In silico identification and interaction analysis of Hub Genes and miRNAs in Gynecological Cancer and their potential role as biomarkers

doi:10.21203/rs.3.rs-4835902/v1

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In silico identification and interaction analysis of Hub Genes and miRNAs in Gynecological Cancer and their potential role as biomarkers

https://doi.org/10.21203/rs.3.rs-4835902/v1

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MicroRNAs (miRNAs) are noncoding RNA molecules that are small, single-stranded, and contain 21 to 23 nucleotides. They are known to play extensive roles in various diseases and their development, including gynaecological cancers such as ovarian, vulvar, and cervical cancer. These cancers are prevalent among women and are often fatal. The heterogeneity of gynecological cancers poses a major challenge to diagnosis in modern medicine. In this study, we carried out extensive in silico analysis to identify hub genes, miRNAs, and their interactions, leading to the identification of potential biomarkers that could assist in the diagnosis and treatment of gynecological cancers. A total of three mRNA expression profiles of cervical cancer (GSE54388, GSE69428, and GSE36668) were retrieved from the Gene Expression Omnibus database. The statistical analysis of GEOR2 yielded 16344 differentially expressed genes (DEGs), and by utilizing robust regression analysis, 229 common DEGs were retrieved. Among them, 94 and 135 genes were downregulated and upregulated, respectively. By utilizing STRING and Cytohuba, we retrieved ten hub genes via a protein-protein interaction network. These genes were named CDK1, AURKA, BUB1B, CCNB1, TOP2A, KIF11, BUB1, CCNB2, CDCA8, and BIRC5. Following extensive computational analysis using the miRDB tool and the NetworkAnalyst, dbDEMC, and MiRNAs databases, a total of 30 miRNAs that interact with hub genes were identified; among these miRNAs, hsa-miR-653-5p, hsa-miR-495-3p, hsa-miR-381-3p, hsa-miR-1266-5p, and hsa-miR-589-3p were the top five interactive miRNAs that targeted the most hub genes and were involved in key functions leading to colorectal cancer, gynecological cancer, glioma, and TGF-beta signalling. These miRNAs show promising interactions with hub genes, and serine/threonine protein kinases are the most strongly associated with these genes and appear to be potential biomarkers and therapeutic targets in gynecological cancers.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

miRNA

noncoding RNAs

RNA sequence data

hub genes

cervical cancer

potential biomarker

Among the most common cause of death, Cervical cancer (CC) is at nubmber four gynecological cancers globally, with an expected 604,000 new cases and 342,000 fatalities in 2020; approximately 483,000 new cases and 274,000 deaths have occurred in low- and middle-income countries [1]. Although, decline has also been noticed in gynological cancer incidences globally thanks to vaccination and screening programs, but CC still poses a significant threat to public health [2]. The high-risk human papillomavirus (HR-HPV) is the primary cause of CC developemnt and classified as a sexually transmitted disease. To date, over 200 genotypes have been identified, with approximately 40 of those genotypes capable of infecting the anogenital region's epithelial cells [3]. Of the 40 genotypes 13 have been known as carcinogenic, and among them the HPV16 being the most common in squamous cell carcinoma (59.3%) and adenocarcinoma (36.3%) [4, 5, 6, 7].

Clinical staging, or the size of the tumor, has proven to be an effective indicator of the response to chemoradiotherapy (CRT); nonetheless, resistance is a crucial problem that is always being researched in oncologic research, especially in relation to its involvement in treatment failure [8]. Clinical data indicates that CRT efficiency varies greatly among CC patients, even those with comparable histological features, and that this greatly raises the risk of metastasis and recurrence. As a result, there is a clear correlation between the high rate of CC mortality and patients' late diagnosis of advanced-stage CC. This issue is most noticeable in low-income areas, where women with advanced CC may not receive prompt treatment due to insufficient coverage and access to healthcare facilities [7].

The necessity to find novel biomarkers whose predictive and prognostic value enable the variety of appropriate therapeutic options based on tumor features and individual is highlighted by the variation in CRT efficacy. Approximately 1% of the genomes in different animals are made up of microRNA genes, each of which has the ability to target hundreds of distinct conserved or nonconserved sequences. An estimated 30% of genes are thought to be controlled by one or more microRNAs [9, 10]. This implies that microRNAs may be helpful in the diagnosis, prognostication, and identification of potential therapeutic targets for cancer. Unlike most genetic material, microRNAs are not used to build proteins, yet they nevertheless have a significant impact on how cells function. Owing to the intricate regulations governing the interactions between target messenger RNAs and microRNAs (mRNAs), it is not surprising that each microRNA has the potential to influence a lot of genes [11]. Comparable to this, it is estimated that 60% of mRNAs interact with microRNAs due to their comparable sequences across species. It was frequently discovered that several microRNAs might target the same gene when these sequences were examined using computer algorithms [12, 13].

Numerous of these hypotheses have been verified by tests, indicating that microRNAs may cooperate to regulate gene activity.Therefore, the present study carried out a comparative analysis of three RNA sequence datasets associated with cervical cancers following DEGs analysis, protein‒protein interaction to prediction of hub genes and their interaction and association with miRNA. Study revealed that hsa-miR-653-5p, hsa-miR-495-3p, hsa-miR-381-3p, hsa-miR- 1266-5p,hsa-miR-589-3p were top five upregulated and hsa-miR-1-5p, has-miR-300, hsa-miR-3913-3p, hsa-miR-6871-5p, hsa-miR-3922-5p were top five downregulated and could be a potential therapeutic targets and biomarkers in gynecological cancers.

