Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Genetic association of circulating interleukins and risk of colorectal cancer: A bidirectional Mendelian randomization study.

BACKGROUND: Previous studies have reported that inflammation, especially interleukin family members, plays an important role in the development of colorectal cancer (CRC). However, because of various confounders and the lack of clinical randomized controlled trial, the causal relationship between genetically predicted level of interleukin family and CRC risk has not been fully explained. OBJECTIVE: Bi-directional Mendelian randomization (MR) was conducted to investigate the causal association between interleukin family members and CRC. METHODS: Several genetic variables were extracted as instrumental variables (IVs) from summary data of genome-wide association studies (GWAS) for interleukin and CRC. IVs of interleukin family were obtained from recently published GWAS studies and the summary data of CRC was from FinnGen Biobank. After a series of quality control measures and strict screening, six models were used to evaluate the causal relationship. Pleiotropy, heterogeneity test, and a variety of sensitivity analysis were also used to estimate the robustness of the model results. RESULTS: Genetically predicted higher circulating levels of IL-2 (odds ratio [OR]: 0.76; 95% confidence interval [CI]: 0.63-0.92; p = .0043), IL-17F(OR: 0.78; 95% CI: 0.62-1.00; p = .015), and IL-31 (OR: 0.88; 95% CI: 0.79-0.98; p = .023) were suggestively associated with decreased CRC risk. However, higher level of IL-10 (OR: 1.40; 95% CI: 1.18-1.65; p = .000094) was causally associated with increased risk of CRC. Reverse MR results indicated that the exposure of CRC was suggestively associated with higher levels of IL-36α (OR: 1.23; 95% CI: 1.01-1.49; p = .040) and IL-17RD (OR: 1.22; 95% CI, 1.00-1.48; p = .048) and lower level of IL-13 (OR: 0.78; 95% CI: 0.65-0.95; p = .013). The overall MR results did not provide evidence for causal relationships between other interleukins and CRC (p > .05). CONCLUSION: We offer suggestive evidence supporting a potential causal relationship between circulating interleukins and CRC, underscoring the significance of targeting circulating interleukins as a strategy to mitigate the incidence of CRC.