Diagnostics and Therapeutic Potential of miR-205 and miR-34a in Ovarian Cancer Management: A miRNA-Target-Based Analysis

Abstract

Epithelial ovarian cancer (EOC) treatment strategies mainly focused on surgery combined with chemotherapy. Recent targeted therapy techniques emerge as milestone and could be used for management of ovarian cancer (OC) progression with more efficacy. The aim is to evaluate the therapeutic and diagnostic potential of microRNA (miRNA) in management of EOC using in silico and quantitative real-time PCR (qRT-PCR) expression analysis. We performed functional enrichment and miRNA-Target genes expression analysis in 48 EOC and 22 normal tissue samples using qRT-PCR and correlated with miRNA expression data in matched samples to evaluate the diagnostic and therapeutic potential of miRNA in OC management. In silico functional enrichment analysis revealed miRNA association with disease. Target genes of miRNAs participate in several biologically important pathways leading to cancer progression. Targets of miRNA-205 and miRNA-34a were significantly downregulated, and upregulated, respectively, in EOC. Moreover, significant negative correlation between relative expression of miRNA-205 and target genes (BCL2, ZEB1, E2F1, and TP53) was observed with r = −0.813; r = −0.755; r = −0.559; and r = −0.767, respectively. Similarly, miRNA-34a also showed higher negative correlation with target genes (MDM4, MAPK3, BRCA1, AREG) with r = −0.840; r = −0.870; r = −0.622; and r = −0.623, respectively. In addition, receiver operating characteristics analysis of combined miRNA panel, miRNA-205-Target gene panel, and miRNA-34a-Target gene panel exhibited higher diagnostics value with area under the curve (AUC) of 92.7 (p < 0.0001), 94.8 (p < 0.0001), and 98.3 (p < 0.0001), respectively. Negative Correlation between miRNA and target genes expression data in matched samples highlights therapeutic potential of miRNA in EOC management. Moreover, combined diagnostic potential of miRNA-target gene panel could predict risk of EOC with higher AUC, sensitivity, and specificity.

Introduction

Ovarian cancer (OC) is the 7th most common and 18th most deadly cancer-causing death in women worldwide (Shabir and Gill, 2020). The asymptomatic characteristic of OC leads to the presentation of most patients at the advance-stage of disease, thus, increasing morbidity. Due to OC, around 1 out of 78 women are affected in their life span, while morbidity accounts for 1 out of 108 women affected with OC (Holschneider and Berek, 2000; Karst and Drapkin, 2010). According to the American Cancer Society, around 19,880 new cases and 12,810 deaths are estimated due to OC in 2022 (Torre et al, 2018). According to cancer statistics data, India recorded 26,834 new cases (crude rate of 4.4), with morbidity of 19,549 (crude rate of 3.2) raking second in Asian countries (Razi et al, 2016).

Moreover, National Cancer Registry Program of India denoted OC as third leading gynecological malignancy in India with incident rate of 9.5 (NCRP, 2020). Similarly, Kulothungan et al (2022) reported the Disability Adjusted Life Years of OC incident rate in India would be 7.4%. Moreover, the age-specific incidence rate of OC upsurge from 35 years and uttermost at 55–64 years in Indian scenario (NCRP, 2020). The incidence rate of OC in India population varies between urban and rural areas. The average incidence rate of 6.4% was recorded in urban population, while 3.6% average incidence rate was recorded in India rural population (Murthy et al, 2009). A variety of factors are associated with developing OC, including smoking, menopausal hormone therapy, nonparity, genetics, endometriosis, and obesity.

The most effective option for the treatment of epithelial ovarian cancer (EOC) is debulking surgery with the combination of chemotherapy. However, the final 5-year relative survival rate is only 49.1%, primarily due to EOC resistance to chemotherapy and recurrence of the disease (Caswell-Jin et al, 2018; Gadducci et al, 2019). The best treatment can only be provided when the incidence of the disease is diagnosed in the early stages, but that is not attained since most of the OC cases are diagnosed at the advanced stage.

Researchers are growing in investigating role of microRNAs (miRNAs) in disease progression since they are involved in essential processes such as cell differentiation, growth, and apoptosis. miRNAs are a family of small single-stranded noncoding RNAs, which are 22–24 nucleotides in length. The expression levels of miRNAs exhibiting tumor suppressor activity are significantly downregulated, whereas miRNAs which act as oncogenes have higher expression levels in OC (Calin et al, 2002). Recently Lv et al (2018) reported the tumor suppressor role of miRNA-34a in OC cell line where increased expression of miRNA-34a reduces the proliferation of OC cell lines. Similarly, induced expression of miRNA-205 increases the proliferation and invasion capability of OC cells via suppressing PTEN/SMAD4 genes expression (Chu et al, 2018).

