Arq Bras Cardiol abc Arquivos Brasileiros de Cardiologia Arq. Bras. Cardiol. 0066-782X 1678-4170 Sociedade Brasileira de Cardiologia - SBC vDRQT7PDmpHdwyYtZ8SqjGg S0066-782X2024000200305 00305 10.36660/abc.20230462 Artigo Original Identificação de Potenciais Biomarcadores Cruciais em IAMCSST por Meio de Análise Bioinformática Integrada Zhao Li-Zhi contribuíram igualmente para este trabalho Concepção e desenho da pesquisa Análise e interpretação dos dados Redação do manuscrito 1 2 Liang Yi contribuíram igualmente para este trabalho Concepção e desenho da pesquisa Redação do manuscrito 3 Yin Ting Análise e interpretação dos dados Análise estatística Redação do manuscrito 4 Liao Hui-Ling Concepção e desenho da pesquisa Análise estatística Revisão crítica do manuscrito quanto ao conteúdo intelectual importante 1 2 0000-0002-1749-6976 Liang Bo Concepção e desenho da pesquisa Obtenção de dados Análise e interpretação dos dados Revisão crítica do manuscrito quanto ao conteúdo intelectual importante 5 The Affiliated Traditional Chinese Medicine Hospital Southwest Medical University Luzhou China The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou – China College of Integration of Traditional Chinese and Western Medicine Southwest Medical University Luzhou China College of Integration of Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou – China Department of Geriatrics Sichuan Second Hospital of T.C.M. Chengdu China Department of Geriatrics, Sichuan Second Hospital of T.C.M., Chengdu – China The Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou – China Chongqing Clinical Research Center of Kidney and Urology Diseases Xinqiao Hospital Third Military Medical University Chongqing China Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing – China Correspondência: Bo Liang e Hui-Ling Liao • Army Medical University (Third Military Medical University) – Chongqing – China; Southwest Medical University – Luzhou – China. E-mail: liangbo2018@126.com; liaohl@swmu.edu.cn

Editor responsável pela revisão: Natália Olivetti

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 05 04 2024 2024 121 2 e20230462 12 07 2023 08 09 2023 14 11 2023 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.

: Identificação de Potenciais Biomarcadores Cruciais em IAMCSST por Meio de Análise Bioinformática Integrada

Identificação de potenciais biomarcadores cruciais em IAMCSST por meio de análise bioinformática integrada. IAMCSST: infarto do miocárdio com elevação do segmento ST; DACE: doença arterial coronariana estável.

Resumo Fundamento

O infarto do miocárdio com elevação do segmento ST (IAMCSST) é uma das principais causas de doenças cardiovasculares fatais, que têm sido a principal causa de mortalidade em todo o mundo. O diagnóstico na fase inicial beneficiaria a intervenção clínica e o prognóstico, mas ainda falta a exploração dos biomarcadores do IAMCSST.

 Objetivos

Neste estudo, conduzimos uma análise bioinformática para identificar potenciais biomarcadores cruciais no progresso do IAMCSST.

 Métodos

Obtivemos GSE59867 para pacientes com IAMCSST e doença arterial coronariana estável (DACE). Genes diferencialmente expressos (GDEs) foram selecionados com o limiar de |log2fold change| > 0,5 e p < 0,05. Com base nesses genes, conduzimos análises de enriquecimento para explorar a relevância potencial entre genes e para rastrear genes centrais. Posteriormente, os genes centrais foram analisados para detectar miRNAs relacionados e DAVID para detectar fatores de transcrição para análise posterior. Finalmente, o GSE62646 foi utilizado para avaliar a especificidade dos GDEs, com genes demonstrando resultados de AUC superiores a 75%, indicando seu potencial como candidatos a biomarcadores. Posteriormente, os genes centrais foram analisados para detectar miRNAs relacionados e DAVID para detectar fatores de transcrição para análise posterior. Finalmente, o GSE62646 foi utilizado para avaliar a especificidade dos GDEs, com genes demonstrando resultados de AUC superiores a 75%, indicando seu potencial como candidatos a biomarcadores.

 Resultados

133 GDEs entre DACE e IAMCSST foram obtidos. Em seguida, a rede PPI de GDEs foi construída usando String e Cytoscape, e análises posteriores determinaram genes centrais e 6 complexos moleculares. A análise de enriquecimento funcional dos GDEs sugere que as vias relacionadas à inflamação, metabolismo e imunidade desempenham um papel fundamental na progressão de DACE para IAMCSST. Além disso, foram previstos miRNAs relacionados, has-miR-124, has-miR-130a/b e has-miR-301a/b regularam a expressão do maior número de genes. Enquanto isso, a análise dos fatores de transcrição indica que EVI1, AML1, GATA1 e PPARG são os genes mais enriquecidos. Finalmente, as curvas ROC demonstram que MS4A3, KLRC4, KLRD1, AQP9 e CD14 exibem alta sensibilidade e especificidade na previsão de IAMCSST.

 Conclusões

Este estudo revelou que imunidade, metabolismo e inflamação estão envolvidos no desenvolvimento de IAMCSST derivado de DACE, e 6 genes, incluindo MS4A3, KLRC4, KLRD1, AQP9, CD14 e CCR1, poderiam ser empregados como candidatos a biomarcadores para IAMCSST.

 Infarto do Miocárdio sem Supradesnível do Segmento ST Doença da Artéria Coronariana Biomarcadores Biologia Computacional Introdução

Nas últimas décadas, as doenças cardiovasculares têm sido a principal causa de mortalidade em todo o mundo.1 Entre as mortes por doenças cardiovasculares, a síndrome coronariana aguda (SCA) é a principal causa.2 Embora o aumento do uso de estratégias terapêuticas baseadas em evidências e mudanças no estilo de vida tenham estimulado reduções consideráveis na mortalidade por doenças cardiovasculares, o número de mortes ainda está aumentando.3 Nos países desenvolvidos, mais de um terço das mortes foram causadas por SCA, e a situação também está a piorar ainda mais nos países em desenvolvimento.3

O infarto do miocárdio com elevação do segmento ST (IAMCSST), o tipo mais grave de ataque cardíaco, é um dos três tipos de SCA. Na maioria dos casos, o IAMCSST é devido à ruptura de uma placa aterosclerótica vulnerável num vaso coronário epicárdico, resultando assim numa oclusão trombótica completa,4 ou seja, pacientes com doença arterial coronariana estável (DACE) são populações de alto risco de IAMCSST.3 Numerosas diretrizes da SCA sugerem que um estilo de vida saudável e um bom desempenho médico reduzem a morbidade, e estratégias de tratamento de reperfusão e revascularização a tempo reduzem a mortalidade.5,6 No entanto, quando ocorre IAMCSST, é muito difícil detectar, transportar, diagnosticar e realizar operações a tempo, o que dificulta aproveitar o momento de ouro para a revascularização. O diagnóstico de IAMCSST baseia-se na evidência de biomarcadores de necrose miocitária. As isoformas I e T da troponina cardíaca surgiram como os biomarcadores diagnósticos preferidos porque são altamente sensíveis e específicos para lesão miocárdica; portanto, tanto as diretrizes europeias quanto as americanas enfatizam que a troponina cardíaca é o biomarcador preferido para o diagnóstico de IAMCSST.7,8 Como biomarcadores tradicionais de SCA, a troponina cardíaca e a banda miocárdica da creatina quinase, que seguem cinética semelhante à da troponina cardíaca, têm sido recomendadas para diagnóstico precoce em casos suspeitos de SCA. No entanto, com o desenvolvimento de microarranjos e sequenciamento de última geração, encontrar novos biomarcadores com alta sensibilidade e especificidade é de grande importância para a prevenção e diagnóstico precoce do IAMCSST, especialmente desenvolvido a partir do DACE.

