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      A Newly Established Cuproptosis-Associated Long Non-Coding RNA Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Soft Tissue Sarcoma

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          Abstract

          Cuproptosis, a new type of programmed cell death, is involved in the development and progression of malignancies. The study of cuproptosis-associated long non-coding RNAs (lncRNAs) in soft tissue sarcomas (STSs) is however limited. There is also uncertainty regarding the prognostic accuracy of cuproptosis-associated lncRNAs in STSs and their relationship to the tumor immune microenvironment. The aim of this study was to determine the prognostic significance of cuprotosis-associated lncRNAs in STSs and their relationship to the tumor immune microenvironment. Transcriptomic and clinical data from patients with STSs were obtained through The Cancer Genome Atlas (TCGA). Overall, 259 patients were randomly allocated to a training group or a testing group. In the training group, a cuproptosis-associated lncRNA signature was constructed, and the signature was verified in the testing group. On the basis of risk scores and clinical features, we later developed a hybrid nomogram. We also performed functional and tumor immune microenvironment analysis based on the cuproptosis-associated lncRNA signature. A signature of 5 cuproptosis-associated lncRNAs was created. Based on this signature, we categorized STS patients into high-risk and low-risk groups. The study showed that patients at high risk had a worse prognosis than those at low risk. A nomogram was then constructed combining clinical characteristics with the risk scores, and it was shown to have credible predictive power. Functional enrichment and tumor immune microenvironmental analyses showed that high-risk STSs tend to be immunologically sensitive tumors. In our study, we found a cuproptosis-associated lncRNAs signature, which serves as an independent prognostic indicator. Cuproptosis-associated lncRNAs may play a role in the tumor immune microenvironment, which might be a therapeutic target for patients with STSs.

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          Robust enumeration of cell subsets from tissue expression profiles

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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            Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

            Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research.
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              Copper induces cell death by targeting lipoylated TCA cycle proteins

              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
                Journal
                J Oncol
                J Oncol
                jo
                Journal of Oncology
                Hindawi
                1687-8450
                1687-8469
                2022
                6 July 2022
                : 2022
                : 8489387
                Affiliations
                1Department of Orthopedics, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, No. 826, Southwestern Road, Shahekou District, Dalian, Liaoning Province 116021, China
                2Dalian Medical University, No. 9, West Section of South Lvshun Road, Dalian, Liaoning Province 116044, China
                3The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
                Author notes

                Academic Editor: Jinghua Pan

                Author information
                https://orcid.org/0000-0002-6388-6895
                Article
                10.1155/2022/8489387
                9279026
                35847354
                392d0ede-55f1-4dde-9954-3a14f2f42a49
                Copyright © 2022 Jun Han et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 May 2022
                : 1 June 2022
                : 7 June 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 31971275
                Categories
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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