A novel cross-gene cluster between ferroptosis and disulfidptosis for the prediction of prognosis, immunity and drug sensitivity in lung adenocarcinoma

Abstract

Background: Ferroptosis and disulfidptosis, emerging forms of programmed cell death linked to tumor progression, play intriguing roles with largely unknown mechanisms. This study aimed to explore gene signatures of ferroptosis and disulfidptosis in lung adenocarcinoma (LUAD) for better prognosis. Methods: Correlation genes for both ferroptosis and disulfidptosis were sourced from FerrDb and literature, while human LUAD transcriptome data came from The Cancer Genome Atlas (TCGA). Cross-genic analysis led to function enrichment, Cox regression, and LASSO regression, establishing prognostic models and constructing a nomogram incorporating risk scores and clinicopathologic features. Additional analyses included Kaplan-Meier, ROC curve, tumor microenvironment, tumor mutation load, immune infiltration, immune checkpoint, and drug sensitivity. Single cell sequencing analyzed FDR genes. Results: Identification and construction of a core network for ferroptosis with disulfidptosis-related genes revealed four signatures (TIMM9, AKT1S1, UTU1, NEDD4) through LASSO regression. A prognostic model categorized samples into high- and low-risk groups, indicating worse prognosis in high-risk LUAD. Multivariate Cox regression affirmed risk score as an independent prognostic factor. Analyses of the tumor microenvironment demonstrated extensive immune infiltration in the low-risk group and high tumor mutation burden in the high-risk group. Drug sensitivity highlighted strong responses in the high-risk group to specific drugs. Single cell sequencing detailed gene expression patterns in distinct cell types. Conclusions: This study unraveled interconnected genes of ferroptosis and disulfidptosis in LUAD, proposing avenues for clinically tailored diagnosis and treatment. Validation of the prognostic model, gene network, and the identification of potential target TIMM9 enhance our understanding for potential interventions in LUAD.