# A Bioinformatics and Wet-Lab-Based Pipeline Identifies CLDN10 and GJB2 as Epigenetically Silenced Tumor Suppressor Genes in Cutaneous Melanoma

## TL;DR

A pipeline combining bioinformatics and lab experiments identifies CLDN10 and GJB2 as tumor suppressor genes silenced by DNA methylation in melanoma.

## Contribution

A practical pipeline is introduced to identify epigenetically silenced tumor suppressor genes using bioinformatics and wet-lab validation.

## Key findings

- CLDN10 and GJB2 are identified as epigenetically silenced tumor suppressor genes in melanoma.
- DNA methylation analysis and gene expression quantification confirm the silencing of these genes in tumors and cell lines.

## Abstract

Studying epigenetic changes in cancer development can reveal the role of tumor suppressor genes and their regulation by DNA methylation. CpG islands, found in promoter regions, are of particular interest, as their hypermethylation can silence tumor suppressor gene expression. Here, we present a practical analysis pipeline for wet-lab biologists with the aim of identify novel epigenetically regulated tumor suppressors using freely available online tools. Bioinformatic platforms such as the R2 Genomics Analysis and Visualization Platform enable analysis of genomic organization, CpG islands, and regulatory elements. Differential methylation and gene expression analyses are based on datasets including TCGA, using tools such as MethSurv, TCGA Wanderer, and GEPIA2 to correlate DNA methylation with gene expression. This bioinformatic step is the basis for the tumor suppressor verification in the wet-lab. Using this pipeline, we identified CLDN10 and GJB2 as potential tumor suppressors in melanoma. Experimentally, our approach includes DNA methylation analysis based on DNA bisulfite conversion, combined bisulfite restriction analysis (CoBRA), pyrosequencing for specific CpG methylation quantification, and RT-PCR for RNA expression quantification. We verify these results in primary tumors, metastases, and cell line models. This approach supports efficient identification of novel epigenetically regulated tumor suppressors, providing practical research guidelines.