Neste trabalho, investigamos os genes diferencialmente expressos (GDEs) entre pacientes com DACE e IAMCSST. Conduzimos a análise das vias de interação proteína-proteína (PPI), Gene Ontology (GO) e Enciclopédia de Genes e Genomas de Kyoto (KEGG), que ajudaram a elucidar a função dos DEGs. Consequentemente, detectamos microRNAs (miRNAs) e fatores de transcrição relacionados para analisar ainda mais as funções potenciais. Por fim, curvas ROC (Receiver Operating Characteristic) foram traçadas para explorar a sensibilidade e especificidade de potenciais biomarcadores e validar os resultados (Figura Central).

 Método Aquisição e processamento de dados brutos

Os dados brutos do conjunto de dados de expressão de microarranjo GSE598679 e seu arquivo de anotação GPL6244 foram obtidos do Gene Expression Omnibus. Um total de 157 amostras, incluindo 46 pacientes com DACE sem história de IM e 111 pacientes com IAMCSST, foram incluídos no presente estudo. Os dados são públicos e não envolvem a privacidade dos pacientes, não sendo necessária a revisão e consentimento do comitê de ética.

 Investigação de GDEs

Após o processamento dos dados brutos, analisamos os dados usando o pacote limma (versão 3.12) com fold change e p para GDEs.10 O limite de GDEs foi |log2fold change| > 0,5 e p < 0,05,11 e os resultados foram visualizados usando os pacotes ggplot2 (versão 3.3.3) e pheatmap (versão 1.0.12).

 Análise PPI

As informações do PPI foram levantadas utilizando o banco de dados String (versão 11.0).12 Em seguida, a rede PPI de GDEs foi carregada no Cytoscape (versão 3.8.2), conforme descrito anteriormente.13 OCitoNCA plugin no Cytoscape foi usado para calcular centralidade e avaliar redes biológicas, e o plugin MCODE foi empregado para detectar potenciais complexos moleculares e módulos de função.

 Análise de enriquecimento funcional

Os termos GO e a análise KEGG de GDEs e potenciais complexos moleculares foram realizados usando Metascape, uma plataforma baseada na web que fornece anotação genética, enriquecimento funcional e serviços de análise de interatoma. Termos GO ou vias KEGG com p <0,01 e enriquecidos com mais de 3 genes foram considerados análise de enriquecimento significativo, conforme descrito anteriormente.14,15

 Análise de Enriquecimento de Conjunto Genético (GSEA)

O GSEA foi conduzido utilizando o software GSEA (versão 7.4) com termos GO, vias KEGG e vias Reactome para complementar o enriquecimento funcional;16 termos com p < 0,05 e |escore de enriquecimento normalizado| >1 foram definidos como termos de enriquecimento significativo.

 Investigação de miRNAs essenciais e fatores de transcrição

Investigamos os miRNAs relacionados de genes centrais para uma explicação funcional adicional usando o software FunRich (versão 3.1.4).17 Os fatores de transcrição foram previstos usando o Database for Annotation, Visualization, and Integrated Discovery (DAVID, versão 6.8), e os genes enriquecidos classificam os resultados.

 Verificação de genes centrais

Desenhamos as curvas da característica de operação do receptor (ROC) usando o pacote pROC e comparamos a expressão dos genes hub em GSE59867 e GSE62646,18 um conjunto de dados genéticos contendo 14 amostras DACE e 28 amostras de IAMCSST, para validar os genes centrais, que poderiam ser os potenciais biomarcadores de IAMCSST. A área sob a curva superior a 75% foi considerada como demonstrando sensibilidade e especificidade excepcionais, o que indicou a sua potencial candidatura como biomarcadores. A expressão dos genes hub foi comparada com um teste t não pareado, e p bicaudal <0,05 foi considerado uma diferença estatística.

 Resultados Investigação de GDEs

As informações básicas dos conjuntos de dados são mostradas na Tabela 1. O conjunto de dados de expressão de microarranjo GSE62646 foi avaliado com um boxplot e um gráfico de degradação de RNA; os resultados sugeriram que GSE62646 é um conjunto de dados qualificado (Figura S1). Em seguida, valores incertos ou ambíguos em GSE62646 foram complementados usando o método K-Nearest Neighbor, e os DEGs foram analisados usando o pacote limma. Após a regulação pelo UniProt, um total de 133 DEGs foram finalmente determinados, incluindo 54 genes regulados negativamente e 79 genes regulados positivamente, como mostrado na Figura 1, visualizados pelo gráfico do vulcão e pelo mapa de calor.

– Informações básicas sobre os conjuntos de dados
GSE59867 GSE62646
Status Público em 21 de maio de 2015 Público em 23 de outubro de 2014
Título O perfil de expressão gênica revela potenciais biomarcadores prognósticos associados à progressão da insuficiência cardíaca. Padrão alterado de expressão gênica em células mononucleares do sangue periférico em pacientes com infarto agudo do miocárdio
População Um total de 157 amostras, incluindo 111 pacientes com IAMCSST e 46 pacientes com DACE sem histórico de IM. Um total de 146 amostras, incluindo 28 pacientes com IAMCSST e 14 pacientes com DACE sem histórico de IM.
Tipo de experimento Perfil de expressão por array Perfil de expressão por array
Plataformas GPL6244 GPL6244
Publicação PMID: 25984239 PMID: 23185530

IAMCSST: infarto do miocárdio com elevação do segmento ST; DACE: doença arterial coronariana estável; IM: infarto do miocárdio.

– GDEs em amostras de IAMCSST e amostras de DACE. A) Mapa de calor. Cada linha representa uma amostra e cada coluna representa um único gene. A cor rosa representou amostras de IAMCSST e a cor azul representou amostras DACE. A escala de cores mostra o nível relativo de expressão gênica em determinados slides: verde indica baixos níveis de expressão relativa; vermelho indica altos níveis de expressão relativa. B) Gráfico do vulcão. IAMCSST: infarto do miocárdio com elevação do segmento ST; DACE: doença arterial coronariana estável.

Análise PPI

Usando a plataforma String, investigamos a rede PPI de linha e o resultado foi carregado no Cytoscape para processamento posterior. Conforme mostrado na Figura 2A, a rede consistia em 73 nós e 167 arestas; 50 nós desconectados foram ocultados, o valor do grau na rede PPI foi detectado usando CytoNCA para descobrir os genes do hub com a mediana do valor do grau, 35 genes incluindo FCGR1A, S100A12, CD163, CCR2, CD14 e outros foram definidos como genes centrais. Então, o plugin MCODE no Cytoscape foi empregado para detectar os módulos de função potencial ou complexo proteico, como mostrado na Figura 2B ~ D; os 3 principais módulos de função potencial (M1, M2 e M3) foram selecionados para análise de enriquecimento subsequente.

