ArticleCancer immunology, immunotherapy : CII2024
Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 8 citations in OpenAlex.
- Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 < 50.Cancer cell international · 2026Article
- RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non-Small Cell Lung Cancer.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma.Cancer drug resistance (Alhambra, Calif.) · 2025Article
- Quality-of-life scale machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer.Frontiers in immunology · 2025Article
- m6A regulator-mediated methylation modifications define the immune infiltration characteristics of the tumor microenvironment in prostate adenocarcinoma.Scientific reports · 2024Article
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Authors and funding
22 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDue to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated.
methodsTotal RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype.
resultsWe used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation.
conclusionsIn summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.
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