Construction of a prognostic model for lung adenocarcinoma based on necroptosis genes and its exploration of the potential for tumor immunotherapy.

BACKGROUND

Lung cancer ranks among the most prevalent malignancies globally, with lung adenocarcinoma (LUAD) being its most frequent histological subtype. Necroptosis is a newly defined mode of programmed cell death that is different from apoptosis and necrosis. However, the role of necroptosis in the occurrence and development of LUAD remains largely unexplored. This study aimed to construct a prognostic model of LUAD based on necroptosis-related genes (NRGs) and analyze the predictive value of this model on the prognosis of LUAD patients.

METHODS

The dataset of LUAD patients was downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the NRGs were downloaded from inside GeneCards and Harmonizome databases. LUAD prognostic models were constructed by one-way Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis and multifactorial Cox regression analysis. Differential analyses of immune function as well as common tumor drugs were performed between high and low risk groups. A ceRNA was constructed to explore the potential lncRNA-miRNA-mRNA regulatory axis in LUAD. In this study, we leveraged bioinformatics to pinpoint genes implicated in necroptosis within LUAD.

RESULTS

Two differentially expressed NRGs (DENRGs: KL, PLK1) were screened and used to construct the prognostic model and validate the RiskScore as an independent prognostic factor. Gene set variation analysis (GSVA) analysis showed that differentially expressed genes were mainly enriched in immune-related pathways. Additionally, we conducted experimental assays to validate the expression of these genes in LUAD cell lines. The GSVA analysis showed that differentially expressed genes were mainly enriched in immune-related pathways. Significant differences (P<0.05) were found between the high and low risk groups in terms of immune function and half-maximal inhibitory concentration (IC) values of five anticancer drugs (doxorubicin, lapatinib, paclitaxel, savolitinib and trametinib). We also identified a lncRNA SNHG14 /hsa-miR-101-3p/KL/PLK1 regulatory axis for LUAD.

CONCLUSIONS

The survival prognosis model of NRGs constructed in this study can predict the prognosis and immune microenvironment of LUAD patients.