Uterine corpus endometrial carcinoma (UCEC) is the sixth most common cancer worldwide and the second most common gynecological cancer in women, with approximately 382,000 new cases and 90,000 deaths globally in 2018. Despite treatment advances including targeted therapies, the incidence continues to rise, and patients with metastatic or recurrent disease face very poor outcomes with overall survival often less than 16 weeks.
Less than 2% of the human genome encodes proteins. The vast majority is transcribed into non-coding RNAs, which were once dismissed as molecular noise but are now recognized as critical regulators of gene expression. Among these, long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that regulate DNA methylation, histone modification, and chromatin architecture.
The competitive endogenous RNA (ceRNA) hypothesis proposes a powerful regulatory mechanism: lncRNAs can act as molecular sponges that absorb microRNAs (miRNAs), preventing those miRNAs from silencing their target messenger RNAs (mRNAs). This lncRNA-miRNA-mRNA triangular network controls gene expression across entire biological programs. Dysregulation of these networks has been documented in breast, lung, liver, and other cancers.
Despite growing evidence of ceRNA networks in other cancers, no study had previously mapped this regulatory architecture specifically in UCEC. Understanding the ceRNA landscape in endometrial cancer could reveal novel diagnostic biomarkers and therapeutic targets that are invisible when studying protein-coding genes alone.
This study analyzed high-throughput RNA sequencing data from 548 patients with UCEC obtained from The Cancer Genome Atlas (TCGA), a publicly available repository of multi-omics cancer data. The dataset included 552 tumor samples and 35 adjacent normal tissue samples, providing a robust comparison between cancerous and healthy endometrial tissue.
The researchers used the bioinformatics package edgeR in R to identify differentially expressed (DE) genes with strict statistical thresholds: a fold-change of at least 4-fold in either direction (|log2 fold change| greater than or equal to 2.0) and a false discovery rate adjusted p-value below 0.01. This conservative approach minimized false positives while capturing biologically meaningful changes.
The ceRNA network was assembled in two steps. First, lncRNA-miRNA interactions were predicted using the miRcode database, which catalogs binding sites across the entire human transcriptome. Second, miRNA-mRNA target relationships were predicted using three independent databases (miRTarbase, TargetScan, and miRDB), and only mRNAs confirmed in all three were included to maximize reliability. The resulting network was visualized using Cytoscape software.
To determine clinical relevance, Kaplan-Meier survival analysis and univariate Cox proportional hazards regression were applied to every RNA node in the network, identifying which lncRNAs, miRNAs, and mRNAs were significantly linked to patient survival. Functional enrichment using Gene Ontology (GO) and KEGG pathway analysis revealed the biological pathways most active in this network.
Comparing 552 tumor samples to 35 normal tissue samples revealed massive transcriptome reprogramming in UCEC. The analysis identified 2,612 differentially expressed mRNAs, 1,111 differentially expressed lncRNAs, and 187 differentially expressed miRNAs. Among lncRNAs, 802 were upregulated and 309 were downregulated in tumor tissue. Among miRNAs, 134 were upregulated and 53 downregulated.
After applying the three-database filter for miRNA-mRNA interactions, the final ceRNA regulatory network for UCEC consisted of 87 lncRNAs, 74 mRNAs, and 20 miRNAs connected through 477 lncRNA-miRNA pairs and 109 miRNA-mRNA regulatory pairs. This network represents the most extensively cross-validated map of lncRNA-miRNA-mRNA interactions in endometrial cancer to date.
Pathway enrichment analysis of the 74 network mRNAs revealed enrichment in cancer-related biological pathways including the p53 signaling pathway, cell cycle regulation, MicroRNAs in cancer, and pathways linked to melanoma, gastric cancer, and prostate cancer. The p53 pathway is one of the most commonly mutated pathways in all human cancers and plays a central role in endometrial carcinogenesis.
