ArticleJournal of proteome research2025
Metabolomics- and Proteomics-Based Disease Diagnostic Classifier Model for the Prediction and Diagnosis of Colorectal Carcinoma.
Article in Journal of proteome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal carcinoma (CRC) is a leading cause of cancer-related deaths globally. Diagnostic biomarkers are essential for risk stratification and early detection, potentially enhancing patient survival. Our study aimed to explore the potential biomarkers of CRC at the protein and metabolic levels.
methodsBlood serum from CRC patients and healthy controls was analyzed using metabolomic and proteomic techniques. A conjoint analysis was conducted, and samples were split into training and validation sets (7:3 ratio) to develop and evaluate a disease diagnosis classifier model. Immunohistochemistry (IHC) analyses were conducted to validate the results.
resultsWe identified 631 differential metabolites and 61 differentially expressed proteins (DEPs) in CRC, involved in pathways such as arginine and proline metabolism, central carbon metabolism in cancer, and signaling pathways including TGF-β, mTOR, PI3K-Akt, and others. Key proteins (CILP2, SLC3A2, EXTL2, hydroxypyruvate isomerase (HYI), ENPEP, LRG1, CTSS, thyrotropin-releasing hormone-degrading ectoenzyme (TRHDE), SELE, and HSPA1A) showed significant expression differences between CRC patients and controls. IHC results showed that compared with the paracancerous tissues, the expression of CILP2, EXTL2, and HYI was significantly downregulated in the CRC tissues (
conclusionsThe identified metabolites and DEPs are promising CRC biomarkers. The developed classifier model based on eight metabolites demonstrates high accuracy for CRC assessment and diagnosis.
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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.