A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

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Abstract

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Molecular mechanisms of epithelial-mesenchymal transition.

Samy Lamouille, Jian Xu, Rik Derynck (2014)

The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial-mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix-loop-helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.

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Copper induces cell death by targeting lipoylated TCA cycle proteins

Peter Tsvetkov, Shannon Coy, Boryana Petrova … (2022)

Copper is an essential cofactor for all organisms, and yet it becomes toxic if concentrations exceed a threshold maintained by evolutionarily conserved homeostatic mechanisms. How excess copper induces cell death, however, is unknown. Here, we show in human cells that copper-dependent, regulated cell death is distinct from known death mechanisms and is dependent on mitochondrial respiration. We show that copper-dependent death occurs by means of direct binding of copper to lipoylated components of the tricarboxylic acid (TCA) cycle. This results in lipoylated protein aggregation and subsequent iron-sulfur cluster protein loss, which leads to proteotoxic stress and ultimately cell death. These findings may explain the need for ancient copper homeostatic mechanisms. Cell death is an essential, finely tuned process that is critical for the removal of damaged and superfluous cells. Multiple forms of programmed and nonprogrammed cell death have been identified, including apoptosis, ferroptosis, and necroptosis. Tsvetkov et al . investigated whether abnormal copper ion elevations may sensitize cells toward a previously unidentified death pathway (see the Perspective by Kahlson and Dixon). By performing CRISPR/Cas9 screens, several genes were identified that could protect against copper-induced cell killing. Using genetically modified cells and a mouse model of a copper overload disorder, the researchers report that excess copper promotes the aggregation of lipoylated proteins and links mitochondrial metabolism to copper-dependent death. —PNK Lipoylation determines sensitivity to copper-induced cell death.

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Author and article information

Contributors

Yi Zhou: URI : https://loop.frontiersin.org/people/1845269/overview

Qi Shu: URI : https://loop.frontiersin.org/people/1654008/overview

Jianrong Gu: URI : https://loop.frontiersin.org/people/1846452/overview

Minghua Xie: URI : https://loop.frontiersin.org/people/732750/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 22 July 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 959456

Affiliations

[1] ¹ Department of Pharmacy , First People’s Hospital of Linping District , Hangzhou, ZG, China

[2] ² The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) , Institute of Basic Medicine and Cancer (IBMC) , Chinese Academy of Sciences , Hangzhou, China

Author notes

Edited by: Sipeng Shen, Nanjing Medical University, China

Reviewed by: Qian Chen, Guangxi Medical University Cancer Hospital, China

Ni Zeng, Affiliated hospital of Zunyi Medical University, China

*Correspondence: Yifang Chen, 1253624670@ 123456qq.com ; Minghua Xie, xmh53072@ 123456163.com

This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics

Article

Publisher ID: 959456

DOI: 10.3389/fgene.2022.959456

PMC ID: 9354044

PubMed ID: 35938036

SO-VID: 70ee5176-7d3f-47dd-bb7b-0cd2110ea8de

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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 01 June 2022

Date accepted : 27 June 2022

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A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

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Most cited references 58

clusterProfiler: an R package for comparing biological themes among gene clusters.

Molecular mechanisms of epithelial-mesenchymal transition.

Copper induces cell death by targeting lipoylated TCA cycle proteins

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