Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer
Abstract Globally, CRC ranks as a principal cause of mortality, with projections indicating a substantial rise in both incidence and mortality by the year 2040. The immunological responses to cancer heavily rely on the function of CD4Tconv. Despite this critical role, prognostic studies on CRC-relat...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-75270-y |
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| author | Zijing Wang Zhanyuan Sun Hengyi Lv Wenjun Wu Hai Li Tao Jiang |
| author_facet | Zijing Wang Zhanyuan Sun Hengyi Lv Wenjun Wu Hai Li Tao Jiang |
| author_sort | Zijing Wang |
| collection | DOAJ |
| description | Abstract Globally, CRC ranks as a principal cause of mortality, with projections indicating a substantial rise in both incidence and mortality by the year 2040. The immunological responses to cancer heavily rely on the function of CD4Tconv. Despite this critical role, prognostic studies on CRC-related CD4Tconv remain insufficient. In this investigation, transcriptomic and clinical data were sourced from TCGA and GEO. Initially, we pinpointed CD4TGs using single-cell datasets. Prognostic genes were then isolated through univariate Cox regression analysis. Building upon this, 101 machine learning algorithms were employed to devise a novel risk assessment framework, which underwent rigorous validation using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent ROC curves, nomograms, and calibration plots. Furthermore, GSEA facilitated the examination of these genes’ potential roles. The RS derived from this model was also analyzed for its implications in the TME, and its potential utility in immunotherapy and chemotherapy contexts. A novel prognostic model was developed, utilizing eight CD4TGs that are significantly linked to the outcomes of patients with CRC. This model’s RS showcased remarkable predictive reliability for the overall survival rates of CRC patients and strongly correlated with malignancy levels. RS serves as an autonomous prognostic indicator, capable of accurately forecasting patient prognoses. Based on the median value of RS, patients were categorized into subgroups of high and low risk. The subgroup with higher risk demonstrated increased immune infiltration and heightened activity of genes associated with immunity. This investigation’s establishment of a CD4TGs risk model introduces novel biomarkers for the clinical evaluation of CRC risks. These biomarkers may enhance therapeutic approaches and, in turn, elevate the clinical outcomes for patients with CRC by facilitating an integrated treatment strategy. |
| format | Article |
| id | doaj-art-324abbdf61dd43459d2a94b934f9be25 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-324abbdf61dd43459d2a94b934f9be252025-08-20T01:50:38ZengNature PortfolioScientific Reports2045-23222024-10-0114111510.1038/s41598-024-75270-yMachine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancerZijing Wang0Zhanyuan Sun1Hengyi Lv2Wenjun Wu3Hai Li4Tao Jiang5First Clinical Medical College, General Hospital of Ningxia Medical UniversityFirst Clinical Medical College, General Hospital of Ningxia Medical UniversityFirst Clinical Medical College, General Hospital of Ningxia Medical UniversityFirst Clinical Medical College, General Hospital of Ningxia Medical UniversityDepartment of Anal-Colorectal Surgery, General Hospital of Ningxia Medical UniversityDepartment of Anal-Colorectal Surgery, General Hospital of Ningxia Medical UniversityAbstract Globally, CRC ranks as a principal cause of mortality, with projections indicating a substantial rise in both incidence and mortality by the year 2040. The immunological responses to cancer heavily rely on the function of CD4Tconv. Despite this critical role, prognostic studies on CRC-related CD4Tconv remain insufficient. In this investigation, transcriptomic and clinical data were sourced from TCGA and GEO. Initially, we pinpointed CD4TGs using single-cell datasets. Prognostic genes were then isolated through univariate Cox regression analysis. Building upon this, 101 machine learning algorithms were employed to devise a novel risk assessment framework, which underwent rigorous validation using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent ROC curves, nomograms, and calibration plots. Furthermore, GSEA facilitated the examination of these genes’ potential roles. The RS derived from this model was also analyzed for its implications in the TME, and its potential utility in immunotherapy and chemotherapy contexts. A novel prognostic model was developed, utilizing eight CD4TGs that are significantly linked to the outcomes of patients with CRC. This model’s RS showcased remarkable predictive reliability for the overall survival rates of CRC patients and strongly correlated with malignancy levels. RS serves as an autonomous prognostic indicator, capable of accurately forecasting patient prognoses. Based on the median value of RS, patients were categorized into subgroups of high and low risk. The subgroup with higher risk demonstrated increased immune infiltration and heightened activity of genes associated with immunity. This investigation’s establishment of a CD4TGs risk model introduces novel biomarkers for the clinical evaluation of CRC risks. These biomarkers may enhance therapeutic approaches and, in turn, elevate the clinical outcomes for patients with CRC by facilitating an integrated treatment strategy.https://doi.org/10.1038/s41598-024-75270-yColorectal cancerCD4+ conventional T cells-related genesPrognosisSingle-cellMachine learning |
| spellingShingle | Zijing Wang Zhanyuan Sun Hengyi Lv Wenjun Wu Hai Li Tao Jiang Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer Scientific Reports Colorectal cancer CD4+ conventional T cells-related genes Prognosis Single-cell Machine learning |
| title | Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer |
| title_full | Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer |
| title_fullStr | Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer |
| title_full_unstemmed | Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer |
| title_short | Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer |
| title_sort | machine learning based model for cd4 conventional t cell genes to predict survival and immune responses in colorectal cancer |
| topic | Colorectal cancer CD4+ conventional T cells-related genes Prognosis Single-cell Machine learning |
| url | https://doi.org/10.1038/s41598-024-75270-y |
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