ArticleMolecular biotechnology2024
A Novel Gene Signature Associated with Protein Post-translational Modification to Predict Clinical Outcomes and Therapeutic Responses of Colorectal Cancer.
Article in Molecular biotechnology, 2024. 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.
- Post-translational modifications of protein and lung cancer.Frontiers in oncology · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulated evidence highlights the biological significance of diverse protein post-translational modifications (PTMs) in tumorigenicity and progression of colorectal cancer (CRC). In this study, ten PTM patterns (ubiquitination, methylation, phosphorylation, glycosylation, acetylation, SUMOylation, citrullination, neddylation, palmitoylation, and ADP-ribosylation) were analyzed for model construction. A post-translational modification index (PTMI) with a 14-gene signature was established. CRC patients with high PTMI had a worse prognosis after validating in nine independent datasets. By incorporating PTMI with clinical features, a nomogram with excellent predictive performance was constructed. Two molecular subtypes of CRC with obvious difference in survival time were identified by unsupervised clustering. Furthermore, PTMI was related to known immunoregulators and key tumor microenvironment components. Low-PTMI patients responded better to fluorouracil-based chemotherapy and immune checkpoint blockade therapy compared to high-PTMI patients, which was validated in multiple independent datasets. However, patients with high PTMI might be sensitive to bevacizumab. In short, we established a novel PTMI model by comprehensively analyzing diverse post-translational modification patterns, which can accurately predict clinical prognosis and treatment response of CRC patients.
Indexed as
Identifiers
37592152What 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.