ArticleDiscover oncology2026
Integrated metabolomics and transcriptomics analysis unveils key metabolic pathways and potential biomarkers in clear cell renal cell carcinoma.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal cancer, with both incidence and mortality rates on the rise in recent years. This underscores the urgent need for precise diagnostic tools, effective monitoring, and improved management strategies. Advances in tumor sequencing have significantly enhanced our understanding of the molecular biology of ccRCC, leading to the identification of several potential genomic biomarkers. However, no widely accepted tissue or blood biomarker for ccRCC has been incorporated into routine clinical practice. In this study, we integrated transcriptomics and metabolomics analyses using paired cancerous and non-cancerous tissue samples from five patients who underwent radical nephrectomy. Our goal was to systematically investigate the molecular mechanisms underlying ccRCC and identify potential therapeutic targets. We identified ten shared KEGG metabolic pathways between the transcriptome and metabolome, with purine metabolism and glycerophospholipid metabolism emerging as particularly relevant to tumorigenesis and disease progression. Network analysis revealed significant correlations between specific metabolites (e.g., guanosine triphosphate and L-malic acid) and key genes (e.g., MET, SDHC, FH, and SDHB) within the molecular network of ccRCC. Additionally, we identified key enzymes in phospholipid metabolism, such as AK, LPCAT, and LPEAT, as potential therapeutic targets. Inhibiting these pathways could effectively disrupt tumor membrane formation and signal transduction. Our findings not only deepen the understanding of the molecular mechanisms driving ccRCC but also provide valuable insights for early diagnosis, prognosis evaluation, and the development of novel therapeutic strategies. Future work should focus on validating these potential biomarkers and therapeutic targets in larger cohorts and exploring their clinical utility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.