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Identification of Genes Predicting Chemoresistance and Short Survival in Ovarian Cancer

Overview
Specialty Oncology
Date 2024 Sep 12
PMID 39262489
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Abstract

Background: Ovarian cancer (OC) is a kind of lethiferous cancer in gynecology, and the development of chemoresistance is the brief reason for treatment failure. The genes which contribute to chemoresistance are often leading to short survival. Thus, this study aims to identify predictive markers for chemoresistance and survival from chemoresistant-related genes.

Methods: Coremine was used to retrieve of genes linked to OC chemoresistance. The relationship of genes with patient survival was analyzed in 489 OC patients of The Cancer Genome Atlas (TCGA) cohort, which the subgroup of 90 resistant and 197 sensitive samples was used to determine gene expression. Kaplan-Meier (KM) plotter of 1,816 OC patients with survival data was retrieved for survival analysis. Survival analysis was carried out by the R survival package in R (version 3.3.1). KM and receiver operating characteristic (ROC) curve were respectively used to access the ability of a gene to predict survival and chemoresistance.

Results: In this study, a group of genes potentially linked to OC chemoresistance was identified, which dysregulated in 90 chemoresistant tissues compared with 197 sensitive tissues. Of them, thirteen genes could predict chemoresistance in 1,347 patients, especially , , were excellent for predicting chemoresistance to any drugs, platin and taxane, and for any drugs and platin, and and for taxane. Meanwhile, 44 genes linked to OC chemoresistance could predict short overall survival (OS) and/or disease-free survival (DFS) in 489 OC patients, and 10 of them could predict short OS in large cohort of up to 1,657 patients. Finally, it is noteworthy that was down-regulated in 90 chemoresistant samples, and low expression of the gene predicted chemoresistance in 1,347 patients, short OS and DFS in 489 patients, and short OS and progression-free survival (PFS) in 1,657 patients.

Conclusions: The identified genes specifically the might be potentially used as predictive marker, prognostic marker and therapeutic target in management of OC.

References
1.
Rung J, Brazma A . Reuse of public genome-wide gene expression data. Nat Rev Genet. 2012; 14(2):89-99. DOI: 10.1038/nrg3394. View

2.
Fekete J, osz A, Pete I, Nagy G, Vereczkey I, Gyorffy B . Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients. Gynecol Oncol. 2020; 156(3):654-661. DOI: 10.1016/j.ygyno.2020.01.006. View

3.
Gao C, Li H, Liu C, Xu X, Zhuang J, Zhou C . Tumor Mutation Burden and Immune Invasion Characteristics in Triple Negative Breast Cancer: Genome High-Throughput Data Analysis. Front Immunol. 2021; 12:650491. PMC: 8097167. DOI: 10.3389/fimmu.2021.650491. View

4.
Barrett T, Suzek T, Troup D, Wilhite S, Ngau W, Ledoux P . NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res. 2004; 33(Database issue):D562-6. PMC: 539976. DOI: 10.1093/nar/gki022. View

5.
Xiang Y, Chen Y, Yan Y, Liu Y, Qiu J, Tan R . MiR-186 bidirectionally regulates cisplatin sensitivity of ovarian cancer cells via suppressing targets PIK3R3 and PTEN and upregulating APAF1 expression. J Cancer. 2020; 11(12):3446-3453. PMC: 7150455. DOI: 10.7150/jca.41135. View