– Rede PPI. A) Toda a rede PPI. B) Módulos biofuncionais M1. C) Módulos biofuncionais M2. D) Módulos biofuncionais M3.

Análise de enriquecimento de função

Numerosas funções moleculares estavam envolvidas na ligação do complexo proteico de classe I do MHC, na ligação a carboidratos, na ligação ao antígeno proteico e na ligação ao receptor RAGE (Figura 3A). Da mesma forma, numerosos processos biológicos estiveram envolvidos na ativação celular envolvida na resposta imune, ativação de leucócitos envolvida na resposta imune, regulação negativa do processo do sistema imunológico, ativação de células mieloides envolvidas na resposta imune, ativação de neutrófilos, imunidade mediada por leucócitos mieloides e ativação de neutrófilos envolvida na resposta imune (Figura 3B). Os resultados indicaram que numerosos componentes celulares estavam envolvidos em grânulos específicos, grânulos terciários, membranas de vesículas citoplasmáticas, membranas granulares secretoras, membranas granulares específicas, o lado externo da membrana plasmática e outros (Figura 3C). As vias KEGG estavam envolvidas no processamento e apresentação de antígenos, citotoxicidade mediada por células natural killer, diferenciação de osteoclastos, linhagem de células hematopoiéticas, desregulação transcricional em câncer, infecção pelo vírus 1 da leucemia de células T humanas, infecção por HTLV-I e via de sinalização PPAR (Figura 3D). Além disso, a análise funcional dos 3 módulos funcionais potenciais (M1, M2 e M3) esteve envolvida na resposta imune (Figura S2A~S2C).

– Análise de enriquecimento GO e KEGG de alvos potenciais. A) Função molecular GO. B) Processos biológicos GO. C) Componentes celulares GO. D) KEGG.

GSEA

Para investigar genes que não são expressos de forma significativamente diferencial, mas são biologicamente importantes e para complementar a análise GO e KEGG, foi realizada uma análise GSEA de todo o conjunto de dados usando GSEA. Tomando o ponto de corte mencionado acima, GSEA estava envolvido na remodelação e depuração do conjunto de lipoproteínas plasmáticas, depuração de lipoproteínas plasmáticas, formação de tampão de agregação plaquetária, RHO GTPases ativam NADPH oxidases, inflamassoma NLRP3, regulação transcricional da diferenciação de adipócitos brancos, depuração de LDL, sinalização de interleucina 10, sinalização de interleucina 4 e interleucina 13, metabolismo de sulfato de heparano/heparina, gliconeogênese e citocromo p450 organizados por tipo de substrato (Figura 4). Obviamente, a análise da GSEA enfatizou a importância da resposta imunológica e forneceu suplementos significativos na coagulação e no IAMCSST.

– GSEA.

Previsão adicional de miRNAs e fatores de transcrição

Os miRNAs dos genes centrais foram previstos usando o software FunRich. Os 8 principais miRNAs classificados por grau, incluindo hsa-miR-124, hsa-miR-130a/b, hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295 e hsa-miR-454 (Figura S3), entre eles foi confirmado que o hsa-miR-124 é fundamental no desenvolvimento de IAMCSST. Enquanto isso, os fatores de transcrição foram analisados utilizando a plataforma DAVID, e os resultados indicam que EVI1, AML1, GATA1 e PPARG são enriquecidos pela maioria dos genes (Figura S4).

 Verificação de genes centrais

Para detectar a sensibilidade e especificidade dos genes centrais, curvas ROC foram empregadas para a verificação dos genes centrais. Em GSE59867, as AUCs de MS4A3, KLRC4, KLRD1, AQP9, CD14 e CCR1 foram 73,6%, 80,5%, 84,7%, 90,3%, 88,2% e 84,2%, respectivamente (todos p <0,0001) (Figura 5A, Tabela 2), o que indica que esses genes possuem excelente sensibilidade e especificidade. Após processar o conjunto de dados GSE62646, incluindo 14 pacientes com DACE e 28 pacientes com IAMCSST, identificamos que MS4A3, KLRC4, KLRD1, AQP9, CD14 e CCR1 apresentaram sensibilidade e especificidade na predição de IAMCSST (Figura 5B). As AUCs desses genes foram de 88,3%, 86,7%, 86,2%, 85,5%, 84,9% e 82,4%, respectivamente (p <0,001) (Figura 5B, Tabela 2). Além disso, MS4A3, KLRC4 e KLRD1 foram regulados negativamente em GSE59867 (todos p <0,0001) (Figura 6A) e GSE62646 (p <0,001) (Figura 6B), enquanto AQP9, CD14 e CCR1 foram regulados positivamente em GSE59867 (p < 0,0001) (Figura 6A) e GSE62646 (p < 0,001) (Figura 6B).

– Curvas ROC de genes centrais. A) GSE59867. B) GSE62646.

– AUCs de genes centrais
GSE59867 GSE62646
AUC EP 95% IC p AUC EP 95% IC p
MS4A3 0,736 0,046 0,654~0,827 <0,0001 0,883 0,054 0,776~0,989 <0,0001
KLRC4 0,805 0,037 0,733~0,878 <0,0001 0,967 0,056 0,758~0,977 0,0001
KLRD1 0,847 0,033 0,782~0,912 <0,0001 0,862 0,056 0,753~0,972 0,0002
AQP9 0,903 0,027 0,850~0,957 <0,0001 0,855 0,060 0,735~0,974 0,0002
CD14 0,882 0,028 0,826~0,937 <0,0001 0,849 0,058 0,734~0,965 0,0003
CCR1 0,842 0,036 0,771~0,913 <0,0001 0,824 0,064 0,698~0,950 0,0007

AUC: área sob a curva característica de operação do receptor; EP: erro padrão; IC: intervalo de confiança.

– Expressão diferencial de genes hub. A) GSE59867. B) GSE62646. IAMCSST: infarto do miocárdio com elevação do segmento ST; DACE: doença arterial coronariana estável.

Discussão

As doenças cardiovasculares são a principal causa de morte no mundo,1 e entre eles o IAMCSST deve ser o primeiro a ser controlado. Nos últimos anos, com o rápido desenvolvimento de microarranjos e sequenciamento de próxima geração, é viável e disponível a busca de biomarcadores confiáveis, que beneficiam o diagnóstico precoce e a prevenção do IM. Neste estudo, obtivemos 133 GDEs entre DACE e IAMCSST, então a rede PPI foi construída, e análises posteriores determinaram genes centrais e 6 complexos moleculares. A análise de enriquecimento funcional revelou que a imunidade, o metabolismo e a inflamação estão envolvidos no desenvolvimento do IAMCSST. Além disso, foram previstos 103 miRNAs relacionados, hsa-miR-124, hsa-miR-130a/b e hsa-miR-301a/b regulam o maior número de genes; entretanto, numerosos fatores de transcrição foram investigados, EVI1, AML1, GATA1 e PPARG são enriquecidos pela maioria dos genes. Por fim, as curvas ROC indicam que MS4A3, KLRC4, KLRD1, AQP9 e CD14 possuem alta sensibilidade e especificidade na predição de IAMCSST.

