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The correlation of miRNA expression and tumor mutational burden in uterine corpus endometrial carcinoma

YANYA CHEN1,#, HONGYUAN WU2,#, RUISI ZHOU5,#, HELING DONG4, XUEFANG ZHANG2, XUEWEI WU1, WENSHAN CHEN1, YANTING YOU5,*, YIFEN WU3,*

1 Department of Gynaecology, Affiliated Dongguan People’s Hospital, Southern Medical University, Dongguan, 523009, China
2 Department of Radiation Oncology, Affiliated Dongguan People’s Hospital, Southern Medical University, Dongguan, 523009, China
3 Department of Oncology, Affiliated Dongguan People’s Hospital, Southern Medical University, Dongguan, 523009, China
4 School of Sports Education, Jinan University, Guangzhou, 510632, China
5 Syndrome Laboratory of Integrated Chinese and Western Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China

* Corresponding Authors: YANTING YOU. Email: email; YIFEN WU. Email: email
# These authors contributed equally to this manuscript

BIOCELL 2023, 47(6), 1353-1364. https://doi.org/10.32604/biocell.2023.027346

Abstract

Background: The relationship between microRNA (miRNA) expression patterns and tumor mutation burden (TMB) in uterine corpus endometrial carcinoma (UCEC) was investigated in this study. Methods: The UCEC dataset from The Cancer Genome Atlas (TCGA) database was used to identify the miRNAs that differ in expression between high TMB and low TMB sample sets. The total sample sets were divided into a training set and a test set. TMB levels were predicted using miRNA-based signature classifiers developed by Lasso Cox regression. Test sets were used to validate the classifier. This study investigated the relationship between a miRNA-based signature classifier and three immune checkpoint molecules (programmed cell death protein 1 [PD-1], programmed cell death ligand 1 [PD-L1], cytotoxic T lymphocyte-associated antigen 4 [CTLA-4]). For the miRNA-based signature classifier, functional enrichment analysis was performed on the miRNAs. An analysis of the relationship between PD-1, PD-L1, and CTLA-4 immune checkpoint genes was carried out using the miRNA-based signature classifier. Results: We identified 27 differentially expressed miRNAs in miRNA-base signature. For predicting the TMB level, 27-miRNA-based signature classifiers had accuracies of 0.8689 in the training cohort, 0.8276 in the test cohort, and 0.8524 in the total cohort. The correlation between the miRNA-based signature classifier and PD-1 was negative, while the correlation between PD-L1 and CTLA4 was positive. Based on the miRNA profiling described above, we validated the expression levels of 9 miRNAs in clinical samples by quantitative reverse transcription PCR (qRT-PCR). Four of them were highly expressed and many cancer-related and immune-associated biological processes were linked to these 27 miRNAs. Thus, the developed miRNA-based signature classifier was correlated with TMB levels that could also predict TMB levels in UCEC samples. Conclusion: In this study, we investigated the relationship between a miRNA-based signature classifier and TMB levels in Uterine Corpus Endometrial Carcinoma. Further, this is the first study to confirm their relationship in clinical samples, which may provide more evidence support for immunotherapy of endometrial cancer.

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APA Style
CHEN, Y., WU, H., ZHOU, R., DONG, H., ZHANG, X. et al. (2023). The correlation of mirna expression and tumor mutational burden in uterine corpus endometrial carcinoma. BIOCELL, 47(6), 1353-1364. https://doi.org/10.32604/biocell.2023.027346
Vancouver Style
CHEN Y, WU H, ZHOU R, DONG H, ZHANG X, WU X, et al. The correlation of mirna expression and tumor mutational burden in uterine corpus endometrial carcinoma. BIOCELL . 2023;47(6):1353-1364 https://doi.org/10.32604/biocell.2023.027346
IEEE Style
Y. CHEN et al., "The correlation of miRNA expression and tumor mutational burden in uterine corpus endometrial carcinoma," BIOCELL , vol. 47, no. 6, pp. 1353-1364. 2023. https://doi.org/10.32604/biocell.2023.027346



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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