4.6 Article

Construction of immunotherapy-related prognostic gene signature and small molecule drug prediction for cutaneous melanoma

Journal

FRONTIERS IN ONCOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.939385

Keywords

cutaneous melanoma; tumor microenvironment; prognostic signature; immunotherapy; bioinformatics

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A riskScore model was developed in this study to analyze patient prognosis, immune tumor microenvironment (TME) cell infiltration features, and treatment responsiveness. This model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.
Background: Cutaneous melanoma (CM), a kind of skin cancer with a high rate of advanced mortality, exhibits a wide variety of driver and transmitter gene alterations in the immunological tumor microenvironment (TME) associated with tumor cell survival and proliferation. Methods: We analyzed the immunological infiltration of TME cells in normal and malignant tissues using 469 CM and 556 normal skin samples. We used a single sample gene set enrichment assay (ssGSEA) to quantify the relative abundance of 28 cells, then used the LASSO COX regression model to develop a riskScore prognostic model, followed by a small molecule drug screening and molecular docking validation, which was then validated using qRT-PCR and IHC. Results: We developed a prognosis model around seven essential protective genes for the first time, dramatically elevated in tumor tissues, as did immune cell infiltration. Multivariate Cox regression results indicated that riskScore is an independent and robust prognostic indicator, and its predictive value in immunotherapy was verified. Additionally, we identified Gabapentin as a possible small molecule therapeutic for CM. Conclusions: A riskScore model was developed in this work to analyze patient prognosis, TME cell infiltration features, and treatment responsiveness. The development of this model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.

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