The detailed steps and flow chart of the methodlogy is shown and described in Fig. 1.

DEG identification

Three gene expression profile datasets for GNY (GSE54388, GSE69428, and GSE36668) were obtained from the well-reputed database Gene Expression Omnibus (GEO), which has been reported for various cervical-associated diseases. The distinct selection criteria were as follows: (1) control, normal GYN test, and GYN-infected samples; (2) type of test used (expression profiling by array); and (3) "Homo sapiens" as species selection. The notes of the chip platform were GPL 570 and GPL 570, Pearl (ver. 5. 30). The chip where the microarray assay was performed was used to collect the gene symbol by translating the microarray probe name, and DEGs were identified with the program limma from the R package after differential expression. The selection standard was |log2 (FC)| > 1, where the p-value was <0.05.

Robustness analysis using rank aggregation

Additionally, they filtered out common DEGs between two datasets using a method called robust rank aggregation (RRA). A probability model is used by the RRA method, which is the result of different ranking lists [14]. Some studies reported positive outcomes after researchers worked on genetic lists from the array of chip data. Therefore, this report used the RRA approach to detect DEGs that were more relevant to the two microarray datasets. Using the R package RRA, genes were sequenced and pronounced robust DEGs whose genes were sequenced in the two datasets were identified. Next, up and downregulated genes from each dataset were systematically ranked based on the FC ranking. The genes with a | log2 (FC) |>1 and a p value <0.05 were considered to be the most significantly differentially expressed genes (DEGs) [15].

Module analysis of the protein‒protein interaction network

The robust DEGs were uploaded to the STRING online tool to construct a protein‒protein interaction (PPI) network [16]. There were no other available network nodes for peers. The term "Homo sapiens" was chosen, and the threshold was set to "highest confidence”. Subsequently, the desired column of PPI network data was extracted, stored in a TSV file and visualized using Cytoscape (v3. 8) [17]. Cluster modules that are the main aspects of the network were constructed by using the MCODE plugin [18] after importing TSV data files to Cytoscape. To identify the genes associated with the core network modules, the modules were first annotated by the KEGG signalling pathway and further analysed by the R programming language. Differences with a p value <0.05 were considered to indicate statistical significance [19].

Screening of Hub genes

The Cytoscape plug-in CytoHubba offers several topological analysis algorithms [20], such as Euclidean Percolated Component (EPC), Density of Maximum Neighborhood Component (DMNC), Degree, Maximum Neighborhood Component (MNC) and Maximal Clique Centrality (MCC), and six centralities: Betweenness, and Stress, Closeness, Radiality, BotcceNeck and ECCentricity. You can answer the question of what could be the hub genes through such algorithms.

Prediction of miRNAs, functional annotations, and analysis

Considering the list of hub genes, we utilized the miRDB database to predict the miRNAs associated with the hub genes. The miRDB is an essential resource for functional annotation or target prediction. It contains predicted miRNA targets for five species: humans, mice, rats, dogs, and chickens [21]. Users can also run their sequences for custom target prediction by employing a rationalized prediction algorithm. In a recent study, miRDB now provides hundreds of cell line expression profiles, and targeted miRNAs can be identified in desired cell lines by setting appropriate search criteria [21]. We carried out our extensive analysis over and targeted 12,000 genes to identify associations with hub genes following the identification of target genes associated with miRNAs.

Gene Ontology enrichment analysis

The miRNAs predicted and identified as having associations and interactions with hub genes were further analysed for functional characterization, which has always been a challenge. To perform functional characterization, we utilized the DIANA-miRPath v3.0 [22] software suite for perfect miRNA regulatory role assessment and controlled pathway analysis. The miRPath web server enables miRNA functional annotation by using unbiased empirical distributions, hypergeometric distributions, and/or meta-analysis statistics. Moreover, the tool has significantly extended support to comprehensive analysis of KEGG molecular pathways and multiple aspects of Gene Ontology (GO) in seven species (Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Homo sapiens, Gallus gallus and Danio rerio).

Prediction and analysis of differentially expressed miRNAs

The functionally enriched miRNAs were analysed by using the database of differentially expressed miRNAs in human cancers (dbDEMC) [23]. It is a comprehensive resource designed to store and display differentially expressed miRNAs in cancers identified by high-throughput and low-throughput sequencing technologies. The updated version of the dbDEMC includes cancer-associated miRNA data from not only humans but also other model organisms, such as mice and rats. In this study, we applied and explored 403 miRNA expression datasets retrieved from public repositories, including ArrayExpress, The Cancer Genome Atlas (TCGA), Sequence Read Archive (SRA), and GEO [24].

miRNA survival analysis

The easy-to-use and simple web application ExplORRNet offers an in-depth analysis of dysregulated miRNA functional associations and survival in patients with breast and gynecological malignancies [25]. In addition to being useful for fundamental and clinical data, the ExplorRNet tool helped the cancer genomics community as a whole by facilitating, promoting, and mining fast-expanding public datasets to advance the science of precision oncology. The final predicted differentially expressed upregulated miRNAs were utilized for survival analysis.

Comprehensive profiling, meta-analysis, and systems-level interpretation of gene expression data

Following the selection of functionally enriched, differentially expressed miRNAs, the network interactions of miRNAs and hub genes were assessed by utilizing NetworkAnalyst (v 3.0). It is a powerful web-based visual analytics platform for comprehensive profiling, meta-analysis, and systems-level interpretation of gene expression data [26].

