Prognostic model of lung adenocarcinoma based on disulfidptosis-related genes and analysis of in vitro cell experiments for PPP1R14B in the model.

Background: Lung adenocarcinoma (LUAD) is one of the common malignant tumors worldwide, and the 5-year survival rate remains unsatisfactory. Reliable prognostic biomarkers are needed to provide references for personalized treatment of patients. Some studies have shown that disulfidptosis-related genes (DRGs) are closely associated with tumorigenesis and development. This study constructed a prognostic risk model to explore the prognostic value of DRGs in LUAD and provide a reference for formulating personalized treatment plans for LUAD patients.

Methods: RNA-seq data of LUAD tissues and adjacent or normal lung tissues were downloaded from TCGA database and GEO database. A risk scores model was constructed through univariate Cox analysis, Lasso analysis, and multivariate Cox analysis. ROC curves and nomogram models were drawn to evaluate the risk model. External validation was performed using LUAD data, data in the LUAD single-cell dataset, and other data in the GEO database. In addition, the immune microenvironment and drug sensitivity of the high-risk and low-risk groups were analyzed. The key gene PPP1R14B in the model was further experimentally verified by in vitro cell experiments.

Results: In this study, a risk model composed of four genes was constructed, and the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.001). The area under the curve (AUC) of the ROC curves of the training set risk model at 1-, 3-, and 5-year were 0.767, 0.759, and 0.711, respectively. Drug sensitivity analysis showed that there was a statistical significance between the high-risk and low-risk groups of patients for drugs such as gefitinib, afatinib, lapatinib, and paclitaxel (P < 0.001). The results of in vitro cell experiments showed that the proliferation and migration of knockdown PPP1R14B LUAD cells were significantly inhibited, and the number of apoptosis of LUAD cells was significantly increased (P < 0.05).

Conclusion: The risk model constructed based on four DRGs can predict the prognosis of LUAD patients with relative accuracy. There are differences in the immune microenvironment between the high-risk and low-risk groups. Patients in the high-risk group are more sensitive to drugs such as gefitinib, afatinib, lapatinib, and paclitaxel, providing a reference for personalized treatment of LUAD patients. Knockdown PPP1R14B significantly inhibited the proliferation and migration of LUAD cells and promoted the apoptosis of LUAD cells.