Endometrioid endometrial adenocarcinoma (EEA) is the most common histological subtype of endometrial cancer. While early-stage EEA has a 74-91% five-year survival rate, patients who relapse or present with advanced disease have poor outcomes. Better prognostic tools are urgently needed to identify which patients are truly high-risk.
Cuproptosis is a recently discovered form of programmed cell death triggered by excessive intracellular copper. Unlike other forms of cell death, cuproptosis works by causing copper ions to bind to certain proteins in the cell's energy-producing machinery (the TCA cycle). This triggers these proteins to clump together abnormally, generating toxic cellular stress that kills the cell. Cancer cells often have elevated copper levels, making cuproptosis a potentially exploitable vulnerability.
Long non-coding RNAs (lncRNAs) are RNA molecules that, unlike regular messenger RNAs, are not translated into proteins. Instead, they regulate how other genes are turned on or off. In cancer, lncRNAs can act as oncogenes or tumor suppressors. This study investigated whether lncRNAs that are co-expressed with cuproptosis-related genes - called cuproptosis-related lncRNAs (CRLs) - could predict prognosis in EEA.
The study used RNA sequencing and clinical data from 318 EEA patients in The Cancer Genome Atlas (TCGA-UCEC) database - a publicly available cancer genomics repository. Starting from 19 known cuproptosis-related genes, the researchers identified all lncRNAs that were co-expressed with these genes across the tumor samples, finding 941 candidate cuproptosis-related lncRNAs.
This large pool of 941 candidates was progressively narrowed down using statistical filtering. First, univariate Cox regression - a statistical method linking gene expression to patient survival time - reduced the list to 11 lncRNAs that showed meaningful prognostic value. Then, LASSO regression (which penalizes model complexity to prevent overfitting) selected the two most informative lncRNAs: AL512353.1 and ACOXL-AS1.
These two lncRNAs were used to calculate a risk score for each patient: RS = (AL512353.1 x 1.264) + (ACOXL-AS1 x 1.658). Patients were then classified as high-risk or low-risk based on the median risk score. This score was validated in independent training and testing splits of the TCGA cohort, and a nomogram - a graphical tool combining the lncRNA risk score with clinical variables - was built to estimate 1-, 3-, and 5-year survival probabilities.
The risk score model successfully stratified patients into two groups with significantly different survival outcomes. Kaplan-Meier survival curves consistently showed that high-risk patients had significantly shorter overall survival than low-risk patients, in both training and validation datasets (p-values below 0.05). The model's AUC for predicting 1-, 3-, and 5-year survival ranged from 0.703 to 0.798, and the comprehensive nomogram reached a C-index (a measure of predictive accuracy) of 0.907.
High-risk classification was significantly associated with several clinicopathological features: higher histological grade (poorly differentiated tumors), deeper myometrial invasion, and specific molecular subtypes. Notably, POLE hypermutation and MSI subtypes (which are associated with better immunotherapy response) clustered in the low-risk group, while the copy-number-high (CNV-high) subtype - associated with worse prognosis - was more common in the high-risk group. Even among early-stage (Stage I-II) patients, high-risk patients had worse survival (p=0.032).
Immune profiling revealed that high-risk patients had significantly lower levels of CD8+ T cell infiltration in their tumors. CD8+ T cells are the immune system's cancer-killing cells. Lower CD8+ T cell presence suggests the tumor microenvironment is immunosuppressive - making high-risk tumors potentially less responsive to immunotherapy. The TIDE algorithm, which predicts immunotherapy response from gene expression, also scored higher dysfunction in the high-risk group.
Using the Genomics of Drug Sensitivity in Cancer (GDSC) database, the researchers predicted which chemotherapy drugs would be most effective in high-risk versus low-risk patients. They found that six drugs showed significantly different predicted sensitivity between the two risk groups.
Most importantly, AKT inhibitors - a class of drugs targeting the AKT signaling pathway, which controls cell survival and proliferation - showed lower IC50 values (meaning greater predicted effectiveness) in high-risk patients. The AKT pathway is frequently activated in endometrial cancer and is already being investigated as a therapeutic target. This finding suggests that high-risk EEA patients might benefit from AKT inhibitor-based therapies that low-risk patients would not need.
This type of pharmacogenomic analysis - predicting drug sensitivity from gene expression profiles - represents an important step toward precision medicine: matching individual patients to treatments most likely to benefit them, based on their tumor's molecular characteristics rather than its clinical stage alone.
The study went beyond correlation to investigate how ACOXL-AS1 mechanistically influences cancer biology. The researchers identified it as acting within a ceRNA (competitive endogenous RNA) network - a regulatory system where lncRNAs act as molecular sponges that bind and sequester microRNAs, thereby controlling the expression of genes those microRNAs would otherwise suppress.
Specifically, ACOXL-AS1 was found to competitively bind miR-421, a microRNA that normally suppresses the gene MTF1 (Metal Regulatory Transcription Factor 1). MTF1 is directly involved in regulating cellular response to metals including copper - placing it at the intersection of cuproptosis biology. When ACOXL-AS1 is present, it sequesters miR-421, allowing MTF1 to be expressed.
These relationships were validated in cell line experiments using two endometrial cancer cell lines (HHUA and HEC-1A). When cells were treated with copper ions plus Elesclomol (a copper ionophore drug that enhances copper uptake), cell proliferation was significantly inhibited. Gene expression analysis confirmed that ACOXL-AS1 and miR-421 levels increased while MTF1 decreased, consistent with the predicted ceRNA mechanism. Overexpression of ACOXL-AS1 via lentiviral vector confirmed that it drives miR-421 upregulation and MTF1 suppression.
The two-lncRNA risk signature (AL512353.1 and ACOXL-AS1) represents a novel molecular tool for stratifying endometrioid endometrial adenocarcinoma patients by prognosis. Because it captures cuproptosis-related biology that is distinct from traditional staging factors like tumor grade and clinical stage, the signature could add value when combined with existing clinical prognostic tools.
The connection to immune infiltration and drug sensitivity makes this signature particularly actionable: high-risk patients could be flagged for closer follow-up, more aggressive therapy, or enrollment in clinical trials of AKT inhibitors or immunotherapy combinations. The nomogram integrating the risk score with clinical variables provides a practical tool for estimating individual survival probabilities.
The main limitation is that the analysis was conducted entirely in the TCGA-UCEC cohort, which has specific demographic and institutional characteristics. Independent validation in prospective cohorts from different institutions and geographic regions is needed before this signature could be used in clinical practice.