Validation of ESM1 Related to Ovarian Cancer and the Biological Function and Prognostic Significance

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Abstract

Background: Ovarian cancer (OC), a serious gynecological malignant disease, remains an enormous challenge in early diagnosis and medical treatment. Based on the GEO and TCGA databases in R language, endothelial cell-specific molecule 1 (ESM1) was confirmed separately with the bioinformatic analysis tool. ESM1 has been demonstrated to be upregulated in multiple cancer types, but the oncogenic mechanism by which ESM1 promotes OC is still largely unknown.

Methods: In this study, we used WGCNA and random survival forest variable screening to filter out ESM1 in OC differentially expressed genes (DEGs). Next, we confirmed the mRNA and protein levels of ESM1 in OC samples via PCR and IHC. The correlation between the ESM1 level and clinical data of OC patients was further confirmed, including FIGO stage, lymph node metastasis, and recurrence. The role of ESM1 in OC development was explored by several functional experiments in vivo and in vitro. Then, the molecular mechanisms of ESM1 were further elucidated by bioinformatic end experimental analysis.

Results: ESM1 was significantly upregulated in OC and was positively correlated with PFS but negatively correlated with OS. ESM1 knockdown inhibited cell proliferation, apoptosis escape, the cell cycle, angiogenesis, migration and invasion in multiple experiments. Moreover, GSVA found that ESM1 was associated with the Akt pathway, and our results supported this prediction.

Conclusion: ESM1 was closely correlated with OC development and progression, and it could be considered a novel biomarker and therapeutic target for OC patients.

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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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Cancer statistics, 2018

Kimberly D Miller, Rebecca Siegel, Ahmedin Jemal (2018)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.

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Profiling Tumor Infiltrating Immune Cells with CIBERSORT.

Binbin Chen, Michael S. Khodadoust, Chih Long Liu … (2018)

Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Biol Sci

Journal ID (iso-abbrev): Int J Biol Sci

Journal ID (publisher-id): ijbs

Title: International Journal of Biological Sciences

Publisher: Ivyspring International Publisher (Sydney )

ISSN (Electronic): 1449-2288

Publication date Collection: 2023

Publication date (Electronic): 1 January 2023

Volume: 19

Issue: 1

Pages: 258-280

Affiliations

[1 ]Department of Assisted Reproductive Centre, Zhuzhou central hospital, Xiangya hospital Zhuzhou central south university, Central South University, Zhuzhou, Hunan, China.

[2 ]Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, Hunan, China.

[3 ]School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China.

[4 ]Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, Hengyang, Hunan, China.

[5 ]Medical College, Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, China.

[6 ]Department of gynecology, Clinical research center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.

[7 ]Department of Pathology, Huizhou Sixth People's Hospital, Huizhou, Guangdong, China.

[8 ]Department of Obstetrics and Gynecology, Foshan First People's Hospital, Foshan, Guangdong, China.

[9 ]Center of Reproductive Medicine, The First-affiliated hospital of Hunan normal university, Hunan Provincial People's Hospital, Changsha, Hunan, China.

Author notes

✉ Corresponding authors: Qun-feng Zhang, Department of Assisted Reproductive Centre, Zhuzhou central hospital, Xiangya hospital Zhuzhou central south university, Central south university, Zhuzhou, Hunan, China, and Department of Obstetrics and Gynecology, The Second Affiliated Hospital of University of South China, Hengyang, Hunan, China, E-mail addresses: xiaofeng29@ 123456163.com . Juan Zou, Department of Assisted Reproductive Centre, Zhuzhou central hospital, Xiangya hospital Zhuzhou central south university, Central south university, Zhuzhou, Hunan, China, and Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, Hunan, China, E-mail addresses: zoujuanusc@ 123456usc.edu.cn .

#These authors contributed equally to this work.

Competing Interests: The authors have declared that no competing interest exists.

Article

Publisher ID: ijbsv19p0258

DOI: 10.7150/ijbs.66839

PMC ID: 9760436

PubMed ID: 36594088

SO-VID: 4cbf11ed-f28c-4531-b360-ce945718b86e

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

History

Date received : 6 September 2021

Date accepted : 1 November 2022

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Validation of ESM1 Related to Ovarian Cancer and the Biological Function and Prognostic Significance

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Cancer Metabolism

Most cited references 52

WGCNA: an R package for weighted correlation network analysis

Cancer statistics, 2018

Profiling Tumor Infiltrating Immune Cells with CIBERSORT.

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