Establishment and Validation of a Necroptosis-Related Long Noncoding RNA Prognostic Model for Non-Small Cell Lung Cancer.

INTRODUCTION/

OBJECTIVE

Non-small cell lung cancer (NSCLC) accounts for over 85% of lung cancers, and current immunotherapies benefit only a subset of patients. Necroptosis, a regulated form of necrotic cell death, has emerged as a potential therapeutic axis in tumours resistant to apoptosis. This study aimed to develop and validate a necroptosis-related long noncoding RNA (lncRNA) prognostic signature for NSCLC and to explore its associations with tumour immune contexture and predicted drug sensitivity.

METHODS

RNA-seq and clinical data from TCGA and GTEx (1,128 tumours; 110 normal lung samples; 1,027 patients after filtering) were analysed. Necroptosis-related lncRNAs were identified by co-expression analysis with 67 necroptosis genes (Pearson r > 0.4, P < .001). Candidate lncRNAs associated with overall survival were selected by univariable Cox regression, refined through LASSO-penalised Cox regression with 10-fold cross-validation repeated 1,000 times, and further filtered by multivariable Cox analysis to construct the final risk model. Model performance was evaluated using time-dependent ROC curves and a clinical nomogram. QRT-PCR validated differential expression in A549 and NCI-H1299 versus BEAS-2B cell lines. Immune profiling was performed using seven deconvolution algorithms and single-sample GSEA (ssGSEA). Consensus clustering was applied to classify tumour immune phenotypes, and drug sensitivity was estimated using the pRRophetic algorithm.

RESULTS

A 12-lncRNA signature stratified overall survival across training, test, and whole cohorts and remained independent of tumour stage in multivariable analysis. Time-dependent ROC curves yielded AUCs of 0.684, 0.652, and 0.624 at 1, 3, and 5 years, respectively. A nomogram integrating the risk score with clinicopathological variables demonstrated strong calibration. qRT-PCR confirmed differential expression of the model lncRNAs between NSCLC and normal bronchial epithelial cell lines. GSEA revealed enrichment of metabolic programmes in low-risk tumours and adhesion/cytoskeletal pathways in high-risk tumours. Immune deconvolution indicated a mixed immune milieu in high-risk tumours, with increased infiltration estimates concurrent with elevated expression of inhibitory checkpoints. Consensus clustering defined two groups: an inflamed "hot-like" cluster with higher immune/stromal scores and lower predicted IC50 values for multiple targeted and cytotoxic agents, and a comparatively "cold-like" cluster.

DISCUSSION: The 12-lncRNA risk model captures biologically distinct tumour states characterised by divergent pathway activation, distinct immune microenvironmental composition, and predicted drug-sensitivity profiles. The co-occurrence of immune infiltration and inhibitory checkpoint enrichment in high-risk tumours suggests immune dysfunction rather than effective antitumour immunity, with direct implications for immunotherapy stratification.

CONCLUSION

This necroptosis-related lncRNA signature provides a framework for prognostic prediction, tumour immune phenotyping, and therapeutic prioritisation in NSCLC. Prospective external validation and mechanistic functional studies are warranted to confirm clinical applicability.