A five‐gene methylation signature predicts overall survival of patients with clear cell renal cell carcinoma

Xiao, Jing; Xu, Gang; Gong, Yu; Li, Junlong; LingfengWu,; Zhu, Wei; He, Yi; Li, Zhongyi; S, Pan

doi:10.1002/jcla.24031

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…Therefore, research on cancer biomarkers and outcome prediction of pRCC has been a research hotspot recently. Jing et al found that methylation of ve genes (CCNB2, CDKN1C, CTSH, E2F2, and ERMP1) was related to the prognosis of pRCC patients and could become potential targets of methylation-mediated immunotherapy [32]. Dang et al identi ed 15 ferroptosis-linked lncRNAs associated with the prognosis of pRCC patients through TCGA data mining to generate a prognosis-linked risk model.…”

Section: Discussionmentioning

confidence: 99%

A cuproptosis-related long non-coding RNA risk coefficient model to predict outcomes and therapeutic responses in papillary renal cell carcinoma

Tang

Yang

Huang

et al. 2023

Preprint

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Background Papillary renal cell carcinoma (pRCC) is the second most common subtype of renal cell carcinoma, second only to clear cell renal cell carcinoma (ccRCC). Compared with ccRCC, metastatic pRCC has worse outcomes. Copper is an essential micronutrient; its accumulation is related to cancer proliferation, growth, angiogenesis, and metastasis. The abnormal expression of long non-coding RNA (lncRNA) plays an essential role in developing pRCC. Our study aimed to predict outcomes in pRCC patients using a prognostic risk coefficient model for cuproptosis-related lncRNAs. Methods We obtained data from pRCC patients and cuproptosis-related genes from The Cancer Genome Atlas and the literature. Cuproptosis-related lncRNAs were obtained using co-expression analysis based on R language software. The prognostic risk model of cuproptosis-related pRCC was established using univariate and multivariate Cox regressions and LASSO regression analysis, and the model was validated. The high- and low-risk groups were divided by the median value of the risk coefficient. Finally, we performed correlation analysis and potential drug identification for high- and low-risk subgroups. Results Seven cuproptosis-related lncRNAs were included (AC019080.5, AC092807.3, AC107464.2, AL5941845.1, GCC2-AS1, NINJ2-AS1, and ZNF710-AS1). Riskscore and tumor stage were independent prognostic factors in pRCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analysis showed that differentially expressed genes in pRCC cuproptosis-related lncRNAs were associated with the pathways of collagen-containing extracellular matrix formation and intracellular and extracellular structure formation. Correlation analysis of immune function showed that type II interferon responses, immune checkpoints, co-inhibition of T cells, cytolysis, co-inhibition of antigen-presenting cells, and human leukocyte antigen were significantly different between high- and low-risk groups. Higher Tumor Immune Dysfunction and Exclusion scores were observed in the low-risk group in the immune escape association analysis, illustrating the suboptimal effect of immunotherapy in low-risk patients. We identified 5-fluorouracil, epothilone B, gemcitabine, paclitaxel, pazopanib, and sunitinib as potentially more beneficial in the high-risk group; bortezomib, erlotinib, and sorafenib were more beneficial in the low-risk group. Conclusion We identified seven cuproptosis-related lncRNAs associated with pRCC outcomes and constructed a prognostic model that provides a basis for basic research and pRCC precision treatment.

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