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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Arq Bras Cardiol</journal-id>
      <journal-id journal-id-type="publisher-id">abc</journal-id>
      <journal-title-group>
        <journal-title>Arquivos Brasileiros de Cardiologia</journal-title>
        <abbrev-journal-title abbrev-type="publisher">Arq. Bras. Cardiol.</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0066-782X</issn>
      <issn pub-type="epub">1678-4170</issn>
      <publisher>
        <publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id specific-use="scielo-v3" pub-id-type="publisher-id">BjThssdMrWBVZHRh5swpSGy</article-id>
      <article-id specific-use="scielo-v2" pub-id-type="publisher-id">S0066-782X2024001200303</article-id>
      <article-id pub-id-type="other">00303</article-id>
      <article-id pub-id-type="doi">10.36660/abc.20230674</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Artigo Original</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Biomarcadores Potenciais na Fibrose Mioc&#225;rdica: Uma An&#225;lise Bioinform&#225;tica</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0001-5212-9418</contrib-id>
          <name>
            <surname>Cheng-Mei</surname>
            <given-names>Wang</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <role>An&#225;lise estat&#237;stica</role>
          <role>Obten&#231;&#227;o de financiamento</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Luo</surname>
            <given-names>Gang</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do</role>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Liu</surname>
            <given-names>Ping</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <role>An&#225;lise estat&#237;stica</role>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ren</surname>
            <given-names>Wei</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <role>An&#225;lise estat&#237;stica</role>
          <role>Obten&#231;&#227;o de financiamento</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do</role>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yang</surname>
            <given-names>Sijin</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <role>An&#225;lise estat&#237;stica</role>
          <role>Obten&#231;&#227;o de financiamento</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do</role>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution content-type="orgname">Beibei Traditional Chinese Medicine Hospital</institution>
        <addr-line>
          <named-content content-type="city">Chongqing</named-content>
        </addr-line>
        <country country="CN">China</country>
        <institution content-type="original">Beibei Traditional Chinese Medicine Hospital, Chongqing - China</institution>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution content-type="orgdiv1">The Affiliated Hospital of Traditional Chinese Medicine</institution>
        <institution content-type="orgname">Southwest Medical University</institution>
        <addr-line>
          <named-content content-type="city">Luzhou</named-content>
          <named-content content-type="state">Sichuan</named-content>
        </addr-line>
        <country country="CN">China</country>
        <institution content-type="original">The Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Luzhou, Sichuan &#8211; China</institution>
      </aff>
      <author-notes>
        <corresp id="c01"><label>Correspond&#234;ncia:</label> Wang Cheng-Mei &#8226; Beibei Traditional Chinese Medicine Hospital - No Chong Qing, 400700 &#8211; China E-mail: <email>wangchengmei157@163.com</email> </corresp>
        <fn fn-type="edited-by">
          <label>Editor respons&#225;vel pela revis&#227;o:</label>
          <p>Nat&#225;lia Olivetti</p>
        </fn>
        <fn fn-type="conflict">
          <label>Potencial conflito de interesse:</label>
          <p>N&#227;o h&#225; conflito com o presente artigo</p>
        </fn>
      </author-notes>
      <pub-date date-type="pub" publication-format="electronic">
        <day>13</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <month>11</month>
        <year>2024</year>
      </pub-date>
      <volume>121</volume>
      <issue>12</issue>
      <elocation-id>e20230674</elocation-id>
      <history>
        <date date-type="received">
          <day>03</day>
          <month>10</month>
          <year>2023</year>
        </date>
        <date date-type="rev-recd">
          <day>17</day>
          <month>07</month>
          <year>2024</year>
        </date>
        <date date-type="accepted">
          <day>26</day>
          <month>08</month>
          <year>2024</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="en">
          <license-p> This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. </license-p>
        </license>
      </permissions>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01">
            <label>Figura Central</label>
            <caption>
              <title>: Biomarcadores Potenciais na Fibrose Mioc&#225;rdica: Uma An&#225;lise Bioinform&#225;tica</title>
            </caption>
            <alternatives>
              <graphic xlink:href="https://minio.scielo.br/documentstore/1678-4170/BjThssdMrWBVZHRh5swpSGy/2da53f8118e4c91d15b07326ec020c88ada968b1.tif"/>
              <graphic xlink:href="https://minio.scielo.br/documentstore/1678-4170/BjThssdMrWBVZHRh5swpSGy/02b7de5437b72fd2e442a59d6da6adc63c7a3898.png" specific-use="scielo-web"/>
              <graphic xlink:href="https://minio.scielo.br/documentstore/1678-4170/BjThssdMrWBVZHRh5swpSGy/5ee1edf322bd75a01894bf84e7d4b4daf8f4d5da.jpg" specific-use="scielo-web" content-type="scielo-267x140"/>
            </alternatives>
          </fig>
        </p>
      </abstract>
      <abstract>
        <title>Resumo</title>
        <sec>
          <title>Fundamento</title>
          <p> A fibrose mioc&#225;rdica (FM) ocorre durante o in&#237;cio e a progress&#227;o da doen&#231;a cardiovascular, e o diagn&#243;stico precoce da FM &#233; ben&#233;fico para melhorar a fun&#231;&#227;o card&#237;aca, mas h&#225; uma falta de pesquisa sobre biomarcadores precoces da FM.</p>
        </sec>
        <sec>
          <title>Objetivos</title>
          <p> Utilizando t&#233;cnicas de bioinform&#225;tica, identificamos potenciais biomarcadores para FM.</p>
        </sec>
        <sec>
          <title>M&#233;todos</title>
          <p> Os conjuntos de dados relacionados &#224; FM foram obtidos do banco de dados Gene Expression Omnibus (GEO). Ap&#243;s o processamento dos dados, genes diferencialmente expressos foram rastreados. Genes diferencialmente expressos foram enriquecidos e, subsequentemente, intera&#231;&#227;o prote&#237;na-prote&#237;na (PPI) foi realizado para analisar os genes diferenciais. Os miRNAs associados e fatores de transcri&#231;&#227;o foram previstos para esses genes centrais. Finalmente, a valida&#231;&#227;o ROC foi realizada nos genes centrais para determinar sua especificidade e sensibilidade como potenciais biomarcadores. O n&#237;vel de signific&#226;ncia adotado foi de 5% (p &lt; 0,05).</p>
        </sec>
        <sec>
          <title>Resultados</title>
          <p> Um total de 91 genes diferencialmente expressos foram identificados, e a an&#225;lise PPI produziu 31 genes centrais. A an&#225;lise de enriquecimento mostrou que apoptose, col&#225;geno, matriz extracelular, ades&#227;o celular e inflama&#231;&#227;o estavam envolvidos na FM. Cento e quarenta e dois miRNAs potenciais foram identificados. Os fatores de transcri&#231;&#227;o JUN, NF-&#954;B1, SP1, RELA, SRF e STAT3 foram enriquecidos na maioria dos alvos principais. Por fim, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2 e ITGB8 tiveram maior precis&#227;o diagn&#243;stica e sensibilidade na predi&#231;&#227;o de FM com base na an&#225;lise da curva ROC.</p>
        </sec>
        <sec>
          <title>Conclus&#227;o</title>
          <p> Oito genes, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2 e ITGB8, podem servir como biomarcadores candidatos para FM. Processos como apoptose celular, s&#237;ntese de prote&#237;na de col&#225;geno, forma&#231;&#227;o de matriz extracelular, ades&#227;o celular e inflama&#231;&#227;o est&#227;o implicados no desenvolvimento da FM.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="pt">
        <kwd>Fibrose Endomioc&#225;rdica</kwd>
        <kwd>Biomarcadores</kwd>
        <kwd>MicroRNAs</kwd>
      </kwd-group>
      <funding-group>
        <award-group>
          <funding-source>Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine</funding-source>
          <award-id>ZYYCXTD-C-202207</award-id>
        </award-group>
        <award-group>
          <funding-source>Innovation Team of Sichuan Provincial Administration of Traditional Chinese Medicine</funding-source>
          <award-id>2022C007</award-id>
        </award-group>
        <award-group>
          <funding-source>Sichuan Science and Technology Program</funding-source>
          <award-id>2022YFS0618</award-id>
        </award-group>
        <funding-statement>Fontes de financiamento: O presente estudo foi financiado por Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (Number: ZYYCXTD-C-202207), Innovation Team of Sichuan Provincial Administration of Traditional Chinese Medicine (Number: 2022C007), and Sichuan Science and Technology Program (No. 2022YFS0618).</funding-statement>
      </funding-group>
      <counts>
        <fig-count count="18"/>
        <table-count count="0"/>
        <equation-count count="0"/>
        <ref-count count="65"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introdu&#231;&#227;o</title>