Além disso, as análises GO e KEGG revelaram que a imunidade, o metabolismo e a inflamação estão envolvidos no mecanismo de desenvolvimento de IAMCSST. Como mostrado acima, vários termos de enriquecimento estavam envolvidos na imunidade e na inflamação, incluindo ativação celular envolvida na resposta imune, ativação de leucócitos envolvida na resposta imune, regulação negativa do processo do sistema imunológico, regulação da imunidade mediada por células assassinas naturais e assim por diante. Enquanto guardiões do sistema imunológico, os leucócitos possuem regulação bidirecional para o desenvolvimento de IAMCSST; alguns leucócitos são aterogênicos, enquanto outros são ateroprotetores; alguns sustentam a inflamação após o infarto do miocárdio, enquanto outros a resolvem.19 Uma revisão construiu um modelo das estratégias terapêuticas do IAMCSST, estudos experimentais anteriores revelaram mecanismos complexos em relação ao desenvolvimento, reparação e remodelação do IAMCSST e modulação da inflamação individualmente com base nas características da condição do paciente no benefício dos pacientes com IAMCSST.20 Resultados semelhantes puderam ser encontrados na análise funcional do potencial complexo molecular; todos os 3 complexos moleculares potenciais estavam envolvidos na imunidade. Como complemento à análise GO e KEGG, a análise GSEA verificou os resultados da análise funcional e forneceu mais evidências de metabolismo. Além dos resultados da análise de enriquecimento sugerindo que os DEGs estão relacionados ao metabolismo do colesterol, os resultados do GSEA levantaram vários termos envolvidos na regulação do metabolismo do colesterol, incluindo remodelação e depuração do conjunto de lipoproteínas plasmáticas, depuração de lipoproteínas plasmáticas, depuração de LDL e sulfato de heparano. /metabolismo da heparina, que eram consistentes com a cognição atual. Além disso, os resultados da análise GSEA indicaram que a regulação da formação do tampão de agregação plaquetária foi diferente entre DACE e IAMCSST, enfatizando que a coagulação também é fundamental para o desenvolvimento de IAMCSST.21 Além disso, a GSEA destacou a inflamação, o stress oxidativo e o metabolismo dos medicamentos, o que pôde ser confirmado na investigação existente.22-24 Os miRNAs desempenham papéis fundamentais na gênese e progressão do IAMCSST; após a triagem, hsa-miR-124, hsa-miR-130a/b, hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295 e hsa-miR-454 foram identificados como os principais miRNAs enriquecidos, numerosos estudos ilustram que o hsa-miR-124 regula o estresse oxidativo e a hipóxia no desenvolvimento do infarto do miocárdio, e pode ser um potencial biomarcador, bem como o alvo terapêutico para o IAMCSST.25,26 Na família MiR-130, incluindo miR-130a e miR- 130b, uma análise mostra que o miR-130 agrava o IAMCSST ao atingir a via PPAR-γ.27 São necessárias pesquisas para explorar e validar a conexão entre hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295 e hsa-miR-454 e IM, além de miRNAs apoiados por experimentos envolvidos no desenvolvimento de IM , como miR-19, miR-23 e outros, podem ser encontrados nos resultados da predição.28-30 Os resultados da previsão do fator de transcrição filtraram 4 fatores de transcrição, incluindo EVI1, AML1, GATA1 e PPARG. EVI1, histona-lisina N-metiltransferase MECOM, está envolvido no progresso da imunidade, metabolismo e inflamação.31,32 Da mesma forma, AML1, fator de transcrição 1 relacionado ao runt, está envolvido na regulação funcional da leucemia, células B e células T e regula o sistema imunológico.33 O GATA1, também conhecido como fator de transcrição eritróide, está envolvido no progresso da produção e coagulação de plaquetas.34 Enquanto isso, um estudo elucidou que o GATA1 está relacionado a uma doença vascular familiar com características de DACE e IAMCSST.35 O PPARG, conhecido como receptor gama ativado por proliferador de peroxissoma, é o receptor nuclear que se liga aos proliferadores de peroxissoma, como drogas hipolipemiantes e ácidos graxos, principalmente envolvido no progresso do metabolismo da gordura e da inflamação e é fundamental para o desenvolvimento de IAMCSST.36

MS4A3 regula o nível de fosforilação de CDK2 através de sua ligação direta a CDKN3,37 e um estudo de coorte sugere que CDK2 estava envolvido na proliferação anormal, uma das características da aterosclerose e do STEMI.38 Além disso, um estudo mencionou que o CDK2 está envolvido na regulação do ciclo celular em miócitos após infarto do miocárdio, o que promove a regeneração da massa muscular e a recuperação da função ventricular.39 Tanto o KLRD1 quanto o KLRC4 são receptores naturais de células assassinas, e naturais as células killer são importantes no aparecimento do IAMCSST pela sua capacidade de secretar IFN-γ e outras citocinas inflamatórias.40 Os pesquisadores mencionaram que a superexpressão do miR-212 inibiu a AQP9 ativando a via de sinalização PI3K/Akt, diminuindo assim a apoptose dos cardiomiócitos, promovendo a regeneração vascular e aliviando a remodelação ventricular em ratos com IAMCSST.41 Da mesma forma, um estudo indicou que o silenciamento do gene AQP9 pode inibir a ativação da via de sinalização ERK1/2, atenuar a resposta inflamatória em ratos com IAMCSST, inibir a apoptose de células miocárdicas e melhorar a função cardíaca.42 CD14, o nome completo da proteína é antígeno de diferenciação de monócitos CD14, recentemente, um estudo mencionou que, em comparação com pacientes com DAC, os níveis de monócitos relacionados ao CD14 foram significativamente maiores em pacientes com IAMCSST.43

Este estudo tem algumas limitações. Primeiro, todos os resultados da análise foram derivados de conjuntos de dados anteriores. Apesar dos esforços que temos feito no controle de qualidade, a autenticidade dos resultados ainda necessita de verificação. Além disso, limitado pelas informações contidas em GSE59867 e GSE62646, não podemos comparar o desempenho diagnóstico dos biomarcadores identificados com a troponina I e T nem avaliar diferenças em sua cinética temporal. Terceiro, todos os dados que utilizamos vieram de células mononucleares do sangue periférico, e não da artéria coronária ou do tecido cardíaco, porque é relativamente difícil obter clinicamente a artéria coronária e o tecido cardíaco. Felizmente, estudos anteriores demonstraram que os dados do sangue periférico também têm boa fiabilidade.44,45 Finalmente, embora numerosos estudos apoiassem a potencialidade dos potenciais biomarcadores previstos neste estudo, os resultados das curvas ROC não conseguiram encontrar um gene com elevada confiança (AUC > 90%), e são necessários ensaios consideráveis para validar a sensibilidade e especificidade de potenciais biomarcadores. No entanto, este estudo determinou os potenciais biomarcadores e investigou os mecanismos complexos do IAMCSST desenvolvidos a partir da DACE, o que promoveu a designação do nosso próximo plano para explorar os mecanismos no ensaio clínico em breve.