Data preprocessing and DEG identification

With the advancement of high-throughput sequencing technologies such as RNA sequencing and microarrays in combination with bioinformatics analysis, many datasets have been generated, including lncRNA, miRNA, and mRNA expression profiles. Millions of genes have been detected and are widely used to predict potential biomarkers for various types of cancer [27]. For the identification of the hub genes involved in gynecological cancer, the GEO datasets GSE54388, GSE69428, and GSE36668 were utilized. This retrieved dataset included different samples, including control samples and ovarian carcinoma samples [28], and was subjected to statistical analysis via GEO2R. The statistical analysis yielded 16344 DEGs from the three designated studies by setting a cut-off p-value <0.05 (Fig. 2). All DEG IDs, gene log fold change (FC) values, and p values were saved in Microsoft Excel files(Table S1). The gene volcano plot shown in Fig. 2 A-C revealed red and green indicating increased and decreased gene expression, respectively, while black indicates genes whose expression did not change.

Robust rank aggregation (RRA)

Through the RRA method, 229 common DEGs were identified in the datasets for GYN compared with those for the tissue samples. As shown in Fig. 3, 135 genes among these DEGs were upregulated, while 94 genes were downregulated. The details of these genes is available in Table S2. The list included not only indispensable genes such as transporters, energy-associated genes, and transcription factors but also several key genes that are known to be associated with various types of cancers, such as decorin, mimecan, and the serine/threonine protein PIM kinase. Decorin is an extracellular matrix small leucine-rich proteoglycan protein. It has an impact on several types of cancer by targeting signalling molecules involved in angiogenesis, metastasis, survival, and cell growth [29]. Similarly, mimecan is a significant precursor lesion of colorectal cancer, which is among the most common cancers worldwide. PIM kinases, which belong to the serine/threonine kinase family, are overexpressed in several cancers, including prostate, breast, colon, endometrial, and gastric cancers [30, 31]. The identification of potential differentially expressed key genes associated with various types of cancer reveals the value of the dataset chosen.

Module analysis and PPI network construction

The PPI network built from the STRING database by utilizing the DEGs to assess interactions and in-depth study and results are shown in Fig. 4 [32]. Using the Cytoscape ClusterViz module and the MCODE algorithm, the robust DEGs were subjected to cluster analysis, and the TSV list was imported to the above software as output. The average local clustering coefficient was 0.563, reflecting a moderate tendency for nodes to cluster together. With the expected number of edges being 511, the observed count of 3014 edges significantly surpasses this, suggesting an exceptionally dense network. Furthermore, a p value of PPI enrichment less than 1.0^e-16indicated highly substantial PPI enrichment within the network. To reliably categorize and diagnose diseases using molecular data and provide insight into the mechanisms of disease pathogenesis, it is imperative to identify network biomarkers. The results of the PPI enrichment analysis revealed a persistent correlation between various genes and gene function.

Prediction and analysis of the hub genes

With a set of topological analysis algorithms that are part of the Cytoscape plug-in, CytoHubba, which is used for the identification of significant nodes in PPI networks, assigns a score to each node belonging to the PPI network and is considered a hub gene. The results revealed 10 hub genes, namely, CDK1, AURKA, BUB1B, CCNB1, TOP2A, KIF11, BUB1, CCNB2, CDCA8, and BIRC5, which are shown in Fig. 5. Cyclin-dependent kinases (CDKs), aurora kinase, BUB1B A, CCNB1, BUB1, CCNB2, CCNB2, and CDCA8 are serine/threonine kinases considered promising targets for cancer therapy. These proteins are essential for cell cycle progression, particularly when they form complexes with cyclins [33, 34]. TOP2A is a factor involved in DNA unlinking and not only plays a pivotal role in cell proliferation, DNA replication, transcription, and recombination but also plays a role in other biological processes, such as chromosome separation and concentration. TOP2A has been reported to play an oncogenic role in several tumor types [35]. Survivin is another name for baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) and is known to play a role by preventing caspase activation and adversely controlling programmed cell death or apoptosis. Due to these characteristics, BIRC5 overexpression promotes particular division and survival pathways linked to cancerous tumors [36]. The literature shows that these genes may help to overcome drug resistance and improve treatment outcomes, and understanding these genes can lead to the discovery of novel therapeutic targets and strategies.

Gene Drug interaction

Gene-drug interaction networks are generated using a tool such as NetworkAnalyst. It visualizes how specific genes interact with various drugs. The highlighted genes (TOP2A, AURKA, CDK1, and KIF11) are shown in yellow, indicating their central role in the network, while the drugs that interact with these genes are displayed in red. For instance, TOP2A interacts with a variety of chemotherapeutic agents like Etoposide, Doxorubicin, and Levofloxacin, all of which interfere with DNA replication in cancer cells [37]. AURKA is associated with drugs such as MLN8237 and Phosphorothioate, both used in cancer treatment due to their impact on cell division [38]. CDK1, another key player, interacts with inhibitors like Flavopiridol and AT7519, which are studied for their potential in cancer therapies targeting cell cycle regulation [39]. Last, KIF11 interacts with drugs including Monastrol and Dimethylenastron, which inhibit the kinesin family member involved in mitosis. These networks highlight critical drug-gene interactions, emphasizing their significance in developing targeted cancer therapies as shown in Fig. 6, and detailed functionalities are described in table 1.

