Decoding The Epitranscriptome: In Silico Insights Into m6A Regulatory Network In Breast Cancer

Summary

This protocol presents an approach for conducting in silico genetic, molecular, and prognostic analyses of m6A modification regulators by integrating mutation profiles, copy number alterations, gene expression, and clinical outcomes using publicly available datasets from the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project, and microarray platforms.

Abstract

N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic transcripts and plays a critical role in RNA metabolism, gene expression, and cellular homeostasis. Dysregulation of m6A regulators, including “writers,” “erasers,” and “readers”, has been increasingly implicated in cancer biology; however, their comprehensive roles in breast cancer remain to be understood. The primary objective of this methods article is to provide bioinformatics beginners with a step-by-step framework for utilizing publicly available cancer datasets to perform mutational analyses, assess gene expression alterations, and examine their associations with patient survival. As a case study, m6A regulators in breast cancer were analyzed using datasets from the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project, and microarray platforms. Transcriptomic profiles were systematically analyzed to demonstrate workflows for evaluating the prognostic relevance of m6A regulatory components in breast cancer. Using this analytical framework, distinct patterns of genetic alterations and differential expression among key m6A regulators were identified. Several regulators, including METTL14, CBLL1, YTHDC1, HNRNPC, HNRNPA2B1, and RBMX, were associated with better patient survival, while YWHAG was associated with poor overall survival. This study provides a comprehensive systems genomics overview of m6A regulatory genes in breast cancer while demonstrating a practical and reproducible web-based bioinformatics workflow. These findings advance the understanding of epitranscriptomic regulation in breast cancer and offer a foundation for the development of novel m6A-based diagnostic and therapeutic strategies.

Introduction

Epitranscriptomic modifications represent an important layer of post-transcriptional gene regulation and contribute to diverse cellular processes and disease states. Among more than 170 RNA modifications identified to date, N6-methyladenosine (m6A) is the most prevalent and well-characterized in eukaryotic mRNAs¹. Installed by “writer” complexes including METTL3/METTL14, removed by “erasers” including FTO and ALKBH5, and interpreted by “reader” proteins including YTH and IGF2BP family members, m6A orchestrates RNA splicing, stability, transport, and translation, thereby influencing key biological processes including development, differentiation, and stress response^2,3.

Alterations in m6A regulatory components have been reported across a broad spectrum of malignancies⁴. In many cancers, aberrant m6A activity drives malignant phenotypes; e.g., elevated expression of METTL3 promotes prostate cancer initiation and progression by modulating the hedgehog pathway and MYC RNA methylation^5,6. Initially found to be implicated in exerting oncogenic effects in acute myeloid leukemia, FTO was shown to drive tumor progression in liver, lung, and colorectal cancers^7,8,9,10. However, context-dependent roles of FTO and ALKBH5 were identified that illustrate the dual nature of m6A-mediated regulation, which can promote both the oncogenic and tumor suppressive signaling^11,12,13,14. M6A readers, including YTHDF1/2/3,  heterogeneous nuclear ribonucleoproteins (hnRNPs), and insulin-like growth factor-2 mRNA-binding proteins (IGF2BP1-3), have also been found to be associated with carcinogenesis^15,16,17.

In breast cancer, increasing evidence suggests that m6A regulators are frequently dysregulated and may be associated with tumor subtypes, immune-related features, and clinical outcomes^18,19. Multiple mechanistic studies position METTL3 as a frequently upregulated pro-oncogenic factor in breast cancer. METTL3-mediated m6A installation can stabilize or enhance translation of transcripts that promote proliferation, epithelial-mesenchymal transition (EMT), metastasis, and chemoresistance²⁰. METTL3 has also been shown to promote breast cancer progression via targeting Bcl-2²¹. ALKBH5 has been implicated in regulating cancer stemness programs through NANOG and other stemness-related molecules, but its influence may vary by tumor context²².

As the list of m6A regulators continues to expand in recent years, an update on how the newly identified regulators might be dysregulated in breast cancer is needed. Table 1 provides a list of m6A regulators that include writers, readers, and erasers of m6A modification. Additionally, novel m6A regulators, including LRPPRC and YWHAG, have been identified with implications in cancer progression^23,24,25. Therefore, a comprehensive genetic and molecular characterization of all known m6A regulators was conducted in breast cancer using tools that can be employed by researchers with limited bioinformatic background.

The objective of this Methods article is to present a step-by-step platform-based bioinformatics protocol for analyzing m6A regulators in breast cancer using publicly available cancer genomics resources. Using datasets from The Cancer Genome Atlas (TCGA) (www.cancer.gov/tcga), the Genotype Tissue Expression (GTEx) project²⁶, and web-based analytical platforms such as cBioPortal and UCSC Xena, this protocol demonstrates reproducible workflows for assessing mutational profiles, gene expression alterations, and association with patient survival. This visualized and accessible approach is intended to facilitate the adoption of epitranscriptomic data analysis by researchers new to cancer bioinformatics.

Protocol

NOTE: The list of genes encoding m6A methylation regulators, categorized as writers, readers, and erasers, is presented in Table 1. All listed genes were included in the subsequent analyses of mutations, expression patterns, and overall survival. All software and tools used in this study are listed in the Table of Materials.

