Evidence map›Paper›PMID 39748947›Full record

ArticleFrontiers in genetics2024

A comprehensive analysis of vasculogenic mimicry related genes to predict the survival rate of HCC and its influence on the tumor microenvironment.

Jingyun Wang, Rong Gao, Jian Qi, Yingru Xing, Bo Hong, Hongzhi Wang, Jinfu Nie

Abstract read
In one paragraph

Article in Frontiers in genetics, 2024. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jingyun WangSchool of Medicine, Anhui University of Science and Technology, Huainan, China.
Rong GaoSchool of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China.
Jian QiInstitute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Yingru XingSchool of Medicine, Anhui University of Science and Technology, Huainan, China.
Bo HongInstitute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Hongzhi WangInstitute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Jinfu NieSchool of Medicine, Anhui University of Science and Technology, Huainan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Investigate the predictive value of Vasculogenic mimicry (VM) related genes for the survival and prognosis of Hepatocellular carcinoma (HCC) patients and its role in the tumor microenvironment (TME). Methods: VM-related genes were obtained from previous literature, the expression profiles, single-cell data and clinical information of HCC patients were downloaded from public databases. The HCC patients were divided into different clusters by unsupervised clustering, the differences in prognosis and immune characteristics of VM-related clusters were analyzed. A prognostic model related to VM (VM Score) was constructed based on LASSO regression and univariate and multivariate Cox regression, the correlation between this model and chemotherapy drugs and immunotherapy was studied. Seurat package was used to standardize single-cell data for single-cell level analysis. The expression of risk factors in VM Score was verified by RT-qPCR. Results: VM Score composed of SPP1, ADAMTS5 and ZBP1 was constructed and validated. VM Score was an independent prognostic factor for HCC. Through the analysis of single cell data further reveals the VM Score influence on TME. In addition, VM Score could provide ideas for the selection of immunotherapy and chemotherapy drugs. RT-qPCR showed that the expression of risk factors was different in HCC cell lines. Conclusion: Our results suggest that VM Score may serve as a promising prognostic biomarker for HCC and provide new ideas for immunotherapy in HCC patients.

Indexed as

biological informationhepatocellular carcinomamolecular pathologytumor microenvironmentvasculogenic mimicry

Identifiers

PMID39748947
PMCPMC11693674

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