 Conclusão

Revelamos que imunidade, metabolismo e inflamação estão envolvidos no desenvolvimento de IAMCSST derivado de DACE, e 5 genes, incluindo MS4A3, KLRC4, KLRD1, AQP9 e CD14, poderiam ser empregados como candidatos a biomarcadores para IAMCSST.

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Park SH, Choi HJ, Yang H, Do KH, Kim J, Lee DW, et al. Endoplasmic Reticulum Stress-Activated C/EBP Homologous Protein Enhances Nuclear Factor-Kappab Signals Via Repression of Peroxisome Proliferator-Activated Receptor Gamma. J Biol Chem. 2010;285(46):35330-9. doi: 10.1074/jbc.M110.136259. Park SH Choi HJ Yang H Do KH Kim J Lee DW et al Endoplasmic Reticulum Stress-Activated C/EBP Homologous Protein Enhances Nuclear Factor-Kappab Signals Via Repression of Peroxisome Proliferator-Activated Receptor Gamma J Biol Chem 2010 285 46 35330 35339 10.1074/jbc.M110.136259 37. Donato JL, Ko J, Kutok JL, Cheng T, Shirakawa T, Mao XQ, et al. Human Htm4 is a Hematopoietic Cell Cycle Regulator. J Clin Invest. 2002;109(1):51-8. doi: 10.1172/JCI14025. Donato JL Ko J Kutok JL Cheng T Shirakawa T Mao XQ et al Human Htm4 is a Hematopoietic Cell Cycle Regulator J Clin Invest 2002 109 1 51 58 10.1172/JCI14025 38. Dehghan A, van Hoek M, Sijbrands EJ, Oostra BA, Hofman A, van Duijn CM, et al. Lack of Association of Two Common Polymorphisms on 9p21 with Risk of Coronary Heart Disease and Myocardial Infarction; Results from a Prospective Cohort Study. BMC Med. 2008;6:30. doi: 10.1186/1741-7015-6-30. Dehghan A van Hoek M Sijbrands EJ Oostra BA Hofman A van Duijn CM et al Lack of Association of Two Common Polymorphisms on 9p21 with Risk of Coronary Heart Disease and Myocardial Infarction; Results from a Prospective Cohort Study BMC Med 2008 6 30 10.1186/1741-7015-6-30 39. Huang W, Feng Y, Liang J, Yu H, Wang C, Wang B, et al. Loss of Microrna-128 Promotes Cardiomyocyte Proliferation and Heart Regeneration. Nat Commun. 2018;9(1):700. doi: 10.1038/s41467-018-03019-z. Huang W Feng Y Liang J Yu H Wang C Wang B et al Loss of Microrna-128 Promotes Cardiomyocyte Proliferation and Heart Regeneration Nat Commun 2018 9 1 700 10.1038/s41467-018-03019-z 40. Ortega-Rodríguez AC, Marín-Jáuregui LS, Martínez-Shio E, Castro BH, González-Amaro R, Escobedo-Uribe CD, et al. Altered NK Cell Receptor Repertoire and Function of Natural Killer Cells in Patients with Acute Myocardial Infarction: a Three-Month Follow-Up Study. Immunobiology. 2020;225(3):151909. doi: 10.1016/j.imbio.2020.151909. Ortega-Rodríguez AC Marín-Jáuregui LS Martínez-Shio E Castro BH González-Amaro R Escobedo-Uribe CD et al Altered NK Cell Receptor Repertoire and Function of Natural Killer Cells in Patients with Acute Myocardial Infarction: a Three-Month Follow-Up Study Immunobiology 2020 225 3 151909 10.1016/j.imbio.2020.151909 41. Ren N, Wang M. Microrna-212-Induced Protection of the Heart Against Myocardial Infarction Occurs Via the Interplay Between AQP9 and PI3K/Akt Signaling Pathway. Exp Cell Res. 2018;370(2):531-41. doi: 10.1016/j.yexcr.2018.07.018. Ren N Wang M Microrna-212-Induced Protection of the Heart Against Myocardial Infarction Occurs Via the Interplay Between AQP9 and PI3K/Akt Signaling Pathway Exp Cell Res 2018 370 2 531 541 10.1016/j.yexcr.2018.07.018 42. Huang X, Yu X, Li H, Han L, Yang X. Regulation Mechanism of Aquaporin 9 Gene on Inflammatory Response and Cardiac Function in Rats with Myocardial Infarction Through Extracellular Signal-Regulated Kinase1/2 Pathway. Heart Vessels. 2019;34(12):2041-51. doi: 10.1007/s00380-019-01452-8. Huang X Yu X Li H Han L Yang X Regulation Mechanism of Aquaporin 9 Gene on Inflammatory Response and Cardiac Function in Rats with Myocardial Infarction Through Extracellular Signal-Regulated Kinase1/2 Pathway Heart Vessels 2019 34 12 2041 2051 10.1007/s00380-019-01452-8 43. Fan Q, Tao R, Zhang H, Xie H, Lu L, Wang T, et al. Dectin-1 Contributes to Myocardial Ischemia/Reperfusion Injury by Regulating Macrophage Polarization and Neutrophil Infiltration. Circulation. 2019;139(5):663-78. doi: 10.1161/CIRCULATIONAHA.118.036044. Fan Q Tao R Zhang H Xie H Lu L Wang T et al Dectin-1 Contributes to Myocardial Ischemia/Reperfusion Injury by Regulating Macrophage Polarization and Neutrophil Infiltration Circulation 2019 139 5 663 678 10.1161/CIRCULATIONAHA.118.036044 44. Lin B, Zheng W, Jiang X. Crosstalk between Circulatory Microenvironment and Vascular Endothelial Cells in Acute Myocardial Infarction. J Inflamm Res. 2021;14:5597-610. doi: 10.2147/JIR.S316414. Lin B Zheng W Jiang X Crosstalk between Circulatory Microenvironment and Vascular Endothelial Cells in Acute Myocardial Infarction J Inflamm Res 2021 14 5597 5610 10.2147/JIR.S316414 45. Askari N, Lipps C, Voss S, Staubach N, Grün D, Klingenberg R, et al. Circulating Monocyte Subsets are Associated with Extent of Myocardial Injury but not with Type of Myocardial Infarction. Front Cardiovasc Med. 2021;8:741890. doi: 10.3389/fcvm.2021.741890. 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 Artigo Original Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis Zhao Li-Zhi contributed to this work equally Conception and design of the research Analysis and interpretation of the data Writing of the manuscript 1 2 Liang Yi contributed to this work equally Conception and design of the research Acquisition of data Writing of the manuscript 3 Yin Ting Analysis and interpretation of the data Statistical analysis Writing of the manuscript 4 Liao Hui-Ling Conception and design of the research Statistical analysis; Critical revision of the manuscript for important intellectual content 1 2 0000-0002-1749-6976 Liang Bo Conception and design of the research Analysis and interpretation of the data Critical revision of the manuscript for important intellectual content 5 China The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou – China China School of Integration of Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou – China China Department of Geriatrics, Sichuan Second Hospital of T.C.M., Chengdu – China China Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou – China China Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University),Chongqing – China Mailing Address: Bo Liang and Hui-Ling Liao • Army Medical University (Third Military Medical University) – Chongqing – China; Southwest Medical University – Luzhou – China. E-mail: liangbo2018@126.com; liaohl@swmu.edu.cn

Potential conflict of interest

No potential conflict of interest relevant to this article was reported.

