Bioinformatic analysis of m6A “reader” YTH family in pan-cancer as a clinical prognosis biomarker

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Abstract

The m6A methylation of mRNA has been demonstrated to interact with the “Reader”. YTH domain family is one of the readers containing five members involved in the progression of multiple tumors. The present study aimed to explore the YTH family's role in seventeen cancer types. Data were downloaded from The Cancer Genome Atlas (TCGA) dataset and analyzed by Software R 3.6.3. Using different bioinformatics methods, including analyses of the overall survival (OS) and disease-free survival (DFS), Gene Set Variation Analysis (GSVA) enrichment. Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT algorithm, multivariate and lasso cox regression analysis our results reveal that, while the expression of the YTH domain family varies distinctively in different cancer types the expression of YTH family is upregulated in most cancer types, especially in liver cancer, and the liver cancer prediction model established herein includes YTHDF1 and YTHDF2. Therefore, the results of the present study have demonstrated that the YTH domain family has the potential to predict the prognosis of cancer and the sensitivity to immunotherapy.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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The Cancer Genome Atlas Pan-Cancer analysis project.

John N Weinstein, Eric Collisson, Gordon Mills … (2014)

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

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GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

Zefang Tang, Boxi Kang, Chenwei Li … (2019)

Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.

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Author and article information

Contributors

Chao Tang: chtang@zju.edu.cn

Bing Liu: 13501616398@163.com

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 13 October 2023

Publication date PMC-release: 13 October 2023

Publication date Collection: 2023

Volume: 13

Electronic Location Identifier: 17350

Affiliations

[1 ]GRID grid.414375.0, ISNI 0000 0004 7588 8796, Department of Urology, , The Third Affiliated Hospital of Second Military Medical University, ; 700 North Moyu Road, Shanghai, 201805 China

[2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, National Clinical Research Center for Child Health of the Children’s Hospital, , Zhejiang University School of Medicine, ; No. 3333, Binsheng Road, Hangzhou, 310052 China

[3 ]Department of Urology, School of Medicine, Xinhua Hospital, Shanghai Jiaotong University, ( https://ror.org/04dzvks42) 1665 Kongjiang Road, Shanghai, 200092 China

Article

Publisher ID: 44143

DOI: 10.1038/s41598-023-44143-1

PMC ID: 10575994

PubMed ID: 37833468

SO-VID: c2fd3f23-628b-41dc-b3c7-f28ac66dff5d

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 5 May 2023

Date accepted : 4 October 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100007219, Natural Science Foundation of Shanghai Municipality;

Award ID: 22ZR1477800

Award Recipient : Bing Liu

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Keywords: biomarkers,oncology

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ScienceOpen disciplines: Uncategorized

Keywords: biomarkers, oncology

Bioinformatic analysis of m6A “reader” YTH family in pan-cancer as a clinical prognosis biomarker

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Karger: Oncology

Most cited references 44

GSVA: gene set variation analysis for microarray and RNA-Seq data

The Cancer Genome Atlas Pan-Cancer analysis project.

GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

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