Table 1. Drug–gene interaction and analysis

Gene	Drug	FDA	Description	Interaction Score
CDK1	CLOTRIMAZOLE	Approved	It is used to treat oral thrush, diaper rash, tinea versicolor, vaginal yeast infections, and various forms of ringworm, such as jock itch and athlete's foot.	0.4
AURA	PACLITAXEL	Approved	A chemotherapeutic drug such as Paclitaxel is used to treat esophageal cancer, lung cancer, ovarian cancer, Kaposi's sarcoma, breast cancer, cervical cancer, and pancreatic cancer. It has several marketed names including Taxol.	0.11
CCNB1	SELICICLIB	In trail	Different ovarian cancer cell lines can undergo cell cycle and apoptosis arrest when employed seliciclib. In vitro experiments has shown that seliciclib efficiently inhibited CDK activity in ovarian cancer cells, resulting in increased apoptotic cell death and decreased viability.	2.5
TOP2A	DOXORUBICIN HYDROCHLORIDE	Approved	The FDA has approved the prescription medication doxorubicin hydrochloride (liposomal), an antineoplastic, for the treatment of multiple myeloma, ovarian cancer, and Kaposi sarcoma associated with AIDS	0.03
KIF11	LITRONESIB	Approved	Trials examining the therapy of solid tumors, ovarian cancer, gastric cancer, prostate cancer, and acute leukemia have all involved intrathenesib.	6.56
BUB1	OMEPRAZOLE	Approved	Medicine used to treat Zollinger-Ellison syndrome, peptic ulcer disease, and gastroesophageal reflux disease.	0.17
BIRC5	ARSENIC TRIOXIDE	Approved	The IC50 values of arsenic trioxide (As2O3) and tetraarsenic oxide (As4O6) for each cell line showed a dose-dependent substantial inhibition of the cell proliferation of ovarian cancer cell lines.	0.06

Identification of differentially expressed microRNAs associated with GYN cancer

For the identification of miRNAs involved in gynecological cancer and their association with various genes, particularly with identified hub genes, the same GEO datasets, GSE54388, GSE69428, and GSE36668, were utilized. A total of 30 miRNAs were retrieved to be associated with GSE54388, GSE69428, and GSE36668 from ten hub genes, which were further categorized into downregulated and upregulated miRNAs based on the log Fc and p-value. The expression levels of these 30 upregulated and downregulated miRNAs are shown in Table 2 and in Tables S3 and S4. miRNAs with more than 80% target scores, such as hsa-miR-190a-3p, had a target score of 98, hsa-miR-670-3p had a target score of 97, and the minimum target score was hsa-miR-3922-5p 85. By binding to their corresponding sites, miRNAs regulate target genes and silence the posttranscriptional modification that follows this binding [40].

All the miRNAs were more or less associated with the hub genes CDK1, AURKA, BUB1B, CCNB1, TOP2A, KIF11, BUB1, CCNB2, CDCA8, and BIRC5. The most important element to be noted was serine/threonine protein kinase, which was associated with most hub genes, followed by those hub genes linked with miRNAs. In a previous study, bioinformatics analysis revealed that each miRNA can regulate several hundred gene targets and participate in various gene signalling pathways [41]. Consequently, miRNAs influence numerous biological functions, including apoptosis, differentiation, and cell proliferation [42]. Altered miRNA expression can significantly impact cellular functional activity. Genomic research on human cancer has shown variability in miRNA expression [43]. Extensive diagnostic and prognostic biological information is linked to miRNA expression. Thus, miRNA expression could serve as a predictor of cervical cancer prognosis, as imbalances in miRNA expression are common across all tumor types [44]. As a result, these expression characteristics may be useful for potential value in diagnosis and prognosis.

Interactions between different microRNAs (miRNAs, indicated by purple squares) and genes (indicated by red circles). The lines represent interactions or regulatory relationships. The orange circles indicate specific genes of interest or key nodes in the network. The density and connections suggest a complex regulatory network, possibly involved in a specific biological process or disease context. The figure shows a complex network of miRNA-gene interactions. Subnetwork 1 comprises 379 nodes, 420 edges, and 10 seed nodes. The nodes include miRNAs (purple squares) and genes (red circles), with interactions depicted by connecting lines, as shown in Fig. S1. The dataset features a detailed table of 30 microRNAs (miRNAs) and their corresponding target genes, showcasing critical parameters such as the target rank, target score, miRNA, gene symbol, and gene description. Each entry highlights a specific miRNA and its target, emphasizing their role in gene regulation.

The target rank and score indicate the efficacy of miRNA-gene interactions, while the gene symbol and description provide insights into the gene's function. This information is vital for understanding the regulatory networks involving miRNAs, which play crucial roles in gene expression and cellular processes. Consequently, miRNAs are an invaluable resource for researchers exploring the impacts of miRNAs on biological functions and disease mechanisms, as described in Table 2.