Epigenetic events occurring before the onset of disease, lead to overexpression of oncogenic miRNA and downregulation of tumor suppressor miRNA due to hypo/hypermethylation events (Loginov et al, 2015; Lopez-Serra and Esteller, 2012). Downstream effect of miRNA dysregulation results in abnormal expression of tumor suppressor genes (PTEN, SMAD4, TCF21, TP53, and ZEB1) and oncogenes (MDM4, MAPK3, AREG, and BRCA1) leading to carcinogenesis (Chu et al, 2018; Hu et al, 2016; Kong and Wang, 2020; Mandke et al, 2012; Nie et al, 2015; Niu et al, 2015; Tung et al, 2017). Thus, identifying miRNA-based dysregulation of target genes and their biological importance would render miRNA therapeutic role in OC.

The present study aims to evaluate the impact of epigenetic status of miRNA in OC progression through bioinformatic tools followed by assessment of candidate miRNA association with disease, miRNA-target enrichment analysis and functional annotation. Moreover, evaluation of miRNAs and their target gene expression will help in manifesting their role in OC progression and will also envisage their therapeutic potential.

Materials and Methodology

Study design

This study involves evaluation of therapeutic potential miRNA in EOC management. We selected candidate miRNA based on our previous study. Selected miRNA was further evaluated in-silico for their association specifically with OC, target-enrichment, and functional enrichment analysis. Further, best EOC-associated miRNA-Target genes were quantified using quantitative real-time PCR (qRT-PCR) and correlated with miRNA expression in matched samples (48 tissue samples and 22 normal samples) to evaluate the therapeutic potential of miRNA.

Patients and clinical features

A total of 70 EOC tissue samples (48 EOC and 22 normal) were recruited for this study (Supplementary Data). All samples were collected from Department of Surgical Oncology, King's George medical University, Lucknow, with dually signed informed consent form by patient. Study was performed according to Declaration of Helsinki and approved by the Institute Ethics Committee (Ref. No: IEC/2019–20/01). Further, samples were segregated according to their age, FIGO stage, histology, and metastatic nature of the samples. The segregated samples included 18 combined FIGO stage I + II (37.5%), and 30 stage III+IV (62.5%) and, similarly, 32 serous (66.0%), 6 mucinous (12.5%), 5 clear cell (10.4%), and 5 endometrioid (10.4%). In addition, samples were segregated as 30 metastatic samples (62.5%) and 18 nonmetastatic cases (37.5%). Around 58.3% of patients were aged above 45 years while 41.6% of patients were below 45 years (Table 1).

Table 1.

Represents the Clinical Features of Patients Recruited in this Study

Variable	Case (48)	Control (22)
Age n (%)
<45	20 (41.6)	10 (45.5)
>45	28 (58.3)	12 (55.5)
Histological type, n (%)
Mucinous	6 (12.5)	—
Serous	32 (66.0)	—
Clear cell	5 (10.4	—
Endometrioid	5 (10.4)	—
Distant metastasis
Absent	18 (37.5)	22 (100)
Present	30 (62.5)	0
FIGO stages
I+II	18 (37.5)	—
III+IV	30 (62.5)	—
Menopausal status, n (%)
Yes	28 (58.3)	14 (63.6)
No	20 (41.6)	8 (36.3)

Screening of candidate miRNA

Several miRNAs have been identified in EOC with reported hypo- and hypermethylation at gene level in several individual studies. Similarly, in our previous study, we reported hypo- and hypermethylation of several miRNA genes based on MeDIP NGS sequencing and further correlated them with miRNA expression in tissue and matched tissue samples (Kumar et al, 2022; Kumar et al, 2021). As an outcome of these studies, miRNA-205 and miRNA-34a exhibited highest correlation with miRNA-gene methylation and dysregulated expression in EOC compared to normal samples. Therefore, these miRNAs may qualify as potential candidate miRNA for further investigating their role in miRNA-based therapeutics in EOC management.

Bioinformatics analysis of the therapeutic role of miRNA in OC

miRNA-disease association enrichment analysis

Selected candidate miRNA was evaluated for association with disease using an online tool (mirnet.org). Analysis was performed with filter “miRNA associated exclusively with OC disease.” Apart from this, all parameters such as betweenness, shortest path, and Steiner forest network were kept as default. Further, predicted miRNA-disease network was evaluated for its significance.