      <p>As doen&#231;as cardiovasculares s&#227;o comuns e a sua taxa de mortalidade ultrapassa a de outras doen&#231;as sist&#233;micas.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Eles ceifam milh&#245;es de vidas anualmente, constituindo aproximadamente 25% das mortes em todo o mundo.<sup><xref ref-type="bibr" rid="B2">2</xref></sup> A fibrose mioc&#225;rdica (FM) desempenha um papel fundamental no in&#237;cio e na progress&#227;o de condi&#231;&#245;es como fibrila&#231;&#227;o atrial, doen&#231;a arterial coron&#225;ria, infarto do mioc&#225;rdio, cardiomiopatia dilatada e cardiomiopatia hipertr&#243;fica.<sup><xref ref-type="bibr" rid="B3">3</xref>-<xref ref-type="bibr" rid="B6">6</xref></sup> A FM &#233; causada pela deposi&#231;&#227;o anormal de tecidos fibrosos no mioc&#225;rdio. &#201; caracterizada por um ac&#250;mulo excessivo da matriz extracelular (MEC) e um aumento significativo de col&#225;geno, levando &#224; rigidez card&#237;aca e ao decl&#237;nio da fun&#231;&#227;o card&#237;aca, seguido por remodela&#231;&#227;o el&#233;trica, arritmias e insufici&#234;ncia card&#237;aca.<sup><xref ref-type="bibr" rid="B7">7</xref></sup></p>
      <p>Do ponto de vista fisiopatol&#243;gico, a FM est&#225; intrinsecamente ligada aos mecanismos reparadores ap&#243;s dano card&#237;aco. Cardiomi&#243;citos s&#227;o c&#233;lulas n&#227;o regenerativas. Quando essas c&#233;lulas s&#227;o danificadas ou morrem, as c&#233;lulas circundantes liberam uma s&#233;rie de mol&#233;culas de sinaliza&#231;&#227;o, como mediadores neuro-humorais, citocinas e fatores de crescimento, que induzem a ativa&#231;&#227;o dos fibroblastos para entrar na &#225;rea danificada e produzir grandes quantidades de col&#225;geno.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> O col&#225;geno &#233; um componente prim&#225;rio da estrutura do tecido e seu ac&#250;mulo excessivo pode levar &#224; deposi&#231;&#227;o anormal da matriz extracelular, reduzindo assim a complac&#234;ncia card&#237;aca e prejudicando sua fun&#231;&#227;o.<sup><xref ref-type="bibr" rid="B9">9</xref></sup> &#192; medida que a fibrose progride, as fun&#231;&#245;es sist&#243;lica e diast&#243;lica do cora&#231;&#227;o ficam ainda mais comprometidas, resultando em insufici&#234;ncia card&#237;aca e afetando significativamente a qualidade de vida dos pacientes.</p>
      <p>Muitas doen&#231;as cardiovasculares podem se manifestar como FM em est&#225;gio inicial.<sup><xref ref-type="bibr" rid="B10">10</xref></sup> Com o envelhecimento da popula&#231;&#227;o global e o aumento de doen&#231;as relacionadas ao estilo de vida, como fibrila&#231;&#227;o atrial, hipertens&#227;o, doen&#231;a card&#237;aca coron&#225;ria e diabetes, a incid&#234;ncia de FM est&#225; aumentando constantemente, representando uma grave amea&#231;a &#224; sa&#250;de humana. Lamentavelmente, os tratamentos atuais se concentram principalmente em retardar a progress&#227;o da fibrose, mas n&#227;o podem revert&#234;-la efetivamente.<sup><xref ref-type="bibr" rid="B11">11</xref></sup> Isso ocorre porque os cardiomi&#243;citos n&#227;o conseguem substituir adequadamente as c&#233;lulas mortas, e o processo de fibrose envolve c&#233;lulas e vias de sinaliza&#231;&#227;o extremamente complexas. Portanto, com os avan&#231;os tecnol&#243;gicos em chips de genes, t&#233;cnicas de sequenciamento e sequenciamento de genoma completo, a identifica&#231;&#227;o precoce da FM e a busca por biomarcadores altamente sens&#237;veis e espec&#237;ficos para elaborar novas estrat&#233;gias terap&#234;uticas para reverter essa condi&#231;&#227;o se tornam primordiais.</p>
      <p>Origin&#225;rio de uma perspectiva de bioinform&#225;tica, o estudo identificou e analisou genes hub diferencialmente expressos na FM, investigando suas perspectivas como biomarcadores. Os processos centrais deste estudo podem ser visualizados na <xref ref-type="fig" rid="f01">Figura Central</xref>.</p>
    </sec>
    <sec sec-type="methods">
      <title>M&#233;todos</title>
      <sec>
        <title>Obtendo um GeneChip</title>
        <p>O <italic>Gene Expression Omnibus</italic><sup><xref ref-type="bibr" rid="B12">12</xref></sup> (GEO) foi usado para rastrear tr&#234;s conjuntos de dados de microarray relacionados &#224; FM e seus arquivos de anota&#231;&#227;o gen&#233;tica, especificamente GSE 123018/GPL11154, GSE152250/GPL20301 e GSE225336/GPL24676. Os dados brutos do GSE123018 continham dados de quatro indiv&#237;duos; GSE152250 e GSE225336 usaram dados de tr&#234;s indiv&#237;duos. Todos os tr&#234;s conjuntos de dados continham cardiomi&#243;citos induzidos por 24 horas usando TGF&#946;1, ent&#227;o escolhemos o conjunto de dados processado em 24 horas e, para tornar os dados menos diferenciados, selecionamos aleatoriamente os dados celulares dos tr&#234;s pacientes no GSE123018. Todos esses dados s&#227;o de c&#243;digo aberto e, portanto, n&#227;o exigem revis&#227;o por um comit&#234; de &#233;tica.</p>
      </sec>
      <sec>
        <title>Triagem de genes expressos diferencialmente</title>
        <p>Arquivos relacionados ao chip gen&#233;tico foram obtidos e baixados do banco de dados GEO. Esta pesquisa utilizou conjuntos de dados de tr&#234;s origens distintas, levando a varia&#231;&#245;es no manuseio laboratorial de cada chip gen&#233;tico. Consequentemente, a remo&#231;&#227;o de efeitos de lote e a normaliza&#231;&#227;o de dados se tornaram primordiais.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> O pacote sva em R (Vers&#227;o 4.2.1) foi inicialmente aplicado para estimativa de efeito de lote, seguido pelo emprego da fun&#231;&#227;o de combate para sua mitiga&#231;&#227;o. Modelos lineares e m&#233;todos bayesianos no pacote <italic>limma</italic> foram usados para avaliar os n&#237;veis de express&#227;o g&#234;nica sob diferentes condi&#231;&#245;es experimentais, corrigir as estimativas de vari&#226;ncia de genes individuais e normalizar as matrizes g&#234;nicas usando a fun&#231;&#227;o <italic>normalize BetweenArrays</italic>, respectivamente. Para selecionar genes com express&#227;o diferencial significativa, definimos um limite de |log2-fold change| valor maior que 2 e valor p menor que 0,05 nos crit&#233;rios de sele&#231;&#227;o. T&#233;cnicas de visualiza&#231;&#227;o foram implementadas com os pacotes <italic>ggplot2</italic> e <italic>pheatmap</italic>.</p>
      </sec>
      <sec>
        <title>An&#225;lise de intera&#231;&#245;es prote&#237;na-prote&#237;na</title>
        <p>A an&#225;lise da intera&#231;&#227;o prote&#237;na-prote&#237;na (PPI)<sup><xref ref-type="bibr" rid="B14">14</xref></sup> oferece insights sobre com quais outras prote&#237;nas uma prote&#237;na espec&#237;fica interage dentro de um organismo. Isso ajuda a caracterizar melhor as fun&#231;&#245;es dessas prote&#237;nas e pode lan&#231;ar luz sobre o in&#237;cio e a progress&#227;o da doen&#231;a. Genes diferencialmente expressos (GDE) foram inseridos no banco de dados STRING (<ext-link ext-link-type="uri" xlink:href="https://stringdb.org">https://stringdb.org</ext-link>),<sup><xref ref-type="bibr" rid="B15">15</xref></sup> que &#233; uma ferramenta de an&#225;lise online que inclui pontua&#231;&#227;o de integra&#231;&#227;o de dados, modelagem probabil&#237;stica, teste hipergeom&#233;trico, an&#225;lise de topologia de rede e corre&#231;&#227;o de taxa de descoberta falsa (TDF), e o arquivo TSV da rede PPI foi exportado. Este arquivo foi posteriormente carregado no Cytoscape (vers&#227;o 3.8.2)<sup><xref ref-type="bibr" rid="B16">16</xref></sup> para visualiza&#231;&#227;o. O plugin CytoNCA calcula m&#233;tricas de alvo como grau, proximidade e intermedia&#231;&#227;o, permitindo a sele&#231;&#227;o de alvos principais. O plugin MCODE &#233; ent&#227;o utilizado para identificar potenciais complexos biomoleculares.</p>
      </sec>
      <sec>
        <title>An&#225;lise de enriquecimento gen&#233;tico</title>
        <p>Utilizando o Metascape (<ext-link ext-link-type="uri" xlink:href="https://Metascape.org/">https://Metascape.org/</ext-link>),<sup><xref ref-type="bibr" rid="B17">17</xref></sup> uma plataforma web para an&#225;lise de enriquecimento funcional de grandes conjuntos de genes, Gene Ontology (GO) e Enciclop&#233;dia de Genes e Genomas de Kyoto (KEGG) an&#225;lises de GDE e potenciais complexos biomoleculares foram realizadas usando testes hipergeom&#233;tricos, m&#233;todo de corre&#231;&#227;o de teste m&#250;ltiplo do m&#233;todo Benjamini-Hochberg. Entradas com um valor p &lt; 0,01 foram consideradas significativamente enriquecidas, e os 15 principais resultados da an&#225;lise de enriquecimento GO e das vias KEGG foram selecionados.</p>
      </sec>
      <sec>
        <title>An&#225;lise de enriquecimento do conjunto de genes</title>
        <p>Para melhorar de forma abrangente a compreens&#227;o das fun&#231;&#245;es dos genes, o software GSEA (vers&#227;o 4.3.2)<sup><xref ref-type="bibr" rid="B18">18</xref></sup> foi empregado em todo o conjunto de dados para an&#225;lise de enriquecimento funcional, abrangendo as vias GO, KEGG e an&#225;lise Reactome. Para determinar o enriquecimento significativo, os crit&#233;rios foram estabelecidos da seguinte forma: um escore de enriquecimento normalizado superior a 1,5, um valor de p nominal inferior a 0,05 e uma TDF inferior a 0,25.</p>
      </sec>
      <sec>
        <title>Aquisi&#231;&#227;o de miRNAs cr&#237;ticos e fatores de transcri&#231;&#227;o</title>
        <p>Os genes Hub foram analisados para potencial miRNA usando o software FunRich (vers&#227;o 3.1.3),<sup><xref ref-type="bibr" rid="B19">19</xref></sup> e a predi&#231;&#227;o do fator de transcri&#231;&#227;o foi realizada usando TRRUST vers&#227;o 2 (<ext-link ext-link-type="uri" xlink:href="https://www.grnpedia.org/trrust/">https://www.grnpedia.org/trrust/</ext-link>).<sup><xref ref-type="bibr" rid="B20">20</xref></sup></p>
      </sec>
      <sec>
        <title>Valida&#231;&#227;o do conjunto de genes com base na curva caracter&#237;stica de opera&#231;&#227;o do receptor (ROC)</title>
        <p>Para validar a robustez dos genes hub identificados e evitar overfitting dos dados originais, &#233; crucial conduzir a valida&#231;&#227;o ROC<sup><xref ref-type="bibr" rid="B21">21</xref></sup> em um conjunto de dados independente para avaliar os genes hub como potenciais biomarcadores. Utilizamos o conjunto de dados de microarray de genes GSE97358, que inclui 84 amostras normais e 84 amostras fibr&#243;ticas. A an&#225;lise e visualiza&#231;&#227;o ROC foram conduzidas no software R usando o pacote pROC.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Resultados</title>
      <sec>
        <title>An&#225;lise de GDEs</title>
