Molecular subtyping and prognostic modeling of non-small cell lung cancer based on disulfidptosis-related genes.

BACKGROUNDNon-small cell lung cancer (NSCLC) shows substantial molecular and immune heterogeneity, and prognosis varies widely even among patients with similar clinicopathologic stage, highlighting the need for biologically grounded biomarkers. Disulfidptosis is a newly described form of regulated cell death triggered by disulfide stress and linked to tumor metabolic vulnerability, but its molecular patterns and prognostic relevance in NSCLC remain unclear. This study aimed to define disulfidptosis-related molecular subtypes in NSCLC, construct and externally validate a disulfidptosis-based prognostic gene signature, and investigate its associations with the tumor immune microenvironment (TIME) and potential immunotherapy response.

METHODSWe analyzed NSCLC gene expression and clinical follow-up data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Initial unsupervised clustering identified disulfidptosis-related molecular subtypes. Further analysis used weighted gene co-expression network analysis (WGCNA), univariate and least absolute shrinkage and selection operator (LASSO)-COX regression, and stepwise Cox regression to identify disulfidptosis-related prognostic genes, which were then validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in surgically resected NSCLC tumor specimens in our center. A Disulfidptosis Score-based risk model was built and evaluated with Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Single-cell RNA sequencing (RNA-seq) data characterised gene-expression patterns in the TIME.

RESULTSTwo molecular subtypes (C1 and C2) based on 16 disulfidptosis genes were identified, among which subtype C2 had a lower immune infiltration score and poorer prognosis. Furthermore, eight disulfidptosis-related prognostic genes were identified and validated through our local cohort by RT-PCR. The Disulfidptosis Score-based on these eight genes effectively stratified patients into high- and low-risk categories. Kaplan-Meier analysis demonstrated that disulfidptosis-related genes were significantly associated with survival outcomes. A prognostic model based on the Disulfidptosis Score and Stage was validated using independent datasets, and the area under the curve (AUC) was 0.728, 0.763, and 0.73 at one, three and five years respectively. Single-cell analysis revealed the expression of disulfidptosis-related prognostic genes across various immune cell types, particularly in plasma cells.

CONCLUSIONSThis study identified disulfidptosis-related gene characteristics and their correlation with tumor microenvironment and prognosis in NSCLC. These findings could provide new insights into NSCLC heterogeneity and offer potential biomarkers for prognosis and therapeutic strategies.