1. Identification of genetic alterations in m6A regulators

1. Access the cBioPortal for cancer genomics. Navigate to the cBioportal website (www.cbioportal.org)^27,28. From the homepage, select the “Query” tab to begin a new analysis.
2. Select the appropriate cancer study and cohort.
3. In the “Select Studies for Visualization and Analysis” search bar, type “Breast Invasive Carcinoma“ and select “Breast Invasive Carcinoma (TCGA, Pan-Cancer Atlas)”.
4. At the bottom, select “Query by Gene”.
   CRITICAL: Ensure that the selected cohort (996 samples) includes both mutation and copy-number alteration (CNA) data.
5. Define the Genetic Query. In the “Enter Genes”, enter the HUGO gene symbols for the full list of m6A regulators under investigation.
   NOTE: Genes can be entered as a list separated by spaces. Under “Select Genomic Profiles”, ensure the following two data types are checked: Mutations and Copy-number Alterations.
6. Under “Select Patient/Case Set,” choose the default sample set that corresponds to the cohort with all profile cases.
7. Click the blue “Submit Query” button.
8. Retrieve and interpret the genetic alteration data. Upon submission, the result will load on the “summary” tab. The central “OncoPrint” visualization provides an immediate overview of genetic alterations across all queried genes in the cohort, which can be downloaded.
9. Next to the OncoPrint, locate the “Cancer Type Summary” plot. This provides a quantitative breakdown of alterations across breast cancer subtypes.
10. Perform a Pan-Cancer Analysis. Return to the cBioPortal homepage and select “TCGA PanCancer Atlas Studies” under the “Query” tab.
11. In the gene input box, enter the same list of m6A regulator genes.
12. Click “Submit Query” and on the results page, navigate to the “Cancer Types Summary” tab. This provides a pan-cancer view.

2. Comparative transcriptomic analysis of m6A regulators using UCSC Xena.

1. Access the UCSC Xena Platform. Navigate to the UCSC Xena website (https://xena.ucsc.edu)²⁹.
2. From the homepage, click on the “Launch Xena” button to enter the main analysis browser.
3. In the Xena browser, click “DATA SETS”.
4. Among the datasets, select “TCGA TARGET GTEx”. This contains uniformly processed RNA-Seq data from the TCGA and GTEx projects' normal tissues.
5. On the next page, click “VISUALIZE”.
6. Define the Phenotype (sample group) variable. In the “Select Your First Variable”, select “Main Category” in the Phenotypic data type.
7. Click "TO SECOND VARIABLE”. Then, in the Genomic data type, tick “Gene Expression” in the dataset. Add the gene list in the “Add Gene or Position” box. Click “Done”.
8. Visualize expression patterns with a heatmap.
9. To separate the breast (TCGA+GTEx) samples from the TCGA TARGET GTEx, type ”Breast” and use the filter option to keep samples.
10. The heatmap is now visible and can be downloaded as a PDF.
11. Generate comparative box plots for individual genes. To quantify and visualize expression differences for a specific gene, use the “View as chart”. Using this option, data can be viewed as a box plot, a dot plot, and a violin plot, comparing the expression distribution between the two sample groups.
12. Use “Download as PDF” option to download the charts.
13. Statistical significance (p-value) can be obtained by clicking on “STATISTICS”.

3. Assessing prognostic significance of m6A regulators using Kaplan-Meier Plotter.

1. Access the Kaplan-Meier Plotter tool. Navigate to the Kaplan-Meier Plotter website (https://kmplot.com/analysis)³⁰.
2. From the homepage, select the “breast cancer” tab to initiate an analysis specific to breast cancer datasets.
3. Configure the gene query for a single gene.
4. In the primary input section, locate the “Gene symbol” box.
5. Enter the official symbol of the m6A regulator gene to be analyzed (e.g., METTL3).
   CRITICAL: Directly below the gene input box, locate and enable the checkbox for “Only JetSet best probe set”. This ensures the most reliable and specific microarray probe is automatically selected for your gene, optimizing data quality and reproducibility.
6. Define the survival analysis parameters. In the “Survival” section, select “Overall survival (OS)” as the primary endpoint for this analysis. The tool will automatically utilize data from 1880 breast cancer patients when this setting is selected.
7. Ensure the “Split patients by” option is set to “median”. This will stratify patients into two equal groups; high-expression and low-expression, based on the median expression value of the queried gene across all samples.
8. “Follow-up threshold” can be used to select the follow-up period. For this study, 180 months was selected.
9. Generate and interpret the Kaplan-Meier Plot.
10. Click the “Draw Kaplan-Meier Plot” button.
11. A new window will load, displaying the survival curve.
12. Interpret the key plot elements; The X-axis indicates time in months, the Y-axis shows the probability of overall survival, the two colored lines represent the survival curves for the high-expression (red) and low-expression (black) patient groups. The log-rank P-value is displayed, indicating the statistical significance of the difference between the two survival curves.

Results

Mutational landscape of m6a methylation regulators in breast cancer

In an earlier study on the genomic analysis of TCGA datasets, recurrent mutations in several genes encoding regulators of DNA methylation were reported³¹. In the present study, cBioPortal was utilized to analyze the “Breast Invasive Carcinoma (TCGA, PanCancer Atlas)” dataset in order to examine mutational profiles of genes encoding the writers, readers, and erasers of m6A RNA methylatio...

Discussion

This Method's article provides a comprehensive, accessible, and integrated workflow for the systematic multi-omics profiling and clinical translation of any gene signature in cancer research, demonstrated here through the analysis of m6A RNA methylation regulators in breast cancer. By combining these major public bioinformatics platforms, this approach enables researchers to efficiently progress from genomic discovery to clinically relevant hypotheses without requiring advanced computational expertise.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
cBioPortal	Memorial Sloan-Kettering Cancer Center	https://www.cbioportal.org
Genotype-Tissue Expression (GTEx)	GTEx Consortium	https://gtexportal.org
Kaplan-Meier Plotter	Gyorffy lab/A5 Genetics Ltd	https://kmplot.com
The Cancer Genome Atlas (TCGA)	National Cancer Institute (NCI)	https://www.cancer.gov/tcga
UCSC Xena Browser	University of California Santa Cruz	https://xenabrowser.net