Target prediction and functional enrichment analysis

Candidate miRNA putative target genes were predicted using seven online tools (miRbase, miRDB, miRTarBase, Tools4miRs, miRnet, Diana-microT, miRwalk). Only miRNA-Target genes overlapping in all seven databases were selected for further analysis. miRNA-Target enrichment network was built using mirnet.org. We applied a filter that exclusively made an interaction network for OC disease during miRNA-target network enrichment analysis. All additional parameter was kept as default, and statistical significance was measured as p < 0.05. In addition, functional enrichment analysis was performed to evaluate the role of the predicted target in cancer progression. Gene Ontology analysis was performed for the miRNA-Target gene, while KEGG (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) analysis of predicted target was performed using DAVID (https://david.ncifcrf.gov/) (Kumar et al, 2022).

Quantification of miRNA-target genes

mRNA samples were isolated from 48 EOC and 22 normal samples. All mRNAs from OC and normal samples were converted into cDNA using QuantiTech^® Reverse Transcription Kit (Cat. No. 205313; Qiagen, India) following manufacturer's instructions. In brief, 1 μg of mRNA was treated with gDNA Wipeout solution by incubating at 42°C for 2 min, followed by the addition of QuantScript Reverse Transcriptase, QuantScript RT Buffer, and RT Primer mix (Cat. No. 205313; Qiagen) and incubated at 42°C for 15 min followed by heat inactivation at 95°C for 3 min. Next, all mRNA samples were diluted to bring final concentration of 10 ng/μL and stored at −20°C till further processing.

Further, qRT-PCR-based quantification of each target gene was performed in triplicates using SYBR Green Method (Cat. No. F416L; ThermoFisher, India) with the respective primer set and cycling condition. In brief, for 10 μL of reaction DyNamo Color Flash SYBR Green 5.0 μL, ROX dye 0.2 μL, forward primer 1.0 μL, reverse primer 1.0 μL, template 0.5, and 2.3 μL of DNase free water was added followed by respective qRT-PCR cycling condition for the primer set of each gene are depicted in Table 2.

Table 2.

Represent MicroRNA-Target Genes Primer Sequences with Optimized Annealing Temperature and Quantitative Real-Time PCR Cycling Condition for Each Gene

miRNA-34a target genes	Primer	Primer sequence (5′→3′)	Annealing temp (°C)	PCR cycling condition
MDM4	F	GGTGCGCAAGGTGAAATGTT	58	Initial denaturation at 95°C, 10 min Final denaturation at 95°C for 15 s Annealing at an optimized temperature of each gene for 30 s Extension at 72°C for 1 min for 40 cycles followed by melt curve stage
MDM4	R	TGGGTCTTTCACGGAGAAGC	58
MAPK3	F	ACTCCAAAGCCCTTGACCTG	59
MAPK3	R	GACTGGCCCACCTCATCC	59
BRCA1	F	ATCACCCCTCAAGGAACCAG	60
BRCA1	R	AGATGCTGCTTCACCCTGAT	60
AREG	F	GACACCTACTCTGGGAAGCG	57
AREG	R	AGGCATTTCACTCACAGGGG	57

miRNA-205 target genes	Primer	Primer sequence	Annealing temp	PCR condition
BCL2	F	ATGTGTGTGGAGAGCGTCAA	60	Initial Denaturation at 95°C, 10 min Final denaturation at 95°C for 15 s Annealing at an optimized temperature of each gene for 30 s Extension at 72°C for 1 min for 40 cycles followed by melt curve stage
BCL2	R	CCGTACAGTTCCACAAAGGC	60
ZEB1	F	GCTGTTTCAAGATGTTTCCTTCCA	55
ZEB1	R	CCTATGCTCCACTCCTTGCT	55
E2F1	F	CCGGGGAATGAAGGTGAACA	58
E2F1	R	GAGCAAAAGGGCCGAAAGTG	58
TP53	F	AGAAAACCTACCAGGGCAGC	60
TP53	R	ACATCTTGTTGAGGGCAGGG	60
β-actin	F	AAATCTGGCACCACACCTTC	58
β-actin	R	CTCCTTAATGTCACGCACGA	58

miRNA, microRNA.

