Renal cell carcinoma treatment

This study shows bisantrene enhances the effectiveness of tyrosine kinase inhibitors in ccRCC by sustaining AKT inhibition and overcoming TKI resistance.

This study identifies CDK4/6 and mTORC1 pathway activation in tRCC and shows that their dual inhibition synergistically suppresses tRCC cell growth in vitro and xenografts in vivo.

The paper identifies a transcriptional transition state in ccRCC cells that drives sarcomatoid transformation and enhances immunotherapy response via macrophage and T-cell interactions.

This paper develops a machine learning framework for therapeutic target discovery in ccRCC, identifying 16 targets and novel mechanisms with superior efficacy to standard-of-care drugs.

The paper assesses neoadjuvant axitinib response in RCC with VTT using multiparametric analysis and identifies angiogenic and immune predictors of treatment response.

This study identifies pRb as a novel VHL substrate, showing that its hyperstabilization promotes ccRCC tumorigenesis through transcriptional changes.

The paper uses proteomics data and machine learning to identify protein signatures distinguishing renal cell carcinomas from normal tissues and different subtypes.

This study uses AI-based image analysis and multi-omics profiling to link morphological features with molecular changes in ccRCC, identifying unique molecular profiles in aggressive rhabdoid tumors.

This paper identifies a novel molecular subtype of papillary renal cell carcinoma (pRCC) characterized by HIF-pathway overactivation, associated with poor prognosis and distinct biological features.

The paper identifies DNA methylation subtypes from transcriptomes to guide sunitinib and immunotherapy selection in renal cancer, finding hyper-methylated tumors respond better to atezolizumab plus bevacizumab.

The paper identifies FAP-expressing EMT-associated cancer cells in highly inflamed localized ccRCC as an aggressive, immunosuppressive subtype linked to poor prognosis and metastasis.

The paper uses long-read spatial transcriptomics on ccRCC tumoroids to reveal gene and isoform expression heterogeneity, identifying distinct functional tumor clusters and isoform-specific patterns.

The paper analyzes RNAseq data to identify alternative splicing patterns associated with tumor thrombus formation in clear-cell renal cell carcinoma.

The paper identifies epigenomic signatures in sarcomatoid renal cell carcinoma (sRCC) that predict response to immune checkpoint inhibitors and are detectable in plasma, enabling non-invasive diagnosis.

This study identifies germline rare variants associated with clear and non-clear cell RCC, accounting for population stratification, and finds distinct risk patterns and somatic associations.

This study identifies hub genes associated with cure in chromophobe cell renal carcinoma using WGCNA and the defective Gompertz model, finding ten genes with strong predictive value.

This study investigates the role of CHPF in ccRCC, finding it promotes proliferation and invasion, suggesting it as a prognostic marker and therapeutic target.

This study evaluates the expression and prognostic significance of PSMBs in clear cell renal cell carcinoma, identifying PSMB4 and PSMB10 as potential therapeutic targets and biomarkers.

This study identifies PNCK as a differentially overexpressed kinase in RCC, showing its role in cell proliferation, apoptosis, and disease progression, suggesting it as a potential drug target.

The paper develops a prognostic model and drug selection framework for clear cell renal cell carcinoma using multi-omics data from TCGA, identifying high- and low-risk patient subtypes with significant survival differences.

A multi-regional single-cell study of kidney tumours reveals clonotypic heterogeneity, immune cell exhaustion, and potential therapeutic targets through transcriptomic and exomic analysis.

This study identifies 10 hub genes associated with ccRCC tumorigenesis and prognosis through bioinformatics analysis of gene expression data.

This study stratifies clear cell renal cell carcinoma patients using transcription factor activity derived from RNA-seq data to predict prognosis and identify subtypes with distinct biomarkers and pathways.

The paper identifies two ccRCC subtypes based on PKM alternative splicing and proposes drug repurposing for each subtype.