Integrative analysis of multiple cell death model for precise prognosis and drug response prediction in gastric cancer.

BACKGROUND

Gastric cancer (GC) is a common upper gastrointestinal tumor. However, the evaluation of prognosis and treatment response in patients with gastric cancer remains a challenge. Programmed cell death (PCD) is one of the important terminal paths for the cells of metazoans, and is involved in a variety of biological events that include morphogenesis, maintenance of tissue homeostasis, and elimination of harmful cells. The objective of this project is to investigate the predictive significance of cell death pathways and create prognostic signatures associated to cell death, with the purpose of forecasting prognosis and providing guidance for the treatment of gastric cancer.

METHODS

Gene transcription profiles and corresponding clinical data of gastric cancer patients were collected from The Cancer Genome Atlas (TCGA-STAD, n = 448) and the Gene Expression Comprehensive Database (GSE84437, n = 483). Thirteen types of cell death-related genes, including apoptosis, necroptosis, pyroptosis, ferroptosis, autophagy, cuprotosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, were analysed. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Patients were categorized into high and low-risk groups based on the median risk score for survival difference analysis. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Additionally, the response to immunotherapy in the high-risk and low-risk groups was calculated using the oncoPredict algorithm. Futhermore, the model genes were validated in the GEO validation set.

RESULTS

A total of 324 differential programmed cell death (PCD)-related genes were identified, and 65 were selected through single-factor Cox analysis. Six PCD-related genes were ultimately identified by Lasso regression to construct a prognostic risk score model. The log-rank test revealed that patients in the high-risk group had inferior survival time compared with those in the low-risk group. The area under the ROC curve (AUC) for the training group at years 1, 3, and 5 were 0.684, 0.713, 0.743, respectively, while the AUC for the validation cohort at years 1, 3, and 5 were 0.695, 0.704, and 0.707, respectively. Unsupervised clustering identified potential subtypes included in the model, and a survival difference was also observed between the two subgroups. Multifactor Cox results, combined with clinical information, demonstrated that the prognostic risk score can serve as an independent prognostic factor, irrespective of other clinical features.

CONCLUSION

By comprehensively analyzing multiple cell death patterns, we have established a novel model that accurately forecasts the clinical prognosis and drug sensitivity of gastric cancer. It was found that all 12 representative drugs may not be suitable for patients in high-risk groups.