Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes
Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1148470/full |
| _version_ | 1797897113572474880 |
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| author | Dongmei Ai Mingmei Wang Qingchuan Zhang Longwei Cheng Yishu Wang Xiuqin Liu Li C. Xia |
| author_facet | Dongmei Ai Mingmei Wang Qingchuan Zhang Longwei Cheng Yishu Wang Xiuqin Liu Li C. Xia |
| author_sort | Dongmei Ai |
| collection | DOAJ |
| description | Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients’ prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git. |
| first_indexed | 2024-04-10T07:53:43Z |
| format | Article |
| id | doaj.art-f90abe0e49904f5f8fee7cb858f62871 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-10T07:53:43Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-f90abe0e49904f5f8fee7cb858f628712023-02-23T07:51:22ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-02-011410.3389/fgene.2023.11484701148470Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genesDongmei Ai0Mingmei Wang1Qingchuan Zhang2Longwei Cheng3Yishu Wang4Xiuqin Liu5Li C. Xia6School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaNational Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics, South China University of Technology, Guangzhou, ChinaColon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients’ prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.https://www.frontiersin.org/articles/10.3389/fgene.2023.1148470/fullLASSOmultivariate cox analysisprognosisimmune genecolorectal cancer |
| spellingShingle | Dongmei Ai Mingmei Wang Qingchuan Zhang Longwei Cheng Yishu Wang Xiuqin Liu Li C. Xia Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes Frontiers in Genetics LASSO multivariate cox analysis prognosis immune gene colorectal cancer |
| title | Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes |
| title_full | Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes |
| title_fullStr | Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes |
| title_full_unstemmed | Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes |
| title_short | Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes |
| title_sort | regularized survival learning and cross database analysis enabled identification of colorectal cancer prognosis related immune genes |
| topic | LASSO multivariate cox analysis prognosis immune gene colorectal cancer |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1148470/full |
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