ArticleTranslational lung cancer research2024
Necroptosis-related lncRNAs: biomarkers for predicting prognosis and immune response in lung adenocarcinoma.
Article in Translational lung cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- XAF1 nonsense mutation rs117407731 enhances lung adenocarcinoma susceptibility via apoptosis suppression.Molecular genetics and genomics : MGG · 2026Article
- Caspase-independent cell death in lung cancer: from mechanisms to clinical applications.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Clinical significance and biological function of PRKCQ-AS1/miR-582-3p expression in LUAD.Hereditas · 2025Article
- Construction of a prognostic model for lung adenocarcinoma based on necroptosis genes and its exploration of the potential for tumor immunotherapy.Translational cancer research · 2025Article
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13 authors.
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Abstract
Background: Lung adenocarcinoma (LUAD) is one of the most prevalent types of lung cancer (LC), accounting for 50% of all LC cases. Despite therapeutic advancements, patients suffer from adverse drug reactions. Furthermore, the prognosis of LC patients remains poor. Necroptosis is a novel mode of cell death and is critically involved in regulating immunotherapy in patients. However, the correlation between the necroptosis-related long non-coding RNA (lncRNA) (necro-related lnc) signature (NecroLncSig) and the response of patients with LUAD to immunotherapy is unclear. This study developed a model using lncRNAs to predict the prognosis of patients with LUAD. Methods: We obtained the transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Next, we conducted a co-expression analysis to identify the necro-related lnc. In addition, we constructed the NecroLncSig using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then we evaluated and validated the NecroLncSig using a Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, principal component analysis (PCA), Gene Ontology (GO) enrichment analysis, a nomogram, and calibration curves. Finally, we used the NecroLncSig to predict the responses of patients to immunotherapy. Results: We constructed the NecroLncSig based on seven necro-related lnc. The patients were classified into a high-risk group (HRG) and a low-risk group (LRG). The overall survival (OS) of patients in the HRG was significantly poorer in the training, testing, and entire sets (P<0.05) than that of the patients in the LRG. Univariate and multivariate Cox regression analyses demonstrated that the risk score could predict the OS of patients in an independent manner (P<0.001). Time-dependent ROC analysis demonstrated that the area under the curve values of the NecroLncSig for 1-, 2-, and 3-year OS were 0.689, 0.700, and 0.685, respectively, for the entire set. Furthermore, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm showed that the response of patients in the HRG to immunotherapy was better than that of patients in the LRG. Conclusions: Necro-related lnc can affect disease progression and patient prognosis. In addition, these lncRNAs can be used to design therapeutic strategies, such as immunotherapy, to treat patients with LUAD.
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