==== Front Skin Res Technol Skin Res Technol 10.1111/(ISSN)1600-0846 SRT Skin Research and Technology 0909-752X 1600-0846 John Wiley and Sons Inc. Hoboken 10.1111/srt.13358 SRT13358 Letter Letters Identification of three master regulatory genes with prognostic value for uveal melanoma by means of weighted co‐expression network analysis DIAZ et al. Diaz Michael Joseph 1 michaeldiaz@ufl.edu Tran Jasmine Thuy 2 Montanez‐Wiscovich Marjorie 3 1 College of Medicine University of Florida Gainesville Florida USA 2 School of Medicine University of Indiana Indianapolis Indiana USA 3 Department of Dermatology University of Florida Gainesville Florida USA * Correspondence Michael Joseph Diaz, College of Medicine, University of Florida, 1600 SW Archer Rd, Gainesville, FL 32610, USA. Email: michaeldiaz@ufl.edu 03 7 2023 7 2023 29 7 10.1111/srt.v29.7 e1335820 4 2023 13 5 2023 © 2023 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. source-schema-version-number2.0 cover-dateJuly 2023 details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:03.07.2023 ==== Body pmc1 To the Editor, Uveal melanoma (UM) is a rare intraocular cancer of the uvea or uveal tract, usually implicating a GNAQ or GNA11 mutation. 1 Patient outcomes are poor, with probability of metastasis above 50% and subsequent low long‐term survival rates. 2 Based on epidemiologic estimates are equally sobering: UM represents 5% of all primary melanoma diagnoses and has a mean‐age‐adjusted incidence > 5 cases per million individuals per year in the United States. 3 Prior expression‐based profiling efforts have revealed potential predictors of metastatic progression, 4 , 5 but this has yet translated to clinical benefits. The aim of this study is to elucidate regulatory genes that are associated with patient outcomes, so as to identify new targets for future UM therapies. A weighted gene co‐expression network analysis was performed to identify candidate hub genes. Bulk RNA sequencing data and corresponding survival information were sourced from the Broad GDAC Firehose portal (cohort = UVM) (N = 80). Genes were retained for downstream analysis if they (1) were among the 80th percentile by variance, (2) were represented by >4 nonmissing samples, and (3) had a calculated median absolute deviation >0. Samples were clustered hierarchically (method = unweighted pair group method with arithmetic mean) to identify outliers. The analyzed expression matrix represented 78 samples and 3839 genes. Counts subjected to transcripts per million normalization and log2‐transformation were used for downstream analysis. Gene co‐expression analysis was performed with R package “WGCNA” v1.71 (networkType = “signed,” corType = “bicor,” power = 18, minModuleSize = 20, deepSplit = 4, maxPOutliers = 0.1). 6 The soft thresholding power was determined by consulting the scale free topology (Figure 1). Hub genes were defined as the most connected gene (i.e., highest correlation) within each candidate module. Kaplan–Meier analysis of overall and disease‐free survival (OS, DFS) outcomes was conducted using R package “survminer.” For each hub gene, UM patients with above‐median gene expression were compared to UM patients with below‐median gene expression. p‐Values less than 0.05 were considered statistically significant. FIGURE 1 Analysis of network scale independence and mean connectivity at soft‐thresholding powers 1–10 stepwise, 12–20 incrementing by 2, 25, and 30. Relevant parameters were networkType = “signed,” verbose = 5, corFnc = “bicor,” and corOptions = list(use = ‘p’, maxPOutliers = 0.1). Red line demarcates signed R^2 of 0.8. A total of six regulatory networks were generated. Lower expression of SASH3 (p‐value = 0.0011) and HM13 (p‐value = 0.0001) correlated with better OS outcomes. Higher expression of PLXNB1 correlated with better OS and DFS outcomes (p‐value < 0.0001). DFS analysis of HM13 and PLXNB1 revealed similarly significant trends. Table 1 has a comprehensive report of these gene‐survival associations. Prior Pan‐Cancer analysis linked high expression of HM13 (Histocompatibility Minor 13) to poor UM prognosis, 7 but associations between UM and SASH3 (SAM And SH3 Domain Containing 3) or PLXNB1 (Plexin B1) have not been reported in the available literature to date. However, there does exist a wealth of convincing data correlating SASH3 and PLXNB1 activity with distinct clinical outcomes in several other cancers. 8 , 9 , 10 TABLE 1 Overall survival probabilities associated with each hub gene, based on KM analysis. Gene Median OS of high expression group Median OS of low expression group p‐Value SASH3 36.59 N/A 1.08e‐03 PLXNB1 N/A 31.07 5.36e‐07 FAM107A 36.59 51.98 3.73e‐01 VSX2 N/A 45.90 8.06e‐01 TNKS2 45.27 45.90 8.29e‐01 HM13 36.59 N/A 1.12e‐04 John Wiley & Sons, Ltd. In this study, we describe novel master regulatory networks in the UM interactome. From these networks, we have further identified new genes associated with UM survival. Studies that further explore these relationships may realistically guide and predict immune therapy efforts. Potential limitations of our study include utilization of a single dataset and lack of specificity for local co‐expression, which is inherent to global gene co‐expression analytical techniques. CONFLICT OF INTEREST STATEMENT Dr. Montanez‐Wiscovich serves as principal investigator for the CorEvitas registry sponsored by the National Psoraisis Foundation and the LITE study sponsored by the Patient Centered Outcomes Research Institute (PCORI). She also has an educational grant from Pfizer Global Medical Grants. FUNDING INFORMATION The authors received no specific funding for this work. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available in the Broad GDAC Firehose portal at https://gdac.broadinstitute.org/. ==== Refs REFERENCES 1 Shoushtari AN , Carvajal RD . GNAQ and GNA11 mutations in uveal melanoma. Melanoma Res. 2014;24 (6 ):525‐534. doi:10.1097/CMR.0000000000000121 25304237 2 Carvajal RD , Schwartz GK , Tezel T , Marr B , Francis JH , Nathan PD . Metastatic disease from uveal melanoma: treatment options and future prospects. Br J Ophthalmol. 2017;101 (1 ):38‐44. Epub 2016 Aug 29. PMID: 27574175; PMCID: PMC5256122 doi:10.1136/bjophthalmol-2016-309034 27574175 3 Mahendraraj K , Lau CS , Lee I , Chamberlain RS . 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