Table 2. The top 30 miRNAs associated with hub genes

Target Rank	Target Score	miRNA	Gene Symbol	Gene Description
1	96	hsa-miR-5011-5p	CDK1	cyclin-dependent kinase 1
2	95	hsa-miR-3913-3p	CDK1	cyclin-dependent kinase 1
3	94	hsa-miR-4760-3p	CDK1	cyclin-dependent kinase 1
4	97	hsa-miR-3941	AURKA	aurora kinase A
5	95	hsa-miR-6871-5p	AURKA	aurora kinase A
6	91	hsa-miR-1-5p	AURKA	aurora kinase A
7	87	hsa-miR-524-5p	BUB1B	BUB1 mitotic checkpoint serine/threonine kinase B
8	87	hsa-miR-520d-5p	BUB1B	BUB1 mitotic checkpoint serine/threonine kinase B
9	85	hsa-miR-3922-5p	BUB1B	BUB1 mitotic checkpoint serine/threonine kinase B
10	98	hsa-miR-548n	CCNB1	cyclin B1
11	96	hsa-miR-559	CCNB1	cyclin B1
12	95	hsa-miR-548ar-5p	CCNB1	cyclin B1
13	92	hsa-miR-8077	TOP2A	DNA topoisomerase II alpha
14	91	hsa-miR-4641	TOP2A	DNA topoisomerase II alpha
15	89	hsa-miR-4663	TOP2A	DNA topoisomerase II alpha
16	98	hsa-miR-190a-3p	KIF11	kinesin family member 11
17	97	hsa-miR-381-3p	KIF11	kinesin family member 11
18	97	hsa-miR-300	KIF11	kinesin family member 11
19	94	hsa-miR-5688	BUB1	BUB1 mitotic checkpoint serine/threonine kinase
20	93	hsa-miR-495-3p	BUB1	BUB1 mitotic checkpoint serine/threonine kinase
21	92	hsa-miR-653-5p	BUB1	BUB1 mitotic checkpoint serine/threonine kinase
22	97	hsa-miR-670-3p	CCNB2	cyclin B2
23	91	hsa-miR-4251	CCNB2	cyclin B2
24	72	hsa-miR-10525-3p	CCNB2	cyclin B2
25	92	hsa-miR-4518	CDCA8	cell division cycle associated 8
26	92	hsa-miR-1266-5p	CDCA8	cell division cycle associated with 8
27	91	hsa-miR-589-3p	CDCA8	cell division cycle associated with 8
28	93	hsa-miR-548t-3p	BIRC5	baculoviral IAP repeat containing 5
29	93	hsa-miR-548ap-3p	BIRC5	baculoviral IAP repeat containing 5
30	93	hsa-miR-548aa	BIRC5	baculoviral IAP repeat containing 5

miRNA and differential expression analysis

Using the dbDEMC, an integrated database a source of DEM in cancers collected by low-throughput or high-throughput sequencing technologies, identified top five up and top five downregulated genes. Conseqeunctly, using Cytoscape, a regulatory network of miRNAs and their target genes was built for both five up and downregulated miRNAs. The results revealed the top five upregulated miRNAs (hsa-miR-653-5p, hsa-miR-495-3p,hsa-miR-381-3p,hsa-miR-1266-5p,hsa-miR-589-3p) regulate approximately 229 common genes in the network, displaying interconnections between the genes and miRNAs. While the top five (hsa-miR-1-5p, hsa-miR-300, hsa-miR-3913-3p, hsa-miR-6871-5p, hsa-miR-3922-5p) downregulated miRNAs have no proper interconnection. These upregulated miRNAs show potential as biomarkers and therapeutic targets in gynecological cancers as shown in Table 3.

Studies revealed the emerging role of hsa-miR-653-5p, hsa-miR-495-3p, and hsa-miR-381-3p in various cancer diseases. Such as hsa-miR-653-5p has been connected to aggressive tumors and poor prognosis in human cancer. At the same time, it has an inhibitory effect on some other types of cancer, such as lung cancers, renal, liver, breast, and cervical [45] which is contrary to our study where upregulation of miR-653 showed aggressiveness in cervical cancer. The hsa-miR-495-3p is known to have inhibitory effects in the progression of various types of cancer such as colorectal cancer [46] while we noted that it is upregulated. In the case of miR-381-3p it is known to inhibit breast cancer progression [47] while in our study it is upregulated. Overall we noted that these miRNAs which are associated with differentially expressed hub genes seem to be potential diagnostic markers and therapeutic studies.

Table 3. Expression level of ten shortlisted miRNA

miRNA ID	Source ID	Cancer Type	Cancer Subtype	Design	LogFC	Expression Status	Expression ID
hsa-miR-653-5p	GSE106817	ovarian cancer	GYN/ovarian cancer	Blood	0.76	Up	EXP00527
hsa-miR-495-3p	GSE106817	ovarian cancer	GYN/ovarian cancer	Blood	0.6	Up	EXP00528
hsa-miR-381-3p	GSE106817	ovarian cancer	GYN/ovarian cancer	Blood	1.05	Up	EXP00528
hsa-miR-1266-5p	GSE31801	ovarian cancer	GYN/ovarian cancer	Blood	0.05	Up	EXP00327
hsa-miR-589-3p	GSE113486	ovarian cancer	GYN/ovarian cancer	Blood	1.38	Up	EXP00537
hsa-miR-1-5p	DRP001085	breast cancer	bladder cancer	cancer vs normal	0	Down	EXP00706
hsa-miR-300	GSE5244	uterus cancer	uterus cancer	cancer vs normal	-0.46	Down	EXP00030
hsa-miR-3913-3p	GSE106817	ovarian cancer	GYN/ovarian cancer	Blood	-0.91	Down	EXP00528
hsa-miR-6871-5p	GSE106817	ovarian cancer	ovarian cancer	Blood	-1.85	Down	EXP00528
hsa-miR-3922-5p	GSE113486	ovarian cancer	GYN/ovarian cancer	Blood	-1.49	Down	EXP00537

Pathway enrichment and gene ontology analysis of miRNAs and hub genes

The miRNAs (upregulated and downregulated) were analysed for pathway analysis as well as their interaction with previously identified hub genes (CDK1, AURKA, BUB1B, CCNB1, TOP2A, KIF11, BUB1, CCNB2, CDCA8, and BIRC5). Pathway enrichment analysis revealed the enrichment analysis results (Fig. S2 A and B). Identifying miRNAs that can alter the expression of hub genes, which are essential for controlling various types of cancer, particularly genealogical cancer, is critical. The DIANA-miRPath program helped us identify miRNAs that affect hub genes. Several of them have been previously mentioned in the literature as controlling the emergence of different forms of cancer.

