Nan Zhang, Wenli Yue, Bihang Jiao, Duo Cheng, Jingjing Wang, Fang Liang, Yingnan Wang, Xiyue Liang, Kunkun Li, Junwei Liu, Yadong Li
{"title":"揭示JAK/STAT信号通路相关基因在结直肠癌中的预后价值：孟德尔随机化分析研究","authors":"Nan Zhang, Wenli Yue, Bihang Jiao, Duo Cheng, Jingjing Wang, Fang Liang, Yingnan Wang, Xiyue Liang, Kunkun Li, Junwei Liu, Yadong Li","doi":"10.1186/s13027-025-00640-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) ranks among the frequently occurring malignant neoplasms affecting the gastrointestinal tract. This study aimed to explore JAK-STAT signaling pathway related genes in CRC and establish a new prognostic model.</p><p><strong>Methods: </strong>The data set used in this study is from a public database. JAK-STAT-differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). Prognostic genes were selected from JAK-STAT-DEGs through Mendelian randomization (MR), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) analyses. The expressions of prognostic genes were verified by RT-qPCR. Then, a risk model was built and validated by the GSE39582. Independent prognostic factors were screened underlying risk scores and different clinical indicators, resulting in the construction of a nomogram. Additionally, immune infiltration, immune scores and immune checkpoint inhibitors analyses and gene set enrichment analysis (GSEA) were carried out.</p><p><strong>Results: </strong>The 3,668 JAK-STAT-DEGs were obtained by intersection of 5826 CRC-DEGs and 9766 JAK-STAT key module genes. Five prognostic genes were selected (ANK3, F5, FAM50B, KLHL35, MPP2), and their expressions were significantly different between CRC and control groups. A risk model was constructed according to prognostic genes and verified by GSE39582. In addition, the nomogram exhibited superior predictive accuracy for CRC. Furthermore, immune analysis results indicated a notable positive correlation between risk score and the scores of immune (R = 0.486), stromal (R = 0.309), and ESTIMATE (R = 0.422). Immune checkpoint inhibitor ADORA2A (Cor = 0.483263) exhibited the strongest positive correlation with risk score. And MPP2 exhibited the most potent activating influence on the cell cycle pathway, whereas ANK3 demonstrated the most significant inhibitory effect within the apoptosis pathway.</p><p><strong>Conclusions: </strong>A new JAK-STAT related CRC prognostic model was constructed and validated, which possessed an underlying predictive potential for CRC patients' prognosis and could potentially enhance tailored guidance for immunotherapy.</p>","PeriodicalId":13568,"journal":{"name":"Infectious Agents and Cancer","volume":"20 1","pages":"9"},"PeriodicalIF":3.1000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806682/pdf/","citationCount":"0","resultStr":"{\"title\":\"Unveiling prognostic value of JAK/STAT signaling pathway related genes in colorectal cancer: a study of Mendelian randomization analysis.\",\"authors\":\"Nan Zhang, Wenli Yue, Bihang Jiao, Duo Cheng, Jingjing Wang, Fang Liang, Yingnan Wang, Xiyue Liang, Kunkun Li, Junwei Liu, Yadong Li\",\"doi\":\"10.1186/s13027-025-00640-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Colorectal cancer (CRC) ranks among the frequently occurring malignant neoplasms affecting the gastrointestinal tract. This study aimed to explore JAK-STAT signaling pathway related genes in CRC and establish a new prognostic model.</p><p><strong>Methods: </strong>The data set used in this study is from a public database. JAK-STAT-differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). Prognostic genes were selected from JAK-STAT-DEGs through Mendelian randomization (MR), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) analyses. The expressions of prognostic genes were verified by RT-qPCR. Then, a risk model was built and validated by the GSE39582. Independent prognostic factors were screened underlying risk scores and different clinical indicators, resulting in the construction of a nomogram. Additionally, immune infiltration, immune scores and immune checkpoint inhibitors analyses and gene set enrichment analysis (GSEA) were carried out.</p><p><strong>Results: </strong>The 3,668 JAK-STAT-DEGs were obtained by intersection of 5826 CRC-DEGs and 9766 JAK-STAT key module genes. Five prognostic genes were selected (ANK3, F5, FAM50B, KLHL35, MPP2), and their expressions were significantly different between CRC and control groups. A risk model was constructed according to prognostic genes and verified by GSE39582. In addition, the nomogram exhibited superior predictive accuracy for CRC. Furthermore, immune analysis results indicated a notable positive correlation between risk score and the scores of immune (R = 0.486), stromal (R = 0.309), and ESTIMATE (R = 0.422). Immune checkpoint inhibitor ADORA2A (Cor = 0.483263) exhibited the strongest positive correlation with risk score. And MPP2 exhibited the most potent activating influence on the cell cycle pathway, whereas ANK3 demonstrated the most significant inhibitory effect within the apoptosis pathway.</p><p><strong>Conclusions: </strong>A new JAK-STAT related CRC prognostic model was constructed and validated, which possessed an underlying predictive potential for CRC patients' prognosis and could potentially enhance tailored guidance for immunotherapy.</p>\",\"PeriodicalId\":13568,\"journal\":{\"name\":\"Infectious Agents and Cancer\",\"volume\":\"20 1\",\"pages\":\"9\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806682/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infectious Agents and Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13027-025-00640-8\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infectious Agents and Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13027-025-00640-8","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Unveiling prognostic value of JAK/STAT signaling pathway related genes in colorectal cancer: a study of Mendelian randomization analysis.
