Endometrial cancer (EC) is a urogenital cancer mostly affecting post-menopausal women. Globally, it is ranked as the fourth most prevalent cancer affecting women and the 15th most frequently diagnosed type of cancer overall (Gu et al., 2021; World Cancer Research Fund International, 2020). The most common symptoms of EC are abnormal vaginal bleeding, pelvic pain, unexplained weight loss, and difficulty urinating. EC is basically divided into types 1 and 2. Type 1 tumors (endometrioid adenocarcinomas) show slow growth, less aggression, and are caused by excessive estrogen. Whereas type-2 tumors (non-endometrioid endometrial cancer) are fast-growing, more aggressive and are not caused by excessive estrogen. The key risk factors implicated in EC include excessive estrogen, obesity, diabetes mellitus, late menopause, early menarche, and nulliparity (American Cancer Society, 2022). Early detection of EC by physical examination, medical imaging, and biopsies is necessary to offer early treatment for better clinical outcomes. More than 75% of the patients are diagnosed at stage 1, which has a higher survival rate (<1% of all cancer deaths, 2% of cancer deaths in women) (Gu et al., 2021; World Cancer Research Fund International, 2020). Standard management of EC involves surgical removal of the uterus, ovaries, and fallopian tubes but sometimes may also involve radiation therapy, chemotherapy, and hormonal therapy (Crosbie et al., 2022).

In general, genetic factors account for 2%–10% of endometrial cancer. Twin studies estimated that the heritability of EC is about 27%–52%. The large-scale Genome Wide Association Studies (GWAS) have identified many common genetic variants influencing EC risk, which were mapped to 16 loci on different chromosomes with their approximate relative risk in family members is as high as 7%. The most reported genes with mutation in EC are P53, CCND2, and KRAS, along with the defective DNA mismatch repair genes. Furthermore, more genes were discovered, and their roles became clearer with the help of bioinformatic approaches. And these studies revealed the involvement of several genetic mutations in endometrial cells, including tumor suppressor genes and oncogenes such as PTEN, and PIK3CA, respectively (Mucci et al., 2016; Johnatty et al., 2017; Cheng et al., 2016). However, molecular pathogenesis of EC is highly complex due to the genetic, and environmental interactions, where the later ones alter the expression of many genes connected to cell growth, metabolism, and cell division in endometrium (Galaal et al., 2014; Dörk et al., 2020).

There has been an emerging interest in the role of miRNAs in altering the gene expression landscape in cancer pathogenesis. Recent research has linked increased and unregulated expression of microRNAs to carcinogenesis in endometrial cancer. A comparison between endometrial cancer tissue and normal tissue shows that the expression of certain microRNAs is higher in the cancerous than in the healthy tissues. Examples are mir-103, mir-106a, mir-107, mir-181a, mir-185, mir-205, mir-210, mir-423, mir-429 and mir-449. On the contrary, the expression of number of other microRNAs is lower in cancerous cells. These include mir-30c, mir-99b, mir-193, mir-152, mir-193b, mir-204, mir-221 along with miRlet7e. Also, miRNAs are responsible for high expression of SRY-related high mobility group box4 (SOX4) in endometrial cancer, as well as loss of DNA mismatch repair (DNA-MMR) genes, PTEN and many other key genes that play an important role in endometrial carcinogenesis (Boren et al., 2008; Chung et al., 2009; Wang Q et al., 2020).

Over the recent years, advanced computational methods have shown their strength in identifying tissue based molecular biomarkers by analyzing the complex global gene expression data of different human diseases. Identifying these key gene expression signatures may hold the key to monitor cancer progression, which would eventually allow early clinical intervention and to develop personalized cancer therapy. The dynamic molecular landscape underlying the hyperproliferation and transformation of normal endometrium into endometrial cancer can be better understood by global profiling of genes and miRNAs. But the information on the role of differentially expressed miRNAs (DEMiRs) and their target genes (DEGs) between normal and endometrial cancer tissues is still not well explored. Therefore, this study aims to study the complex and dynamic molecular interactions among miRNA, target genes by employing robust bioinformatic gene network analysis and advanced statistical tools.