Statistical analysis

The miRNA-target genes' relative expression was evaluated using the LIVAK method (2^−ΔΔCT). The Mann–Whitney U test was used to discover the statistical significance of differences between two groups of variables. Further, the correlation between miRNA expression and target gene expression was evaluated using Pearson's Coefficient Correlation analysis. The Association of miRNA and target genes with disease progression was evaluated using binary logistic regression analysis and predicted probability values obtained from the regression model were used to evaluate the combined diagnostic potential of the miRNA panel and gene panel using Receiver Operating Characteristics curve analysis.

All statistical analyses were executed in SPSS^® (Version 26; IBM Chicago, Chicago, IL) and graphs were plotted in GraphPad Prism (Version 8.0). All statistical tests were two-sided, and a p-value <0.05 was considered statistically significant.

Results

Selection of candidate miRNA

In our previous study, MeDIP-NGS sequencing-based differentially methylated region associated with miRNA-205 and miRNA-34a genes promoter region was evaluated and found to have significant hypomethylation and hypermethylation on miRNA-205 and miRNA-34a genes with log2FC value of −0.21 (p = 0.002) and 0.71 (p = 0.032), respectively. In addition, our previous expression analysis of these miRNA showed to have significant dysregulation in EOC compared to normal tissue samples and were significantly correlated with methylation status miRNA-genes obtained from MeDIP-NGS sequencing (Kumar et al, 2022; Kumar et al, 2021). Therefore, based on our previous analysis, we selected these two miRNAs (miRNA-205 and miRNA-34a) for evaluation of their therapeutic role in OC progression and management.

Candidate miRNA-disease association and miRNA-target enrichment analysis

In the first step toward identifying the potential role of miRNA in OC therapeutics, we performed bioinformatics analysis to identify the miRNA-Disease enrichment and putative targets of our candidate hypo- and hypermethylated miRNA in cancer. To see the association of miRNA with OC, we built a miRNA-disease interaction network using mirNet2.0 tools using the filter “only to ovarian cancer”; which revealed a significant association of 350 miRNAs with OC. However, only 250 OC disease-associated miRNAs were obtained after applying statistical filters (p < 0.05). miRNA-disease enrichment analysis revealed that our candidate hypermethylated and hypomethylated miRNA (miR-34a and miR-205) were significantly associated with OC disease (Fig. 1a).

FIG. 1.

Represents miRNA-disease enrichment and miRNA-Target gene enrichment analysis of candidate miRNA. (a) miRNA-205 and miRNA-34a were significantly enriched in miRNA-disease enrichment analysis exclusively with OC and ovarian neoplasm. (b) miRNA-target enriched for miRNA-205, most significantly enriched genes were highlighted in dark grey color circular shape. (c) Enriched target genes for miRNA-34a, and most significant genes are highlighted in grey color square shape. miRNA, microRNA; OC, ovarian cancer.

Further, we used seven different miRNA databases (miRbase, miRDB, miRTarBase, Tools4miRs, miRnet, Diana-microT, miRwalk) to evaluate miRNA target molecules, more than thousands of genes were significantly targeted by individual candidate hypomethylated and hypermethylated miRNAs, however, we only selected overlapping target genes involved in OC from seven databases and used to build miRNA-Target enrichment network using online tools miRnet 2.0. During analysis 550 hypermethylated and 670 hypomethylated miRNA-target genes were enriched. Further, these putative targets were narrowed down for their exclusive association with OC disease using DAVID online tools. We found that 230 genes were targeted by hypermethylated miRNA and 320 genes were targeted by hypomethylated in OC with a higher enrichment score (Fold enrichment = 2.341; p < 0.0005; FDR 0.391; Fold enrichment = 3.233; p < 0.0002; FDR = 0.28883).

In our study, most significantly enriched miRNA-205 target genes were BCL2, ZEB1, E2F1, TP53, WNT5A, WAC, TET3, PANK1, HMGA2, PFN2, LRRFIP1, ZNF217, RAB23, POU2F2, RASSF8, RSF1, CDK14, GAS1, STAT3, MYO10, THBS2, ZMIZ1, TSC22D2, MYC, NOT, and AGO (Fig. 1b) and most significant miRNA34a-target genes were MDM4, MAPK3, BRCA1, AREG, VEGFA, AGO2, CXCL5, MYC, NOTCH2, SMAD2, RASSF2, CDK1, MBD4, MYCN, TRPS1, SOX2, MYC, MAPK8, MAPK1, BCL2L2, E2F3, MYCBP2, AGO3, CDK6, FAM135A, TBP, MYB, BCL11A, HIPK3, and MYO5C (Fig. 1c). Moreover, MDM4, MAPK3, BRCA1, and AREG were selected as the most significant downstream targets of miRNA-34a while BCL2, ZEB1, E2F1, and TP53 for miRNA-205 with a higher degree of betweenness, shortest path value, and p-value.