The functional enrichment of the top upregulated and downregulated DEMs. The heatmap shows the enrichment of biological processes for five upregulated miRNAs, with high enrichment (red) observed in processes such as "Fc-gamma receptor signalling pathway" for hsa-miR-653-5p, "positive regulation of transcription, DNA-templated" for hsa-miR-381-3p, and "Toll-like receptor signalling pathway" for hsa-miR-495-3p. The log (p-value) indicates the significance of enrichment, with more negative values (darker red) representing greater enrichment (Fig. S3 A). The enrichment of the cellular components "organelle" in hsa-miR-653-5p, "protein complex" in hsa-miR-589-3p, and "nucleoplasm" in hsa-miR-381-3p is shown in Fig. S3 (B). High enrichment (red) of molecular functions was observed for "nucleic acid binding transcription factor activity" in hsa-miR-653-5p, "protein binding transcription factor activity" in hsa-miR-381-3p, and "RNA binding" in hsa-miR-495-3p (Fig. S3 C).

The biological processes for downregulated miRNAs with the highest enrichment (most significant) were related to transcription, response to stress, signalling pathways, and catabolic processes, indicating strong associations of these miRNAs with these functions (Fig. S4 A). The cellular components with the highest enrichment (most significant) were organelles, protein complexes, and nucleoplasm, suggesting strong associations of these miRNAs with these cellular structures (Fig. S4 B). The molecular functions with the greatest enrichment (most significant) included nucleic acid binding transcription factor activity, protein binding transcription factor activity, and ion binding, highlighting the strong associations of these miRNAs with these molecular activities (Fig. S4 C).

Network of miRNA and their target genes

Network of microRNAs (miRNAs) and their target genes visualized using Cytoscape. In the network, yellow nodes represent miRNAs, while red nodes represent the target genes regulated by these miRNAs. Edges (lines) connecting the nodes indicate regulatory relationships between miRNAs and their target genes. This visualization helps in understanding the complex regulatory mechanisms of miRNAs and identifying potential key miRNAs that play crucial roles in gene regulation as shown in Fig. 7. The exact relationship between the hub gene and miRNA showed that the four genes CDK1, AURKA, CCNB1, BUB1 and BIRC5 target more miRNAs than the other hub genes, BUB1B, TOP2A, KIF11, CCNB2 and CDCAB. Among these, the highest interaction was related to BIRC5 (Grade=80), and the lowest interaction was related to BUB1B (grade=4). Additionally, hsa-miR-653-5p, hsa-miR-495-3p,hsa-miR-381-3p, hsa-miR-1266-5p, and hsa-miR-589-3p were the top five interactive miRNAs, respectively, that targeted the most hub genes. Recently, various studies specified that miR-653 is intricate in many biological processes such as cancer by differentially expressing in various types of cancer [48, 49]. The miR-495-3p reported to inhibit the colorectal cancer progression by HMGB1 downregulation [50].

A suffiecnt data of literature has demonstrated the correlation between dysregulated miR-589-3p and a variety of diseases inclduing cancer [51]. While the hsa-miR-1266-5p is not well reported in litertaure but on various databses, it is know tomplay role pericardium cancer and pericardial mesothelioma. So overall, the role of these miRNA in antioncogenic and prooncogenic functions, in various cancer types reveal that n combination of hub genes these miRNAs could be a potential targets for the diagnosis and treatment of gynlogcical cancer. While miR-495-3p which is not well studied in cancer and seems to be a novel miRNA identified in our transcriptomics data analysis and may become a potential therapeutic target for gynological cancer.

Survival analysis of upregulated miRNAs

Kaplan‒Meier survival curves for patients with high and low expression levels of hsa-miR-653-5p across different cancer stages (I-IV). The p values indicate significant differences in survival probabilities between high (p = 0.0093) and low (p = 0.0018) expression groups. Kaplan‒Meier survival curves for patients with high and low expression levels of hsa-miR-495-3p across different cancer stages (I-IV). The p values indicate significant differences in survival probabilities between high (p = 0.00016) and low (p = 0.2) expression groups. Kaplan‒Meier survival curves for patients with high and low expression levels of hsa-miR-495-3p across different cancer stages (I-IV). The p values indicate significant differences in survival probabilities between high (p = 0.0011) and low (p =0.007) expression groups. Kaplan‒Meier survival curves for patients with high and low expression levels of hsa-miR-495-3p across different cancer stages (I-IV). The p values indicate significant differences in survival probabilities between high (p = 0.025) and low (p =0.0011) expression groups. Kaplan‒Meier survival curves for patients with high and low expression levels of hsa-miR-495-3p across different cancer stages (I-IV). The p values indicate significant differences in survival probabilities between high (p = 0.015) and low (p < 0.0001) expression groups as shown in Fig 8.

To confirm that indeed all four miRNAs as well as hub genes are essential for classification into good or poor prognoses, we performed the survival analysis and results showed that statistically overall survival analysis were associated with good survival rate, confirming that all four miRNAs could be potential biomarkers.

TF-miRNA Coregulatory Network

The transcription factor (TF)-miRNA coregulatory network highlights the complex interactions between transcription factors (shown in red circles) and microRNAs (miRNAs, shown in blue squares and green diamonds). Key nodes such as TP53, CDK1, and AURKA are central regulatory hubs. The network illustrates how TFs and miRNAs coregulate target genes, emphasizing their coordinated role in gene expression regulation. This kind of network is crucial for understanding the regulatory mechanisms involved in various biological processes and diseases, as shown in Fig. 9.

