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Transcriptomic analysis reveals tumor stage- or grade-dependent expression of miRNAs in serous ovarian cancer

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Abstract

Ovarian cancer (OC) is the most lethal gynecological malignancy and cellular mechanisms regulating OC progression are not completely understood. miRNAs are involved in many signaling pathways which are critical for the progression of malignant tumors, including OC. In the present study, we aim to identify miRNAs whose expression change in a tumor stage- and/or grade-dependent manner in serous OC. Computational analysis was performed in R using The Cancer Genome Atlas miRNA dataset. Kaplan–Meier plots were constructed to compare the survival of patients with low and high expressions of identified miRNAs. We found that 91 and 90 miRNAs out of 799 are differentially expressed in terms of tumor stage and grade, respectively. miR-152, miR-375 and miR-204 were top three hits in terms of tumor stage; and similarly, miR-125b, miR-768-5p and -3p in terms of tumor grade. Among top 15 miRNAs whose expression most significantly changed between tumor stages, 66.7% were upregulated in late stage. However, 53.3% of top 15 miRNAs identified in terms of tumor grade were upregulated in high grade. 11 miRNAs are differentially expressed in terms of both tumor stage and grade. Expression changes of some of the top miRNAs were found to be associated with shorter survival in serous OC. Text mining analysis showed that most of these miRNAs have not been previously studied in the context of OC. Mechanistic studies of these miRNAs in OC progression, differentiation and metastasis will be of high importance to develop novel strategies for the treatment of serous ovarian cancer.

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Availability of data and material

Data used in the present study are available from The Cancer Genome Atlas (TCGA)—Ovarian Cancer project (https://portal.gdc.cancer.gov/projects/TCGA-OV). These data can be accessed using curatedOvarianData Bioconductor data package in R statistical computing language as done in this study. GEO datasets can be accessed in https://www.ncbi.nlm.nih.gov/geo/ and analyzed using GEO2R tool.

Code availability

R code written to analyze the data in the current study can be found in supplementary files as pdf and Rmarkdown documents. We provided all the R code to make this computational study completely reproducible by other researchers.

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Caglar Berkel is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) 2211-E graduate program.

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  1. Department of Molecular Biology and Genetics, Faculty of Science and Arts, Tokat Gaziosmanpasa University, Tokat, 60250, Turkey

    Caglar Berkel & Ercan Cacan

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Berkel, C., Cacan, E. Transcriptomic analysis reveals tumor stage- or grade-dependent expression of miRNAs in serous ovarian cancer. Human Cell 34, 862–877 (2021). https://doi.org/10.1007/s13577-021-00486-3

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