A succinylation-related prognostic model for predicting lung adenocarcinoma prognosis and guiding immunotherapy.

Abstract

Background: Lung adenocarcinoma (LUAD) is a common culprit of cancer-related deaths. Recent studies have revealed that succinylation-related genes (SRGs) are pivotal in cancer. However, the comprehensive characteristics and clinical significance of SRGs in LUAD occurrence are not clear. Therefore, our goal is to dig out the succinylation-related prognostic feature genes in LUAD.

Methods: We identified differentially expressed SRGs in LUAD, and established the LUAD prognostic model using analyses of multivariate, LASSO, and univariate Cox regression. Based on clinical information and riskscore, we graphed a nomogram of the prognostic model and analyzed the independent prognostic ability of the riskscore. Analyses of immune assessment, mutation frequency, and drug sensitivity were carried out on LUAD patients.

Results: A 9-gene prognostic model was successfully set up in this project. The receiver operation characteristic (ROC) curves illustrated that the model effectively predicted the risk of LUAD patients. The levels of immune infiltration and immune scores of LUAD patients in the high-risk (HR) group were greatly lower than those in the low-risk (LR) group. Furthermore, compared to the LR group, the HR group had a significantly elevated gene mutation rate. ENPP3 and SLC22A8 may respond to targeted drugs more sensitively. The low-expression groups of ENPP3 and SLC22A8 genes may have higher drug sensitivity to Nilotinib, ARRY-614, and Megestrol acetate, with lower drug resistance.

Conclusion: The above results indicated that the prognostic model established using SRGs can be a predictive marker for LUAD prognosis, offering references for LUAD treatment and evaluation.