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Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma |
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| Authors: | Rong-Hua Yang Bo Liang Jie-Hua Li Xiao-Bing Pi Kai Yu Shi-Jian Xiang Ning Gu Xiao-Dong Chen Si-Tong Zhou |
| Institution: | 1. Department of Burn Surgery and Skin Regeneration, The First People’s Hospital of Foshan, Foshan, China
Contribution: Data curation (equal), Investigation (equal), Software (equal), Validation (equal), Visualization (equal);2. Nanjing University of Chinese Medicine, Nanjing, China;3. Department of Dermatology, The First People’s Hospital of Foshan, Foshan, China Contribution: Data curation (equal), Formal analysis (equal), Methodology (equal);4. Department of Dermatology, The First People’s Hospital of Foshan, Foshan, China Contribution: Data curation (equal), Resources (equal), Validation (equal);5. Department of Emergency, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China Contribution: Data curation (equal), Resources (equal), Validation (equal);6. Department of Pharmacy, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China Contribution: Formal analysis (equal), Resources (equal), Software (equal);7. Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, China;8. Department of Burn Surgery and Skin Regeneration, The First People’s Hospital of Foshan, Foshan, China;9. Department of Dermatology, The First People’s Hospital of Foshan, Foshan, China |
| Abstract: | Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM. |
| Keywords: | ESTIMATE LASSO prognostic biomarker PRSS35 skin cutaneous melanoma tumour microenvironment |