Editor responsible for the review: Natália Olivetti

: Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis

Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis. STEMI: ST-segment elevation myocardial infarction; SCAD: stable coronary artery disease.

Abstract Background

ST-segment elevation myocardial infarction (STEMI) is one of the leading causes of fatal cardiovascular diseases, which have been the prime cause of mortality worldwide. Diagnosis in the early phase would benefit clinical intervention and prognosis, but the exploration of the biomarkers of STEMI is still lacking.

 Objectives

In this study, we conducted a bioinformatics analysis to identify potential crucial biomarkers in the progress of STEMI.

 Methods

We obtained GSE59867 for STEMI and stable coronary artery disease (SCAD) patients. Differentially expressed genes (DEGs) were screened with the threshold of |log2fold change| > 0.5 and p <0.05. Based on these genes, we conducted enrichment analysis to explore the potential relevance between genes and to screen hub genes. Subsequently, hub genes were analyzed to detect related miRNAs and DAVID to detect transcription factors for further analysis. Finally, GSE62646 was utilized to assess DEGs specificity, with genes demonstrating AUC results exceeding 75%, indicating their potential as candidate biomarkers.

 Results

133 DEGs between SCAD and STEMI were obtained. Then, the PPI network of DEGs was constructed using String and Cytoscape, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis of the DEGs suggests that pathways related to inflammation, metabolism, and immunity play a pivotal role in the progression from SCAD to STEMI. Besides, related-miRNAs were predicted, has-miR-124, has-miR-130a/b, and has-miR-301a/b regulated the expression of the largest number of genes. Meanwhile, Transcription factors analysis indicate that EVI1, AML1, GATA1, and PPARG are the most enriched gene. Finally, ROC curves demonstrate that MS4A3, KLRC4, KLRD1, AQP9, and CD14 exhibit both high sensitivity and specificity in predicting STEMI.

 Conclusions

This study revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 6 genes, including MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1, could be employed as candidate biomarkers to STEMI.

 ST Elevation Myocardial Infarction Coronary Artery Disease Biomarkers Computational Biology Introduction

In the past decades, cardiovascular diseases have been the prime cause of mortality worldwid.1 Among the deaths from cardiovascular diseases, acute coronary syndrome (ACS) is the leading cause.2 Although increased use of evidence-based therapy strategies and lifestyle changes have spurred considerable reductions in mortality from cardiovascular diseases, the number of deaths is still increasing.3 In developed nations, more than a third of deaths were caused by ACS, and the situation is also rising even worse in developing countries.3

ST-segment elevation myocardial infarction (STEMI), the most severe type of heart attack, is one of three types of ACS. In the majority of cases, STEMI is due to disruption of a vulnerable atherosclerotic plaque in an epicardial coronary vessel, thereby, a complete thrombotic occlusion,4 that is to say, stable coronary artery disease (SCAD) patients are high-risk populations of STEMI,3 Numerous guidelines from ACS suggest that a healthy lifestyle and good medical performance reduce morbidity and reperfusion and revascularization treatment strategies in time reduce mortality.5,6 Nevertheless, when STEMI occurs, it is very hard to detect, transport, diagnose, and perform operations in time, which makes it difficult to seize the golden time for revascularization. The diagnosis of STEMI relies on biomarker evidence of myocyte necrosis. Cardiac troponin isoforms I and T have emerged as the preferred diagnostic biomarkers because they are highly sensitive and specific for myocardial injury; therefore, both European and American guidelines emphasize that cardiac troponin is the preferred biomarker for diagnosis of STEMI.7,8 As traditional biomarkers of ACS, cardiac troponin and creatine kinase myocardial band, which follow similar kinetics as cardiac troponin, have been recommended for early diagnosis in suspected cases of ACS. However, with the development of microarray and next-generation sequencing, finding new biomarkers with high sensitivity and specificity is of great significance for the prevention and early diagnosis of STEMI, specially developed from SCAD.

In this work, we investigated the differentially expressed genes (DEGs) between patients with SCAD and STEMI. We conducted the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, which helped to elucidate the function of DEGs. Consequently, we detected related microRNAs (miRNAs) and transcription factors to analyze the potential functions further. At last, receiver operating characteristic (ROC) curves were plotted to explore the sensitivity and specificity of potential biomarkers and validate the results (Central Illustration).

 Method Acquisition and processing of raw data

The raw data of the microarray expression dataset GSE598679 and its annotation file GPL6244 were obtained from Gene Expression Omnibus. A total of 157 samples, including 46 SCAD patients without a history of MI and 111 ST-segment elevation myocardial infarction (STEMI) patients, were included in the present study. The data are public and do not involve the privacy of patients, so the review and consent of the ethics committee are not required.

 Investigation of DEGs

After the processing of raw data, we analyzed the data using the limma package (version 3.12) with a fold change and p for DEGs.10 The threshold of DEGs was |log2fold change| > 0.5 and p <0.05,11 and the results were visualized using the ggplot2 (version 3.3.3) and the pheatmap (version 1.0.12) packages.

 PPI analysis

PPI information was surveyed using the String database (version 11.0).12 Next, the PPI network of DEGs was uploaded to Cytoscape (version 3.8.2), as described previously.13 The CytoNCA plugin in Cytoscape was used to calculate centrality and evaluate biological networks, and the MCODE plugin was employed for detecting potential molecular complexes and function modules.

 Functional enrichment analysis

GO terms and KEGG analysis of DEGs and potential molecular complexes were carried out using Metascape, a web-based platform providing gene annotation, functional enrichment, and interactome analysis services. GO terms or KEGG pathways with both p < 0.01 and enriched with more than 3 genes were considered significant enrichment analysis, as described previously.14,15

 Gene Set Enrichment Analysis (GSEA)

GSEA was conducted using GSEA software (version 7.4) with GO terms, KEGG pathways, and Reactome pathways to supplement the functional enrichment;16 terms with p < 0.05 and |normalized enrichment score| >1 were defined as significant enrichment terms.

 Investigation of pivotal miRNAs and transcription factors

We investigated the related miRNAs of hub genes for a further functional explanation using FunRich software (version 3.1.4).17 The transcription factors were predicted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8), and the enriched genes rank the results.

 Verification of hub genes

We draw the receiver operating characteristic (ROC) curves using the pROC package and compared the expression of hub genes in both GSE59867 and GSE62646,18 a gene dataset containing 14 SCAD samples and 28 STEMI samples, to validate the hub genes, which could be the potential biomarkers of STEMI. The area under the curve exceeding 75% was regarded as demonstrating exceptional sensitivity and specificity, which indicated their potential candidacy as biomarkers. The expression of hub genes was compared with an unpaired t-test, and two-tailed p < 0.05 was considered a statistical difference.