        <p>Ap&#243;s o conjunto de dados original ter sido corrigido em lote e normalizado, uma an&#225;lise diferencial foi conduzida usando o pacote limma. Por fim, 91 GDE foram identificados, com 56 genes significativamente regulados positivamente e 35 genes significativamente regulados negativamente. Mapas de calor de genes e gr&#225;ficos de vulc&#227;o de GDE s&#227;o ilustrados na <xref ref-type="fig" rid="f02">Figura 1</xref>. Os par&#226;metros topol&#243;gicos espec&#237;ficos dos GDE podem ser encontrados na <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Tabela S1</ext-link>.</p>
        <p>
          <fig id="f02">
            <label>Figura 1</label>
            <caption>
              <title>&#8211; A) Gr&#225;fico do vulc&#227;o gen&#233;tico. B) Mapa de calor gen&#233;tico.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf01.tif"/></fig>
        </p>
      </sec>
      <sec>
        <title>An&#225;lise PPI</title>
        <p>Os 91 GDE foram carregados no site STRING para an&#225;lise PPI. Os resultados da an&#225;lise foram importados para o Cytoscape para investiga&#231;&#227;o posterior. A <xref ref-type="fig" rid="f03">Figura 2A</xref> descreve a rede consistindo em 37 n&#243;s e 71 arestas. Os graus de gene foram calculados usando CytoNCA e, ao classificar valores maiores que a mediana, 28 genes hub foram identificados. Os par&#226;metros topol&#243;gicos espec&#237;ficos dos genes hub podem ser encontrados na <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Tabela S2</ext-link>. Enquanto isso, o plugin MCODE revelou um potencial m&#243;dulo funcional de complexo de prote&#237;na (<xref ref-type="fig" rid="f03">Figura 2B</xref>).</p>
        <p>
          <fig id="f03">
            <label>Figura 2</label>
            <caption>
              <title>&#8211; A) Rede PPI. B) m&#243;dulos biofuncionais.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf02.tif"/></fig>
        </p>
      </sec>
      <sec>
        <title>Enriquecimento funcional</title>
        <p>A an&#225;lise da express&#227;o g&#234;nica diferencial destacou que, como mostrado na <xref ref-type="fig" rid="f04">Figura 3A</xref>, os genes participam de processos biol&#243;gicos como migra&#231;&#227;o mesenquimal, desenvolvimento do tecido conjuntivo, desenvolvimento do tecido mioc&#225;rdico, resposta a est&#237;mulos mec&#226;nicos, processo do sistema circulat&#243;rio e desenvolvimento do tecido muscular estriado. A <xref ref-type="fig" rid="f04">Figura 3B</xref> demonstra o envolvimento em v&#225;rias fun&#231;&#245;es biol&#243;gicas moleculares, como liga&#231;&#227;o da mol&#233;cula de ades&#227;o celular, liga&#231;&#227;o da integrina, atividade do fator de crescimento, liga&#231;&#227;o do glicosaminoglicano, liga&#231;&#227;o do col&#225;geno, constituintes estruturais da matriz extracelular e atividade da oxidorredutase. Os componentes celulares implicados incluem a MEC, estruturas encapsulantes externas, MEC contendo col&#225;geno, l&#250;men do gr&#226;nulo secretor, l&#250;men da ves&#237;cula citoplasm&#225;tica e miofibrilas, como mostrado na <xref ref-type="fig" rid="f04">Figura 3C</xref>. A an&#225;lise da via KEGG indicou envolvimento em m&#250;ltiplas vias de sinaliza&#231;&#227;o, como a via de sinaliza&#231;&#227;o do hipop&#243;tamo, intera&#231;&#227;o citocina-receptor de citocina, cardiomiopatia hipertr&#243;fica, intera&#231;&#227;o MEC-receptor, cardiomiopatia arritmog&#234;nica do ventr&#237;culo direito, senesc&#234;ncia celular, fator de crescimento transformador-&#946;(TGF-&#946;)via de sinaliza&#231;&#227;o, Cardiomiopatia dilatada e via de sinaliza&#231;&#227;o p53, que podem ser observadas na <xref ref-type="fig" rid="f04">Figura 3D</xref>. Todas as an&#225;lises de enriquecimento com valores de p menores que 0,05 s&#227;o mostradas na <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Tabela S3</ext-link>. A an&#225;lise de enriquecimento do m&#243;dulo biol&#243;gico mostrou envolvimento na montagem da MEC e na migra&#231;&#227;o mesenquimal, conforme mostrado na <xref ref-type="fig" rid="f05">Figura 4</xref>.</p>
        <p>
          <fig id="f04">
            <label>Figura 3</label>
            <caption>
              <title>&#8211; A) Processos biol&#243;gicos do GO. B) Fun&#231;&#227;o molecular do GO. C) Componentes celulares do GO. D) KEGG.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf03.tif"/></fig>
        </p>
        <p>
          <fig id="f05">
            <label>Figura 4</label>
            <caption>
              <title>&#8211; An&#225;lise de enriquecimento do m&#243;dulo biol&#243;gico.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf04.tif"/></fig>
        </p>
      </sec>
      <sec>
        <title>Resultados GSEA</title>
        <p>Ao conduzir GSEA no conjunto de dados de genes totalmente ajustado, o Reactome revelou enriquecimentos prim&#225;rios em TGF-&#946;, sinaliza&#231;&#227;o do fator de necrose tumoral Hallmark-&#945; (TNF-&#945;) via NF-&#954;B, sinaliza&#231;&#227;o mTorc1, sinaliza&#231;&#227;o Hedgehog e resposta de prote&#237;na desdobrada. Os principais componentes celulares inclu&#237;ram o processoma da subunidade, pr&#233;-ribossomo e tr&#237;meros complexos de col&#225;geno. Em termos de fun&#231;&#245;es biol&#243;gicas, a participa&#231;&#227;o do gene foi observada na atividade do inibidor do regulador de transcri&#231;&#227;o, constituinte estrutural da MEC e atividade do fator de crescimento. A an&#225;lise da via KEGG mostrou envolvimento na via de sinaliza&#231;&#227;o TGF-&#946;, bioss&#237;ntese de glicosaminoglicanos, sulfato de condroitina e cardiomiopatia dilatada. Lamentavelmente, nos processos biol&#243;gicos GO, nenhum conjunto de genes foi significativo em uma TDF abaixo de 25%, conforme ilustrado na <xref ref-type="fig" rid="f06">Figura 5</xref>, e os par&#226;metros relevantes podem ser encontrados na <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Tabela S4</ext-link>.</p>
        <p>
          <fig id="f06">
            <label>Figura 5</label>
            <caption>
              <title>&#8211; GSEA.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf05.tif"/></fig>
        </p>
      </sec>
      <sec>
        <title>Predi&#231;&#227;o de miRNAs e fatores de transcri&#231;&#227;o</title>
        <p>Utilizando FunRich para prever miRNAs, a an&#225;lise de abund&#226;ncia reteve miRNA-148a-3p, miRNA-148b-3p, miR-152-3p, miR-372-3p, miR-506-3p, a fam&#237;lia miR-130, a fam&#237;lia miR-302, a fam&#237;lia miR-29 e a fam&#237;lia miR-520, conforme exibido na <xref ref-type="fig" rid="f07">Figura 6</xref>. A previs&#227;o p&#243;s-an&#225;lise de fatores de transcri&#231;&#227;o por meio da plataforma TRRUST revelou que o proto-oncogene Jun (JUN), NF-&#954;B1, fator de transcri&#231;&#227;o Sp1(SP1), proto-oncogene RELA (RELA), fator de resposta s&#233;rica (SRF), e transdutor de sinal e ativador da transcri&#231;&#227;o 3 (STAT3) foram altamente enriquecidos entre a maioria dos alvos principais, conforme ilustrado na <xref ref-type="fig" rid="f08">Figura 7</xref>.</p>
        <p>
          <fig id="f07">
            <label>Figura 6</label>
            <caption>
              <title>&#8211; miRNAs.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf06.tif"/></fig>
        </p>
        <p>
          <fig id="f08">
            <label>Figura 7</label>
            <caption>
              <title>&#8211; An&#225;lise dos fatores de transcri&#231;&#227;o.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf07.tif"/></fig>
        </p>
      </sec>
      <sec>
        <title>Os resultados da valida&#231;&#227;o do ROC</title>
        <p>Durante a an&#225;lise ROC dos principais alvos, o estudo descobriu que as AUCs para os genes interleucina 11 (IL11), beta induz&#237;vel por parada do crescimento e dano ao DNA (GADD45B), fator de diferencia&#231;&#227;o do crescimento 5 (GDF5), NADPH oxidase 4 (NOX4), prote&#237;na de liga&#231;&#227;o ao fator de crescimento semelhante &#224; insulina 3 (IGFBP3), actina alfa do m&#250;sculo card&#237;aco 1 (ACTC1), miozenina 2 (MYOZ2) e subunidade beta 8 da integrina (ITGB8) foram 0,964710884, 0,943239796, 0,936791383, 0,929563492, 0,92651644, 0,926232993, 0,91723356 e 0,906391723, respectivamente. Todas as AUCs foram maiores que 0,9; entre elas, IL11, GADD45B, NOX4, IGFBP3 e ACTC1 s&#227;o reguladas positivamente na fibrose, enquanto GDF5, MYOZ2 e ITGB8 s&#227;o reguladas negativamente na express&#227;o, comprovando esses genes como potenciais marcadores biol&#243;gicos para o diagn&#243;stico de FM.</p>
      </sec>
    </sec>
    <sec sec-type="discussion">
      <title>Discuss&#227;o</title>
      <p>As doen&#231;as cardiovasculares, como principal causa de morte em todo o mundo, representam um fardo significativo para a sa&#250;de p&#250;blica global.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Condi&#231;&#245;es cardiovasculares comuns, como doen&#231;a card&#237;aca coron&#225;ria, infarto do mioc&#225;rdio, doen&#231;a card&#237;aca hipertensiva, cardiomiopatia, fibrila&#231;&#227;o atrial e cardiomiopatia diab&#233;tica, podem levar &#224; FM, resultando em insufici&#234;ncia card&#237;aca. A FM induzida por doen&#231;as cardiovasculares &#233; principalmente reparadora, manifestando-se como a transforma&#231;&#227;o de fibroblastos card&#237;acos em miofibroblastos sob est&#237;mulos patol&#243;gicos quando as c&#233;lulas card&#237;acas s&#227;o lesionadas. Esses miofibroblastos podem secretar uma grande quantidade de MEC, promovendo a forma&#231;&#227;o de tecido cicatricial, aumentando assim a rigidez card&#237;aca, levando &#224; FM e resultando em remodela&#231;&#227;o card&#237;aca e insufici&#234;ncia card&#237;aca. Em contraste, o processo de remodela&#231;&#227;o da MEC requer a deposi&#231;&#227;o de grandes quantidades de col&#225;geno e outras prote&#237;nas; o tipo de col&#225;geno e a reticula&#231;&#227;o de outroAs prote&#237;nas tamb&#233;m est&#227;o inextricavelmente ligadas &#224; remodela&#231;&#227;o.