ArticleJournal of molecular histology2026
Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
42470494What Socratic 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 Socratic graph.