ArticleOncology letters2024
Integrative analysis and risk model construction for super‑enhancer‑related immune genes in clear cell renal cell carcinoma.
Article in Oncology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.Technology in cancer research & treatmentArticle
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11 authors at 7 institutions in 1 country.
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Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer associated with poor prognosis, and accounts for the majority of RCC-related deaths. The lack of comprehensive diagnostic and prognostic biomarkers has limited further understanding of the pathophysiology of ccRCC. Super-enhancers (SEs) are congregated enhancer clusters that have a key role in tumor processes such as epithelial-mesenchymal transition, metabolic reprogramming, immune escape and resistance to apoptosis. RCC may also be immunogenic and sensitive to immunotherapy. In the present study, an Arraystar human SE-long non-coding RNA (lncRNA) microarray was first employed to profile the differentially expressed SE-lncRNAs and mRNAs in 5 paired ccRCC and peritumoral tissues and to identify SE-related genes. The overlap of these genes with immune genes was then determined to identify SE-related immune genes. A model for predicting clinical prognosis and response to immunotherapy was built following the comprehensive analysis of a ccRCC gene expression dataset from The Cancer Genome Atlas (TCGA) database. The patients from TCGA were divided into high- and low-risk groups based on the median score derived from the risk model, and the Kaplan-Meier survival analysis showed that the low-risk group had a higher survival probability. In addition, according to the receiver operating characteristic curve analysis, the risk model had more advantages than other clinical factors in predicting the overall survival (OS) rate of patients with ccRCC. Using this model, it was demonstrated that the high-risk group had a more robust immune response. Furthermore, 61 potential drugs with half-maximal inhibitory concentration values that differed significantly between the two patient groups were screened to investigate potential drug treatment of ccRCC. In summary, the present study provided a novel index for predicting the survival probability of patients with ccRCC and may provide some insights into the mechanisms through which SE-related immune genes influence the diagnosis, prognosis and potential treatment drugs of ccRCC.
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