In gynecological cancers, dysregulation of miRNAs plays a critical role in tumorigenesis and progression. Among the upregulated miRNAs, hsa-miR-653-5p has been implicated in promoting tumor growth and metastasis in various cancers, including ovarian cancer, through its regulatory effects on key signalling pathways. Similarly, hsa-miR-495-3p is upregulated in ovarian cancer. The upregulation of hsa-miR-381-3p has been associated with tumor aggressiveness and poor prognosis in cervical cancer patients. Moreover, hsa-miR-1266-5p, hsa-miR-589-3p, and other upregulated miRNAs have been implicated in various aspects of cancer progression, including cell proliferation, invasion, and metastasis, making them promising candidates for biomarker discovery in gynecological cancers. These dysregulated miRNAs offer promising opportunities for the development of noninvasive diagnostic tools and personalized therapeutic strategies for the management of gynaecological cancers.

Authors Contribution

Yuanjun Zhu, Sisi Chen,Methodology, writing Original draft, Na Duan, WangBo Lu, Reviewing draft, Methodology, Conceptualization and supervision. Hanling Huang, Ke Huang

Funding Statement

No funds were available for this research work.

Data Availability

Three gene expression profile datasets for GNY (GSE54388, GSE69428, and GSE36668) were obtained from the well-reputed database Gene Expression Omnibus (GEO).