The clinical presentation of endometrial cancer is quite variable, ranging from a localized, non-aggressive to highly aggressive, rapidly spreading, and invasive lethal cancer. These variations are due to different intrinsic factors such as genetic alteration, complex gene expression patterns, cell lineage and histological subtypes interacting with variety of environmental factors including exposures to mutagens or carcinogens (Bell and Ellenson, 2019). Identification of these factors at an early stage may have positive impact on developing molecular diagnosis and targeted therapies of endometrial cancer with minimum toxicity. Molecular subtyping of primary endometrial cancers using different bioinformatics tools and analysis methods were reported earlier with many limitations (Banaganapalli et al., 2020; Besso et al., 2020; Kim et al., 2020; Wang G et al., 2020). However, these studies used different study design, sampling method, statistical measures, and validation approaches. Even though the number of publications aimed at studying the effect of miRNAs is increasing, understanding of miRNAs role in the carcinogenesis process and their effect on gene expression is lacking (He et al., 2015; Liu Y et al., 2020; Miao et al., 2021).

In this study we characterized the molecular connections between key genes and microRNAs involved in endometrial metastasis using publicly accessible expression datasets from several endometrial tissues. We identified 66 differently expressed genes in endometrial cancer tissues (65 downregulated and 1 was upregulated). The KEGG pathway identifies no enriched pathways that were associated with the upregulated gene. However, pathways of ECM-receptor interaction, p53 signaling and cancer are highlighted by the enrichment of key downregulated genes. The extracellular matrix (ECM) of the female reproductive tract undergoes extensive structural remodeling on a monthly basis, through its components modification, accumulation or degradation. By directly encouraging cellular transformation and metastasis, abnormal ECM has an impact on the cancer progression. Remarkably, stromal cell activity is also deregulated by ECM abnormalities which promotes inflammation as well as angiogenesis resulting in the formation of cancer microenvironments. The ECM-receptor interaction signal pathway has also been identified as a contributor to cancer progression with low survival rate in many gynecological malignancies including endometrial cancer, breast cancer and ovarian cancer (Bao et al., 2019; Liu et al., 2021; Yadav et al., 2020). In this study, the expression of COL3A1, COL5A1 and COL5A2 were downregulated in EC, are of the collagen family, and they interact with elements of the extracellular matrix (ECM) including matrix metalloproteinases, tyrosine kinase receptors, integrins, and signaling pathways to affect the behavior and activity of cancer cells. Studies found that the outcome/survival rate of EC patients with low COL5A2 expression was considerably lower than patients with high COL5A2 expression. Despite that, farther studies are needed to clarify the exact mechanism so we can understand the prognostic and therapeutic values of such dysregulation (Jabłońska-Trypuć et al., 2016; Zhu et al., 2020).

In addition, prognosis in EC was predicted by the P53/P21 signaling pathway, which was linked to patients’ age, stages, and mortality. Since P53 is responsible for the regulation of the cell growth and proliferation, dysfunction in P53 signaling pathway was associated with elevation of cyclin D1 and proliferating cell nuclear antigen (PCNA) which promote higher proliferation in endometrial tissue (Liu Q et al., 2020).

We also discovered miRNA changes that target genes controlling the transformation of normal endometrial tissue to aggressive malignant ones. High estrogen level, overweight, diabetes mellitus and other human disorders that are known to raise the risk of endometrial cancer can be better understood by finding hub genes, with the highest degree of centrality in the key module (Zang et al., 2018). Therefore, when generating miRNA-mRNA functional network that are composed of 66 miRNA target genes and 26 miRNAs, we identified 18 hub target genes and 5 miRNAs with a high degree of centrality parameter (>50). Out of the hub miRNAs exclusively dysregulated in EC, only 2 (hsa-mir-200a, hsa-mir-429) were targeting 6 hub genes (ALDH1A1, ABL1, FOSB, PDGFRA, CCND2, PTCH1). Interestingly, all hub miRNAs were upregulated, and all the hub genes were downregulated.

Pathway analysis of the hubs (genes and miRNAs) revealed their engagement in a variety of biological processes such as cell division, migration, motility as well as regulation of stem cells in response to extracellular signals, histones modifications, carbohydrate metabolism and oncogenic transformation. Therefore, our findings are mostly consistent with earlier research that revealed differential regulation of key genes and microRNAs in the transformation of normal endometrial tissue to endometrial cancer (Lee et al., 2011; Wu et al., 2017; Huang et al., 2018; Guo et al., 2020; Zheng et al., 2021).

