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Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis

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Abstract

We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy.

Key messages

  • Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis.

  • Model usefulness should be considered when using models that contained more features.

  • We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm.

  • ER stress-associated signature has excellent ability for predicting prognosis for melanoma.

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Availability of data and materials

The data that support the findings of this study are available in The Cancer Genome Atlas (TCGA) datasets at https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

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Acknowledgements

We also acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Funding

This work was supported by the Projects of the National Natural Science Foundation of China (grant number 82073019 and 82073018), the Shenzhen Science and Technology Innovation Commission, China (Natural Science Foundation of Shenzhen, grant number JCYJ20210324113001005), Scientific Research Fund of Hunan Provincial Education Department (grant number 20C0402), and Hunan First Normal University (grant number XYS16N03), and Natural Science Foundation of Hunan Province China under Grants 2022JJ30673;.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, China

    Alphonse Houssou Hounye, Jiaoju Wang, Cong Cao & Muzhou Hou

  2. Department of Dermatology, Xiangya Hospital of Central South University, Changsha, 410000, China

    Bingqian Hu

  3. School of Computer Science, Hunan First Normal University, Changsha, 410205, China

    Zheng Wang

  4. Department of Dermatology, The Second Clinical Medical College, Shenzhen People’s Hospital Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, 518020, China

    Jianglin Zhang

  5. Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China

    Min Qi

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  1. Alphonse Houssou Hounye
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Contributions

Conceptualization: Alphonse Houssou Hounye; investigation: Bingqian Hu; writing—original draft: Alphonse Houssou Hounye and Bingqian Hu; writing—review and editing: Alphonse Houssou Hounye, Bingqian Hu, Zheng Wang, Jiaoju Wang, Cong Cao, Min Qi, Jianglin Zhang, and Muzhou Hou; visualization: Alphonse Houssou Hounye; supervision: Zheng Wang, Min Qi, Jianglin Zhang, and Muzhou Hou; funding acquisition: Min Qi and Jianglin Zhang.

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Correspondence to Muzhou Hou or Min Qi.

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The current study followed data requirements and Clinical Medical Ethics Committee approval, Xiangya Hospital, Central South University with ethic number: 202112271.

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Alphonse Houssou Hounye and Bingqian Hu contributed equally to this work

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Hounye, A.H., Hu, B., Wang, Z. et al. Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis. J Mol Med 101, 1267–1287 (2023). https://doi.org/10.1007/s00109-023-02365-w

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