Ano1 is a Prognostic Biomarker That is Correlated with Immune Infiltration in Colorectal Cancer

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Abstract

Background

Anoctamin 1 (ANO1) has been observed to be overexpressed in gastrointestinal and pulmonary epithelial cells, as well as in a number of cancers. Although Ano1 is involved in the prognosis of colorectal cancer (CRC), its mechanism of action in metastatic CRC has not been fully elucidated.

Methods

The expression of Ano1 was assessed in samples obtained from The Cancer Genome Atlas (TCGA) database. Then, we used Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), and Weighted Correlation Network Analysis (WGCNA) to determine the functions of Ano1. Additionally, random survival forest, Cox multivariate analysis, Kaplan Meier analysis, and ROC were used to determine the predictive value of Ano1 on clinical outcomes in CRC patients. Finally, HE staining, immunohistochemical (IHC) analysis and qRT-PCR were used to explore the expression of the Ano1 gene in CRC tissue.

Results

The expression level of Ano1 in CRC was significantly elevated, and the prognosis was poor. The modules with a higher proportion of upregulated genes tended to be positively correlated with Ano1-high. KNG1, GNG4, F2, POSTN, THBS2, SPP1 and FGA were identified as hub proteins of the PPI network. The heatmap showed that the expression level of the Ano1-high group was significantly negatively correlated with immune infiltrate. The overexpression of the Ano1 gene in CRC tissue samples was also confirmed by HE staining, immunohistochemical (IHC) analysis and qRT-PCR.

Conclusion

High expression of Ano1 is closely related to a poor prognosis in patients with colorectal cancer. Ano1 may participate in the metastasis and progression, as well as the immune regulation of CRC. In summary, Ano1 can act as a potential prognostic biomarker and a novel target for CRC therapy.

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Most cited references 41

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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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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 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

Journal

Journal ID (nlm-ta): Int J Gen Med

Journal ID (iso-abbrev): Int J Gen Med

Journal ID (publisher-id): ijgm

Title: International Journal of General Medicine

Publisher: Dove

ISSN (Electronic): 1178-7074

Publication date (Electronic): 15 February 2022

Publication date Collection: 2022

Volume: 15

Pages: 1547-1564

Affiliations

[1 ]Laboratory Animal Center, Dalian Medical University , Dalian, 116044, Liaoning Province, People’s Republic of China

[2 ]Cardiology Department, The Second Hospital of Dalian Medical University , Dalian, 116023, Liaoning Province, People’s Republic of China

[3 ]Pathology Department, The Second Hospital of Dalian Medical University , Dalian, 116023, Liaoning Province, People’s Republic of China

[4 ]Emergency Department, The Second Hospital of Dalian Medical University , Dalian, 116023, Liaoning Province, People’s Republic of China

Author notes

Correspondence: Bingbing Shang; Liang Wang, Tel +86-17709875175; +86-13332225676, Email shangbingbing@dmu.edu.cn; wangliang-dy@dmu.edu.cn

[*]

These authors contributed equally to this work

Article

Publisher ID: 348296

DOI: 10.2147/IJGM.S348296

PMC ID: 8858027

SO-VID: c703bb09-5d74-413b-ae5a-838cd493f1d3

License:

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

History

Date received : 18 November 2021

Date accepted : 21 January 2022

Page count

Figures: 12, References: 41, Pages: 18

Funding

Funded by: No funding was received for this article;

No funding was received for this article.

Ano1 is a Prognostic Biomarker That is Correlated with Immune Infiltration in Colorectal Cancer

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Karger: Oncology

Most cited references 41

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

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

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

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