Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma
BackgroundRectal adenocarcinoma (READ) is a common malignant tumor. This study aims to establish a risk model based on anoikis-related genes (ARGs) to predict prognosis and the tumor microenvironment in READ.MethodsTranscriptomic data and clinical data downloaded from the TCGA and GEO databases were...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1604541/full |
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| author | Bing Zhao Xuegui Tang |
| author_facet | Bing Zhao Xuegui Tang |
| author_sort | Bing Zhao |
| collection | DOAJ |
| description | BackgroundRectal adenocarcinoma (READ) is a common malignant tumor. This study aims to establish a risk model based on anoikis-related genes (ARGs) to predict prognosis and the tumor microenvironment in READ.MethodsTranscriptomic data and clinical data downloaded from the TCGA and GEO databases were used for differential analysis and Cox regression analysis. An ARGs-based prognostic risk model was constructed for READ. The survival curves and ROC curves were plotted to determine the predictive ability of the model for READ patients. The model was externally validated in the GSE87211 dataset. A nomogram, immune analysis, drug sensitivity analysis, and functional enrichment analysis were also performed to comprehensively validate the model.ResultsThe risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG). Kaplan-Meier curves for the TCGA training cohort (P < 0.0001), testing cohort (P = 0.018), and GSE87211 dataset (P = 0.036) showed better prognoses in the low-risk group. The AUC for 1-year, 3-year, and 5-year overall survival in the TCGA training cohort, testing cohort, and GSE87211 dataset were (0.962, 0.923, 0.956), (0.887, 0.838, 0.833), and (0.73, 0.817, 0.743), respectively. The nomogram showed that the risk score served as an independent predictor of overall survival. Drug sensitivity analysis revealed differences in the IC50 values of OSI-027, PLX-4720, UMI-77, and Sapitinib between the high-risk and low-risk groups. Immune microenvironment analysis suggested distinct differences in immune cells between the two risk groups. Enrichment analysis revealed that these prognostic ARGs were primarily enriched in pathways and biological processes related to tumorigenesis.ConclusionThe risk model of ARGs can effectively predict READ prognosis and provide potential therapeutic targets. |
| format | Article |
| id | doaj-art-3a1c749a879c4f8da38c97fe44c7032a |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-3a1c749a879c4f8da38c97fe44c7032a2025-08-20T04:02:50ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-08-011610.3389/fgene.2025.16045411604541Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinomaBing Zhao0Xuegui Tang1Department of Integrated Traditional and Western Medicine Anorectal, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaAnorectal Department, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaBackgroundRectal adenocarcinoma (READ) is a common malignant tumor. This study aims to establish a risk model based on anoikis-related genes (ARGs) to predict prognosis and the tumor microenvironment in READ.MethodsTranscriptomic data and clinical data downloaded from the TCGA and GEO databases were used for differential analysis and Cox regression analysis. An ARGs-based prognostic risk model was constructed for READ. The survival curves and ROC curves were plotted to determine the predictive ability of the model for READ patients. The model was externally validated in the GSE87211 dataset. A nomogram, immune analysis, drug sensitivity analysis, and functional enrichment analysis were also performed to comprehensively validate the model.ResultsThe risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG). Kaplan-Meier curves for the TCGA training cohort (P < 0.0001), testing cohort (P = 0.018), and GSE87211 dataset (P = 0.036) showed better prognoses in the low-risk group. The AUC for 1-year, 3-year, and 5-year overall survival in the TCGA training cohort, testing cohort, and GSE87211 dataset were (0.962, 0.923, 0.956), (0.887, 0.838, 0.833), and (0.73, 0.817, 0.743), respectively. The nomogram showed that the risk score served as an independent predictor of overall survival. Drug sensitivity analysis revealed differences in the IC50 values of OSI-027, PLX-4720, UMI-77, and Sapitinib between the high-risk and low-risk groups. Immune microenvironment analysis suggested distinct differences in immune cells between the two risk groups. Enrichment analysis revealed that these prognostic ARGs were primarily enriched in pathways and biological processes related to tumorigenesis.ConclusionThe risk model of ARGs can effectively predict READ prognosis and provide potential therapeutic targets.https://www.frontiersin.org/articles/10.3389/fgene.2025.1604541/fullanoikis-related genesrisk modelrectal adenocarcinoma (READ)prognosistumor microenvironment |
| spellingShingle | Bing Zhao Xuegui Tang Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma Frontiers in Genetics anoikis-related genes risk model rectal adenocarcinoma (READ) prognosis tumor microenvironment |
| title | Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| title_full | Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| title_fullStr | Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| title_full_unstemmed | Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| title_short | Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| title_sort | identification of anoikis related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma |
| topic | anoikis-related genes risk model rectal adenocarcinoma (READ) prognosis tumor microenvironment |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1604541/full |
| work_keys_str_mv | AT bingzhao identificationofanoikisrelatedgenestodevelopariskmodelandpredicttheprognosisandtumormicroenvironmentinrectaladenocarcinoma AT xueguitang identificationofanoikisrelatedgenestodevelopariskmodelandpredicttheprognosisandtumormicroenvironmentinrectaladenocarcinoma |