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> Existem col&#225;genos tipo I e tipo III na MEC. O col&#225;geno tipo I comp&#245;e 85% do col&#225;geno total e est&#225; principalmente associado &#224;s fibras grossas que fornecem resist&#234;ncia &#224; tra&#231;&#227;o, alongamento e deformabilidade. Em contraste, o col&#225;geno tipo III comp&#245;e 11% do col&#225;geno total e est&#225; associado &#224;s fibras finas que fornecem elasticidade, e a deposi&#231;&#227;o de col&#225;geno de ambos os tipos pode diminuir a complac&#234;ncia mioc&#225;rdica.<sup><xref ref-type="bibr" rid="B23">23</xref></sup> Atualmente, o tratamento cl&#237;nico da FM emprega principalmente interven&#231;&#245;es farmacol&#243;gicas visando o sistema renina-angiotensina-aldosterona para desacelerar a progress&#227;o da fibrose; no entanto, os resultados t&#234;m sido sub&#243;timos.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> O transplante card&#237;aco pode prolongar a sobreviv&#234;ncia do paciente, mas traz consigo seu pr&#243;prio conjunto de desafios, como riscos elevados, complica&#231;&#245;es m&#250;ltiplas e suscetibilidade &#224; rejei&#231;&#227;o do enxerto, que afetam negativamente os resultados do paciente.<sup><xref ref-type="bibr" rid="B25">25</xref></sup> Consequentemente, compreender os mecanismos subjacentes da FM, identificar alvos de tratamento vi&#225;veis, inibir a prolifera&#231;&#227;o de fibroblastos card&#237;acos e atenuar a progress&#227;o da fibrose s&#227;o de vital import&#226;ncia no tratamento de dist&#250;rbios cardiovasculares. Este estudo teve como objetivo identificar a FM em um est&#225;gio inicial e encontrar marcadores biol&#243;gicos com alta sensibilidade e especificidade para o desenvolvimento de novas estrat&#233;gias de tratamento. Nesta pesquisa, GDE na FM foram obtidos dos conjuntos de dados GSE123018, GSE152250 e GSE225336, construindo uma rede PPI compreendendo 37 n&#243;s e 71 arestas. Alvos principais foram identificados por meio da rede, e um potencial cluster funcional foi descoberto usando o plugin MCODE.</p>
      <p>Por meio da an&#225;lise de enriquecimento GO e KEGG, os genes-chave foram enriquecidos principalmente em vias intimamente relacionadas &#224; fibrose, como a via de sinaliza&#231;&#227;o TGF-&#946;, intera&#231;&#227;o MEC-receptor, cardiomiopatia dilatada e cardiomiopatia hipertr&#243;fica. Al&#233;m disso, esses genes est&#227;o envolvidos em processos biol&#243;gicos, como a intera&#231;&#227;o citocina-receptor citocina, a via de sinaliza&#231;&#227;o p53, a via de sinaliza&#231;&#227;o Hippo e migra&#231;&#227;o mesenquimal, enfatizando os pap&#233;is centrais da apoptose, col&#225;geno, MEC e ades&#227;o celular na fibrose. A via de sinaliza&#231;&#227;o TGF-&#946; foi amplamente confirmada como tendo um papel regulador significativo na FM.<sup><xref ref-type="bibr" rid="B26">26</xref></sup> A express&#227;o elevada de TGF-&#946; &#233; quase universalmente observada em pacientes com FM.<sup><xref ref-type="bibr" rid="B27">27</xref>-<xref ref-type="bibr" rid="B29">29</xref></sup> Essa express&#227;o diferencial leva &#224; ativa&#231;&#227;o de fibroblastos, estimula a express&#227;o de inibidores teciduais de metaloproteinases da matriz e inibe a atividade das metaloproteinases da matriz, resultando em aumento da deposi&#231;&#227;o de MEC e exacerba&#231;&#227;o da FM.<sup><xref ref-type="bibr" rid="B30">30</xref>,<xref ref-type="bibr" rid="B31">31</xref></sup> Numerosos estudos indicam que a FM pode ser significativamente melhorada pela inibi&#231;&#227;o da via de sinaliza&#231;&#227;o do TGF-&#946; por meio de medicamentos.<sup><xref ref-type="bibr" rid="B32">32</xref>-<xref ref-type="bibr" rid="B34">34</xref></sup> Ambas as vias de sinaliza&#231;&#227;o P53 e Hippo medeiam a apoptose celular.</p>
      <p>Estudos demonstraram que a inibi&#231;&#227;o dessas vias pode suprimir a apoptose dos cardiomi&#243;citos e reduzir a forma&#231;&#227;o de FM.<sup><xref ref-type="bibr" rid="B35">35</xref>,<xref ref-type="bibr" rid="B36">36</xref></sup> Al&#233;m disso, a pesquisa indica que o SO2 pode inibir a via Hippo-MST, aliviando a apoptose celular e o estresse do ret&#237;culo endoplasm&#225;tico, atenuando significativamente a FM em ratos diab&#233;ticos.<sup><xref ref-type="bibr" rid="B37">37</xref></sup> Da mesma forma, a an&#225;lise de enriquecimento do m&#243;dulo central revelou que suas fun&#231;&#245;es est&#227;o intimamente relacionadas aos principais mecanismos da FM, como a montagem da MEC e a migra&#231;&#227;o mesenquimal. Como um suplemento &#224; an&#225;lise de enriquecimento do gene central, a GSEA continua a destacar a deposi&#231;&#227;o da MEC como um processo central na FM. Al&#233;m disso, ela introduz as vias inflamat&#243;rias de sinaliza&#231;&#227;o Hedgehog, mTORC1 e TNF-&#945; por meio do NF-&#954;B, que foram todas confirmadas como relacionadas &#224; FM em v&#225;rios estudos globais.<sup><xref ref-type="bibr" rid="B38">38</xref>-<xref ref-type="bibr" rid="B40">40</xref></sup></p>
      <p>A express&#227;o g&#234;nica em organismos biol&#243;gicos depende da regula&#231;&#227;o de miRNAs. Ap&#243;s prever miRNAs para genes hub, foi descoberto que miRNA-148a-3p, miRNA-148b-3p, miR-152-3p, miR-372-3p, miR-506-3p, a fam&#237;lia miR-130, a fam&#237;lia miR-302, a fam&#237;lia miR-29 e a fam&#237;lia miR-520 s&#227;o compartilhadas pela maioria dos genes. A fam&#237;lia miR-29, em particular, &#233; uma das fam&#237;lias de miRNA mais amplamente estudadas na FM. Numerosos estudos mostraram que a fam&#237;lia miR-29 pode atingir uma variedade de genes relacionados &#224; s&#237;ntese de MEC, como v&#225;rias prote&#237;nas de col&#225;geno. Sua regula&#231;&#227;o negativa na fibrose pode levar &#224; deposi&#231;&#227;o excessiva de componentes da MEC.<sup><xref ref-type="bibr" rid="B41">41</xref>,<xref ref-type="bibr" rid="B42">42</xref></sup> Em um experimento animal, Sheng-song Xu et al. descobriram que a express&#227;o de miR-152-3p reduzia a prolifera&#231;&#227;o de fibroblastos card&#237;acos (FC) e aliviava a FM ao inibir a via de sinaliza&#231;&#227;o Wnt1/&#946;-catenina.<sup><xref ref-type="bibr" rid="B43">43</xref></sup></p>
      <p>Da mesma forma, a regula&#231;&#227;o negativa do miR-130 promove efeitos cardioprotetores mediados pelo PPAR- &#947; por meio da inibi&#231;&#227;o da inflama&#231;&#227;o e da FM.<sup><xref ref-type="bibr" rid="B44">44</xref></sup> Portanto, a fam&#237;lia hsa-miR-29, miR-152-3p e miR-130 poderiam potencialmente servir como marcadores biol&#243;gicos e alvos terap&#234;uticos para FM. A an&#225;lise de fatores de transcri&#231;&#227;o indicou que JUN, NF-&#954;B1, SP1, RELA, SRF e STAT3 est&#227;o envolvidos em vias relacionadas &#224; inflama&#231;&#227;o, prolifera&#231;&#227;o celular, apoptose e respostas ao estresse celular. NF-&#954;B1 e RELA s&#227;o parte do complexo NF-&#922;b.<sup><xref ref-type="bibr" rid="B45">45</xref></sup> Geng-Rui Xu descobriu que, ao inibir a via de sinaliza&#231;&#227;o TLR4/MyD88/NF-&#954;B, a deposi&#231;&#227;o de col&#225;geno foi significativamente reduzida, melhorando assim a FM.<sup><xref ref-type="bibr" rid="B46">46</xref></sup> STAT3 est&#225; intimamente relacionado &#224; FM e est&#225; envolvido em m&#250;ltiplas vias de sinaliza&#231;&#227;o. Estudos demonstraram que a inibi&#231;&#227;o das vias IL-6/STAT3 e JAK-STAT3 alivia o grau de FM.<sup><xref ref-type="bibr" rid="B47">47</xref>,<xref ref-type="bibr" rid="B48">48</xref></sup></p>
      <p>Ap&#243;s realizar an&#225;lise de especificidade e sensibilidade usando ROC, identificamos genes com AUC maior que 0,9, incluindo IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2 e ITGB8. IL11 &#233; um gene profibr&#243;tico cl&#225;ssico. Experimentos recentes mostraram que a secre&#231;&#227;o de IL11 pode promover sinaliza&#231;&#227;o par&#225;crina em fibroblastos adjacentes, bem como transi&#231;&#227;o epitelial-mesenquimal e transi&#231;&#227;o endotelial-mesenquimal, exibindo efeitos profibr&#243;ticos e pr&#243;-inflamat&#243;rios evidentes.<sup><xref ref-type="bibr" rid="B49">49</xref>,<xref ref-type="bibr" rid="B50">50</xref></sup> Em outro estudo, foi descoberto que a IL-11 pode sinergizar ou aumentar os efeitos fibr&#243;ticos do TGF-&#946;.<sup><xref ref-type="bibr" rid="B51">51</xref></sup> Tongtong Song et al. descobriram em experimentos celulares que a inibi&#231;&#227;o de IL11 em c&#233;lulas reduziu significativamente a express&#227;o de mRNA e prote&#237;na do col&#225;geno tipo I e col&#225;geno tipo III, melhorando assim a remodela&#231;&#227;o da MEC.<sup><xref ref-type="bibr" rid="B52">52</xref></sup> GADD45B &#233; um gene que &#233; regulado positivamente em danos ao DNA e respostas ao estresse, desempenhando pap&#233;is em v&#225;rios processos biol&#243;gicos, particularmente na regula&#231;&#227;o do ciclo celular, apoptose, reparo do DNA e respostas ao estresse celular.<sup><xref ref-type="bibr" rid="B53">53</xref></sup> A pesquisa mostrou que a driamicina aumentou significativamente o col&#225;geno tipo I, e o col&#225;geno tipo III, diminuiu a fun&#231;&#227;o card&#237;aca, e induziu FM na MEC pela regula&#231;&#227;o positiva de GADD45B e a ativa&#231;&#227;o de p38 e pJNK no cora&#231;&#227;o.<sup><xref ref-type="bibr" rid="B54">54</xref></sup> GDF5 pode regular a express&#227;o de p38-MAPK, subsequentemente inibindo a transcri&#231;&#227;o de col&#225;geno tipo I e Col1a1 e col&#225;geno tipo III, bem como genes Col3a1 em c&#233;lulas card&#237;acas. Isso leva a uma redu&#231;&#227;o na FM ap&#243;s infarto do mioc&#225;rdio.<sup><xref ref-type="bibr" rid="B55">55</xref></sup> NOX4 &#233; um membro da fam&#237;lia NADPH oxidase que medeia a produ&#231;&#227;o de ROS. Numerosos estudos demonstraram que a inibi&#231;&#227;o do estresse oxidativo induzido por NOX4 pode ter efeitos antifibr&#243;ticos no mioc&#225;rdio.<sup><xref ref-type="bibr" rid="B56">56</xref></sup> Al&#233;m disso, o NOX4 &#233; regulado pelo TGF-&#946;, uma via fundamental na FM.<sup><xref ref-type="bibr" rid="B57">57</xref></sup> Al&#233;m disso, em um experimento in vivo e in vitro, Nox4 foi encontrado mediando no meio da via TGF&#946;1 para promover a s&#237;ntese de col&#225;geno, mas, infelizmente, o experimento n&#227;o elucidou qual col&#225;geno especificamente.<sup><xref ref-type="bibr" rid="B58">58</xref></sup> Da mesma forma, experimentos recentes mostraram que IGFBP3 &#233; altamente expresso em fibroblastos em modelos murinos de FM.