GPR143 is a Prognostic Biomarker Associated with Immune Inltration in Melanoma Zixuan Xing ( [email protected] ) Xi'an Jiaotong University School of Medicine Shaobo Wu Xi'an Jiaotong University Qijuan Zang Xi'an Jiaotong University School of Medicine Hao Lei Xi'an Jiaotong University Second Aliated Hospital Yi Wei Xi'an Jiaotong University Medical College First Aliated Hospital Chaonan Zhang Xi'an Jiaotong University Medical College First Aliated Hospital Bingling Dai Xi'an Jiaotong University School of Medicine Research Article Keywords: GPR143, SKCM, Immune inltration, Biomarker, TCGA, Bioinformatics Posted Date: May 4th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-430494/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/15 Abstract Background: Skin cutaneous melanoma (SKCM) is considered one of the most aggressive and lethal cancers of the skin. G-protein coupled receptor 143 (GPR143), which has been reported to cause congenital nystagmus, belongs to the superfamily of G protein- coupled receptors. Methods and Results: We analyzed the expression of GPR143 and survival of SKCM patients in SKCM via Gene Expression Proling Interactive Analysis (GEPIA). Then, logistic regression and multivariate cox analysis was used to analyze the inuence of GPR143 expression on clinicopathological elements and survival. We explored the immune response of 22 TIICs in SKCM via CIBERSORT and used TIMER to assess the correlation of GPR143 expression and immune inltration level. Finally, we used gene set enrichment analysis (GSEA) to assess the TCGA dataset. The outcomes suggest that GPR143 expression in tumor samples is remarkedly higher than in normal samples and high GPR143 expression is associated with poorer prognosis. The result of multivariate analysis indicated that increased GPR143 expression is an independent prognostic factor for prognosis. We found GPR143 expression level has prominent negative correlations with inltrating levels of B cell, CD8+ T cells, etc. GSEA indicated that pigment metabolic process, pigment biosynthetic process and other pathways were identied as differentially enriched pathways in Gene Ontology (GO). Oxidative phosphorylation, Parkinson’s disease and other pathways were showed signicantly differential enrichment in GPR143 high expression phenotype in Kyoto Encyclopedia of Genes and Genomes (KEGG). Conclusions: In conclusion, GPR143 may be a novel prognostic biomarker and associated with immune inltrates in SKCM. Introduction Skin cutaneous melanoma (SKCM) is considered one of the most aggressive and lethal cancers of the skin [1–3]. Among skin tumors, melanoma is frequently characterized by its poor prognosis and low survival rate [4–6]. To make things worse, there are no effective systemic therapies to treat advanced stages of SKCM [4, 7, 8]. The main prognostic indicators following diagnosis of cutaneous melanoma can be divided into histological and clinical factors [9, 10]. Recently, ve chemokine members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) was proved to be associated with SKCM tumorigenesis, progression, prognosis and immune inltrations[11]. However, we need to nd better biomarkers. G-protein coupled receptor 143 (GPR143) belongs to the superfamily of G protein-coupled receptors. Ocular albinism (OA) can be caused by mutations in GPR143, which is located on the X-chromosome [12]. The GPR143 gene, which has also been reported to cause congenital nystagmus, possesses nine exons and spans ~ 40 kb of genomic DNA [13]. GPR143 encodes proteins that are important for the development and maturation of melanosomes, and is only localized in the lysosomes and melanosomes of cells [13]. In Skin cutaneous melanoma, only a little information has been made available about GPR143. In order to explore the role of GPR143 in SKCM, we conducted a further analysis using the data downloaded from The Cancer Genome Atlas (TCGA). Moreover, the correlation of GPR143 with SKCM was identied by Gene Expression Proling Interactive Analysis (GEPIA), the R and COX regression analysis. Meanwhile, CIBERSORT, a recent metagene approach, was used along with the Tumor Immune Estimation Resource (TIMER) to test the relative proportions of different types of tumor inltrating immune cells (TIICs) in various tumor microenvironments. In addition, these methods helped us to study the association of GPR143 with tumor- inltrating immune cells. The results allowed us to have a comprehensive understanding for the potential function of GPR143 in SKCM. Also, the ndings demonstrated a possible correlation as well as a latent mechanism between GPR143 and tumor-immune interactions. Therefore, GPR143 may become a novel sign for the evaluation of prognosis and immune inltration for