WGCNA identification of TLR7 as a novel diagnostic biomarker, progression and prognostic indicator, and immunotherapeutic target for stomach adenocarcinoma

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Abstract

Stomach adenocarcinoma (STAD) is a malignant tumor with high histological heterogeneity. However, the potential mechanism of STAD tumorigenesis remains to be elucidated. The purpose of our research was to identify candidate genes associated with the diagnosis, progression, prognosis, and immunotherapeutic targets of STAD. Based on tumor samples from the GSE28541 dataset, weighted gene co‐expression network analysis revealed 16 modules related to STAD stage and grade. The salmon module emerged as the most relevant module (cor = 0.34), and functional enrichment analysis showed that the genes in the salmon were primarily related to major histocompatibility complex, immune response, and cell differentiation. Toll‐like receptor 7 (TLR7) was recognized as the real hub gene in the salmon module. Compared to normal stomach tissues, the transcriptional and translational levels of TLR7 were significantly elevated in STAD. Receiver operating characteristic curves verified that TLR7 displayed remarkable sensitivity and specificity for the diagnosis of STAD. The functions of TLR7 were primarily enriched in the regulation of Toll‐like receptor signaling pathway, pattern recognition receptor signaling pathway, and innate immune response. Overexpression of TLR7 tended to indicate more advanced STAD, higher degree of STAD, and poorer prognosis of STAD. In addition, TLR7 expression was positively correlated with immune cell infiltration and immune checkpoint expression. Somatic copy number alteration of TLR7 was also significantly related to immune cell infiltration. In conclusion, this study revealed the crucial role of TLR7 in STAD and provided new perspectives for the selection of biomarkers, progression and prognosis indicators, and immunotherapeutic targets for STAD.

Abstract

Combined with WGCNA analysis and data mining, TLR7 were found to be significantly associated with the progression and prognosis of STAD. TLR7 could serve as diagnostic biomarkers, prognostic indicatiors, and immunotherapeutic targets of stomach adenocarcinoma.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

Taiwen Li, Jingyu Fan, Binbin Wang … (2017)

Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.

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STRING v10: protein–protein interaction networks, integrated over the tree of life

Damian Szklarczyk, Andrea Franceschini, Stefan Wyder … (2014)

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

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Author and article information

Contributors

Dong Shang:

ORCID: https://orcid.org/0000-0002-2768-5244

shangdong@dmu.edu.cn

Shilin Xia:

ORCID: https://orcid.org/0000-0003-1187-3192

xiashilin@dmu.edu.cn

Journal

Journal ID (nlm-ta): Cancer Med

Journal ID (iso-abbrev): Cancer Med

Journal ID (doi): 10.1002/(ISSN)2045-7634

Journal ID (publisher-id): CAM4

Title: Cancer Medicine

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Electronic): 2045-7634

Publication date (Electronic): 12 May 2021

Publication date Collection: June 2021

Volume: 10

Issue: 12 ( doiID: 10.1002/cam4.v10.12 )

Pages: 4004-4016

Affiliations

[ ¹ ] Department of General Surgery The First Affiliated Hospital of Dalian Medical University Dalian, Liaoning China

[ ² ] Clinical Laboratory of Integrative Medicine The First Affiliated Hospital of Dalian Medical University Dalian, Liaoning China

[ ³ ] Department of Immunology College of Basic Medical Science Dalian Medical University Dalian, Liaoning China

[ ⁴ ] Department of Oncology The First Affiliated Hospital of Dalian Medical University Dalian, Liaoning China

Author notes

[*] [* ] Correspondence

Shilin Xia and Dong Shang, Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Email: xiashilin@ 123456dmu.edu.cn ; shangdong@ 123456dmu.edu.cn

Author information

Qihang Yuan https://orcid.org/0000-0001-7516-7620

Dong Shang https://orcid.org/0000-0002-2768-5244

Shilin Xia https://orcid.org/0000-0003-1187-3192

Article

Publisher ID: CAM43946

DOI: 10.1002/cam4.3946

PMC ID: 8209604

PubMed ID: 33982398

SO-VID: 023c1cd3-e287-4d3d-a0a7-bdee0319e920

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 17 April 2021

Date accepted : 19 April 2021

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Figures: 10, Tables: 0, Pages: 13, Words: 5806

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source-schema-version-number 2.0

cover-date June 2021

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:17.06.2021

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: biomarkers,immunotherapeutic targets,prognosis,stomach adenocarcinoma,weighted gene co‐expression network analysis

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: biomarkers, immunotherapeutic targets, prognosis, stomach adenocarcinoma, weighted gene co‐expression network analysis

WGCNA identification of TLR7 as a novel diagnostic biomarker, progression and prognostic indicator, and immunotherapeutic target for stomach adenocarcinoma

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Most cited references 34

WGCNA: an R package for weighted correlation network analysis

TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

STRING v10: protein–protein interaction networks, integrated over the tree of life

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