ArticleFrontiers in oncology2023
Development and validation of a combined hypoxia- and metabolism-related prognostic signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Gene Expression in Muscle-Invasive and Non-Muscle-Invasive Bladder Cancer Cells Exposed to Hypoxia.Cancers · 2025Article
- Phospholipase C Beta 2 as a Key Regulator of Tumor Progression and Epithelial-Mesenchymal Transition via PI3K/AKT Signaling in Renal Cell Carcinoma.Biomedicines · 2025Article
- NK cells in renal cell carcinoma and its implications for CAR-NK therapy.Frontiers in cell and developmental biology · 2025Review
- PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives.Molecular cancer · 2024Review
- Identification of early Alzheimer's disease subclass and signature genes based on PANoptosis genes.Frontiers in immunology · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hypoxia and metabolism are closely correlated with the progression of cancer. We aimed to construct a combined hypoxia- and metabolism-related genes (HMRGs) prognostic signature to predict survival and immunotherapy responses in patients with clear cell renal cell carcinoma (ccRCC). Methods: The RNA-seq profiles and clinical data of ccRCC were acquired from the TCGA and the ArrayExpress (E-MTAB-1980) databases. Least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox regression analyses were applied to establish a prognostic signature. The E-MTAB-1980 cohort was selected for validation. The effectiveness and reliability of the signature were further evaluated by Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) curves. Further analyses, including functional enrichment, ssGSEA algorithm, CIBERSORT algorithm, and expression of immune checkpoints, were explored to investigate immune status and immunotherapy responses. Results: We constructed a prognostic eight-gene signature with IRF6, TEK, PLCB2, ABCB1, TGFA, COL4A5, PLOD2, and TUBB6. Patients were divided into high-risk and low-risk groups based on the medium-risk score. The K-M analysis revealed that patients in the high-risk group had an apparently poor prognosis compared to those in the low-risk group in the TCGA ( Conclusion: The HMRGs signature could be used to predict clinical prognosis, evaluate the efficacy of immunotherapy, and guide personalized immunotherapy in ccRCC patients.
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