MicroRNAs and transcription factors (TFs) influence gene expression at the post-transcriptional and post-translational stages, respectively. Notably, the formation of a feed-forward loop (FFL) unit allows miRNAs and TFs to regulate each other as well as co-regulate a shared target gene, which further develops gene regulatory networks (Jiang et al., 2016). In this study, we found that both miR-429 and hsa-mir-200a-5p miRNAs regulate several TF-target genes such as ZEB1, ZIB2 (Smad-interacting protein 1), TGFB1 and SP1 (specificity protein 1). In endometrial cancer cells, they regulate cytoskeleton remodeling, independent of the zinc finger E-box binding homeobox (ZEB)/E-cadherin axis, which in turn affects cell migration, and elongation plays a crucial role in specifying the cell phenotypes. However, overexpression of ZEB2 is associated with oncogenic transformation and tumor metastasis in several cancer types such as endometrial, hepatocellular, and thyroid cancers (Liu et al., 2017). On the contrary, down regulation of ZEB2 is associated with poor prognosis and showed an enhanced potency and invasiveness of colon cancer cells. So, its interaction with other genes in certain tumor/cancer microenvironment is still unclear and need further studies (Li et al., 2017). They also regulate TGF-β (transforming growth factor β) which in turn regulates the cellular pathways such as SMAD and ERK1/2 by positively influencing stress fiber production, cellular migration, survival and proliferation of cancer cells (Gregory et al., 2008; Wang et al., 2016; Xiong et al., 2016). SP1-TF plays a role in growth factors signaling, involved in immune response, chromatin remodeling and response to DNA damage (Klicka et al., 2021). Furthermore, we found SIX1 (Sine Oculis Homeobox Homolog 1), GATA3 and SMAD3-TFs are regulated by hsa-mir-200a-5p miRNA. SMAD3 suppresses tumor growth by preventing cell division and encouraging apoptosis. Additionally, Smad3 controls transcriptional responses that contribute to metastasis and is necessary for TGF-beta-mediated immune suppression. Accordingly, depending on the kind of cell and clinical stage of the cancer, Smad3 works both as a negative and positive regulator of carcinogenesis by controlling various transcriptional responses (Millet and Zhang, 2007).

The comprehensive system biology validation of the six hub genes has shown that upregulated hsa-mir-200a-b is associated with the decreased expression of the PTCH1, CCND2, PDGFRA, FOSB and ABL1 genes in endometrial cancer tissue. The upregulated hsa-mir-429 is correlated with the decreased expression of the ALDH1A1 gene. By targeting inhibition of PTEN gene, miR-200a b can accelerate the growth of endometrial cancer cells (Wu et al., 2017). Of note, hsa-miR-429 is frequently increased in a variety of malignancies. It may act as an oncogene and associated with the decreased overall survival, increase in the cancerous cells growth and cell proliferation by inhibiting CDKN2B in cancers such EC (Banaganapalli et al., 2020; Liu et al., 2018). Also, it has been found that anti-miR-429 and mir200a-b could improve the cytotoxic activity of chemotherapy in EC’s patient (Lee et al., 2011; Banaganapalli et al., 2020). Based on this output, both miRNAs have the potential to be biomarkers and therapeutic target for EC.

Proteolysis Targeting Chimeras (PROTACs) can target all ALDH1A1, ABL1, PDGFRA, CCND2 and PTCH1 genes. Additionally, PDGFRA and PTCH1 are targeted by antibody molecules. ABL1 is targeted by Imatinib Mesylate inhibitor which, in a phase I clinical trial for endometrial cancer, while PDGFRA is targeted by Nintedanib, and Dovitinib inhibitors which are currently under phase II clinical trial and Vatalanib as well as Cediranib, inhibitors under phase I clinical trial.

There are a few limitations to this study. To establish non-invasive biomarkers for endometrial carcinogenesis, it is critical to map relevant miRNAs or genes in body fluids such as blood, urine, and vaginal discharges. This work has discovered a number of significant hub genes and miRNAs, which need to be further tested on a large pool of samples using validation methods such as real-time PCR, and functional biology assays. The number of clinical samples analyzed in this study is small. However, this limit is unreasonable given that we used secondary data retrieved from GEO and had no control over the study design. Although analyzing larger sample size, would result lower background noise and standard error of the effect fraction, but may not drastically change the study conclusion.

To sum up, the present study identifies 6 hub genes (ALDH1A1, ABL1, FOSB, PDGFRA, CCND2, PTCH1) along with two miRNAs (hsa-mir-200a, hsa-mir-429) significantly influencing multiple crucial cellular processes in endometrial cancer. This study demonstrates the efficacy of computational concepts, such as functional enrichment of biological pathways, and the construction of miRNA-mRNA and transcription factor gene networks, in the identification of endometrial cancer biomarkers from massive gene expression data. There is a correlation between tissue-based endometrial cancer indicators and cancer progression, which may allow for early clinical intervention and therapy. This study also lays the groundwork for future knowledge-driven functional analyses of the microRNAs and their gene targets in endometrial cancer.