M <sup>5</sup>C regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in lung adenocarcinoma

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Abstract

Background

In recent years, immunotherapy has made great progress, and the regulatory role of epigenetics has been verified. However, the role of 5-methylcytosine (m ⁵C) in the tumor microenvironment (TME) and immunotherapy response remains unclear.

Methods

Based on 11 m ⁵C regulators, we evaluated the m ⁵C modification patterns of 572 lung adenocarcinoma (LUAD) patients. The m ⁵C score was constructed by principal component analysis (PCA) algorithms in order to quantify the m ⁵C modification pattern of individual LUAD patients.

Results

Two m ⁵C methylation modification patterns were identified according to 11 m ⁵C regulators. The two patterns had a remarkably distinct TME immune cell infiltration characterization. Next, 226 differentially expressed genes (DEGs) related to the m ⁵C phenotype were screened. Patients were divided into three different gene cluster subtypes based on these genes, which had different TME immune cell infiltration and prognosis characteristics. The m ⁵C score was constructed to quantify the m ⁵C modification pattern of individual LUAD patients. We found that the high m ⁵C score group had a better prognosis. The role of the m ⁵C score in predicting prognosis was also verified in the dataset GSE31210.

Conclusions

Our study revealed that m ⁵C modification played a significant role in TME regulation of LUAD. Investigation of the m ⁵C regulation mode may have some implications for tumor immunotherapy in the future.

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The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

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BEDTools: a flexible suite of utilities for comparing genomic features

Aaron Quinlan, Ira Hall (2010)

Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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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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Author and article information

Journal

Journal ID (nlm-ta): Transl Lung Cancer Res

Journal ID (iso-abbrev): Transl Lung Cancer Res

Journal ID (publisher-id): TLCR

Title: Translational Lung Cancer Research

Publisher: AME Publishing Company

ISSN (Print): 2218-6751

ISSN (Electronic): 2226-4477

Publication date (Print and electronic): May 2021

Publication date (Print): May 2021

Volume: 10

Issue: 5

Pages: 2172-2192

Affiliations

[1 ]deptDepartment of Radiation Oncology , The First Affiliated Hospital of Nanjing Medical University , Nanjing, China;

[2 ]deptDepartment of Radiation Oncology , Fudan University Shanghai Cancer Center , Shanghai, China;

[3 ]deptDepartment of Oncology, Shanghai Medical College , Fudan University , Shanghai, China;

[4 ]Shanghai Key Laboratory of Radiation Oncology , Shanghai, China;

[5 ]deptDepartment of Synthetic Internal Medicine , The First Affiliated Hospital of Nanjing Medical University , Nanjing, China

Author notes

Contributions: (I) Conception and design: H Chen, HC Zhu; (II) Administrative support: HC Zhu, HY Cheng, XC Sun; (III) Provision of study materials or patients: XL G, ZY Zhang; (IV) Collection and assembly of data: H Chen, ZY Zhang, M Liu, RY Wu, XF Zhang; (V) Data analysis and interpretation: XL Ge, HC Zhu, LP Xu, M Liu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

[#]

These authors contributed equally to this work.

Correspondence to: Xin-Chen Sun. Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 300 Guangzhou Road, Nanjing 210029, China. Email: sunxinchen@ 123456njmu.edu.cn ; Hong-Cheng Zhu. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China. Email: zhuhc90@ 123456163.com .

Article

Publisher ID: tlcr-10-05-2172

DOI: 10.21037/tlcr-21-351

PMC ID: 8182725

PubMed ID: 34164268

SO-VID: dac0bb1c-caaa-4a86-82fd-c9e7b6edec6a

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

History

Date received : 16 March 2021

Date accepted : 21 May 2021

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