Comprehensive pan-cancer analysis identifies FHL2 associated with poor prognosis in lung adenocarcinoma

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Abstract

Background

The FHL family (four-and-a-half-LIM-only protein family) contains five multifunctional proteins (FHL1-5) that are involved in cell survival, transcriptional regulation, and signal transduction. Among these proteins, FHL2 is one of the most reported members in tumors, which is differentially expressed in numerous tumors. However, no systematic pan-cancer analysis of FHL2 has been performed so far.

Methods

We obtained The Cancer Genome Atlas (TCGA) expression profiles and clinical data from Xena database and the Tumor Immune Estimation Resource (TIMER) database. Gene expression, prognosis, mRNA modification, and immune infiltration of FHL2 in pan-cancer were analyzed. Functional analysis validated the potential mechanism of FHL2 in lung adenocarcinoma (LUAD).

Results

FHL2 is differentially expressed in a wide range of tumors and has prognostic value. Digging into the immune landscape of FHL2, we found that FHL2 is significantly associated with tumor-associated fibroblasts. Furthermore, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) suggested that FHL2 may be involved in epithelial-mesenchymal transition (EMT)-associated pathways such as NF-KB and TGF-β in LUAD.

Conclusions

Our comprehensive bioinformatics analysis identified mRNA level expression of FHL2 correlates with prognosis in different cancers. This study may help to more fully explore the role of FHL2 in tumor progression and metastasis.

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Most cited references 37

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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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Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Journal

Journal ID (nlm-ta): Transl Cancer Res

Journal ID (iso-abbrev): Transl Cancer Res

Journal ID (publisher-id): TCR

Title: Translational Cancer Research

Publisher: AME Publishing Company

ISSN (Print): 2218-676X

ISSN (Electronic): 2219-6803

Publication date (Electronic): 20 June 2023

Publication date (Print): 30 June 2023

Volume: 12

Issue: 6

Pages: 1516-1534

Affiliations

[1 ]deptDepartment of Thoracic Surgery , The First Affiliated Hospital of Soochow University , Suzhou, China;

[2 ]deptInstitute of Thoracic Surgery , The First Affiliated Hospital of Soochow University , Suzhou, China;

[3 ]deptSoochow University Laboratory of Cancer Molecular Genetics , Medical College of Soochow University , Suzhou, China

Author notes

Contributions: (I) Conception and design: B Pan, C Li; (II) Administrative support: L Wan; (III) Provision of study materials or patients: Y Li; (IV) Collection and assembly of data: J Yang, Z Chen; (V) Data analysis and interpretation: X Tong, J Zhao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

[#]

These authors contributed equally to this work.

Correspondence to: Chang Li, MD, PhD. Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China. Email cli@ 123456suda.edu.cn ; Jun Zhao, MD, PhD. Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China. Email junzhao@ 123456suda.edu.cn ; Xin Tong, MD. Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China. Email txin@ 123456suda.edu.cn .

Article

Publisher ID: tcr-12-06-1516

DOI: 10.21037/tcr-22-2786

PMC ID: 10331713

PubMed ID: 37434686

SO-VID: c862fad7-328f-4709-b06e-9bb38f80969d

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 : 07 December 2022

Date accepted : 06 May 2023

Funding

Funded by: National Natural Science Foundation of China

Award ID: No. 81873417

Funded by: Natural Science Foundation of Jiangsu province

Award ID: No. BK20220250

Comprehensive pan-cancer analysis identifies FHL2 associated with poor prognosis in lung adenocarcinoma

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Abstract

Background

Methods

Results

Conclusions

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Core Readings in Statistical Mediation Analysis

Most cited references 37

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

limma powers differential expression analyses for RNA-sequencing and microarray studies

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