Cuproptosis is a recently identified mechanism of cell death triggered by intracellular copper accumulation, which disrupts mitochondrial metabolic pathways through aberrant binding to lipoylated proteins in the tricarboxylic acid cycle. This newly described form of regulated cell death is distinct from apoptosis, ferroptosis, and necroptosis and has attracted rapid attention in cancer biology due to its potential role in tumor progression and treatment response.
Long non-coding RNAs (lncRNAs) are RNA molecules exceeding 200 nucleotides in length that do not encode proteins but regulate gene expression through diverse mechanisms including chromatin remodeling, transcription factor modulation, and post-transcriptional processing. LncRNAs associated with cuproptosis-related gene networks represent a novel class of candidate biomarkers for cancer prognosis.
Clear cell renal cell carcinoma (ccRCC) accounts for the majority of kidney cancer cases and is characterized by frequent metabolic reprogramming and resistance to conventional chemotherapy. Identifying prognostic signatures derived from cuproptosis biology could inform patient stratification and reveal new therapeutic vulnerabilities in this treatment-resistant tumor type.
The study used RNA sequencing data from 539 ccRCC tumor samples and 72 adjacent normal kidney tissue samples available through The Cancer Genome Atlas (TCGA), supplemented by an independent validation cohort from the International Cancer Genome Consortium (ICGC). Cuproptosis-related genes were identified from published literature, and lncRNAs co-expressed with these genes were computationally identified using Pearson correlation analysis with a correlation coefficient threshold of 0.4 and an adjusted p-value below 0.001.
This approach identified 280 cuproptosis-related lncRNAs (CRLRs) that showed significant co-expression with cuproptosis pathway genes. Univariate Cox proportional hazards regression was then applied to identify lncRNAs with significant associations with overall survival among ccRCC patients, narrowing the candidate list to those with prognostic relevance in the TCGA training cohort.
LASSO penalized Cox regression was applied to the survival-associated CRLRs to construct a parsimonious prognostic model, reducing the signature to three hub lncRNAs: FOXD2-AS1, AC026401.3, and LASTR. Each lncRNA was assigned a regression coefficient, and individual patient risk scores were calculated as the linear combination of expression values weighted by their respective LASSO coefficients.
The three-lncRNA risk score model demonstrated time-dependent AUC values of 0.741 at one year, 0.680 at three years, and 0.700 at five years in the TCGA training cohort. Patients were stratified into high-risk and low-risk groups using the median risk score as the cutoff, and Kaplan-Meier survival analysis confirmed significantly worse overall survival in the high-risk group (log-rank p less than 0.001).
The prognostic performance was independently validated in the ICGC cohort, where the model maintained significant separation of survival curves between high- and low-risk patient groups. This cross-cohort validation supports the generalizability of the three-lncRNA signature beyond the discovery dataset and reduces the likelihood of overfitting as the sole explanation for observed performance.
Multivariate Cox regression analysis confirmed that the CRLR risk score remained an independent prognostic factor after adjustment for clinical variables including age, sex, tumor grade, and TNM pathological stage. This independence from established clinical prognosticators indicates that the lncRNA signature captures biological information not fully encoded in conventional staging systems.
Immune cell infiltration analysis using CIBERSORT deconvolution revealed that high-risk ccRCC patients had a distinctly immunosuppressive tumor microenvironment characterized by elevated proportions of M2 macrophages, regulatory T cells, and reduced cytotoxic CD8+ T cell infiltration compared to low-risk patients. This immune profile is associated with resistance to anti-tumor immune responses.
Immune checkpoint gene expression analysis showed that high-risk patients had elevated expression of PD-L1, CTLA-4, and other checkpoint molecules, suggesting that the cuproptosis-related lncRNA signature may correlate with sensitivity to immune checkpoint inhibitor therapy. High-risk patients may derive greater benefit from PD-1 or PD-L1 blockade given their immunosuppressed tumor microenvironment.
Analysis using the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm and the Subclass Mapping (SubMap) method further suggested that high-risk patients showed patterns of immune dysfunction and exclusion consistent with expected response to immune checkpoint therapy. These findings provide a biological rationale for prospective evaluation of the risk score as an immunotherapy response predictor in ccRCC.
To confirm the biological relevance of the three identified hub lncRNAs, quantitative PCR (qPCR) expression analysis was performed across four ccRCC cell lines: 769-P, 786-O, ACHN, and Caki-1. All three lncRNAs showed significantly elevated expression in ccRCC cell lines compared to a normal renal tubular epithelial cell line (HK-2), providing experimental support for their role in the cancerous phenotype.
FOXD2-AS1 demonstrated the highest relative expression among the three hub lncRNAs across the tested cell lines, consistent with its larger regression coefficient in the LASSO model. AC026401.3 and LASTR also showed reproducible overexpression in cancer cells compared to normal kidney epithelium, suggesting their involvement in pathways active in ccRCC biology.
These in vitro findings validate the transcriptomic observations from the TCGA dataset at the cellular level and suggest that functional experiments targeting these lncRNAs could reveal their mechanistic roles in cuproptosis pathway modulation, metabolic reprogramming, and tumor invasion in ccRCC cell models.
The three-cuproptosis-related lncRNA signature provides a new prognostic tool for ccRCC that complements conventional clinical staging, with the potential to identify patients at elevated risk of poor survival who may benefit from intensified surveillance or alternative therapeutic strategies. The integration of cuproptosis biology into prognostic modeling represents a conceptually novel approach aligned with emerging understanding of copper metabolism in cancer.
The signature's association with immune checkpoint expression and tumor microenvironment composition positions it as a potential predictor of immunotherapy benefit, a clinically important application given the growing role of PD-1 and PD-L1 inhibitors in first-line ccRCC treatment. Prospective clinical validation studies would be required to confirm this predictive utility.
Future research should investigate the functional mechanisms by which FOXD2-AS1, AC026401.3, and LASTR contribute to cuproptosis resistance or sensitivity in ccRCC cells, and should explore whether modulating these lncRNAs or the copper metabolism pathways they regulate could represent therapeutic targets in this malignancy.