<sup><xref ref-type="bibr" rid="B59">59</xref></sup> Li et al. descobriram que a inibi&#231;&#227;o da express&#227;o de IGFBP3 atenuou a express&#227;o de col&#225;geno I e III na FM induzida por diabetes.<sup><xref ref-type="bibr" rid="B60">60</xref></sup> Um estudo de Alessandra Ruggiero descobriu que camundongos mutantes MYOZ2 podem desenvolver hipertrofia de c&#233;lulas card&#237;acas e fibrose intersticial.<sup><xref ref-type="bibr" rid="B61">61</xref></sup> ACTC1 &#233; uma prote&#237;na-chave nos mi&#243;citos card&#237;acos e est&#225; envolvida em sua fun&#231;&#227;o contr&#225;til. Embora muta&#231;&#245;es em ACTC1 sejam conhecidas por estarem associadas a certas doen&#231;as card&#237;acas, como a cardiomiopatia hipertr&#243;fica familiar (HCM),<sup><xref ref-type="bibr" rid="B62">62</xref></sup> atualmente n&#227;o h&#225; evid&#234;ncias definitivas que liguem ACTC1 diretamente &#224; FM. Mais pesquisas s&#227;o necess&#225;rias para elucidar essa rela&#231;&#227;o. ITGB8 &#233; um membro da fam&#237;lia integrina e est&#225; envolvido em intera&#231;&#245;es entre c&#233;lulas, bem como entre c&#233;lulas e a MEC.<sup><xref ref-type="bibr" rid="B63">63</xref></sup> Estudo indica que ITGB8 participa principalmente do processo de fibrose pulmonar, mas seu papel na FM ainda n&#227;o foi totalmente estabelecido.<sup><xref ref-type="bibr" rid="B64">64</xref></sup> No entanto, outros membros da fam&#237;lia das integrinas, como o ITGB1, s&#227;o conhecidos por estarem associados &#224; FM,<sup><xref ref-type="bibr" rid="B65">65</xref></sup> garantindo uma investiga&#231;&#227;o mais aprofundada sobre as fun&#231;&#245;es do ITGB8.</p>
      <p>
        <fig id="f09">
          <label>Figura 8</label>
          <caption>
            <title>&#8211; ROCs de genes centrais.</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf08.tif"/></fig>
      </p>
      <p>Este estudo tem certas limita&#231;&#245;es, pois os resultados que obtivemos foram derivados de conjuntos de dados do banco de dados GEO. Embora esfor&#231;os tenham sido feitos para controlar e normalizar os efeitos em tr&#234;s conjuntos de dados experimentais, as descobertas n&#227;o foram verificadas em experimentos do mundo real. Al&#233;m disso, ao adquirir os conjuntos de dados originais, por uma quest&#227;o de consist&#234;ncia entre os tr&#234;s conjuntos de dados, optamos pelo grupo experimental induzido por TGF-&#946; de 24 horas sem selecionar os grupos experimentais de 48 ou 72 horas. Embora uma literatura substancial sugira que fibrose significativa pode ser observada ap&#243;s 24 horas de indu&#231;&#227;o, essa abordagem pode ter perdido informa&#231;&#245;es cruciais no processo din&#226;mico. Al&#233;m disso, o biomarcador central da FM, TGF-&#946;, n&#227;o exibiu altos n&#237;veis de confian&#231;a no conjunto de dados GSE97358, apesar de sua AUC ser 0,882015306. Isso requer uma experimenta&#231;&#227;o extensiva para verificar a especificidade e a sensibilidade desses biomarcadores potenciais.</p>
    </sec>
    <sec sec-type="discussion">
      <title>Conclus&#227;o</title>
      <p>Os oito genes, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2 e ITGB8, podem servir como biomarcadores candidatos para FM. Processos como apoptose celular, s&#237;ntese de prote&#237;na de col&#225;geno, forma&#231;&#227;o de MEC, ades&#227;o celular e inflama&#231;&#227;o est&#227;o implicados no desenvolvimento da FM.</p>
    </sec>
  </body>
  <back>
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      <fn fn-type="other">
        <label>Vincula&#231;&#227;o acad&#234;mica:</label>
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      </fn>
      <fn fn-type="other">
        <label>Aprova&#231;&#227;o &#233;tica e consentimento informado:</label>
        <p> Este artigo n&#227;o cont&#233;m estudos com humanos ou animais realizados por nenhum dos autores.</p>
      </fn>
      <fn fn-type="supplementary-material" id="fn1">
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      </fn>
    </fn-group>
    <fn-group>
      <fn fn-type="financial-disclosure">
        <label>Fontes de financiamento:</label>
        <p> O presente estudo foi financiado por Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (Number: ZYYCXTD-C-202207), Innovation Team of Sichuan Provincial Administration of Traditional Chinese Medicine (Number: 2022C007), and Sichuan Science and Technology Program (No. 2022YFS0618).</p>
      </fn>
    </fn-group>
  </back>
  <sub-article article-type="translation" id="TRen" xml:lang="en">
    <front-stub>
      <article-id pub-id-type="doi">10.36660/abc.20230674i</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Potential Biomarkers in Myocardial Fibrosis: A Bioinformatic Analysis</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0001-5212-9418</contrib-id>
          <name>
            <surname>Cheng-Mei</surname>
            <given-names>Wang</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <role>Statistical analysis</role>
          <role>Obtaining financing</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Luo</surname>
            <given-names>Gang</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <role>Critical revision of the manuscript for content</role>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Liu</surname>
            <given-names>Ping</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <role>Statistical analysis</role>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ren</surname>
            <given-names>Wei</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <role>Statistical analysis</role>
          <role>Obtaining financing</role>
          <role>Critical revision of the manuscript for content</role>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yang</surname>
            <given-names>Sijin</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <role>Statistical analysis</role>
          <role>Obtaining financing</role>
          <role>Critical revision of the manuscript for content</role>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1001">
        <label>1</label>
        <country country="CN">China</country>
        <institution content-type="original">Beibei Traditional Chinese Medicine Hospital, Chongqing - China</institution>
      </aff>
      <aff id="aff2001">
        <label>2</label>
        <country country="CN">China</country>
        <institution content-type="original">The Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Luzhou, Sichuan &#8211; China</institution>
      </aff>
      <author-notes>
        <corresp id="c01001"><label>Mailing Address:</label> Wang Cheng-Mei &#8226; Beibei Traditional Chinese Medicine Hospital - No Chong Qing, 400700 &#8211; China E-mail: wangchengmei157@163.com </corresp>
        <fn fn-type="conflict">
          <label>Potential conflict of interest:</label>
          <p>No potential conflict of interest relevant to this article was reported.</p>
        </fn>
        <fn fn-type="edited-by">
          <label>Editor responsible for the review:</label>
          <p>Nat&#225;lia Olivetti</p>
        </fn>
      </author-notes>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01001">
            <label>Central Illustration</label>
            <caption>
              <title>: Potential Biomarkers in Myocardial Fibrosis: A Bioinformatic Analysis</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf01-en.tif"/></fig>
        </p>
      </abstract>
      <abstract>
        <title>Abstract</title>
        <sec>
          <title>Background</title>
          <p> Myocardial fibrosis (MF) occurs throughout the onset and progression of cardiovascular disease, and early diagnosis of MF is beneficial for improving cardiac function, but there is a lack of research on early biomarkers of MF.</p>
        </sec>
        <sec>
          <title>Objectives</title>
          <p> Utilizing bioinformatics techniques, we identified potential biomarkers for MF.</p>
        </sec>
        <sec>
          <title>Methods</title>
          <p> Datasets related to MF were sourced from the GEO database. After processing the data, differentially expressed genes were screened. Differentially expressed genes were enriched, and subsequently, protein-protein interaction (PPI) was performed to analyze the differential genes. The associated miRNAs and transcription factors were predicted for these core genes. Finally, ROC validation was performed on the core genes to determine their specificity and sensitivity as potential biomarkers. The level of significance adopted was 5% (p &lt; 0.05).</p>
        </sec>
        <sec>
          <title>Results</title>
          <p> A total of 91 differentially expressed genes were identified, and PPI analysis yielded 31 central genes. Enrichment analysis showed that apoptosis, collagen, extracellular matrix, cell adhesion, and inflammation were involved in MF. One hundred and forty-two potential miRNAs were identified. the transcription factors JUN, NF-&#954;B1, SP1, RELA, serum response factor (SRF), and STAT3 were enriched in most of the core targets. Ultimately, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8 had higher diagnostic accuracy and sensitivity in predicting MF based on ROC curve analysis.</p>
        </sec>
        <sec>
          <title>Conclusion</title>
          <p> Eight genes, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8, can serve as candidate biomarkers for MF. Processes such as cellular apoptosis, collagen protein synthesis, extracellular matrix formation, cellular adhesion, and inflammation are implicated in the development of MF.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>Fibrosis Endomioc&#225;rdica</kwd>
        <kwd>Biomarkers</kwd>
        <kwd>MicroRNAs</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introduction</title>