Functional enrichment analysis

After identifying putative candidate hypomethylated and hypermethylated downstream miRNA-target genes, we tried to analyze (1) the functional role of these target genes in cancer progression and (2) the therapeutic potential of candidate miRNAs. Therefore, we used DAVID software for functional annotation (Gene Ontology and KEGG analysis) of predicted miRNA-Target genes. We evaluated highly enriched top 10 Gene Ontology terms for biological process (BP), Molecular Function (MF), and Cellular Component (CC) of hypomethylated and hypermethylated candidate miRNA-Targets. Most of the Gene Ontology terms of hyper- and hypomethylated miRNA-Target genes were found to play a pivotal role in cancer progression and are presented in Supplementary Tables S1 and S2.

Further, we assessed KEGG enrichment analysis of selected candidate hyper- and hypomethylated miRNA-target genes to assess their involvement in biologically important pathways. Our hypermethylated miRNA targets were highly enriched in the small cell lung cancer, apoptosis, pathways in cancer, P53 signaling pathways, and several other cancers with a high enrichment score. Similarly, hypomethylated miRNA-target genes were significantly enriched in the cell cycle, pathway in cancer, PI3K-Akt signaling pathways, miRNA in cancer, and other important cancer pathways with higher enrichment scores (Supplementary Table S3).

Quantification of miRNA-target genes in OC

Further, we performed qRT-PCR-based relative expression analysis of miRNA-Target genes in EOC tissue samples compared to control tissue samples. The relative expression of miRNA-34a target genes MDM4, MAPK3, BRCA1, and AREG was significantly upregulated in EOC samples compared to healthy control with an increase in fold change of 7.22 (p < 0.0001), 6.39 (p < 0.0001), 4.69 (p < 0.0001), and 7.87 (p < 0.0001), respectively (Fig. 2a). Similarly, the relative expression of miRNA-205 target genes BCL2, ZEB1, E2F1, and TP53 was significantly downregulated in EOC samples compared to healthy control with a drop-in fold change of 4.09 (p < 0.0001), 0.94 (p < 0.0001), 3.1 (p < 0.0001), and 3.48 (p < 0.0001)], respectively (Fig. 2b).

FIG. 2.

Represents relative expression of enriched target genes of miRNA-205 and miRNA-34a in matched EOC tissue samples (48) and normal tissue samples (22). (a) miRNA-34a target genes such as MDM4, MAPK3, BRCA1, and AREG were significantly elevated in matched EOC tissue samples compared to normal controls with a fold change of 7.22 (p < 0.0001), 6.39 (p < 0.0001), 4.69 (p < 0.0001), and 7.87 (p < 0.0001), respectively. (b) Similarly, miRNA-205 target genes such as BCL2, ZEB1, E2F1, and TP53 were significantly downregulated in matched EOC tissue samples compared to normal with a drop-in fold change of 4.09 (p < 0.0001), 0.94 (p < 0.0001), 3.1 (p < 0.0001), and 3.48 (p < 0.0001), respectively. Statistical significance was determined by p < 0.05 by Mann–Whitney U test. Data represented as mean on SEM, ***p < 0.0001. EOC, epithelial ovarian cancer; SEM, standard error of the mean.

Correlation between miRNA and target gene expression

Our in silico analysis of miRNA disease association, miRNA-target enrichment analysis, and functional enrichment analysis revealed several potential downstream target genes involve in OC progression. To understand the molecular significance and potential regulatory role of miRNA in cancer progression, we performed Pearson's correlation analysis between altered/dysregulated miRNA and downstream target genes expression in matched samples (48 EOC and 22 normal). The expression data of miRNA-205 and miRNA-34a were extracted from matched samples of previous study (Kumar et al, 2022; Kumar et al, 2021). Correlation analysis revealed that downregulated expression of miRNA-34a has a strong negative correlation with the downstream target MDM4 (r = −0.840; p < 0.0001), MAPK3 (r = −0.870; p < 0.001), AREG (r = −0.623; p < 0.0001), and BRCA1 (r = −0.622; p < 0.0001) (Fig. 3) (Table 3a).

FIG. 3.