Background: Colorectal cancer (CRC) ranks among the frequently occurring malignant neoplasms affecting the gastrointestinal tract. This study aimed to explore JAK-STAT signaling pathway related genes in CRC and establish a new prognostic model.
Methods: The data set used in this study is from a public database. JAK-STAT-differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). Prognostic genes were selected from JAK-STAT-DEGs through Mendelian randomization (MR), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) analyses. The expressions of prognostic genes were verified by RT-qPCR. Then, a risk model was built and validated by the GSE39582. Independent prognostic factors were screened underlying risk scores and different clinical indicators, resulting in the construction of a nomogram. Additionally, immune infiltration, immune scores and immune checkpoint inhibitors analyses and gene set enrichment analysis (GSEA) were carried out.
Results: The 3,668 JAK-STAT-DEGs were obtained by intersection of 5826 CRC-DEGs and 9766 JAK-STAT key module genes. Five prognostic genes were selected (ANK3, F5, FAM50B, KLHL35, MPP2), and their expressions were significantly different between CRC and control groups. A risk model was constructed according to prognostic genes and verified by GSE39582. In addition, the nomogram exhibited superior predictive accuracy for CRC. Furthermore, immune analysis results indicated a notable positive correlation between risk score and the scores of immune (R = 0.486), stromal (R = 0.309), and ESTIMATE (R = 0.422). Immune checkpoint inhibitor ADORA2A (Cor = 0.483263) exhibited the strongest positive correlation with risk score. And MPP2 exhibited the most potent activating influence on the cell cycle pathway, whereas ANK3 demonstrated the most significant inhibitory effect within the apoptosis pathway.
Conclusions: A new JAK-STAT related CRC prognostic model was constructed and validated, which possessed an underlying predictive potential for CRC patients' prognosis and could potentially enhance tailored guidance for immunotherapy.
期刊介绍：
Infectious Agents and Cancer is an open access, peer-reviewed online journal that encompasses all aspects of basic, clinical, epidemiological and translational research providing an insight into the association between chronic infections and cancer.
The journal welcomes submissions in the pathogen-related cancer areas and other related topics, in particular:
• HPV and anogenital cancers, as well as head and neck cancers;
• EBV and Burkitt lymphoma;
• HCV/HBV and hepatocellular carcinoma as well as lymphoproliferative diseases;
• HHV8 and Kaposi sarcoma;
• HTLV and leukemia;
• Cancers in Low- and Middle-income countries.
The link between infection and cancer has become well established over the past 50 years, and infection-associated cancer contribute up to 16% of cancers in developed countries and 33% in less developed countries.
Preventive vaccines have been developed for only two cancer-causing viruses, highlighting both the opportunity to prevent infection-associated cancers by vaccination and the gaps that remain before vaccines can be developed for other cancer-causing agents. These gaps are due to incomplete understanding of the basic biology, natural history, epidemiology of many of the pathogens that cause cancer, the mechanisms they exploit to cause cancer, and how to interrupt progression to cancer in human populations. Early diagnosis or identification of lesions at high risk of progression represent the current most critical research area of the field supported by recent advances in genomics and proteomics technologies.
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