Open AccessIdentification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma
Author(s) -
Yang RongHua,
Liang Bo,
Li JieHua,
Pi XiaoBing,
Yu Kai,
Xiang ShiJian,
Gu Ning,
Chen XiaoDong,
Zhou SiTong
Publication year - 2021
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.17021
Subject(s) - melanoma , biomarker , identification (biology) , pathology , medicine , skin cancer , tumour heterogeneity , tumor microenvironment , cancer research , oncology , dermatology , biology , cancer , biochemistry , botany
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.