Ethics approval and consent to participate

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021
Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J and Bray F: Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. Lancet Glob Health. 8:e191–203. 2020
The effects of the national HPV vaccination programme in England, UK, on cervical cancer and grade 3 cervical intraepithelial neoplasia incidence: A register-based observational study. Lancet. 398:2084–2092. 2021.
Moscicki AB, Schiffman M, Burchell A, Albero G, Giuliano AR, Goodman MT, Kjaer SK and Palefsky J: Updating the natural history of human papillomavirus and anogenital cancers. Vaccine. 30 (Suppl 5): F24-F33. 2012
Tommasino M: The human papillomavirus family and its role in carcinogenesis. Semin Cancer Biol. 26:13–21. 2014
Liu SC, Huang EY, Hu CF, Ou YC, ChangChien CC, Wang CJ, Tsai CC, Fu HC, Wu CH and Lin H: Pretreatment factors associated with recurrence for patients with cervical cancer international federation of gynecology and obstetrics stage IB1 disease. Gynecol Obstet Invest. 81:339–345. 2016.
Rodriguez NM: Participatory innovation for human papillomavirus screening to accelerate the elimination of cervical cancer. Lancet Glob Health. 9:e582–e583. 2021.
Zhou J, Lei N, Tian W, Guo R, Chen M, Qiu L, Wu F, Li Y and Chang L: Recent progress of the tumor microenvironmental metabolism in cervical cancer radioresistance. Front Oncol. 12:9996432022.
Friedman, R. C., et al. (2009). "Most mammalian mRNAs are conserved targets of microRNAs." Genome research 19(1): 92-105.
6. Iorio, M. V. and C. M. Croce (2012). "MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review." EMBO molecular medicine 4(3): 143-159.
7. Krek, A., et al. (2005). "Combinatorial microRNA target predictions." Nature genetics 37(5): 495-500.
Lewis, B. P., et al. (2005). "Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets." Cell 120(1): 15-20.
Bartel DP. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell. 2004 ;116:281–297
Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012, 28:573-80.
McCarthy DJ, Smyth GK. Testing significance relative to a fold-change threshold is a Treat. Bioinformatics. 2009, 25:765-71
von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003, 31:258-61.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks Genome Research 2003, 13:2498-504
Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003 Jan 13;4(1):2.
Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res, 44(D1), D457-D462.
Chin, CH., Chen, SH., Wu, HH. et al.cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol8 (Suppl 4), S11 (2014).
Wang X. miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA. 2008 Jun;14(6):1012-7.
Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, Dalamagas T, Hatzigeorgiou AG. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015 Jul 1;43(W1):W460-6.
Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, Zhao Y, Zhao H, He Y, Zhang G, Yang Z. dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms. Genomics Proteomics Bioinformatics. 2022 Jun;20(3):446-454
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature578, 82–93 (2020)
Lawarde A, Sharif Rahmani E, Nath A, Lavogina D, Jaal J, Salumets A, Modhukur V. ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers. Noncoding RNA Res. 2023 Oct 17;9(1):125-140. doi: 10.1016/j.ncrna.2023.10.006. PMID: 38035042; PMCID: PMC10686811.
Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019 Jul 2;47(W1):W234-W241.
Yeung TL, Leung CS, Wong KK, Gutierrez-Hartmann A et al. ELF3 is a negative regulator of epithelial-mesenchymal transition in ovarian cancer cells. Oncotarget 2017 Mar 7;8(10):16951
Zhou, Y., Tao, L., Qiu, J. et al. Tumor biomarkers for diagnosis, prognosis and targeted therapy. Sig Transduct Target Ther 9, 132 (2024).
Bi XL, Yang W. Biological functions of decorin in cancer. Chin J Cancer. 2013 May;32(5):266-9.
Zheng CX, Zhao SX, Wang P, Yu HM, Wang CF, Han B, Su B, Xiang Y, Li XS, Li SX, Ma QY, Zhang RX, Wan HY, Song HD. Different expression of mimecan as a marker for differential diagnosis between NSCLC and SCLC. Oncol Rep. 2009; 22:1057-61.
Rout AK, Dehury B, Parida SN, Rout SS, Jena R, Kaushik N, Kaushik NK, Pradhan SK, Sahoo CR, Singh AK, Arya M, Behera BK. A review on structure-function mechanism and signaling pathway of serine/threonine protein PIM kinases as a therapeutic target. Int J Biol Macromol. 2024 Jun;270(Pt 1):132030.
von Mering, C. et al. (2003). "STRING: A database of predicted functional associations between proteins". Nucleic Acids Res, 31(1), 258-261.
Wang, Q., Bode, A.M. & Zhang, T. Targeting CDK1 in cancer: mechanisms and implications. npj Precis. Onc.7, 58 (2023)
Lin X, Xiang X, Hao L, Wang T, Lai Y, Abudoureyimu M, Zhou H, Feng B, Chu X, Wang R. The role of Aurora-A in human cancers and future therapeutics. Am J Cancer Res. 2020 Sep 1;10(9):2705-2729.
Porter, A. C. & Farr, C. J. Topoisomerase II: Untangling its contribution at the centromere. Chromosome Res.12(6), 569–583 (2004).
Li, F., Aljahdali, I. & Ling, X. Cancer therapeutics using survivin BIRC5 as a target: what can we do after over two decades of study?. J Exp Clin Cancer Res38, 368 (2019).
Uusküla-Reimand L, Wilson MD. Untangling the roles of TOP2A and TOP2B in transcription and cancer. Sci Adv. 2022 Nov 4;8(44):eadd4920
Yang, Y., Ding, L., Zhou, Q. et al. Silencing of AURKA augments the antitumor efficacy of the AURKA inhibitor MLN8237 on neuroblastoma cells. Cancer Cell Int20, 9 (2020).
Peyressatre M, Prével C, Pellerano M, Morris MC. Targeting cyclin-dependent kinases in human cancers: from small molecules to Peptide inhibitors. Cancers (Basel). 2015 Jan 23;7(1):179-237
O'Brien J, Hayder H, Zayed Y, Peng C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front Endocrinol (Lausanne). 2018 Aug 3;9:402.
Li Q, Yang Z, Chen M, Liu Y. Downregulation of microRNA-196a enhances the sensitivity of non-small cell lung cancer cells to cisplatin treatment. Int J Mol Med. 2016;37:1067–1074.
Wu L, Pu X, Wang Q, Cao J, Xu F, Xu LI, Li K. miR-96 induces cisplatin chemoresistance in non-small cell lung cancer cells by downregulating SAMD9. Oncol Lett. 2016;11:945–952.
Zhang Z, Zhang L, Yin ZY, Fan XL, Hu B, Wang LQ, Zhang D. miR-107 regulates cisplatin chemosensitivity of A549 non small cell lung cancer cell line by targeting cyclin dependent kinase 8. Int J Clin Exp Pathol. 2014;7:7236–7241.
Lu Z, Wang M, Wu S, Ye M, Lin Z, Shun T, Duan C. MicroRNA-137-regulated AKT serine/threonine kinase 2 inhibits tumor growth and sensitizes cisplatin in patients with non-small cell lung cancer. Oncol Lett. 2018 Aug;16(2):1876-1884
Omorou M, Huang Y, Liu N, Bafei SEC, Gao M, Mu C, Zhang L, Hui X. The emerging role of miR-653 in human cancer. Cancer Epidemiol. 2022 ; 79:102208.
Zhang JL, Zheng HF, Li K, Zhu YP. miR-495-3p depresses cell proliferation and migration by downregulating HMGB1 in colorectal cancer. World J Surg Oncol. 2022; 20:101
Yu, YZ., Mu, Q., Ren, Q. et al. miR-381-3p suppresses breast cancer progression by inhibition of epithelial–mesenchymal transition. World J Surg Onc19, 230 (2021).
Omorou M., Huang Y, Liu N., Bafei S.E.C., Gao M, Mu C., Zhang L., Hui X. The emerging role of miR-653 in human cancer. Cancer Epidemiol. 79(2022) 102208.
Z. Li, H. Fan, W. Chen, J. Xiao, X. Ma, P. Ni, Z. Xu, L. Yang. MicroRNA-653-5p Promotes Gastric Cancer Proliferation and Metastasis by Targeting the SOCS6-STAT3 Pathway. Front Mol. Biosci. 15 (2021) 8:655580.
Zhang JL, Zheng GF, Li K, Zhu. YP. miR-495-3p depresses cell proliferation and migration by downregulating HMGB1 in colorectal cancer. World J. Surg Oncol. 20(2022)101.
Shi F, He R., Zhu J, Lu T, Zhong T. miR-589-3p promoted osteogenic differentiation of periodontal ligament stem cells through targeting ATF1. J Orthop Surg Res. 17(2022)221

No competing interests reported.

Fig.S1.jpg
Figure S1. Interactions between different microRNAs and genes
Fig.S2.jpg
Figure S2. KEGG pathway enrichment analysis. (A) Downregulated miRNAs. (B) Upregulated miRNAs.
Fig.S3.jpg
Figure S3. GO enrichment analysis results across three main categories for the upregulated miRNAs: (A) Biological Process, (B) Cellular Component, and (C) Molecular Function
Fig.S4.jpg
Figure S4. The GO enrichment analysis results across three main categories for the downregulated miRNAs: (A) Biological Process, (B) Cellular Component, and (C) Molecular Function.
TableS1ListofCommongenes.xlsx
Table S1. List of common genes under three transcriptomics datasets
TableS2.docx
Table S2.Biological Process, cellular component and molecular function of down-regulated gene.
TableS3.docx
Tables S3. miRNA and their target gene

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In silico identification and interaction analysis of Hub Genes and miRNAs in Gynecological Cancer and their potential role as biomarkers

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