ArticleScientific reports2023
The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 13 citations in OpenAlex.
- Single-cell transcriptomics reveals ADRB1 as a key immune checkpoint regulating T cell exhaustion in lung cancer and its prognostic value in lung cancer.Discover oncology · 2026Article
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- Integrative bulk RNA analysis unveils immune evasion mechanisms and predictive biomarkers of osimertinib resistance in non-small cell lung cancer.Discover oncology · 2025Article
- AI-Driven Analysis Unveils Functional Dynamics of Müller Cells in Retinal Autoimmune Inflammation.bioRxiv : the preprint server for biology · 2025Article
- Translating premalignant biology to accelerate non-small-cell lung cancer interception.Nature reviews. Cancer · 2025Review
- Construction and Validation of T-cell Exhaustion-related Gene Signature for Predicting Prognosis in Diffuse Large B-cell Lymphoma.Current pharmaceutical biotechnology · 2025Article
- The Impact of Genetic Mutations on the Efficacy of Immunotherapies in Lung Cancer.International journal of molecular sciences · 2024Review
- Alternative Strategies for Delivering Immunotherapeutics Targeting the PD-1/PD-L1 Immune Checkpoint in Cancer.Pharmaceutics · 2024Review
- Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024Article
- In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma.Frontiers in oncology · 2024Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.
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