ArticleAnimal cells and systems2020
Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis.
Article in Animal cells and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Targeting Plasma Membrane CaCancers · 2026Review
- Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma.Scientific reports · 2023Article
- Article
- Article
- Identification of epigenetic dysregulation gene markers and immune landscape in kidney renal clear cell carcinoma by comprehensive genomic analysis.Frontiers in immunology · 2022Article
- Transcriptome alterations in spermatogonial stem cells exposed to bisphenol A.Animal cells and systems · 2022Article
- DNA damage repair: historical perspectives, mechanistic pathways and clinical translation for targeted cancer therapy.Signal transduction and targeted therapy · 2021Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.
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