Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer

Abstract

Background

In the face of poor prognosis and immunotherapy failure of gastric cancer (GC), this project tried to find new potential biomarkers for predicting prognosis and precision medication to ameliorate the situation.

Methods

To form synthetic matrices, we retrieved stomach adenocarcinoma transcriptome data from Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA). Necroptosis-related prognostic lncRNA was identified by coexpression analysis and univariate Cox regression. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the necroptosis-related lncRNA model. Next, the Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox (uni-Cox) regression, multivariate Cox (multi-Cox) regression, nomogram, and calibration curves were made to verify and evaluate the model. Gene set enrichment analyses (GSEA), principal component analysis (PCA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) in risk groups were also analyzed. For further discussing immunotherapy between the cold and hot tumors, we divided the entire set into two clusters based on necroptosis-related lncRNAs.

Results

We constructed a model with 16 necroptosis-related lncRNAs. In the model, we found the calibration plots showed a good concordance with the prognosis prediction. The area's 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.726, 0.763, and 0.770, respectively. Risk groups could be a guide of systemic treatment because of significantly different IC50 between risk groups. Above all, clusters could help distinguish between the cold and hot tumors effectively and contribute to precise mediation. Cluster 2 was identified as the hot tumor and more susceptible to immunotherapeutic drugs.

Conclusion

The results of this project supported that necroptosis-related lncRNAs could predict prognosis and help make a distinction between the cold and hot tumors for improving individual therapy in GC.