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Integrated machine learning and bioinformatic analyses used to construct a copper-induced cell death-related classifier for prognosis and immunotherapeutic response of hepatocellular carcinoma patients

Affiliation
Department of Transplantation ,Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Zhao, Shuai;
Affiliation
Department of Oncology ,Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Chen, Shuxian;
Affiliation
Department of Interventional Oncology ,Renji Hospital, Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Liu, Wangrui;
Affiliation
Affiliated Hospital of Youjiang Medical University for Nationalities ,Baise ,China
Wei, Shiyin;
Affiliation
Department of Urology ,Renji Hospital ,Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Wu, Xinrui;
Affiliation
Department of Interventional Oncology ,Renji Hospital, Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Cui, Dan;
Affiliation
Department of Gastroenterology ,The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University ,Changzhou ,Jiangsu ,China
Jiang, Lifeng;
Affiliation
Department of Oncology ,Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Chen, Siyu;
Affiliation
Department of Transplantation ,Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine ,Shanghai ,China
Wang, Jian

Background: Copper as phytonutrient has powerful activity against health diseases. A newly discovered mechanism of cell death that affects energy metabolism by copper (“cuproptosis”) can induce multiple cuproptosis-related genes. Hepatocellular carcinoma (HCC) is a poorly prognosed widespread cancer having danger of advanced metastasis. Therefore, earlier diagnosis followed by the specific targeted therapy are required for improved prognosis. The work herein constructed scoring system built on ten cuproptosis-related genes (CRGs) to predict progression of tumor and metastasis more accurately and test patient reaction toward immunotherapy. Methods: A comprehensive assessment of cuproptosis patterns in HCC samples from two databases and a real-world cohort was performed on ten CRGs, that were linked to immune cell infiltration signatures of TME (tumor microenvironment). Risk signatures were created for quantifying effect of cuproptosis on HCC, and the effects of related genes on cellular function of HCC were investigated, in addition to the effects of immunotherapy and targeted therapy drugs. Results: Two distinct cuproptosis-associated mutational patterns were identified, with distinct immune cell infiltration characteristics and survival likelihood. Studies have shown that assessment of cuproptosis-induced tumor mutational patterns can help predict tumor stage, phenotype, stromal activity, genetic diversity, and patient prognosis. High risk scores are characterized by lower survival and worse treatment with anti-PD-L1/CTAL4 immunotherapy and first-line targeted drugs. Cytological functional assays show that CDKN2A and GLS promote proliferation, migration and inhibit copper-dependent death of HCC cells. Conclusion: HCC patients with high-risk scores exhibit significant treatment disadvantage and survival rates. Cuproptosis plays a non-negligible role in the development of HCC. Quantifying cuproptosis-related designs of tumors will aid in phenotypic categorization, leading to efficient personalized and targeted therapeutics and precise prediction of prognosis and metastasis.

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License Holder: Copyright © 2023 Zhao, Chen, Liu, Wei, Wu, Cui, Jiang, Chen and Wang.

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