Open Access

ARTICLE

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

YANYA CHEN^1,#, HONGYUAN WU^2,#, RUISI ZHOU^5,#, HELING DONG⁴, XUEFANG ZHANG², XUEWEI WU¹, WENSHAN CHEN¹, YANTING YOU^5,*, YIFEN WU^3,*

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: ; YIFEN WU. Email:

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

Received 25 October 2022; Accepted 06 February 2023; Issue published 19 May 2023

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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