Evidence map›Paper›PMID 42432583›Full record

ArticleBMC cancer2026

Uncovering the mechanism of DCLK3 in colorectal cancer progression through WGCNA and machine learning.

Lu Yang, Yanru Han, Juanjuan Ji, Yuyu Wang, Fang Yang, Wenjing Li, Xiaohe Guo

Abstract read
In one paragraph

Article in BMC cancer, 2026. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lu YangDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China.
Yanru HanXinxiang Key Laboratory of Precision Diagnosis and Treatment for Inflammatory Bowel Disease, Xinxiang, Henan, 453100, China.
Juanjuan JiDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China.
Yuyu WangDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China.
Fang YangDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China.
Wenjing LiDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China.
Xiaohe GuoDepartment of Gastroenterology, The First Affiliated Hospital of Henan, University of Medicine, Xinxiang, Henan, 453100, China. 15294857353@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) is a prevalent malignant tumor of the digestive tract with high morbidity and mortality rates. Although doublecortin like kinase 3 (DCLK3) is upregulated in CRC and promotes disease progression, its specific mechanisms remain unclear.

methodsCore factors of CRC were identified through bioinformatics analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms. The expression level of DCLK3 and ELK1 were evaluated using bioinformatics databases (GSE142279, The Cancer Genome Atlas (TCGA)) and experimental validation (quantitative real-time PCR (qRT-PCR), Western blot). Functional assays (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, flow cytometry, Transwell assay, wound healing assay, sphere formation assay, tube formation assay, in vivo model, hematoxylin and eosin staining (HE), and immunohistochemistry (IHC)) and mechanism studies (prediction via Genecards and JASPAR databases, dual-luciferase reporter assay, and chromatin immunoprecipitation (ChIP)) were performed to validate and explore the molecular mechanism of DCLK3 in CRC.

resultsBioinformatics analysis combined with WGCNA and machine learning algorithms identified DCLK3 as a core gene. DCLK3 was significantly upregulated in CRC, and its high expression was associated with poorer overall survival in patients. Knockdown of DCLK3 inhibited the proliferation of CRC cells, promoted apoptosis, and suppressed cell invasion, migration, cancer stemness, and angiogenesis. ETS like-1 protein (ELK1) promoted malignant phenotypes in CRC cells by transcriptionally activating DCLK3. Also, in vivo studies demonstrated that ELK1 activated DCLK3 to facilitate CRC tumor growth.

conclusionDCLK3, identified through bioinformatic analysis, promotes CRC progression via its transcriptional activation by the transcription factor ELK1, highlighting its potential as a therapeutic target.

Indexed as

Colorectal NeoplasmsMachine LearningProtein Serine-Threonine KinasesAnimalsCell Line, TumorCell MovementCell ProliferationComputational BiologyDisease ProgressionDoublecortin-Like Kinasesets-Domain Protein Elk-1FemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMiceDoublecortin-Like KinasesELK1 protein, humanets-Domain Protein Elk-1Protein Serine-Threonine KinasesColorectal cancerDoublecortin like kinase 3ETS like-1 protein

Identifiers

PMID42432583
PMCPMC13602744

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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.