        <p>Cardiovascular diseases are common, and their mortality rate surpasses that of other systemic illnesses.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> They claim millions of lives annually, constituting approximately 25% of deaths worldwide.<sup><xref ref-type="bibr" rid="B2">2</xref></sup> Myocardial fibrosis (MF) plays a pivotal role in the onset and progression of conditions such as atrial fibrillation, coronary artery disease, myocardial infarction, dilated cardiomyopathy, and hypertrophic cardiomyopathy.<sup><xref ref-type="bibr" rid="B3">3</xref>-<xref ref-type="bibr" rid="B6">6</xref></sup> MF is caused by the abnormal deposition of fibrous tissues in the myocardium. It is characterized by an excessive accumulation of the extracellular matrix (ECM) and a significant increase in collagen, leading to cardiac stiffness and a decline in heart function, followed by electrical remodeling, arrhythmias, and heart failure.<sup><xref ref-type="bibr" rid="B7">7</xref></sup></p>
        <p>From a pathophysiological standpoint, MF is intricately tied to the reparative mechanisms after cardiac damage. Cardiomyocytes are non-regenerative cells. When these cells are damaged or die, surrounding cells release a series of signaling molecules, such as neurohumoral mediators, cytokines, and growth factors, which induce fibroblast activation to enter the damaged area and produce large amounts of collagen.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> Collagen is a primary component of tissue structure, and its excessive accumulation can lead to abnormal deposition of the extracellular matrix, thereby reducing cardiac compliance and impairing its function.<sup><xref ref-type="bibr" rid="B9">9</xref></sup> As fibrosis progresses, the heart&#8217;s systolic and diastolic functions are further compromised, eventually resulting in heart failure and significantly affecting patients&#8217; quality of life.</p>
        <p>Many cardiovascular diseases can manifest as MF at an early stage.<sup><xref ref-type="bibr" rid="B10">10</xref></sup> With the global aging population and the rise of lifestyle-related diseases such as atrial fibrillation, hypertension, coronary heart disease, and diabetes, the incidence of MF is steadily increasing, posing a severe threat to human health. Regrettably, current treatments mainly focus on slowing the progression of fibrosis but cannot effectively reverse it.<sup><xref ref-type="bibr" rid="B11">11</xref></sup> This is because cardiomyocytes cannot adequately replace dead cells, and the fibrosis process involves extremely complex cells and signaling pathways. Therefore, with the technological advancements in gene chips, sequencing techniques, and whole-genome sequencing, early identification of MF and searching for highly sensitive and specific biomarkers to devise new therapeutic strategies to reverse this condition becomes paramount.</p>
        <p>Originating from a bioinformatics perspective, the study identified and analyzed hub genes differentially expressed in MF, investigating their prospects as biomarkers. The core processes of this study can be viewed in the <xref ref-type="fig" rid="f01001">Central Figure</xref>.</p>
      </sec>
      <sec sec-type="methods">
        <title>Methods</title>
        <sec>
          <title>Obtaining a GeneChip</title>
          <p>The Gene Expression Omnibus<sup><xref ref-type="bibr" rid="B12">12</xref></sup> (GEO) was used to screen three microarray datasets related to MF and their gene annotation files, specifically GSE 123018/GPL11154, GSE152250/GPL20301, and GSE225336/GPL24676. The GSE123018 raw data contained data from four individuals; GSE152250 and GSE225336 used data from three individuals. All three sets of data contained cardiomyocytes induced for 24 hours using TGF&#946;1, so we chose the data set processed at 24 hours, and in order to make the data less differentiated, we randomly picked the cell data from the three patients in GSE123018. All these data are open-source and thus do not require review by an ethics committee.</p>
        </sec>
        <sec>
          <title>Screening for differentially expressed genes</title>
          <p>Gene chip-related files were sourced and downloaded from the GEO database. This research utilized datasets from three distinct origins, leading to variances in the laboratory handling of each gene chip. Consequently, the removal of batch effects and data normalization became paramount.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> The sva package in R (Version 4.2.1) was initially applied for batch effect estimation, followed by the employment of the combat function for its mitigation. Linear models and Bayesian methods in the limma package were used to evaluate gene expression levels under different experimental conditions, correct the variance estimates of individual genes, and normalize the gene matrices using the normalize BetweenArrays function, respectively. In order to select genes with significant differential expression, we set a threshold of |log2-fold change| value more than 2 and P-value less than 0.05 on the selection criteria. Visualization techniques were implemented with the ggplot2 and pheatmap packages.</p>
        </sec>
        <sec>
          <title>Analysis of protein-protein interactions</title>
          <p>Protein-protein interaction (PPI)<sup><xref ref-type="bibr" rid="B14">14</xref></sup> analysis offers insights into which other proteins a specific protein interacts with within an organism. This aids in better characterizing the functions of these proteins and can shed light on disease onset and progression. Differentially expressed genes (DEGs) were input into the STRING database (https://stringdb.org),<sup><xref ref-type="bibr" rid="B15">15</xref></sup> which is an online analysis tool that includes data integration scoring, probabilistic modeling, hypergeometric testing, network topology analysis, and false discovery rate (FDR) correction, and the TSV file of the PPI network was exported. This file was subsequently loaded into Cytoscape (version 3.8.2)<sup><xref ref-type="bibr" rid="B16">16</xref></sup> for visualization. The CytoNCA plugin calculates target metrics such as degree, closeness, and betweenness, allowing for the selection of core targets. The MCODE plugin is then utilized to identify potential biomolecular complexes.</p>
        </sec>
        <sec>
          <title>Gene enrichment analysis</title>
          <p>Utilizing Metascape (<ext-link ext-link-type="uri" xlink:href="https://Metascape.org/">https://Metascape.org/</ext-link>),<sup><xref ref-type="bibr" rid="B17">17</xref></sup> a web platform for functional enrichment analysis of large gene sets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs and potential biomolecular complexes were performed using hypergeometric tests, Benjamini-Hochberg method multiple test correction method. Entries with a p value &lt;0.01 were deemed significantly enriched, and the top 15 results from the GO enrichment analysis and KEGG pathways were selected.</p>
        </sec>
        <sec>
          <title>Gene set enrichment analysis</title>
          <p>To comprehensively enhance the understanding of gene functions, GSEA software (version 4.3.2)<sup><xref ref-type="bibr" rid="B18">18</xref></sup> was employed on the entire dataset for functional enrichment analysis, encompassing GO, KEGG pathways, and Reactome analysis. For determining significant enrichment, criteria were established as follows: a normalized enrichment score exceeding 1.5, a nominal p-value less than 0.05, and a FDR below 0.25.</p>
        </sec>
        <sec>
          <title>Acquisition of critical miRNAs and transcription factors</title>
          <p>The Hub genes were analyzed for potential miRNA using FunRich software (version 3.1.3),<sup><xref ref-type="bibr" rid="B19">19</xref></sup> and transcription factor prediction was carried out using TRRUST version 2 (<ext-link ext-link-type="uri" xlink:href="https://www.grnpedia.org/trrust/">https://www.grnpedia.org/trrust/</ext-link>).<sup><xref ref-type="bibr" rid="B20">20</xref></sup></p>
        </sec>
        <sec>
          <title>Gene set validation based on the receiver operating characteristic curve (ROC)</title>
          <p>To validate the robustness of the identified hub genes and to avoid overfitting the original data, it is crucial to conduct ROC<sup><xref ref-type="bibr" rid="B21">21</xref></sup> validation on an independent dataset to evaluate the hub genes as potential biomarkers. We utilized the gene microarray dataset GSE97358, which includes 84 normal samples and 84 fibrotic samples. ROC analysis and visualization were conducted in R software using the pROC package.</p>
        </sec>
      </sec>
      <sec sec-type="results">
        <title>Results</title>
        <sec>
          <title>Analysis of DEGs</title>
          <p>After the original dataset was batch-corrected and normalized, a differential analysis was conducted using the limma package. Ultimately, 91 DEGs were identified, with 56 genes significantly upregulated and 35 genes significantly downregulated. Gene heatmaps and volcano plots of DEGs are illustrated in <xref ref-type="fig" rid="f02001">Figure 1</xref>. The specific topological parameters of the DEGs can be found in <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Table S1</ext-link>.</p>
          <p>
            <fig id="f02001">
              <label>Figure 1</label>
              <caption>
                <title>&#8211; A) Gene volcano plot. B) Gene heat map.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf02-en.tif"/></fig>
          </p>
        </sec>
        <sec>
          <title>PPI analysis</title>
          <p>The 91 DEGs were uploaded to the STRING website for PPI analysis. The analysis results were imported into Cytoscape for further investigation. <xref ref-type="fig" rid="f03001">Figure 2A</xref> depicts the network consisting of 37 nodes and 71 edges. Gene degrees were calculated using CytoNCA, and by sorting values greater than the median,28 hub genes were identified. The specific topological parameters of the hub genes can be found in <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Table S2</ext-link>. Meanwhile, the MCODE plugin revealed a potential protein complex functional module (<xref ref-type="fig" rid="f03001">Figure 2B</xref>).</p>
          <p>
            <fig id="f03001">
              <label>Figure 2</label>
              <caption>
                <title>&#8211; A) PPI network. B) bio-functional modules.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf03-en.tif"/></fig>