Represents correlation between dysregulated miRNA-34a expression with target gene expression in matched tissue samples. Upregulated expression of MDM4 (a), MAPK3 (b), BRCA1 (c), and AREG (d) has a strong negative correlation with decreased expression of miRNA-34a with r = −0.840; p < 0.0001, r = −0.870; p < 0.0001, r = −0.622; p < 0.0001 and r = −0.623, p < 0.0001, respectively. Statistical significance was determined by p < 0.05 by Pearson coefficient correlation analysis.

Table 3.

Represents Correlation Analysis of MicroRNA-34 and MicroRNA-205 Expression with Corresponding Target Genes Expression in Matched Tissue Samples

	miRNA-34a-Target genes correlation^a			miRNA-205-Target genes correlation^b
Parameter		miR-34a	Age	Parameter		miR-205	Age
MDM4	r	−0.840	0.122	BCL2	r	−0.813	0.221
MDM4	p	0.0001	0.231	BCL2	p	0.0001	0.213
MAPK3	r	−0.870	0.034	ZEB1	r	−0.755	0.342
	r	−0.870	0.034		p	0.0001	0.086
	p	0.001	0.165		p	0.0001	0.086
AREG	r	−0.623	0.034	E2F1	r	−0.559	0.311
AREG	p	0.0001	0.332	E2F1	p	0.001	0.543
BRCA1	r	−0.622	0.234	TP53	r	−0.767	0.299
BRCA1	p	0.0001	0.189	TP53	p	0.0001	0.134

miRNA-34a has significant negative correlation with MDM4, MAPK3, AREG, BRCA1, while age does not show any significant correlation.

miRNA-205 showed strong negative correlation with BCL2, ZEB1, E2F1, and TP53, while, age does not show significant correlation with any tested parameters. r = regression coefficient, p = probability value.

Similarly, upregulated miRNA-205 was negatively correlated with downstream target genes BCL2 (r = −0.813; p < 0.0001), ZEB1 (r = −0.755; p < 0.0001), E2F1 (r = −0.559; p < 0.001), and TP53 (r = −0.767; p < 0.0001), respectively (Fig. 4) (Table 3b).

FIG. 4.

Represents correlation between dysregulated expression of miRNA-205 with target gene expression in matched tissue samples. Downregulated expression of BCL2 (a), ZEB1 (b), E2F1 (c), and TP53 (d) has a strong negative correlation with upregulated expression of miRNA-205 with r = −0.813; p < 0.0001, r = −0.755; p < 0.0001, r = −0.559; p < 0.0001, and r = −0.767, p < 0.0001, respectively. Statistical significance was determined by p < 0.05 by Pearson coefficient correlation analysis.

Diagnostic potential miRNA panel and target gene panel

Further, to evaluate the diagnostic potential of miRNA and their downstream target genes, we conducted multivariate binary logistic regression analysis on a combined miRNA panel (miRNA-205+miRNA-34a), hypermethylated miRNA-34a target gene panel (MDM4+MAPK3+AREG+BRCA1), and hypomethylated miRNA-205 target gene panel (BCL2+ZEB1+E2F1+TP53). The expression data of miRNA were used from the previous study (Kumar et al, 2022; Kumar et al, 2021). The expression of the miRNA panel and hyper- and hypomethylated target genes panel were significantly associated with disease progression (Supplementary Table S4).

Furthermore, predicted probability values obtained from multivariate regression model was used to assess the diagnostic potential of each panel using receiver operating characteristics analysis. The combined diagnostic potential of miRNA-34a and miRNA-205 was evaluated by area under the curve (AUC) at 95% confidence interval (CI), p-value was 82.7% (0.715–0.940; p < 0.0001) with sensitivity = 91.3% and specificity = 78% at cutoff value = 0.523 (Fig. 5a). Combined diagnostic potential of miRNA-34a target genes (MDM4+MAPK3+AREG+BRCA1) was evaluated by AUC at 95% CI, p-value was 98.3% (0.960–1.002; p < 0.0001) with sensitivity = 93.5% and specificity = 90.9% at cutoff value = 0.523 (Fig. 5b).

FIG. 5.

ROC curve analysis of miRNA panel, miR-205-associated target gene panel, and miR-34a-associated target gene panel toward diagnosis of EOC. (a) Combined diagnostic potential of miR-205 and miR-34a was evaluated by AUC at 95% CI, p-value was 82.7% (0.715–0.940; p < 0.0001) with sensitivity = 91.3%, specificity = 78% at cutoff value = 0.567. (b) Combined diagnostic potential of MDM4+MAPK3+AREG+BRCA1was evaluated by AUC at 95% CI, p-value was 98.3% (0.960–1.002; p < 0.0001) with sensitivity = 93.5%, specificity = 90.9% at cutoff value = 0.523 (c) Similarly, combined diagnostic potential of BCL2+ZEB1+E2F1+TP53 was evaluated by AUC at 95% CI, p-value was 94.8% (0.862–1.204; p < 0.0001) with sensitivity = 95.7% and specificity = 93.7% at cutoff value = 0.871. AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristics.

