ArticleiScience2025
Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Challenges and Opportunities in Single-Sample Network Modeling.bioRxiv : the preprint server for biology · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
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
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small subset of patients benefit, and robust molecular predictors remain elusive. We present a computational framework leveraging sample-specific gene co-expression networks to identify features predictive of immunotherapy response in kidney cancer. Our results reveal that patients with similar clinical outcomes exhibit comparable gene co-expression patterns. Notably, increased gene connectivity and stronger negative gene-gene associations are hallmarks of poor responders. We further developed sample-specific pathway-level network scores to detect dysregulated biological pathways linked to treatment outcomes. Finally, incorporating these sample-level network features improves the predictive performance of gene expression-based machine learning models. This work highlights the value of personalized gene network features for stratifying patients with cancer and optimizing immunotherapy strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.