SKCM patients. Materials And Methods Data acquisition SKCM patient datasets, with gene expression proles and clinical information, were downloaded from the publicly available TCGA (https://TCGAData.nci.nih.gov/TCGA/) [14], and included 1 normal and 471 tumor tissues. Subsequent processing excluded cases with insucient or missing data on age, local invasion, lymph node metastasis, distant metastasis, TNM stage and overall survival time. Then, we transformed RNA sequencing data to convert count data to value more similar to those from microarrays. Page 2/15 Furthermore, in light of the expression of GPR143 in further study, tumor tissues were divided into 2 groups to study the effects of GPR143 expression on immune microenvironment. Survival and expression analysis by GEPIA The correlation between GPR143 expression and clinicopathologic information in SKCM was conrmed by Gene Expression Proling Interactive Analysis (GEPIA) (http:// gepia.cancer-pku.cn/index.html). In GEPIA[15], a standard processing pipeline was used to analyzes 9,736 tumors specimens from the RNA sequencing expression data and 8,587 normal samples from GTEx and TCGA. The correlation of the gene expression with SKCM patients’ prognosis could be found by the analysis of survival curve of differential GPR143 expression in GEPIA. Moreover, stage plot that used pathological stage as variable was analyzed to compare GPR143 expression in various pathological stage. Additionally, boxplots that used disease state (Tumor or Normal) as variable was drew to show differential expression of GPR143. Evaluation of tumor-inltrating immune cells TIMER [16] as a comprehensive resource (https://cistrome.shinyapps.io/timer/) was employed in the systematic analysis of immune inltrates across a wide spectrum of cancer types. A novel statistical approach applied by TIMER called the deconvolution using the gene expression proles [17] to deduce an effect on the number of tumor-inltrating immune cells (TIICs). The Cancer Genome Atlas provided the TIMER database 10,897 specimens across 32 cancer types applied for the approximate calculation of immune inltrates. A sequence of analysis was conducted on the expression of GPR143 in various types of cancer and its correlation with plentiful immune inltrates. B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells was included in these immune inltrates. CIBERSORT [18] (http://cibersort.stanford.edu/), an online analytical platform based on a deconvolution algorithm, was used to assess the relevance of correlation between TIICs in tumor and gene expression. Thus, CIBERSORT could be used to precisely estimate TIIC concentration. In order to analyze its correlation with survival and molecular subpopulation, we evaluated the immune response of 22 TIICs in SKCM via CIBERSORT. Meanwhile, 472 samples from the TCGA were used to assess the inuence of GPR143 expression. Also, 472 samples were divided into two groups, low expression group and high expression group, and then we used the data to make vioplot. Gene set enrichment analysis A computational method that determined the statistical signicance of a priori dened set of genes and the existence of concordant differences between two biological states was known as the GSEA [19, 20]. Gene set enrichment analysis (GSEA), a computational method, assessed whether a specic gene set is conspicuous different in any two biological states. In our study, in light of their correlation with the GPR143 expression, a list was rstly generated on the classication of the genes. This computational method expounded on the prominent differences on the survival between high- and low- GPR143 groups. In addition, in order to sort the enriched pathways in each phenotype, we used the nominal P value and normalized enrichment score (NES) [21]. Those gene set with a discovery rate (FDR) < 0.05 were deemed to be markedly enriched. Statistical analysis Data acquired from TCGA were merged and processed by R3.6.3. The correlations between the clinical information and GPR143 expression were assessed by using logistic regression. Furthermore, Multivariate Cox analysis was used to analyze the inuence of GPR143 expression and other clinicopathological elements (age and gender) on survival. P value < 0.05 was set up as the cut-off criterion. To study the correlation between 22 types of immune cells, we conducted the correlation heatmap to show the correlation between every two different immune cells in specimens. Results Page 3/15 Patient characteristics and multivariate analysis Our study cohort represented SKCM with tumor stages of I and II, where stage I embodied 77 patients (35.5%) and stage II encompassed 140 (64.5%). Survival curve, pathological stage plot and expression on boxplot