Evidence map›Paper›PMID 41373777›Full record

ArticleInternational journal of molecular sciences2025

Identification of Colorectal Cancer-Related RNA Markers from Whole Blood Using Integrated Bioinformatics Analysis.

Jin Han, Jung Chul Na, Tae Il Kim, Jae Myun Lee, Jong Koo Kim, Jae Jun Park, Jaemee Jung, Hyeyoung Lee

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jin HanDepartment of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University Mirae Campus, Wonju 26493, Republic of Korea.
Jung Chul NaDepartment of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University Mirae Campus, Wonju 26493, Republic of Korea.
Tae Il KimDivision of Gastroenterology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.ORCID 0000-0003-4807-890X
Jae Myun LeeDepartment of Family Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.ORCID 0000-0002-5273-3113
Jong Koo KimDepartment of Microbiology and Immunology, Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.ORCID 0000-0003-2880-277X
Jae Jun ParkDivision of Gastroenterology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Jaemee JungDepartment of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University Mirae Campus, Wonju 26493, Republic of Korea.
Hyeyoung LeeDepartment of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University Mirae Campus, Wonju 26493, Republic of Korea.ORCID 0000-0003-1572-5250

Funding

the Ministry of Health & Welfare, Republic of Korea HI22C0878
6 · The paper itself

Abstract

Despite advances in blood-based screening tests for colorectal cancer (CRC), most existing assays focus on DNA-based biomarkers, which predominantly reflect tumor-derived fragments released at later disease stages. In contrast, whole-blood transcriptomic profiling can capture systemic immune responses and tumor-host interactions, offering a complementary strategy for earlier disease detection. However, clinically validated whole-blood transcriptomic signatures remain limited. Here, we investigated a whole-blood RNA-based biomarker discovery strategy by integrating multi-cohort transcriptomic resources. Public GEO datasets (GSE164191 and GSE11545) were harmonized and analyzed, yielding 956 differentially expressed genes (DEGs). Multi-layer biological filtering incorporating PPI networks, transcription factors, CRC-related GWAS variants, whole-blood eQTL signals, DigSeE, and CoReCG disease associations refined these to 375 high-confidence transcripts (WB-PADs). In parallel, RNA-seq analysis of a Korean cohort (10 CRC vs. 10 controls) identified 217 DEGs (WB-K). Cross-dataset convergence highlighted seven overlapping transcripts, and five candidates (

Indexed as

Biomarkers, TumorColorectal NeoplasmsComputational BiologyAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksGenome-Wide Association StudyHumansMaleMiddle AgedTranscriptomeBiomarkers, Tumorbiomarker discoverycirculating transcriptscolorectal cancerearly detectionliquid biopsyRNA-seqRT-qPCRwhole blood

Identifiers

PMID41373777
PMCPMC12692440

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LicenceCC BY
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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 OpenQuestion graph.