Evidence map›Paper›PMID 42470494›Full record

ArticleJournal of molecular histology2026

Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning.

Song Zheng, Lei Meng, Xiaoye Fan, Xiaoqing He

Abstract read
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Article in Journal of molecular histology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

4 authors.

Song ZhengDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Xingtai Medical University, 376 Shunde Road, Xiangdu District, Xingtai City, 054000, Hebei Province, China. 15227869283@163.com.
Lei MengDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Xingtai Medical University, 376 Shunde Road, Xiangdu District, Xingtai City, 054000, Hebei Province, China.
Xiaoye FanDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Xingtai Medical University, 376 Shunde Road, Xiangdu District, Xingtai City, 054000, Hebei Province, China.
Xiaoqing HeDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Xingtai Medical University, 376 Shunde Road, Xiangdu District, Xingtai City, 054000, Hebei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Understanding the complex molecular networks that underlie this aggressive behavior is critical for developing novel diagnostic and therapeutic strategies. This study aimed to identify key molecular regulators of CRC progression by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning algorithms. Hub genes were initially identified by intersecting genes from the most significant module with CRC-related and glycolysis-related targets from the GeneCards database, as well as upregulated differentially expressed genes (DEGs) from the GSE113513 dataset. Lasso regression and random forest (RF) algorithms were employed to screen for key genes from this intersection. The expression of the identified key gene was validated using quantitative real-time PCR (qRT-PCR) and Western blotting. Functional assays, including Cell Counting Kit-8 (CCK-8), colony formation, Transwell invasion, flow cytometry, and metabolic analyses, were conducted to analyze the malignant behaviors of CRC cells. The regulatory relationship between cyclin dependent kinase 1 (CDK1) and transcription factor AP-4 (TFAP4) was validated through chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays. A xenograft mouse model was used to evaluate the effect of TFAP4 knockdown on the malignant progression of CRC cells in vivo. WGCNA and machine learning analyses identified three key genes: MET, MYC, and CDK1. CDK1 was selected for further investigation and found to be significantly upregulated in CRC tissues and cell lines. Functionally, CDK1 knockdown markedly inhibited CRC cell proliferation, invasion, and glycolysis while promoting apoptosis. Mechanistically, the transcription factor TFAP4 was identified as an upstream regulator that directly activated CDK1 transcription. Moreover, CDK1 and TFAP4 expression were associated with metastatic stage. TFAP4 exerted its oncogenic effects by positively regulating CDK1. Furthermore, silencing TFAP4 significantly suppressed tumor growth in vivo. This study establishes the TFAP4-CDK1 axis as a critical driver of malignant progression in CRC. Targeting this pathway could lead to the development of novel interventions for CRC.

Indexed as

Colorectal NeoplasmsGene Expression Regulation, NeoplasticGene Regulatory NetworksMachine LearningAnimalsCDC2 Protein KinaseCell Line, TumorCell ProliferationDisease ProgressionGene Expression ProfilingHumansMiceCDC2 Protein KinaseColorectal cancerCyclin dependent kinase 1Machine learning algorithmsTranscription factor AP-4Weighted gene co-expression network analysis

Identifiers

PMID42470494

What Socratic holds

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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.