Evidence map›Paper›PMID 39937284›Full record

ArticleClinical and experimental medicine2025

Identification of the entosis-related prognostic signature and tumour microenvironment in hepatocellular carcinoma on the basis of bioinformatics analysis and experimental validation.

Chen Wu, Shixu Fang, Liangliang Wu, Zhengcheng Mi, Yao Yin, Yuan Liao, Yongxiang Zhao, Tinghua Wang, Jintong Na

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Chen Wu *State Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Shixu Fang *State Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Liangliang WuState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Zhengcheng MiState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Yao YinState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Yuan LiaoState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Yongxiang ZhaoState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Tinghua WangLaboratory Animal Department, Kunming Medical University, Kunming, 650031, Yunnan, China. wangtinghua@vip.163.com.
Jintong NaState Key Laboratory of Targeting Oncology, National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-Targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi, 530021, China. najintong@sr.gxmu.edu.cn.

Funding

Scientific and Technological Innovation Major Base of Guangxi No.2022-36-Z05
6 · The paper itself

Abstract

Liver cancer ranks among the deadliest cancers worldwide. Entosis, a recently uncovered method of cell death, has not yet been fully explored for its relevance to HCC. A bioinformatics analysis was performed to determine the expression and mutational landscapes of Entosis-related genes (ERGs). A subset of differentially expressed Entosis-related genes (DEERGs) was generated. A risk model for entosis was subsequently constructed employing LASSO and Cox regression methodologies. The correlations among ERGs, genes associated with risk, the developed risk model, and the immune context of the tumour were explored. Furthermore, the study investigated the varying drug sensitivities between high-risk and slight-risk patient groups. The expression patterns of four pivotal risk genes were delineated via qRT‒PCR and WB. A prognostic model comprising four DEERGs (KIF18A, SPP1, LCAT and TRIB3) was developed. The ability of this model to predict the survival outcomes of patients with HCC was confirmed through receiver operating characteristic curve analysis. Patients were grouped according to their risk assessments, revealing that the low-risk population demonstrated a more favourable survival outcome than did the high-risk population. The high-risk population presented reduced tumour stroma, immune and ESTIMATE scores, along with an increased proportion of cancer stem cells and tumour mutation burden. Additionally, a connection between the risk model and the responsiveness of various chemotherapy drugs as well as the efficacy of immunotherapies in patients was noted. These findings provide significant guidance for the development of targeted clinical treatment strategies. qRT‒PCR and WB analysis revealed that the gene expression of KIF18A and SPP1 were elevated in HCCLM3 cells compared with that in THLE2 cells; whereas, the expression level of LCAT and TIRB3 was decreased. The four genes KIF18A, SPP1, LCAT and TRIB3 could effectively predict the survival prognosis of patients with liver cancer. KIF18A and SPP1 were elevated in HCC tissues compared with that in THLE2 cells.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsTumor MicroenvironmentBiomarkers, TumorComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisROC CurveBiomarkers, TumorEntosisHepatocellular carcinomaPrognostic signatureTumour microenvironment

Identifiers

PMID39937284
PMCPMC11821697

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.