Identification of a gefitinib resistance-associated signature for predicting prognosis and therapeutic response in lung adenocarcinoma via integrated multi-omics analysis and machine learning

Abstract

Gefitinib resistance (GR) is widespread; therefore, alternative treatments for lung adenocarcinoma (LUAD) are needed. The study of gefitinib-resistance gene sets may lead to a better understanding of the mechanism underlying GR, methods for predicting and preventing GR, and alternative therapies. GR gene sets, single-cell data, and transcriptome data were obtained from public databases. Univariate and multivariate regression analyses and machine learning techniques were used to screen genes and construct a signature, respectively. Survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to assess signature performance in internal and external data sets. Enrichment and tumor immune-microenvironment analyses were used to explore the mechanism of the signature genes in GR. Novel immunological and non-immunological therapies were explored. A signature consisting of 22 genes was successfully constructed in LUAD cohort, which performed well in both internal and external validation. The signature was closely related to chromosomal processes, DNA replication, important immune-cell infiltration, and multiple immune scores in enrichment and tumor microenvironment analyses. Further, the signature predicted immunotherapy efficacy in patients with LUAD to a certain extent, and we identified various agents other than gefitinib that may have better treatment effects in high-risk and low-risk groups, providing treatment guidance for gefitinib-resistant patients. The 22-gene signature can predict the prognosis of gefitinib-resistant patients with LUAD and immunotherapy efficacy, and provides new guidance for non-immunotherapy.