ArticleCancer science2026
Decoding Senescence-Driven Heterogeneity in Early-Onset Colorectal Cancer for Prognostic and Therapeutic Stratification.
Article in Cancer science, 2026. 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.
- CircARHGAP10 inhibits colorectal cancer cell proliferation, migration and invasion by governing the miR-29a-5p/LPP axis and regulating Wnt/β-catenin signaling pathway.Molecular and cellular biochemistry · 2026Article
- ACSM5 c.1273 C> A Polymorphism Promotes Malignant Progression of Lung Adenocarcinoma in Xuanwei Region.Applied biochemistry and biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Early-onset colorectal cancer (EOCRC), diagnosed in patients under 50, is particularly aggressive, yet lacks targeted therapeutic strategies. This study aimed to explore the role of cellular senescence in driving EOCRC's malignancy and developed a senescence scoring system (EO-Senscore) to guide precision oncology. Through a multi-omics analysis of 2961 patients, we discovered that cellular senescence is more pronounced and varied in EOCRC and is not tied to chronological age. Two distinct senescence subtypes were identified: Cluster 1 (low-senescence tumors) showed prolonged survival, enhanced immunogenicity, and cell cycle activation, while Cluster 2 (high-senescence tumors) exhibited aggressive phenotypes and an immunosuppressive microenvironment. We further developed a machine learning model, the EO-Senscore, to quantify a tumor's senescence level. This score effectively stratified patients by prognosis and potential treatment response. Patients with a low EO-Senscore were predicted to respond well to immunotherapy and chemotherapy. In contrast, those with a high score had more invasive tumors but showed significant sensitivity to senolytic drugs (like ABT-263) in lab-based experiments. In conclusion, this research establishes cellular senescence as a crucial factor in EOCRC's aggressiveness. The EO-Senscore provides a practical, quantitative tool to guide clinical decisions, suggesting that patients could be directed toward immunotherapy or novel senolytic-based combination therapies for more personalized and effective cancer care.
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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.