 Results Investigation of DEGs

The basic information of the datasets is shown in Table 1. Microarray expression dataset GSE62646 was evaluated with a boxplot and RNA degradation plot; the results suggested that GSE62646 is a qualified dataset (Figure S1). Then, uncertain or ambiguous values in GSE62646 were supplemented using the K-Nearest Neighbor method, and the DEGs were analyzed using the limma package. After regulating by UniProt, a total of 133 DEGs were finally determined, including 54 downregulated genes, and 79 upregulated genes, as shown in Figure 1, visualized by volcano plot and heat map.

– Basic information about the datasets
GSE59867 GSE62646
Status Public on May 21, 2015 Public on Oct 23, 2014
Title Gene expression profiling reveals potential prognostic biomarkers associated with the progression of heart failure. Altered gene expression pattern in peripheral blood mononuclear cells in patients with acute myocardial infarction
Population A total of 157 samples, including 111 STEMI patients and 46 SCAD patients without a history of MI. A total of 146 samples, including 28 STEMI patients and 14 SCAD patients without a history of MI.
Experiment type Expression profiling by array Expression profiling by array
Platforms GPL6244 GPL6244
Publication PMID: 25984239 PMID: 23185530

STEMI: ST-segment elevation myocardial infarction; SCAD: stable coronary artery disease; MI: myocardial infarction.

– DEGs in STEMI samples and SCAD samples. A) Heat map. Each row represents a sample, and each column represents a single gene. Pink color represented STEMI samples, and blue color represented SCAD samples. The color scale shows the relative gene expression level in certain slides: green indicates low relative expression levels; red indicates high relative expression levels. B) Volcano plot. STEMI: ST-segment elevation myocardial infarction; SCAD: stable coronary artery disease.

PPI analysis

Using the String platform, we investigated the row PPI network, and the result was uploaded to Cytoscape for further processing. As shown in Figure 2A, the network consisted of 73 nodes and 167 edges; 50 disconnected nodes were hidden, the value of the degree in the PPI network was detected using CytoNCA to figure out the hub genes with the median of the degree value, 35 genes including FCGR1A, S100A12, CD163, CCR2, CD14, and others were defined as hub genes. Then, the MCODE plugin in Cytoscape was employed to detect the potential function modules or protein complex, as shown in Figure 2B~D; the top 3 potential function modules (M1, M2, and M3) were selected for subsequent enrichment analysis.

– PPI network. A) The whole PPI network. B) M1 bio-functional modules. C) M2 bio-functional modules. D) M3 bio-functional modules.

Function enrichment analysis

Numerous molecular functions were involved in MHC class I protein complex binding, carbohydrate binding, protein antigen binding, and RAGE receptor binding (Figure 3A). Similarly, numerous biological processes were involved in cell activation involved in the immune response, leukocyte activation involved in the immune response, negative regulation of the immune system process, myeloid cell activation involved in the immune response, neutrophil activation, myeloid leukocyte mediated immunity, and neutrophil activation involved in immune response (Figure 3B). The results indicated that numerous cellular components were involved in specific granules, tertiary granules, cytoplasmic vesicle membranes, secretory granule membranes, specific granule membranes, the external side of the plasma membrane, and others (Figure 3C). KEGG pathways were involved in Antigen processing and presentation, natural killer cell-mediated cytotoxicity, osteoclast differentiation, hematopoietic cell lineage, transcriptional misregulation in cancer, Human T-cell leukemia virus 1 infection, HTLV-I infection, and PPAR signaling pathway (Figure 3D). Besides, the functional analysis of the 3 potential function modules (M1, M2, and M3) was involved in the immune response (Figure S2A~S2C).

– GO and KEGG enrichment analysis of potential targets. A) GO molecular function. B) GO biological processes. C) GO cellular components. D) KEGG.

GSEA

To investigate genes that are not significantly differentially expressed but are biologically important and to supplement GO and KEGG analysis, a GSEA analysis of the whole dataset was conducted using GSEA. Take the cutoff as mentioned above, GSEA was involved in plasma lipoprotein assembly remodeling and clearance, plasma lipoprotein clearance, platelet aggregation plug formation, RHO GTPases activate NADPH oxidases, NLRP3 inflammasome, transcriptional regulation of white adipocyte differentiation, LDL clearance, interleukin 10 signaling, interleukin 4 and interleukin 13 signaling, heparan sulfate/heparin metabolism, gluconeogenesis, and cytochrome p450 arranged by substrate type (Figure 4). Obviously, GSEA analysis emphasized the importance of immune-related response and has provided significant supplements on coagulation and STEMI.

– GSEA.

Further prediction of miRNAs and transcription factors

The miRNAs of hub genes were predicted using FunRich software. Top 8 miRNA ranked by degree, including hsa-miR-124, hsa-miR-130a/b, hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295, and hsa-miR-454 (Figure S3), among them hsa-miR-124 was confirmed are pivotal in the development of STEMI. Meanwhile, transcription factors were analyzed using the DAVID platform, and the results indicate that EVI1, AML1, GATA1, and PPARG are enriched by most genes (Figure S4).

 Verification of hub genes

To detect the sensitivity and specificity of hub genes, ROC curves were employed for the verification of the hub genes. In GSE59867, the AUCs of MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1 were 73.6%, 80.5%, 84.7%, 90.3%, 88.2%, and 84.2%, respectively (all P < 0.0001) (Figure 5A, Table 2), which indicate that these genes have excellent sensitivity and specificity. After processing the GSE62646 dataset, including 14 SCAD patients and 28 STEMI patients, we identified that MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1 had sensitivity and specificity in the prediction of STEMI (Figure 5B). The AUCs of these genes were 88.3%, 86.7%, 86.2%, 85.5%, 84.9%, and 82.4%, respectively (p < 0.001) (Figure 5B, Table 2). In addition, MS4A3, KLRC4, and KLRD1 were down-regulated in GSE59867 (p < 0.0001) (Figure 6A) and GSE62646 (p < 0.001) (Figure 6B), whereas AQP9, CD14, and CCR1 were upregulated in GSE59867 (p < 0.0001) (Figure 6A) and GSE62646 (p < 0.001) (Figure 6B).

– ROC curves of hub genes. A) GSE59867. B) GSE62646.

– AUCs of hub genes
GSE59867 GSE62646
AUC SE 95% CIs p AUC SE 95% CIs p
MS4A3 0.736 0.046 0.654~0.827 < 0.0001 0.883 0.054 0.776~0.989 < 0.0001
KLRC4 0.805 0.037 0.733~0.878 < 0.0001 0.967 0.056 0.758~0.977 0.0001
KLRD1 0.847 0.033 0.782~0.912 < 0.0001 0.862 0.056 0.753~0.972 0.0002
AQP9 0.903 0.027 0.850~0.957 < 0.0001 0.855 0.060 0.735~0.974 0.0002
CD14 0.882 0.028 0.826~0.937 < 0.0001 0.849 0.058 0.734~0.965 0.0003
CCR1 0.842 0.036 0.771~0.913 < 0.0001 0.824 0.064 0.698~0.950 0.0007

AUC: area under the receiver operating characteristic curve; SE: standard error; CI: confidence interval.

