已发表论文

卵巢癌铂耐药关键候选基因的预测

 

Authors Guo K , Li L 

Received 7 September 2021

Accepted for publication 26 October 2021

Published 16 November 2021 Volume 2021:14 Pages 8237—8248

DOI https://doi.org/10.2147/IJGM.S338044

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Scott Fraser

Purpose: Ovarian cancer is one of the common malignant tumors of female reproductive organs, which seriously threatens the life and health of women. Resistance to chemotherapeutic drugs for ovarian cancer is the root cause of recurrence in most patients. The purpose of this study is to determine the differentially expressed genes of platinum resistance in ovarian cancer, and to screen out molecular targets and diagnostic markers that could be used to treat ovarian cancer platinum resistance.
Methods: We downloaded 5 gene microarray datasets GSE58470, GSE45553, GSE41499, GSE33482, and GSE15372 from the Gene Expression Omnibus database, all of which are associated with ovarian cancer platinum resistance. Subsequently, the intersection of the statistically significant differentially expressed genes in 5 gene chips was taken, and relevant bioinformatics and clinical parameters were performed on the screened differential genes. qRT-PCR was utilized to examine the mRNA expression levels in ovarian cancer sensitive and cisplatin-resistant cells.
Results: Three differential genes, IFI27, JAG1, DNM3 , may be closely related to platinum resistance of ovarian cancer, were screened by microarray datasets. According to the combined verification of bioinformatics, clinical case analyses and experiments, it was inferred that the increased expression of DNM3  was beneficial to patients with platinum resistance, but the high expression of IFI27  and JAG1  may lead to the risk of platinum resistance.
Conclusion: IFI27, JAG1  and DNM3  screened by relevant gene chips may serve as new biomarkers of platinum resistance in ovarian cancer.
Keywords: ovarian cancer, platinum resistance, bioinformatical analysis, differentially expressed genes