{"title":"空气污染相关的肝细胞癌免疫基因预后特征：网络毒理学、机器学习和多组学分析。","authors":"Lei Pu, Xiaoyan Zhang, Cheng Pu, Peng Sun","doi":"10.3389/fimmu.2025.1638445","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Air pollution may crosstalk with immune system to promote hepatocellular carcinoma (HCC) development, but its precise mechanisms and prognostic significance remain unclear.</p><p><strong>Objective: </strong>This study aims to construct a prognostic signature for HCC based on air pollutant-related immune genes (APIGs).</p><p><strong>Methods: </strong>We obtained mRNA-seq and scRNA of HCC from GEO, TCGA and ICGC. AP-related target genes were retrieved from several online databases. APIGs were obtained using WGCNA, differential gene expression analysis and immune infiltration analysis. Molecular subtypes were conducted based on APIG expression to characterize immune features. A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). Furthermore, we performed qRT-PCR, survival analyses, functional enrichment, immune infiltration and single-cell analyses. Subsequently, LASSO, RF, and RFE-SVM were employed to identify diagnostic genes, followed by pan-cancer analysis.</p><p><strong>Results: </strong>We identified 19 APIGs. HCC samples were divided into 3 subtypes, with C1 exhibiting a pro-tumor immune microenvironment and poorer prognosis. APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. The biological pathway differences between APIGPS-based high- and low-risk groups involved immune responses and cell proliferation and migration. APIGPS genes had stable binding to 7 APs and were mainly expressed in macrophages, with HRG exhibiting higher macrophage abundance. CDC25C was identified as the hub gene after intersecting diagnostic genes and APIGPS genes. CDC25C was associated with survival of 10 cancers, MSI in 10 cancers, TMB in 21 cancers, and immune cell abundance in 13 cancers.</p><p><strong>Conclusions: </strong>We identified key APIGs and constructed a robust APIG-based prognostic signature for HCC. CDC25C was a key target through which APs impact HCC and multiple other cancers.</p>","PeriodicalId":12622,"journal":{"name":"Frontiers in Immunology","volume":"16 ","pages":"1638445"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463942/pdf/","citationCount":"0","resultStr":"{\"title\":\"Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.\",\"authors\":\"Lei Pu, Xiaoyan Zhang, Cheng Pu, Peng Sun\",\"doi\":\"10.3389/fimmu.2025.1638445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Air pollution may crosstalk with immune system to promote hepatocellular carcinoma (HCC) development, but its precise mechanisms and prognostic significance remain unclear.</p><p><strong>Objective: </strong>This study aims to construct a prognostic signature for HCC based on air pollutant-related immune genes (APIGs).</p><p><strong>Methods: </strong>We obtained mRNA-seq and scRNA of HCC from GEO, TCGA and ICGC. AP-related target genes were retrieved from several online databases. APIGs were obtained using WGCNA, differential gene expression analysis and immune infiltration analysis. Molecular subtypes were conducted based on APIG expression to characterize immune features. A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). Furthermore, we performed qRT-PCR, survival analyses, functional enrichment, immune infiltration and single-cell analyses. Subsequently, LASSO, RF, and RFE-SVM were employed to identify diagnostic genes, followed by pan-cancer analysis.</p><p><strong>Results: </strong>We identified 19 APIGs. HCC samples were divided into 3 subtypes, with C1 exhibiting a pro-tumor immune microenvironment and poorer prognosis. APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. The biological pathway differences between APIGPS-based high- and low-risk groups involved immune responses and cell proliferation and migration. APIGPS genes had stable binding to 7 APs and were mainly expressed in macrophages, with HRG exhibiting higher macrophage abundance. CDC25C was identified as the hub gene after intersecting diagnostic genes and APIGPS genes. CDC25C was associated with survival of 10 cancers, MSI in 10 cancers, TMB in 21 cancers, and immune cell abundance in 13 cancers.</p><p><strong>Conclusions: </strong>We identified key APIGs and constructed a robust APIG-based prognostic signature for HCC. CDC25C was a key target through which APs impact HCC and multiple other cancers.</p>\",\"PeriodicalId\":12622,\"journal\":{\"name\":\"Frontiers in Immunology\",\"volume\":\"16 \",\"pages\":\"1638445\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463942/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Immunology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fimmu.2025.1638445\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Immunology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fimmu.2025.1638445","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.
Background: Air pollution may crosstalk with immune system to promote hepatocellular carcinoma (HCC) development, but its precise mechanisms and prognostic significance remain unclear.
Objective: This study aims to construct a prognostic signature for HCC based on air pollutant-related immune genes (APIGs).
Methods: We obtained mRNA-seq and scRNA of HCC from GEO, TCGA and ICGC. AP-related target genes were retrieved from several online databases. APIGs were obtained using WGCNA, differential gene expression analysis and immune infiltration analysis. Molecular subtypes were conducted based on APIG expression to characterize immune features. A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). Furthermore, we performed qRT-PCR, survival analyses, functional enrichment, immune infiltration and single-cell analyses. Subsequently, LASSO, RF, and RFE-SVM were employed to identify diagnostic genes, followed by pan-cancer analysis.
Results: We identified 19 APIGs. HCC samples were divided into 3 subtypes, with C1 exhibiting a pro-tumor immune microenvironment and poorer prognosis. APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. The biological pathway differences between APIGPS-based high- and low-risk groups involved immune responses and cell proliferation and migration. APIGPS genes had stable binding to 7 APs and were mainly expressed in macrophages, with HRG exhibiting higher macrophage abundance. CDC25C was identified as the hub gene after intersecting diagnostic genes and APIGPS genes. CDC25C was associated with survival of 10 cancers, MSI in 10 cancers, TMB in 21 cancers, and immune cell abundance in 13 cancers.
Conclusions: We identified key APIGs and constructed a robust APIG-based prognostic signature for HCC. CDC25C was a key target through which APs impact HCC and multiple other cancers.
期刊介绍：
Frontiers in Immunology is a leading journal in its field, publishing rigorously peer-reviewed research across basic, translational and clinical immunology. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
Frontiers in Immunology is the official Journal of the International Union of Immunological Societies (IUIS). Encompassing the entire field of Immunology, this journal welcomes papers that investigate basic mechanisms of immune system development and function, with a particular emphasis given to the description of the clinical and immunological phenotype of human immune disorders, and on the definition of their molecular basis.
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