          </p>
        </sec>
        <sec>
          <title>Functional enrichment</title>
          <p>Differential gene expression analysis highlighted that, as shown in <xref ref-type="fig" rid="f04001">Figure 3A</xref>, the genes participate in biological processes such as mesenchymal migration, connective tissue development, myocardial tissue development, response to mechanical stimulus, circulatory system process, and striated muscle tissue development. <xref ref-type="fig" rid="f04001">Figure 3B</xref> demonstrates involvement in various molecular biological functions, such as cell adhesion molecule binding, integrin binding, growth factor activity, glycosaminoglycan binding, collagen binding, extracellular matrix structural constituents, and oxidoreductase activity. The cellular components implicated include the extracellular matrix, external encapsulating structures, collagen-containing extracellular matrix, secretory granule lumen, cytoplasmic vesicle lumen, and myofibrils, as displayed in <xref ref-type="fig" rid="f04001">Figure 3C</xref>. KEGG pathway analysis indicated involvement in multiple signaling pathways, such as Hippo signaling pathway, Cytokine-cytokine receptor interaction, Hypertrophic cardiomyopathy, ECM-receptor interaction, Arrhythmogenic right ventricular cardiomyopathy, Cellular senescence, transforming growth factor-&#946;(TGF-&#946;)signaling pathway, Dilated cardiomyopathy, and p53 signaling pathway, which can be observed in <xref ref-type="fig" rid="f04001">Figure 3D</xref>, All enrichment analyses with p-values less than 0.05 are shown in <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Table S3</ext-link>. Enrichment analysis of the biological module showed involvement in extracellular matrix assembly and mesenchymal migration, as shown in <xref ref-type="fig" rid="f05001">Figure 4</xref>.</p>
          <p>
            <fig id="f04001">
              <label>Figure 3</label>
              <caption>
                <title>&#8211; A) GO biological processes. B) GO molecular function. C) GO cellular components. D) KEGG.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf04-en.tif"/></fig>
          </p>
          <p>
            <fig id="f05001">
              <label>Figure 4</label>
              <caption>
                <title>&#8211; Enrichment analysis of the biological module.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf05-en.tif"/></fig>
          </p>
        </sec>
        <sec>
          <title>GSEA results</title>
          <p>Upon conducting GSEA on the fully adjusted gene dataset, Reactome revealed primary enrichments in TGF-&#946;, Hallmark tumor necrosis factor-&#945;(TNF-&#945;) signaling via NF-&#954;B, mTorc1 signaling, Hedgehog signaling, and unfolded protein response. The main cellular components included the subunit processome, pre-ribosome, and complex collagen trimers. In terms of biological functions, gene participation was observed in transcription regulator inhibitor activity, extracellular matrix structural constituent, and growth factor activity. KEGG pathway analysis showed involvement in the TGF-&#946; signaling pathway, glycosaminoglycan biosynthesis, chondroitin sulfate, and dilated cardiomyopathy. Regrettably, in GO biological processes, no gene sets were significant at an FDR below 25%, as depicted in <xref ref-type="fig" rid="f06001">Figure 5</xref>, and the relevant parameters can be found in <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">Table S4</ext-link>.</p>
          <p>
            <fig id="f06001">
              <label>Figure 5</label>
              <caption>
                <title>&#8211; GSEA.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf06-en.tif"/></fig>
          </p>
        </sec>
        <sec>
          <title>Prediction of miRNAs and transcription factors</title>
          <p>Utilizing FunRich to predict miRNAs, abundance analysis retained miRNA-148a-3p, miRNA-148b-3p, miR-152-3p, miR-372-3p, miR-506-3p, the miR-130 family, the miR-302 family, the miR-29 family, and the miR-520 family, as displayed in <xref ref-type="fig" rid="f07001">Figure 6</xref>. Postanalysis prediction of transcription factors through the TRRUST platform revealed that Jun proto-oncogene (JUN), NF-&#954;B1, Sp1 transcription factor (SP1), RELA proto-oncogene (RELA), SRF, and signal transducer and activator of transcription 3 (STAT3) were highly enriched among the majority of core targets, as illustrated in <xref ref-type="fig" rid="f08001">Figure 7</xref>.</p>
          <p>
            <fig id="f07001">
              <label>Figure 6</label>
              <caption>
                <title>&#8211; miRNAs.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf07-en.tif"/></fig>
          </p>
          <p>
            <fig id="f08001">
              <label>Figure 7</label>
              <caption>
                <title>&#8211; Transcription factors analysis.</title>
              </caption>
              <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf08-en.tif"/></fig>
          </p>
        </sec>
        <sec>
          <title>The results of the ROC validation</title>
          <p>During the ROC analysis of core targets, the study found that the AUCs for the genes interleukin 11(IL11), growth arrest and DNA damage-inducible beta (GADD45B), growth differentiation factor 5 (GDF5), NADPH oxidase 4 (NOX4), insulin-like growth factor binding protein 3 (IGFBP3,) actin alpha cardiac muscle 1(ACTC1), myozenin 2 (MYOZ2), and integrin subunit beta 8 (ITGB8) were 0.964710884, 0.943239796, 0.936791383, 0.929563492, 0.92651644, 0.926232993, 0.91723356, and 0.906391723, respectively. All AUCs were greater than 0.9; among them, IL11, GADD45B, NOX4, IGFBP3, and ACTC1 are upregulated in fibrosis, while GDF5, MYOZ2, and ITGB8 are downregulated in expression, substantiating these genes as potential biological markers for diagnosing MF.</p>
        </sec>
      </sec>
      <sec sec-type="discussion">
        <title>Discussion</title>
        <p>Cardiovascular diseases, as the leading cause of death worldwide, pose a significant burden on global public health.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Common cardiovascular conditions, such as coronary heart disease, myocardial infarction, hypertensive heart disease, cardiomyopathy, atrial fibrillation, and diabetic cardiomyopathy, can all lead to MF, ultimately resulting in heart failure. MF induced by cardiovascular diseases is primarily reparative, manifesting as the transformation of cardiac fibroblasts into myofibroblasts under pathological stimuli when cardiac cells are injured. These myofibroblasts can secrete a large amount of extracellular matrix, promoting scar tissue formation, thereby increasing cardiac stiffness, leading to MF, and resulting in cardiac remodeling and heart failure. In contrast, the remodeling process of the ECM requires the deposition of large amounts of collagen and other proteins; the type of collagen and cross-linking of other proteins are also inextricably linked to remodeling.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> There are type I and type III collagens in ECM. Type I collagen makes up 85% of the total collagen and is primarily associated with the coarse fibers that provide tensile strength, stretch, and deformability. In contrast, type III collagen makes up 11% of the total collagen and is associated with the fine fibers that provide elasticity, and collagen deposition of both types can decrease myocardial compliance.<sup><xref ref-type="bibr" rid="B23">23</xref></sup> At present, the clinical management of MF mainly employs pharmacological interventions targeting the renin-angiotensin-aldosterone system to decelerate fibrosis progression; however, the results have been suboptimal.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> Cardiac transplantation can prolong patient survival but comes with its own set of challenges, such as elevated risks, multiple complications, and susceptibility to graft rejection, which adversely affect patient outcomes.<sup><xref ref-type="bibr" rid="B25">25</xref></sup> Consequently, understanding the underlying mechanisms of MF, pinpointing viable treatment targets, inhibiting cardiac fibroblast proliferation, and attenuating the progression of fibrosis are of vital importance in the management of cardiovascular disorders. This study aimed to identify MF at an early stage and to find biological markers with high sensitivity and specificity for the development of new treatment strategies. In this research, DEGs in MF were obtained from the GSE123018, GSE152250, and GSE225336 datasets, constructing a PPI network comprising 37 nodes and 71 edges. Core targets were identified through the network, and a potential functional cluster was discovered using the MCODE plugin.</p>
        <p>Through GO and KEGG enrichment analysis, the key genes were mainly enriched in pathways closely related to fibrosis, such as the TGF-&#946; signaling pathway, ECM-receptor interaction, dilated cardiomyopathy, and hypertrophic cardiomyopathy. In addition, these genes are involved in biological processes such as cytokine&#8210;cytokine receptor interaction, the p53 signaling pathway, the Hippo signaling pathway, and mesenchymal migration, emphasizing the central roles of apoptosis, collagen, extracellular matrix, and cell adhesion in fibrosis. The TGF-&#946; signaling pathway has been widely confirmed to play a significant regulatory role in MF.<sup><xref ref-type="bibr" rid="B26">26</xref></sup> Elevated expression of TGF-&#946; is almost universally observed in patients with MF.<sup><xref ref-type="bibr" rid="B27">27</xref>-<xref ref-type="bibr" rid="B29">29</xref></sup> This differential expression leads to the activation of fibroblasts, stimulates the expression of tissue inhibitors of matrix metalloproteinases, and inhibits the activity of matrix metalloproteinases, resulting in increased deposition of extracellular matrix and exacerbation of MF.<sup><xref ref-type="bibr" rid="B30">30</xref>,<xref ref-type="bibr" rid="B31">31</xref></sup> Numerous studies indicate that MF can be significantly improved by inhibiting the TGF-&#946; signaling pathway through medication.<sup><xref ref-type="bibr" rid="B32">32</xref>-<xref ref-type="bibr" rid="B34">34</xref></sup> Both the P53 and Hippo signaling pathways mediate cell apoptosis.</p>
