ArticleDiscover oncology2025
Utilizing multi-omics analysis, a new signature has been identified and validated for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma, which is based on tumor mutation burden.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- MUC16 promotes endometrial cancer progression and modulates sensitivity to lapatinib through the ESR1/PI3K/AKT axis.Translational oncology · 2026Article
- Advances in the application of multi-omics in tumor immunotherapy.Frontiers in genetics · 2026Review
- An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.Technology in cancer research & treatmentReview
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4 authors.
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Abstract
Immunotherapy is used extensively in treating non-small cell lung cancer (NSCLC) patients. Nevertheless, in contrast to lung adenocarcinoma (LUAD), the endeavors to develop effective targeted treatments for lung squamous cell carcinoma (LUSC) have not yielded positive outcomes. Hence, it is crucial to discover biomarkers for immunotherapy and investigate more potent treatments, which is an immediate requirement for individuals with LUSC. The LUSC somatic mutation data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Multivariate analysis was performed to create a signature related to tumor mutation burden (TMB). Next, we utilized the CIBERSORT algorithm to assess the correlation between TMB and immune infiltrates. Additionally, we identified prognostic immune cells of LUSC through Kaplan-Meier analysis. The TCGA and ICGC cohorts covered a combined total of 11 genes that were frequently mutated. SYNE1 and TTN mutation correlated with an increased TMB and suggested a positive clinical outlook. A TMB-related signature (SYNE1 and TTN) was constructed based on this. The outlook for the high-risk group in LUSC was considerably poorer than the low-risk group (p = 0.004). In LUSC, there was a correlation between the TMB-related signature and immune infiltrates, and a positive response to anti-PD-L1 therapy was observed in individuals with low-risk scores. Furthermore, based on Kaplan-Meier analysis, plasma cells were identified as predictive immune cells in LUSC samples. In conclusion, the GSEA examination demonstrated that the TMB-associated signature stimulated immune system-related signaling pathways. To sum up, the TMB-associated signature could be marker to anticipate the immune reaction in individuals with LUSC.
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