Dataset Information

Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer.

ABSTRACT:

Objective

Due to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer.

Methods

An immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined.

Results

Our results showed that high risk scores were indicative of undesirable survival outcomes both in the training set (p < 0.0001) and the validation set (p = 0.002). Moreover, this signature could independently predict patients' survival (HR: 2.656 (1.919-3.676) and p < 0.001). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways (p < 0.05). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs (p < 0.05). There was heterogeneity in immune cells between high- and low-risk samples (p < 0.05). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score (p < 0.05).

Conclusion

This immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.

SUBMITTER: Qiu F

PROVIDER: S-EPMC8602949 | biostudies-literature |

REPOSITORIES: biostudies-literature

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