Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis
BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1142609/full |
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| author | Jie Zhu Jie Zhu Jie Zhu Jie Zhu Weikaixin Kong Weikaixin Kong Weikaixin Kong Liting Huang Suzhen Bi Xuelong Jiao Sujie Zhu Sujie Zhu |
| author_facet | Jie Zhu Jie Zhu Jie Zhu Jie Zhu Weikaixin Kong Weikaixin Kong Weikaixin Kong Liting Huang Suzhen Bi Xuelong Jiao Sujie Zhu Sujie Zhu |
| author_sort | Jie Zhu |
| collection | DOAJ |
| description | BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.ConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types. |
| first_indexed | 2024-04-09T23:36:58Z |
| format | Article |
| id | doaj.art-1c41b5ce181545b9bac4db4ca8d5dcdf |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-09T23:36:58Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-1c41b5ce181545b9bac4db4ca8d5dcdf2023-03-20T04:34:26ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-03-011410.3389/fimmu.2023.11426091142609Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysisJie Zhu0Jie Zhu1Jie Zhu2Jie Zhu3Weikaixin Kong4Weikaixin Kong5Weikaixin Kong6Liting Huang7Suzhen Bi8Xuelong Jiao9Sujie Zhu10Sujie Zhu11Key Laboratory of Birth Regulation and Control Technology of National Health Commission of China, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, Shandong, ChinaInstitute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaInstitute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, FinlandDepartment of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, ChinaInstitute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, FinlandDepartment of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, ChinaGastrointestinal Surgery Department, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaInstitute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaInstitute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaGastrointestinal Surgery Department, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaKey Laboratory of Birth Regulation and Control Technology of National Health Commission of China, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, Shandong, ChinaInstitute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, ChinaBackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.ConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1142609/fullbioinformaticscolon cancermachine learningimmune therapymultiple omics |
| spellingShingle | Jie Zhu Jie Zhu Jie Zhu Jie Zhu Weikaixin Kong Weikaixin Kong Weikaixin Kong Liting Huang Suzhen Bi Xuelong Jiao Sujie Zhu Sujie Zhu Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis Frontiers in Immunology bioinformatics colon cancer machine learning immune therapy multiple omics |
| title | Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis |
| title_full | Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis |
| title_fullStr | Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis |
| title_full_unstemmed | Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis |
| title_short | Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis |
| title_sort | identification of immunotherapy and chemotherapy related molecular subtypes in colon cancer by integrated multi omics data analysis |
| topic | bioinformatics colon cancer machine learning immune therapy multiple omics |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1142609/full |
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