The emerging role of long non-coding RNAs in renal cell carcinoma progression and clinical therapy via targeting metabolic regulation

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TL;DR

This review summarizes how long non-coding RNAs (lncRNAs) regulate renal cell carcinoma (RCC) progression through metabolic pathways, including glucose metabolism, lipid metabolism, amino acid metabolism, and mitochondrial dynamics. Key lncRNAs such as KCNQ1DN, ROR, FILNC1, and SNHG12 are identified as potential biomarkers and therapeutic targets, with several acting through the lncRNA-miRNA-mRNA axis to influence glycolysis, lipid accumulation, and drug resistance. The clinical significance lies in identifying new diagnostic markers and therapeutic strategies for RCC, particularly for overcoming sunitinib resistance and targeting metabolic vulnerabilities.

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**Background:** Renal cell carcinoma (RCC) is the 12th most frequent cancer worldwide, accounting for 3% of adult cancers and 2% of cancer deaths. Approximately 30% of RCC patients have metastasis at diagnosis, and 30–70% relapse after surgery. RCC is insensitive to radiotherapy and chemotherapy, and immunotherapy response is limited by tumor heterogeneity and drug resistance. Long non-coding RNAs (lncRNAs) are transcripts >200 nucleotides that regulate gene expression through multiple mechanisms, including the lncRNA-miRNA-mRNA axis, direct protein binding, and chromatin regulation. Metabolic reprogramming, particularly the Warburg effect (aerobic glycolysis), is a hallmark of RCC, with clear cell RCC (ccRCC) characterized by lipid and glycogen accumulation. This review examines the intersection of lncRNA biology and metabolic regulation in RCC progression and therapy.

**Methods:** This is a narrative review synthesizing published literature on lncRNA expression and function in RCC, with a focus on metabolic regulatory mechanisms. The authors summarize findings from in vitro studies, in vivo animal models, TCGA data analyses, and clinical observations. Key metabolic pathways examined include glucose metabolism (glycolysis, pentose phosphate pathway, TCA cycle), lipid metabolism (fatty acid synthesis, desaturation, elongation), amino acid metabolism (arginine synthesis, glutamate metabolism), and mitochondrial dynamics (oxidative phosphorylation, ROS production, apoptosis regulation).

**Key Results:** Multiple lncRNAs are dysregulated in RCC. Up-regulated lncRNAs include LINC00406 (via PI3K/AKT), DLEU7-AS1 (via miR-26a-5p), FTX (via mir4429-UBE2C), CYTOR (via miR-136-5p), MMP2-AS1 (via miR-34c-5p/MMP2), ASAR (via miR34/miR449/STAT3), and SNHG12 (via multiple miRNA axes including miR-129-5p/MAPK/ERK, SP1/CDCA3, miR-30a-3p, and miR-200c-5p). Down-regulated lncRNAs include XIST (controversial—reported both up and down), NEAT1 (also controversial), KCNQ1DN (via c-MYC/cyclin D1/p27), NR_023387 (via MGP), and SLERCC (via UPF1-Wnt/β-catenin). In glucose metabolism, lncRNA KCNQ1DN suppresses glycolysis by inhibiting c-MYC transcriptional activity; lncRNA ROR promotes glycolysis by reducing p53 and increasing c-MYC; lncRNA FILNC1 down-regulates c-MYC via AUF1 interaction under energy stress. About 90% of ccRCC patients have VHL gene deletion, leading to HIF-1α accumulation and up-regulation of GLUT-1 and LDHA. In lipid metabolism, lncRNA AnxA3 is a negative regulator of adipogenic differentiation in ccRCC. In amino acid metabolism, lncRNA 00312 up-regulates ASS1 via miR-34a-5p inhibition, and lncRNA TUG1 activates Wnt/β-catenin/c-MYC via miR-141-3p. In mitochondrial dynamics, lncRNA TP73-AS1 modulates mTOR pathway; NDUFA4L2 (the highest expressed gene in RCC) reduces mitochondrial oxygen consumption and ROS production; MEG3 down-regulates Bcl-2 and inhibits cytochrome c release; PANDAR suppresses Bcl-2 and Mcl-1 via PI3K/AKT-mTOR; HOTAIR regulates MIUC1 and Bcl-2/cytochrome c; ITGB1 modulates Mcl-1 expression. SNHG12 is significantly up-regulated in sunitinib-resistant RCC and associated with poor clinical outcomes. The "obesity paradox" is noted: obesity is a risk factor for RCC, yet obese patients may have better prognosis due to lower tumor invasiveness and higher immunogenicity.

**Clinical Implications:** LncRNAs represent promising biomarkers for RCC diagnosis, prognosis, and therapeutic targeting. FILNC1 and lncRNA00312 are potential diagnostic markers. SNHG12 knockdown may reverse sunitinib resistance, a major clinical challenge since most patients develop resistance after 5–6 months of treatment. Targeting metabolic pathways through lncRNA manipulation offers new therapeutic avenues, though SCD1 inhibitors (e.g., T-3764518) show anti-tumor potential but may have off-target effects on normal cells. The review emphasizes that while lncRNA research in RCC metabolism is still in early stages, it provides new targets for early clinical diagnosis and treatment of this fatal malignancy.