Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer

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Abstract

Transmembrane 4 L6 family member 1 (TM4SF1) and discoidin domain receptor 1 (DDR1) are expressed in numerous types of cancer, but their expression in epithelial ovarian cancer and the association between their expression and patient prognosis are unclear. The present study aimed to explore the expression of TM4SF1 and DDR1 and their relationship with prognosis in epithelial ovarian cancer. Firstly, the Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) platforms were used to compare the expression levels of TM4SF1 and DDR1 in ovarian cancer and normal ovarian tissue, and Kaplan-Meier plotter was used to analyze the association between gene expression and patient prognosis. The proteins interacting with TM4SF1 and DDR1 were analyzed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways was conducted for the interacting proteins. Furthermore, immunohistochemical staining was performed to detect the expression of TM4SF1 and DDR1 protein in epithelial ovarian cancer tissue and to analyze the association between expression and prognosis. The Oncomine and GEPIA analyses showed that the expression levels of TM4SF1 and DDR1 were significantly higher in epithelial ovarian cancer than in normal ovarian tissue, and the analysis of clinical samples revealed that TM4SF1 and DDR1 were coexpressed in some cases. STRING analysis indicated that the TM4SF1 and DDR1 proteins interact with each other. The overall survival and progression-free survival of patients whose epithelial ovarian cancer coexpressed TM4SF1 and DDR1 were significantly shorter than those of patients lacking TM4SF1 and DDR1 coexpression. Multivariate analysis indicated that TM4SF1 and DDR1 protein coexpression was an independent prognostic factor. In summary, TM4SF1 and DDR1 proteins were coexpressed in some epithelial ovarian cancer tissues and appear to be adverse prognostic factors for epithelial ovarian cancer. In addition, TM4SF1 and DDR1 may have an interactive or mutual regulatory mechanism.

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Most cited references 31

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

Zefang Tang, Chenwei Li, Boxi Kang … (2017)

Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

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Author and article information

Journal

Journal ID (nlm-ta): Oncol Lett

Journal ID (iso-abbrev): Oncol Lett

Journal ID (publisher-id): OL

Title: Oncology Letters

Publisher: D.A. Spandidos

ISSN (Print): 1792-1074

ISSN (Electronic): 1792-1082

Publication date Collection: October 2023

Publication date (Electronic): 30 August 2023

Publication date PMC-release: 30 August 2023

Volume: 26

Issue: 4

Electronic Location Identifier: 448

Affiliations

[1 ]Department of Gynecological Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, P.R. China

[2 ]Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi 530021, P.R. China

[3 ]Guangxi Key Laboratory of Prevention and Treatment for Regional High Frequency Tumor, Nanning, Guangxi 530021, P.R. China

Author notes

Correspondence to: Mr. Zhijun Yang, Department of Gynecological Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi 530021, P.R. China, E-mail: yzj7528@ 123456126.com

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Contributed equally

Article

Publisher ID: OL-26-4-14035

DOI: 10.3892/ol.2023.14035

PMC ID: 10502932

PubMed ID: 37720676

SO-VID: e091d2d5-679c-4a60-a419-fcf6d72cd00e

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

History

Date received : 03 May 2023

Date accepted : 02 August 2023

Funding

Funded by: National Natural Science Foundation of China

Award ID: 81360387

Funded by: Guangxi Natural Science Foundation

Award ID: 2017GXNSFDA198009

Funded by: Guangxi Zhuang Autonomous Region Key Clinical Specialty Construction Project

Award ID: 2018-39

Funded by: Guangxi Medical High-level Backbone Personnel Training ‘139’ Project

Award ID: 2018-22

Funded by: Special Fund of the 17th Guangxi New Century ‘Ten, Hundred, Thousand’ Talent Project

Award ID: 2014210

The present study was supported by grants from the National Natural Science Foundation of China (grant no. 81360387), the Guangxi Natural Science Foundation (grant no. 2017GXNSFDA198009), the Guangxi Zhuang Autonomous Region Key Clinical Specialty Construction Project (grant no. 2018-39), the Guangxi Medical High-level Backbone Personnel Training ‘139’ Project (grant no. 2018-22) and the Special Fund of the 17th Guangxi New Century ‘Ten, Hundred, Thousand’ Talent Project (grant no. 2014210).

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Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer

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Abstract

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Karger: Oncology

Most cited references 31

clusterProfiler: an R package for comparing biological themes among gene clusters.

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

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