Evidence map›Paper›PMID 40433415›Full record

ArticleCurrent genomics2025

CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer.

Xingyu He, Jun Ma, Xue Yan, Xiangyu Yang, Ping Wang, Lijie Zhang, Na Li, Zheng Shi

Abstract read
In one paragraph

Article in Current genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–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

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  13. Deregulated miR-487b-3p in Patients with Non-Alcoholic Fatty Liver Disease and Its Regulatory Effect on Insulin Resistance.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2026
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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.

Xingyu HeClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
Jun MaClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
Xue YanClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
Xiangyu YangWest China Hospital, Sichuan University, 610083, P.R. China.
Ping WangWest China Hospital, Sichuan University, 610083, P.R. China.
Lijie ZhangClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
Na LiClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
Zheng ShiClinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression. Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients. Objectives: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition. Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC. Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups. Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

Indexed as

CDT1early diagnosishepatocellular carcinomamachine learningNonalcoholic fatty liver diseasesingle-cell sequencing

Identifiers

PMID40433415
PMCPMC12107793

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LicenceCC BY
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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.