Evidence map›Paper›PMID 42089037›Full record

ArticleContemporary oncology (Poznan, Poland)2026

Combined analysis of metabolomics and transcriptomics reveals new indicators for the diagnosis and prognosis of colorectal cancer.

Manman Guo, Bingyu Jin

Abstract read
In one paragraph

Article in Contemporary oncology (Poznan, Poland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Manman GuoDepartment of Medical Laboratory, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Bingyu JinDepartment of Medical Laboratory, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Abnormal cellular metabolism is one of the characteristics of tumour cells. However, the differences in the global metabolomics between normal and tumour tissues remain unclear. In this study, we have proposed a diagnostic and prognostic model for colorectal cancer (CRC) based on metabolomics and genomics. Material and methods: Metabolomics data of CRC patients was obtained from the MetaboLights repository, and identified the characteristic metabolites of CRC cells through orthogonal partial least squares discriminant analysis (OPLS-DA). Then we performed differential analysis of these metabolites between normal and tumour tissues. Subsequent enrichment analysis was used to analyse the signalling pathways related to the differential metabolites. Finally, we combined metabolomics and genomics to construct a prognostic model, and detected the expression of key metabolites and genes in CRC cell lines by using ELISA and western blot. Results: Based on the variable important in projection values of OPLS-DA, we identified 318 characteristic metabolites. By conducting differential analysis of these metabolites, we identified 30 downregulated and 42 upregulated metabolites in colorectal cancer. The combined analysis of enrichment pathways revealed that 5 pathways were enriched in both metabolomics and transcriptomics. Conclusions: We established a prognostic model through univariate Cox and least absolute shrinkage and selection operator regression, and then verified the excellent application value of the model for patient prognosis through receiver operating characteristic curves and survival analysis. Finally, ELISA and western blot experiments showed that compared with normal colorectal epithelial cells, the levels of estradiol and formimidoyltransferase cyclodeaminase proteins were increased, while the levels of methionine and SLC5A1 proteins were decreased in CRC cells.

Indexed as

colorectal cancermetabolomicsmulti-omics

Identifiers

PMID42089037
PMCPMC13137426

What Socratic holds

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LicenceCC BY-NC-SA
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Registered trials

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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 Socratic graph.