Evidence map›Paper›PMID 40820150›Full record

ArticleScientific reports2025

FEN1 plays a key role in the transition from HSIL to CSCC.

Fengxian Zhang, Xiaomei Chen, Chenyang Chu, Meilin Chen, Ying Yang, Yanting You, Jie Zhou, Pingping Jiang, Ni Song, Yunhong Yu and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Fengxian Zhang *School of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Xiaomei Chen *School of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Chenyang Chu *School of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Meilin Chen *School of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Ying YangSchool of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Yanting YouSchool of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Jie ZhouSchool of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China.
Pingping JiangDepartment of Traditional Chinese Medicine, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, China.
Ni SongThe First Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China. 1songsun@163.com.
Yunhong YuGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Geriatric Institute, Southern Medical University, Guangzhou, 510080, China. yuyunhong123@126.com.
Yanyan LiuSchool of Traditional Chinese Medicine, Southern Medical University, Baiyun District, Guangzhou, 510515, China. Liuyyan90@163.com.

Funding

Guangdong Provincial Administration of Traditional Chinese Medicine Project 20231004National Science Foundation of China 82074289,81873205,82104910
6 · The paper itself

Abstract

Cervical high-grade squamous intraepithelial lesion (HSIL) is a well-recognized precursor to cervical squamous cell carcinoma (CSCC). This study aims to explore molecular mechanisms underlying this transition. The datasets of HSIL and CSCC were downloaded from gene expression omnibus database (GEO). HSIL and CSCC coexpression modules were analyzed by Weighted gene coexpression network analysis (WGCNA). Enrichment analysis were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). The immune microenvironment (TIME) was analyzed by CIBERSORT. Receiver operating characteristic (ROC) curve, and survival analysis were performed. The binding of FEN1 and its upstream regulator PCNA was determined by protein interaction, correlation analysis and rigid docking. Immune infiltration, immune checkpoint and docking with small molecule inhibitors was also analyzed. In vitro experiments were conducted to investigate the function and underlying mechanisms of FEN1 in HSIL and CSCC. A total of 30 sequentially expressed shared genes were identified as potential drug targets. Immune infiltration revealed an imbalance of M1/M2 macrophages, T γδ cells and T cells CD4 memory resting were negatively associated with tumor progression, forming a pro-cancer effect at the HSIL stage and progressing toward CSCC. The key gene FEN1 was over-expressed in HSIL and CSCC, and correlated with the prognosis of CSCC (P < 0.05). FEN1 interacts with PCNA to regulate macrophage-mediated immune infiltration, increase the risk of immune escape, and decrease the sensitivity to immune checkpoints. Small molecule inhibitor binds to FEN1 docking and inhibits its regulation. FEN1 expression is upregulated in both HSIL and CSCC, with a more pronounced increase in CSCC. Knockdown of FEN1 significantly inhibits the proliferation of cervical cancer cells. This study identifies FEN1 as a biomarker and therapeutic target to truncate or reverse CSCC progression at the HSIL stage.

Indexed as

Carcinoma, Squamous CellFlap EndonucleasesUterine Cervical DysplasiaUterine Cervical NeoplasmsCell Line, TumorDisease ProgressionFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansProliferating Cell Nuclear AntigenTumor MicroenvironmentFEN1 protein, humanFlap EndonucleasesPCNA protein, humanProliferating Cell Nuclear AntigenCervical squamous cell carcinomaFlap endonuclease 1High-grade squamous intraepithelial lesionImmune infiltrationPotential biomarker

Identifiers

PMID40820150
PMCPMC12358609

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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.