Cuproptosis is a fresh form of the copper-elesclomol-triggered, mitochondrial tricarboxylic acid (TCA) dependent cell death. Yet, the subsumed mechanism of cuproptosis-associated lncRNAs in carcinoma is not wholly clarified. Here, We appraised 580 cuproptosis-associated lncRNAs in sarcoma and thereafter construed a module composing of 6 cuproptosis lncRNAs, entitled CuLncScore, utilizing a machine learning methodology. It could outstandingly discern the prognosis of patients in parallel with discriminating tumor immune microenvironment traits. Moreover, we simulate the classification system of cuproptosis lncRNAs by unsupervised learning method to facilitate differentiation of clinical denouement and immunotherapy modality options. Notably, Our Taizhou cohort validated the stability of CuLncScore and the classification system. Taking a step further, we checked these 6 cuproptosis lncRNAs by Quantitative real-time polymerase chain reaction (qRT-PCR) to ascertain their authenticity. All told, our investigations highlight that cuproptosis lncRNAs are involved in various components of sarcoma and assist in the formation of the tumor immune microenvironment. These results provide partial insights to further comprehend the molecular mechanisms of cuproptosis lncRNAs in sarcoma and could be helpful for the development of personalized therapeutic strategies targeting cuproptosis or cuproptosis lncRNAs.
ObjectiveCuproptosis, a nascent and unique pattern of cell death, is poised to spark a new rush of biological research. Yet, the subsumed mechanism of cuproptosis in carcinoma is not wholly clarified. The exclusive aim of this work is to define a novel classification algorithm and risk-prognosis scoring framework based on the expression modalities of cuproptosis genes to monitor clear cell renal cell carcinoma (ccRCC) patients’ prognosis and immunotherapeutic response.MethodsWe pooled ccRCC data from three large-scale databases as the training subset and gathered a panel of clinical queues, termed the Taizhou cohort, which served as the validation setup. Wilcox test was conducted for comparison of expression variation, while the cox analysis and KM curves were utilized to visualize prognosis. Unsupervised clustering analysis was used to identify cuproptosis phenotypes in ccRCC. Concurrently, LASSO regression-based computational scoring model. A step further, gene set enrichment analysis (GSEA) was performed to check potential biological processes and the “CIBERSORT” R package was used to estimate the proportion of immune cells. To last, immunohistochemistry and qRT-PCR were carried out for the assay of critical genes for cuproptosis.ResultsHere, we glimpse the prognostic power of cuproptosis genes in pan-cancer by investigating 33 cancers with multi-omics data to map their genetic heterogeneity landscape. In parallel, we devoted extra attention to their strategic potential role in ccRCC, identifying two phenotypes of cuproptosis with different immune microenvironmental characteristics by pooling ccRCC data from three large-scale databases. Additionally, we compiled a cuproptosis scoring system for clinicians to determine the prognosis, immunotherapy response, and chemosensitivity of ccRCC patients. Notably, we assembled a clinical cohort sample to validate the pivotal gene for cuproptosis, FDX1, to supply more clues to translate the biological significance of cuproptosis in ccRCC.ConclusionIn all, our investigations highlight that cuproptosis is involved in various components of ccRCC and assists in the formation of the tumor immune microenvironment. These results provide partial insights to further comprehend the molecular mechanisms of cuproptosis in ccRCC and could be helpful for the development of personalized therapeutic strategies targeting copper or cuproptosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.