Survival analysis identified 9 lncRNAs significantly associated with overall survival in UCEC patients. Six of these (AC110491.1, ADARB2-AS1, C2orf48, C10orf91, GLIS3-AS1, and LINC00491) were inversely associated with survival, meaning higher expression predicted shorter survival. Three (AL596188.1, LINC00237, and LINC00261) were positively associated with survival, meaning higher expression predicted better outcomes.
The lncRNA C2orf48 emerged as a potential oncogenic regulator. It showed strong positive correlations with five mRNAs known to promote cell proliferation: MCM4, CDC25A, CCNE1, KIF23, and E2F1. C2orf48 is predicted to regulate these targets by competing with 4 key miRNAs (mir-106a, mir-195, mir-216b, and mir-424), effectively releasing the brakes on cell cycle progression.
LINC00261 showed the opposite pattern: higher expression correlated with better patient survival. LINC00261 negatively correlated with the mRNAs CCNE1 and ANKRD33B, and prior research in other cancers has shown LINC00261 to act as a tumor suppressor by upregulating FOXO1, a transcription factor that inhibits cancer cell proliferation, migration, and invasion.
Three miRNAs (hsa-mir-205, hsa-mir-211, and hsa-mir-425) in the network were also significantly associated with UCEC prognosis. Additionally, 22 mRNAs showed prognostic significance, including cell cycle genes CCNE1 and E2F1, the checkpoint regulator CDC25A, the cell division gene KIF23, and the DNA replication factor MCM4.
The finding that C2orf48 positively correlates with cell cycle genes MCM4, CDC25A, CCNE1, KIF23, and E2F1 is biologically consistent with its predicted function. CCNE1 (Cyclin E1) controls the transition from the G1 to S phase of the cell cycle and is overexpressed in multiple cancer types. E2F1 is a transcription factor that drives DNA replication and cell division, and high free E2F1 has been studied as a prognostic marker in endometrial cancer. CDC25A activates cyclin-dependent kinases critical for cell cycle entry and is a known target of tumor suppressors.
LINC00261 has previously been reported as downregulated in gastric cancer, liver cancer, pancreatic cancer, non-small cell lung cancer, and endometrial cancer, consistently acting as a tumor suppressor. Fang and colleagues specifically demonstrated that LINC00261 inhibits endometrial cancer cell proliferation and invasion by absorbing miRNAs that would otherwise silence FOXO1. The present study's finding that higher LINC00261 predicts better UCEC survival is consistent with this tumor-suppressive role.
Several confirmed miRNA-mRNA links in the network have established roles in endometrial cancer. miR-449a has been shown to suppress endometrial tumor development by targeting CDC25A. The miRNA-424/E2F7 axis is known to inhibit endometrial cancer malignancy. PVT1, another lncRNA, promotes endometrial carcinoma cell migration and invasion through miR-195-5p, a miRNA also present in the current network.
This study is limited by its bioinformatics-only design. The ceRNA hypothesis itself remains debated, with some researchers arguing that changes in individual lncRNAs may not meaningfully shift miRNA availability in all contexts. The patient data also derives primarily from a Caucasian population in TCGA, limiting generalizability. All predicted interactions require experimental validation in cell and animal models before they can confirm functional causation.
This study provides the first systematic ceRNA regulatory map for uterine corpus endometrial carcinoma, built from the largest available genomic dataset for the disease. The network of 87 lncRNAs, 74 mRNAs, and 20 miRNAs defines the regulatory architecture underlying transcriptome dysregulation in UCEC.
The identification of 9 prognostically relevant lncRNAs, with C2orf48 and LINC00261 as the most connected hubs, points to actionable molecular targets. Both lncRNAs are predicted to modulate cancer-driving gene expression programs through well-defined miRNA intermediaries, making them tractable for future therapeutic interference using RNA-based drugs or small molecules.
Future studies should validate these network interactions using UCEC cell lines and animal models, and should test whether lncRNA expression levels in tumor biopsies can serve as independent prognostic biomarkers. Expanding the analysis to include other racial groups and non-Caucasian patient populations will be important to assess whether the network findings generalize broadly.