Background

Clear cell renal cell carcinoma (ccRCC), which accounts for the majority of kidney cancers, often goes undetected until late-stage metastasis due to its lack of early symptoms. Once tumors progress beyond a certain stage, they frequently become resistant to conventional chemotherapy or radiation therapy, limiting treatment options. Although targeted therapies, including tyrosine kinase inhibitors (TKIs), and immune checkpoint inhibitors have been introduced as standard first-line treatments, the development of drug resistance and interindividual variability in response remain significant challenges that are yet to be fully resolved. This is because patients exhibit diverse tumor microenvironments and genomic alterations that contribute to treatment failure. This is why there is an unceasing clinical need to identify precise prognostic indicators and discover new therapeutic targets.

Recently, the molecular biology community has begun to focus on lysine crotonylation (LCR), a type of post-translational modification (PTM). This epigenetic mechanism, in which crotonyl groups bind to lysine residues of both non-histone and histone proteins, is deeply involved in regulating gene transcription and reprogramming cellular metabolism. Although reports have indicated that LCR signals regulate cancer cell proliferation and invasion in various solid tumors, including colorectal and liver cancers, the pathological functions and clinical value of LCR-related molecules in ccRCC remain shrouded in mystery. There has been an urgent need to elucidate the interaction network of LCR genes within vast omics data and link it to therapeutic strategies.

Key Findings

The researchers systematically profiled multi-omics data for ccRCC by collecting open cancer databases. First, consensus clustering was performed based on the expression patterns of 18 genes directly associated with LCR (LCR-related genes, LCRGs). The analysis revealed that the tumor patient group was divided into two distinct molecular subtypes, C1 and C2. The two subtypes exhibited marked contrasts not only in overall patient survival rates but also in patterns of tumor immune cell infiltration and the activity of major cancer-related signaling pathways.

To control the complexity of biological signals, key hub gene modules were extracted using weighted gene co-expression network analysis (WGCNA). Subsequently, 101 machine learning algorithm combinations were validated stepwise to establish a 15-gene-based LCR signature (LCR-related signature, LCRS) model with the highest predictive performance. The completed LCRS model was statistically proven to be a strong prognostic factor functioning independently of existing clinical stages in multiple independent cohorts, including the Cancer Genome Atlas (TCGA).

Precision analysis of single-cell RNA sequencing (scRNA-seq) data revealed that the core genes constituting LCRS are predominantly expressed in endothelial cells within the tumor microenvironment. This demonstrates that the LCR regulatory mechanism directly intervenes not only in cancer cells themselves but also in tumor angiogenesis and vascular function maintenance.

In drug response evaluation using pharmacogenomic databases (CTRP, PRISM), opposing treatment sensitivities were observed across risk groups. High-risk patients exhibited high sensitivity to sunitinib, whereas low-risk patients showed superior responses upon administration of axitinib and pazopanib. The Cancer Dependency Map (DepMap) large-scale CRISPR screening data identified CLCNKA, which encodes a chloride ion channel protein, as a critical target molecule that suppresses tumor growth in high-risk patients. Additionally, 10 promising candidate compounds targeting CLCNKA for high-risk patients were selected.

Significance and Prospects

This study comprehensively reconstructed the LCR signaling system, an unexplored area, using computational biology and machine learning techniques to derive clinical value. In particular, the 15-gene model derived from validating over 100 algorithm combinations provides a solid foundation for developing molecular diagnostic panels to stratify risk in patients with small cell lung cancer. By elucidating the expression patterns centered on vascular endothelial cells, the possibility of a multifaceted combination therapy to control the tumor microenvironment has also been opened.

Data establishing the differences in sensitivity among TKI agents are expected to significantly reduce trial and error at the prescription site. The finding that drug responses vary depending on a patient's LCR activity, even within the same class of vascular endothelial growth factor receptor (VEGFR) inhibitors, offers highly realistic insights from a precision medicine perspective. The discovered CLCNKA target can also serve as a strong starting point for the development of first-in-class drugs aimed at refractory patient populations who do not respond to existing treatments.

However, since the results were derived based on a retrospective multi-omics cohort analysis and public database screening, additional validation is required before practical implementation. The clinical validity of the 15-gene signature must be reconfirmed through prospective clinical trials, and the actual molecular mechanism of the CLCNKA gene within tumors and the in vivo safety of candidate compounds also need to be rigorously evaluated. If subsequent studies smoothly demonstrate the specific pathway by which LCR deformation in endothelial cells induces vascular structural changes at the molecular level, it is expected to pave the way for a turning point that will elevate the treatment outcomes of renal cell carcinoma.