Similarly, combined diagnostic potential of miRNA-205 target genes (BCL2+ZEB1+E2F1+TP53) was evaluated by AUC at 95% CI, was 94.8% (95% CI = 0.862–1.204; p < 0.0001) with sensitivity = 95.7% and specificity = 93.7% at cutoff value = 0.871 (Fig. 5c). Combined diagnostic ability of miR-34a and miR-205 downstream target genes has better predictive value for detection of EOC. The detailed AUC, sensitivity, specificity and cutoff values of each panel is given in Table 4.

Table 4.

Represents Receiver Operating Characteristics Curve Analysis for Predicting Risk Epithelial Ovarian Cancer Using Relative Expression Level of MicroRNA Panel and MicroRNA-Target Gene Panel from Tissue Samples

	Diagnostic potential of panels
Biomarker panel	AUC (%)	p	SEN (%)	SPE (%)	95% CI	CV
miR-205+miR-34a	82.7	0.0001	91.3	78.1	0.715–0.940	0.565
MDM4+MAPK3+AREG+BRCA	98.3	0.0001	93.5	90.1	0.960–1.002	0.523
BCL2+ZEB1+E2F1+TP53	94.8	0.0001	95.7	93.7	0.861–1.204	0.871

AUC, area under the curve; CI, confidence interval; CV, optimal cutoff value; EOC, epithelial ovarian cancer; SEN, sensitivity; SPE, specificity.

Discussion

OC occurrence is the result of a multistep process, including epigenetic changes coupled with dysregulated expression of oncogenes and tumor suppressor genes. miRNA act as a tumor suppressor or oncogenic in nature; epigenetic changes on miRNA gene reflect the adverse impact on the downstream expression of miRNA and associated target genes. Several studies have correlated hypo-/hypermethylation-associated downstream dysregulated miRNA expression in cancer. Recently, Gu et al (2017) reported hypermethylation-based downregulation of miR-34a in several cancer. Similarly, hypermethylation associated dysregulation of miRNA-154, miRNA-184, and miRNA-410 reported in EOC and breast cancer, respectively (Attema et al, 2013; Wu et al, 2019). In addition, hypomethylation associated upregulated expression of miRNA-205 was frequently reported in OC, breast cancer, diabetic vasculopathy (Singh et al, 2017).

In this context, our previous analysis on miRNA-205 and miRNA-34a expression and correlation with epigenetic changes on their gene was in line with recent articles (Kumar et al, 2022; Kumar et al, 2021). Therefore, we extended our analysis toward identifying therapeutic potential of miRNA-205 and miRNA-34a in OC management. To begin with, we performed in silico analysis to identify the potential target of candidate miRNA and evaluated their expression in EOC condition.

The first step toward identifying the therapeutic potential of miRNA, we assessed miRNA-disease enrichment, miRNA-Target enrichment, and functional enrichment analysis of candidate hypo-/hypermethylated miRNA. Our analysis revealed that several tumor suppressor genes and oncogenes involved in cancer progression pathways were targeted by candidate hypomethylated (miR-205) and hypermethylated miRNA (miR-34a). Most significant target gene of miR-205 was BCL2, TP53, ZEB1, and E2F1, while, MDM4, MAPK3, BRCA1, and AREG were significantly targeted by miR-34a.

Wang et al (2015) revealed miR-205 upregulation leading to downregulation of BCL2 in cell line, moreover, miRNA-205 targets BCL2 in cell line was confirmed through luciferase assay, qRT-PCR, and immunohistochemical assay. Similarly, miR-205 enhances the motility of OC cells via targeting ZEB1. In this study, miRNA-205 overexpression was significantly correlated with downregulation of ZEB1 in OC cell line (Niu et al, 2015). Zhao et al (2021) reported that miR-205 overexpression in breast cancer cell line induces proliferation and invasion by targeting E2F1 gene. Similar, upregulated expression of miRNA-34a eventually targets the MDM4 and reduces its expression in CLL cells, while reduced expression of miR-34a leads to overexpression of MDM4 leading to cancer progression (Cao et al, 2020).

