ArticleClinical proteomics2025
Unveiling the protein landscape for early detection of colorectal precancerous lesions.
Article in Clinical proteomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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.
- Correction: Unveiling the protein landscape for early detection of colorectal precancerous lesions.Clinical proteomics · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Colorectal cancer (CRC) has emerged as the second most prevalent cause of cancer-related mortality globally. Early identification of precancerous lesions prone to malignant transformation is pivotal in CRC prevention. Proteins, as microscopic reflections of cellular functional states, offer insights into pathological alterations within precancerous lesions through changes in their expression and function. Our review summarizes the protein research on colorectal adenomas under different sample conditions, including traditional adenomas, serrated lesions, LST, FAP and IBD. It highlights the changes in the expression patterns of key proteins and their potential mechanisms underlying the transition from precancerous to cancerous states. Additionally, it summarizes the research on post-translational modifications of characteristic protein families and associated signaling pathways, while discussing current techniques for studying protein expression and function in colorectal cancer, such as proteomics and artificial intelligence. However, current research limitations, such as small sample sizes, limited sample types, and insufficient in-depth mechanistic analysis, hinder comprehensive understanding. Future research should expand study cohorts, diversify sample types, and leverage machine learning and multi-omics approaches to develop predictive models. By doing so, a more comprehensive understanding of protein profiles during the progression from colorectal precancerous to cancerous lesions can be obtained, facilitating early CRC diagnosis and the development of targeted therapeutic interventions.
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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.