A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity

Abstract

Colorectal cancer (CRC) is a common cancer with high mortality rates worldwide. Disulfidptosis is an emerging mode of cancer cell death. In this study, disulfidptosis-related lncRNAs were identified by screening and incorporated into a prognostic model to predict the prognosis and immunotherapy response of colorectal cancer (CRC), providing a new and effective guide for clinical decision making. Transcriptome and clinical data of CRC patients and normal controls were obtained from The Cancer Genome Atlas (TCGA). Pearson correlation, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify disulfidptosis-related lncRNAs. A risk scoring model was constructed, and its predictive performance was comprehensively validated. An accurate nomogram was constructed for CRC prognosis prediction. Model reliability was verified via principal component, survival and receiver operating characteristic (ROC) curve analyses. GO analysis and GSEA were used to identify cellular pathways relevant to the model. Immune cell infiltration was studied via the ESTIMATE and CIBERSORT algorithms. The association of tumor mutational burden (TMB) with the model-derived risk scores was assessed using single-nucleotide variant data. Finally, tThe clinical value of the model was evaluated through the GDSC and CTRP databases, and effective drugs were predicted. A prognostic risk model containing 9 disulfidptosis-related lncRNAs (ATP2A1-AS1, AC011815.1, AC013652.1, AC109992.2, AC069549.1, AC005034.5, SUCLG2-AS1, AP003555.1 and AL590101.1) was successfully constructed. There were significant difference in survival rates between the high-risk and low-risk groups (based on the median risk score) in the training and validation datasets. The risk score serves as an independent prognostic factor when combined with clinical variables. GSEA revealed that the high-risk group was enriched in the cellular processes of epidermis development, kidney differentiation and skin development. The prognostic model could stratify CRC patients into two distinct risk score groups. A high risk score independently predicted poor overall survival and was correlated with reduced immune cell infiltration, high TMB, and decreased tumor immune response activity. Immune checkpoint blockade might improve survival in high-risk CRC patients, whereas low-risk patients might be more responsive to targeted therapy and diverse kinase inhibitors. In summary, we established a disulfidptosis-related lncRNA model that holds promise as a reliable marker of CRC prognosis and immunotherapy response and can be also be used to predict the immune cell infiltration landscape and targeted therapy response.