ArticlePloS one2021
Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A Swedish Haplotype GWAS in Familial and Sporadic Site-Specific Colorectal Cancer.International journal of molecular sciences · 2026Article
- COMP promotes the progression of colorectal cancer by regulating epithelial mesenchymal transition.BMC cancer · 2025Article
- The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.BMC gastroenterology · 2025Article
- Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.Briefings in bioinformatics · 2025Review
- Development and Spatial External Validation of a Predictive Model of Survival Based on Random Survival Forest Analysis for People Living With HIV and AIDS After Highly Active Antiretroviral Therapy in China: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Integrating analysis of multi-omics summary data identifies novel plasma protein biomarkers and drug targets for bladder cancer.Discover oncology · 2025Article
- Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection.Frontiers in bioinformatics · 2025Article
- Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.Scientific reports · 2024Article
- A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma.Lipids in health and disease · 2024Article
- Exploration of ANKRD27 as an immune-related prognostic factor in pan-cancer and hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Development and validation of a machine learning model to predict prognosis in liver failure patients treated with non-bioartificial liver support system.Frontiers in medicine · 2024Article
- Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.Translational cancer research · 2023Article
- Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models.Ophthalmology science · 2023Article
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
- Human Colonoid-Myofibroblast Coculture for Study of Apical NaInternational journal of molecular sciences · 2023Article
- Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories.Archives of medical science : AMS · 2023Article
- Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival.Frontiers in genetics · 2022Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model's predictive performance. In addition, Cox PH predictive performance was better than RSF.
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