– Differential expression of hub genes. A) GSE59867. B) GSE62646. STEMI: ST-segment elevation myocardial infarction; SCAD: stable coronary artery disease.

Discussion

Cardiovascular diseases are the leading cause of death worldwide,1 and among them, STEMI should be the first to be controlled. In recent years, with the rapid development of microarray and next-generation sequencing, it is feasible and available to seek reliable biomarkers, which benefit the early diagnosis and prevention of MI. In this study, we obtained 133 DEGs between SCAD and STEMI, then the PPI network was constructed, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis revealed that immunity, metabolism, and inflammation are involved in the development of STEMI. Besides, 103 related miRNAs were predicted, hsa-miR-124, hsa-miR-130a/b, and hsa-miR-301a/b regulate the largest number of genes; meanwhile, numerous transcription factors were investigated, EVI1, AML1, GATA1, and PPARG are enriched by most genes. At last, ROC curves indicate MS4A3, KLRC4, KLRD1, AQP9, and CD14 own high sensitivity and specificity in the prediction of STEMI.

Further, GO and KEGG analysis have revealed that immunity, metabolism, and inflammation are involved in the mechanism of STEMI development. As is showcased above, numerous enrichment terms were involved in immunity and inflammation, including cell activation involved in immune response, leukocyte activation involved in immune response, negative regulation of the immune system process, regulation of natural killer cell-mediated immunity, and so on. As the keepers of the immune system, Leukocytes possess bidirectional regulation to the development of STEMI; some leukocytes are atherogenic, whereas others are atheroprotective; some sustain inflammation after myocardial infarction while others resolve it.19 A review has built a blueprint of the therapy strategies of STEMI, previous experimental studies have revealed complex mechanisms regarding the development, reparative, and remodeling of STEMI and modulating inflammation individually based on the characteristics of the patient’s condition in the will benefit patients with STEMI.20 Similar results could be found in the functional analysis of the potential molecular complex; all 3 potential molecular complexes were involved in the immunity. As a supplement to GO and KEGG analysis, GSEA analysis verified the results of the functional analysis and provided more evidence of metabolism. In addition to the enrichment analysis results suggesting that DEGs are related to cholesterol metabolism, the results of GSEA raised that numerous terms involved in the regulation of cholesterol metabolism, including plasma lipoprotein assembly remodeling and clearance, plasma lipoprotein clearance, LDL clearance, and heparan sulfate/heparin metabolism, which were consistent with current cognition. Besides, the results of GSEA analysis indicated that the regulation of platelet aggregation plug formation was different between SCAD and STEMI, emphasizing that coagulation is also pivotal to the development of STEMI.21 Moreover, GSEA highlighted inflammation, oxidative stress, and drug metabolism, which could be confirmed in the existing research.22-24

MiRNAs play key roles in the genesis and progression of STEMI; after screening, hsa-miR-124, hsa-miR-130a/b, hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295, and hsa-miR-454 were identified as the main enriched miRNAs, numerous studies illustrate that hsa-miR-124 regulates oxidative stress and hypoxia in the development of MI, and could be a potential biomarker as well as the therapeutic target for STEMI.25,26 MiR-130 family, including miR-130a and miR-130b, an analysis shows miR-130 aggravates STEMI by targeting PPAR-γ pathway.27 Research is needed to explore and validate the connection between hsa-miR-301a/b, hsa-miR-3666, hsa-miR-4295, and hsa-miR-454 and MI, besides miRNAs supported by experiments involved in the development of MI, such as miR-19, miR-23, and others, could be found in the results of prediction.28-30 The results of transcription factor prediction filtered 4 transcription factors, including EVI1, AML1, GATA1, and PPARG. EVI1, Histone-lysine N-methyltransferase MECOM, are involved in the progress of immunity, metabolism, and inflammation.31,32 Similarly, AML1, runt-related transcription factor 1, is involved in the functional regulation of leukemia, B-cell, and T-cell and regulates the immunity system.33 GATA1, also known as the Erythroid transcription factor, is involved in the progress of platelet production and coagulation.34 Meanwhile, a study has elucidated that GATA1 is related to a familial vascular disease with features of SCAD and STEMI.35 PPARG, known as Peroxisome proliferator-activated receptor gamma, is the nuclear receptor that binds peroxisome proliferators such as hypolipidemic drugs and fatty acids, mainly involved in the progress of fat metabolism and inflammation and is pivotal to the development of STEMI.36

MS4A3 regulates the level of phosphorylation of CDK2 through its direct binding to CDKN3,37 and a cohort study suggests that CDK2 was involved in abnormal proliferation, one of the characteristics of atherosclerosis and STEMI.38 Besides, a study has mentioned that CDK2 is involved in the regulation of the cell cycle in myocytes after myocardial infarction, which promotes the regeneration of muscle mass and the recovery of ventricular function.39 Both KLRD1 and KLRC4 are natural killer cell receptors, and natural killer cells are important in the onset of STEMI by their ability to secrete IFN-γ and other inflammatory cytokines.40 Researchers have mentioned that the overexpression of miR-212 inhibited AQP9 by activating the PI3K/Akt signaling pathway, thus decreasing cardiomyocyte apoptosis, promoting vascular regeneration, and alleviating ventricular remodeling in rats with STEMI.41 Similarly, a study indicated that silencing the AQP9 gene can inhibit the activation of the ERK1/2 signaling pathway, attenuate the inflammatory response in rats with STEMI, inhibit apoptosis of myocardial cells, and improve cardiac function.42 CD14, full protein name is monocyte differentiation antigen CD14, recently, a study has mentioned that compared with CAD patients, the CD14-related monocyte levels were significantly higher in patients with STEMI.43

This study has some limitations. First, all the results of the analysis were derived from previous data sets. Despite the efforts we have made in quality control, the authenticity of the results still needs verification. Moreover, limited by the information contained in GSE59867 and GSE62646, we cannot compare the diagnostic performance of the identified biomarkers with troponin I and T nor evaluate differences in their temporal kinetics. Third, all data we used came from peripheral blood mononuclear cells, not from coronary artery or heart tissue, because it is relatively difficult to obtain coronary artery and heart tissue clinically. Fortunately, previous studies have shown that peripheral blood data also have good reliability.44,45 Finally, although numerous studies supported the potentiality of the potential biomarkers predicted in this study, the results of ROC curves failed to find a gene with high confidence (AUC > 90%), and considerable trials are needed to validate the sensitivity and specificity of potential biomarkers. Nevertheless, this study determined the potential biomarkers and investigated the complex mechanisms of STEMI developed from SCAD, which has promoted the designation of our next plan to explore the mechanisms in the clinical trial soon.

 Conclusion

We revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 5 genes, including MS4A3, KLRC4, KLRD1, AQP9, and CD14, could be employed as candidate biomarkers for STEMI.

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