Evidence map›Paper›PMID 40265593›Full record

ArticleInternational journal of immunopathology and pharmacology

Construction of a tumor immune microenvironment-related risk scoring model for prognosis of hepatocellular carcinoma.

Xinyi Li, Zifan Qin, Haozhi Chen, Daichuan Chen, Nafisa Alimu, Duoduo Li, Xiyu Cheng, Qiong Yan, Lishu Zhang, Xingwei Liu and 6 more

Abstract read
In one paragraph

Article in International journal of immunopathology and pharmacology. 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. Article
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

16 authors.

Xinyi LiCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Zifan QinCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Haozhi ChenCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Daichuan ChenCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Nafisa AlimuSchool of Stomatology, Xinjiang Medical University, Urumqi, Xinjiang, China.
Duoduo LiCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Xiyu ChengCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Qiong YanCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Lishu ZhangCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Xingwei LiuCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Zitong ZhouCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Jiayi ZhuCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Hangqi MaCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Xinyue PeiCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Hanli XuCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.
Jiaqiang HuangCollege of Life Sciences & Bioengineering, Beijing Jiaotong University, Beijing, China.ORCID 0000-0002-6610-8159

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to develop a prognostic model for HCC based on TME-related factors.

introductionHepatocellular carcinoma (HCC) is characterized by a poor prognosis, largely due to the complex and heterogeneous interactions between stromal and immune cells within the tumor microenvironment (TME).

methodsGenome and transcriptome data, as well as clinical information of HCC patients, were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The TME score was evaluated using the "ESTIMATE" R package. Differentially expressed genes (DEGs) associated with TME phenotype were analyzed using the LIMMA R-package. Survival outcomes were compared using Kaplan-Meier curves with log-rank test and Cox proportional hazards model. Protein-Protein Interaction (PPI) networks integrated with multivariate survival and LASSO analyses were utilized to identify TME-related hub genes for a risk score model. A nomogram predicting prognosis of HCC patients was developed through four independent cohorts.

resultsThe TME scores showed a negative correlation with tumor progression and survival in HCC patients. We identified 50 core genes with high connectivity in the PPI network, as along with 33 key DEGs associated with survival in HCC. Intersection analysis revealed six hub genes -

conclusionWe have developed a TME-related risk scoring model for HCC patients and identified six hub gene panel that serve as a potential biomarker for personalized prognosis of immunotherapy and non-invasive diagnostics of HCC.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularLiver NeoplasmsTumor MicroenvironmentFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMaleMiddle AgedNomogramsPrognosisProtein Interaction MapsRisk AssessmentRisk FactorsBiomarkers, Tumordifferentially expressed geneshepatocellular carcinomaprognosisrisk scoretumor microenvironment

Identifiers

PMID40265593
PMCPMC12035210

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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.