Similarly, downregulated expression of miRNA-34a was correlated with overexpression of direct target AREG in OC (Tung et al, 2017). Moreover, MAPK3 and BRCA1 were reported as target genes of miRNA-34a in many other cancers and their dysregulation was associated with cancer progression (Fawzy et al, 2020).

In addition, we explored downstream functional analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) of selected candidate miRNA-Target genes exclusively in EOC. Functional enrichment analysis of hypomethylated and hypermethylated miRNA-Target genes using Gene Ontology and KEGG analysis showed significant involvement of miRNA-Targets in different biologically important BP {[Hypomethylated BP (GO:0010628, GO:0006357, GO:0008285); Hypermethylated BP (GO:0008219, GO:0006915, GO:0010941, GO:0012501)); MF (Hypomethylated MF (GO:0005524, GO:0000978, GO:0008134, GO:0019899); Hypermethylated MF (GO:0044877, GO:0051087, GO:0004861,GO:0042802)); CC (Hypomethylated CC (GO:0005654, GO:0005694, GO:0043234, GO:0044427); Hypermethylated CC (GO:0005634, GO:0097136, GO:0043233, GO:0005654)]}, and different cancer-related pathways (Supplementary Table S1).

Further, expression analysis of miRNA-205 and miRNA-34a target genes in EOC and normal samples showed significant downregulation of miRNA-205 target gene (BCL2, TP53, ZEB1, and E2F1) in OC tissue samples compared to normal while significant upregulation of miRNA-34a target genes (MDM4, MAPK3, BRCA1 and AREG) was observed in EOC tissue samples compared to normal controls. Further, therapeutic potential was evaluated by correlating candidate miRNA expression data of matched samples from previous study with miRNA-Target genes expression data and showed promising pattern of expression with significant negative correlation with corresponding miRNA-Target genes. The significant correlation of candidate miRNA expression with miRNA-Target gene expression warrants further investigations toward establishing therapeutic potential of these miRNAs and their target genes in OC management. Moreover, evaluation of combined diagnostic potential of miRNA and miRNA-Target genes showed promising sensitivity and specificity toward detection of EOC.

Conclusion

Overall, our analysis revealed miRNA-205 and miRNA-34a disease association, therapeutic potential, and diagnostic ability. Moreover, identified miRNAs-Target genes showed tumor suppressors and oncogenic roles in several cancer-associated pathways and involved in cancer progression, thus revealing miRNAs therapeutic potential for OC treatment. Most importantly, our study revealed the combined diagnostic potential of miRNA (miRNA-205 and miRNA-34) and miRNA-Target genes having greater AUC, sensitivity, and specificity values for diagnosis of OC. Moreover, dysregulated expression of miRNA-Target genes could act as independent factor for discriminating OC patients from normal patients.

However, this study is an only preliminary report highlighting epigenetic regulation of miRNA, target identification, expression analysis, evaluation of their diagnostic potential, and correlation of miRNA and target gene expression. Therefore, additional high-throughput analysis is required at cellular and molecular level to validate the obtained result in a larger cohort of samples and further claiming the therapeutic role of candidate miRNA and there are target molecules in OC management. Nevertheless, miRNA therapeutics could enhance the survival of the patient and have to overcome several hurdles before accomplishing it as a therapeutic agent.

Footnotes

Acknowledgments

We want to thank the Central instrumentation facility of MNNIT Allahabad, Prayagraj, for facilitating Real-Time PCR. We further extend our gratitude to Department of Gynecology and Obstetrics, Swaroop Rani Nehru Hospital Allahabad, Prayagraj, for providing normal tissue samples recruited for this study.

Authors' Contributions

V.K. performed investigation, writing-original draft preparation, data curation, and software; A.P. performed investigation, writing draft, and software; A.A. data curation and writing draft; P.G. draft formatting and data curation; D.B. software and data curation; M.S. helped in resources, conceptualization, methodology, validation, and supervision of this study.

Ethics Approval and Consent to Participate

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Motilal Nehru National Institute of Technology Allahabad (Ref. No: IEC/2019–20/01). Moreover, informed consent was obtained from all patients involved in the study.

Availability of Data and Materials

The datasets used and/or analyzed during the current study are included in this article and can also be made available from corresponding author on reasonable request.

Disclosure Statement

No competing financial interests exist.

Funding Information

This research received specific grant from Indian Council of Medical Research, Government of India, Grant number 5/13/58/2015/NCD-III.

Supplementary Material

Supplementary Data

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

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