        <p>Studies have shown that inhibiting these pathways can suppress cardiomyocyte apoptosis and reduce the formation of MF.<sup><xref ref-type="bibr" rid="B35">35</xref>,<xref ref-type="bibr" rid="B36">36</xref></sup> Additionally, research indicates that SO2 can inhibit the Hippo-MST pathway, alleviating cell apoptosis and endoplasmic reticulum stress (ERS), thus significantly attenuating MF in diabetic rats.<sup><xref ref-type="bibr" rid="B37">37</xref></sup> Similarly, enrichment analysis of the core module revealed that its functions are closely related to key mechanisms of MF, such as extracellular matrix assembly and mesenchymal migration. As a supplement to the core gene enrichment analysis, GSEA continues to highlight the deposition of the extracellular matrix as a central process in MF. Additionally, it introduces the Hedgehog signaling, mTORC1, and TNF-&#945; inflammatory pathways through NF-&#954;B, which have all been confirmed to be related to MF in various global studies.<sup><xref ref-type="bibr" rid="B38">38</xref>-<xref ref-type="bibr" rid="B40">40</xref></sup></p>
        <p>Gene expression in biological organisms relies on the regulation of miRNAs. After predicting miRNAs for hub genes, it was found that miRNA-148a-3p, miRNA-148b-3p, miR-152-3p, miR-372-3p, miR-506-3p, the miR-130 family, the miR-302 family, the miR-29 family, and the miR-520 family are shared by a majority of genes. The miR-29 family, in particular, is one of the most extensively studied miRNA families in MF. Numerous studies have shown that the miR-29 family can target a variety of genes related to ECM synthesis, such as various collagen proteins. Its downregulation in fibrosis can lead to excessive deposition of ECM components.<sup><xref ref-type="bibr" rid="B41">41</xref>,<xref ref-type="bibr" rid="B42">42</xref></sup> In an animal experiment, Sheng-song Xu et al. discovered that the expression of miR-152-3p was found to reduce cardiac fibroblast (CF) proliferation and alleviate MF by inhibiting the Wnt1/&#946;-catenin signaling pathway.<sup><xref ref-type="bibr" rid="B43">43</xref></sup></p>
        <p>Similarly, the downregulation of miR-130 promotes cardioprotective effects mediated by PPAR-&#947; through the inhibition of inflammation and MF.<sup><xref ref-type="bibr" rid="B44">44</xref></sup> Therefore, the hsa-miR-29 family, miR-152-3p, and miR-130 could potentially serve as biological markers and therapeutic targets for MF. Analysis of transcription factors indicated that JUN, NF-&#954;B1, SP1, RELA, SRF, and STAT3 are involved in pathways related to inflammation, cell proliferation, apoptosis, and cellular stress responses. NF-&#954;B1 and RELA are part of the NF-&#954;B complex.<sup><xref ref-type="bibr" rid="B45">45</xref></sup> Geng-Rui Xu found that by inhibiting the TLR4/MyD88/NF-&#954;B signaling pathway, collagen deposition was significantly reduced, thereby ameliorating MF.<sup><xref ref-type="bibr" rid="B46">46</xref></sup> STAT3 is closely related to MF and is involved in multiple signaling pathways. Studies have shown that inhibiting the IL-6/STAT3 and JAK-STAT3 pathways alleviates the degree of MF.<sup><xref ref-type="bibr" rid="B47">47</xref>,<xref ref-type="bibr" rid="B48">48</xref></sup></p>
        <p>After performing specificity and sensitivity analysis using ROC, we identified genes with an AUC greater than 0.9, including IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8. IL11 is a classic profibrotic gene. Recent experiments have shown that IL11 secretion can promote paracrine signaling in adjacent fibroblasts, as well as epithelial-to-mesenchymal transition (EMT) and endothelial-to-mesenchymal transition (EndMT), displaying evident profibrotic and proinflammatory effects.<sup><xref ref-type="bibr" rid="B49">49</xref>,<xref ref-type="bibr" rid="B50">50</xref></sup> In another study, it was found that IL-11 can synergize with or enhance the fibrotic effects of TGF-&#946;.<sup><xref ref-type="bibr" rid="B51">51</xref></sup> Tongtong Song et al. found in cellular experiments that inhibition of IL11 in cells significantly reduced mRNA and protein expression of type I collagen and type III collagen, thereby improving ECM remodeling.<sup><xref ref-type="bibr" rid="B52">52</xref></sup> GADD45B is a gene that is upregulated in DNA damage and stress responses, playing roles in various biological processes, particularly in cell cycle regulation, apoptosis, DNA repair, and cellular stress responses.<sup><xref ref-type="bibr" rid="B53">53</xref></sup> Research has shown that adriamycin significantly increased type I collagen, and type III collagen, decreased cardiac function, and induced MF in the ECM by upregulating GADD45B and activating p38 and pJNK in the heart.<sup><xref ref-type="bibr" rid="B54">54</xref></sup> GDF5 can regulate the expression of p38-MAPK, subsequently inhibiting the transcription of type I collagen and Col1a1 and type III collagen, as well as Col3a1 genes in cardiac cells. This leads to a reduction in MF following myocardial infarction.<sup><xref ref-type="bibr" rid="B55">55</xref></sup> NOX4 is a member of the NADPH oxidase family that mediates the production of ROS. Numerous studies have shown that inhibiting NOX4-induced oxidative stress can have antifibrotic effects on the myocardium.<sup><xref ref-type="bibr" rid="B56">56</xref></sup> Additionally, NOX4 is regulated by TGF-&#946;, a fundamental pathway in MF.<sup><xref ref-type="bibr" rid="B57">57</xref></sup> Furthermore, in an in vivo and in vitro experiment, Nox4 was found mediating in the middle of the TGF&#946;1 pathway to promote collagen synthesis, but, unfortunately, the experiment did not elucidate which collagen specifically.<sup><xref ref-type="bibr" rid="B58">58</xref></sup> Likewise, recent experiments have shown that IGFBP3 is highly expressed in fibroblasts in mouse models of MF.<sup><xref ref-type="bibr" rid="B59">59</xref></sup> Changlin Li et al. found that inhibition of IGFBP3 expression attenuated collagen I and III expression in diabetes-induced MF.<sup><xref ref-type="bibr" rid="B60">60</xref></sup> A study by Alessandra Ruggiero found that MYOZ2 mutant mice can develop cardiac cell hypertrophy and interstitial fibrosis.<sup><xref ref-type="bibr" rid="B61">61</xref></sup> ACTC1 is a key protein in cardiac myocytes and is involved in their contractile function. Although mutations in ACTC1 are known to be associated with certain cardiac diseases, such as familial hypertrophic cardiomyopathy (HCM),<sup><xref ref-type="bibr" rid="B62">62</xref></sup> there is currently no definitive evidence linking ACTC1 directly to MF. Further research is needed to elucidate this relationship. ITGB8 is a member of the integrin family and is involved in interactions between cells as well as between cells and the ECM.<sup><xref ref-type="bibr" rid="B63">63</xref></sup> Study indicates that ITGB8 primarily participates in the process of pulmonary fibrosis, but its role in MF has not yet been fully established.<sup><xref ref-type="bibr" rid="B64">64</xref></sup> However, other members of the integrin family, such as ITGB1, are known to be associated with MF,<sup><xref ref-type="bibr" rid="B65">65</xref></sup> warranting further investigation into the functions of ITGB8.</p>
        <p>This study has certain limitations, as the results we obtained were derived from GEO database datasets. While efforts have been made to control and normalize the effects in three experimental datasets, the findings have not been verified in real-world experiments. Furthermore, in acquiring the original datasets, for the sake of consistency across the three datasets, we opted for the 24-hour TGF-&#946;-induced experimental group without selecting the 48-hour or 72-hour experimental groups. Although substantial literature suggests that significant fibrosis can be observed after 24 hours of induction, this approach might have missed crucial information in the dynamic process. Moreover, the core biomarker of MF, TGF-&#946;, did not exhibit high confidence levels in the GSE97358 dataset despite its AUC being 0.882015306. This necessitates extensive experimentation to verify the specificity and sensitivity of these potential biomarkers.</p>
        <p>
          <fig id="f09001">
            <label>Figure 8</label>
            <caption>
              <title>&#8211; ROCs of hub genes.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-121-12-e20230674-30674-gf09-en.tif"/></fig>
        </p>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclusion</title>
        <p>The eight genes, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8, can serve as candidate biomarkers for MF. Processes such as cellular apoptosis, collagen protein synthesis, extracellular matrix formation, cellular adhesion, and inflammation are implicated in the development of MF.</p>
      </sec>
    </body>
    <back>
      <fn-group>
        <fn fn-type="other">
          <label>Study association:</label>
          <p> This study is not associated with any thesis or dissertation work.</p>
        </fn>
        <fn fn-type="other">
          <label>Ethics approval and consent to participate:</label>
          <p> This article does not contain any studies with human participants or animals performed by any of the authors.</p>
        </fn>
        <fn fn-type="supplementary-material" id="fn1001">
          <label>*Supplemental Materials</label>
          <p>For additional information, <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2024/12112/2023-0674_suplementar.pdf">please click here</ext-link>.</p>
        </fn>
      </fn-group>
      <fn-group>
        <fn fn-type="financial-disclosure">
          <label>Sources of funding:</label>
          <p> This study was partially funded by Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (Number: ZYYCXTD-C-202207), Innovation Team of Sichuan Provincial Administration of Traditional Chinese Medicine (Number: 2022C007), and Sichuan Science and Technology Program (No. 2022YFS0618).</p>
        </fn>
      </fn-group>
    </back>
  </sub-article>
</article>
