Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

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Abstract

Background: Hepatocellular carcinoma (HCC), which has high rates of recurrence and metastasis and is the main reason and the most common tumor for cancer mortality worldwide, has an unfavorable prognosis. N7-methylguanosine (m7G) modification can affect the formation and development of tumors by affecting gene expression and other biological processes. In addition, many previous studies have confirmed the unique function of long noncoding RNAs (lncRNAs) in tumor progression; however, studies exploring the functions of m7G-related lncRNAs in HCC patients has been limited.

Methods: Relevant RNA expression information was acquired from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), and m7G-related lncRNAs were identified via gene coexpression analysis. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate regression analyses were implemented to construct an ideal risk model whose validity was verified using Kaplan–Meier survival, principal component, receiver operating characteristic (ROC) curve, and nomogram analyses. In addition, the potential functions of lncRNAs in the novel signature were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and gene set enrichment analysis (GSEA). At last, in both risk groups and subtypes classified based on the expression of the risk-related lncRNAs, we analyzed the immune characteristics and drug sensitivity of patients.

Results: After rigorous screening processes, we built a model based on 11 m7G-related lncRNAs for predicting patient overall survival (OS). The results suggested that the survival status of patients with high-risk scores was lower than that of patients with low-risk scores, and a high-risk score was related to malignant clinical features. Cox regression analysis showed that the m7G risk score was an independent prognostic parameter. Moreover, immune cell infiltration and immunotherapy sensitivity differed between the risk groups.

Conclusion: The m7G risk score model constructed based on 11 m7G-related lncRNAs can effectively assess the OS of HCC patients and may offer support for making individualized treatment and immunotherapy decisions for HCC patients.

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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The Molecular Signatures Database (MSigDB) hallmark gene set collection.

Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir … (2015)

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

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Author and article information

Contributors

Yu-yang Dai: URI : https://loop.frontiersin.org/people/1765528/overview

Yi-ping Gao: URI : https://loop.frontiersin.org/people/1729886/overview

Jin-song Liu: URI : https://loop.frontiersin.org/people/1754239/overview

Cheng Zeng: URI : https://loop.frontiersin.org/people/1798070/overview

Hong-lin Wu: URI : https://loop.frontiersin.org/people/821305/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 30 August 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 930446

Affiliations

[1] ¹ Department of Radiology, Wujin Hospital Affiliated with Jiangsu University , Changzhou, Jiangsu Province, China

[2] ² Department of Radiology, The Wujin Clinical College of Xuzhou Medical University , Changzhou, Jiangsu, China

[3] ³ Department of Interventional Radiology, Nanfang Hospital Affiliated to Southern Medical University , Guangzhou, Guangdong, China

[4] ⁴ State Key Laboratory of Ophthalmology, Optometry and Vision Science, Eye Hospital, School of Ophthalmology and Optometry, School of Biomedical Engineering, Wenzhou Medical University , Wenzhou, Zhejiang, China

[5] ⁵ Department of Oncology, Wujin Hospital Affiliated with Jiangsu University , Changzhou, Jiangsu, China

[6] ⁶ Department of Oncology, The Wujin Clinical College of Xuzhou Medical University , Changzhou, Jiangsu, China

[7] ⁷ Department of Nephrology, Wujin Hospital Affiliated with Jiangsu University , Changzhou, Jiangsu, China

[8] ⁸ Department of Nephrology, The Wujin Clinical College of Xuzhou Medical University , Changzhou, Jiangsu, China

Author notes

Edited by: Zhenjian Zhuo, Guangzhou Medical University, China

Reviewed by: Yu Meng, Jinan University, China

Wei Liu, Heilongjiang Institute of Technology, China

*Correspondence: Hong-lin Wu, wuhljs@ 123456163.com

[ † ]

These authors have contributed equally to this work

This article was submitted to Cancer Genetics and Oncogenomics, a section of the journal Frontiers in Genetics

Article

Publisher ID: 930446

DOI: 10.3389/fgene.2022.930446

PMC ID: 9468367

PubMed ID: 36110218

SO-VID: 1ba4e02d-310d-4cad-8309-14df28ec6752

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

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Karger: Oncology

Most cited references 65

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

The Molecular Signatures Database (MSigDB) hallmark gene set collection.

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