Evidence map›Paper›PMID 36860325›Full record

ArticleFrontiers in oncology2023

Metabolism-related signatures is correlated with poor prognosis and immune infiltration in hepatocellular carcinoma

Jiapei Shen, Weijie Sun, Jiaying Liu, Jiali Li, Ying Li, Yufeng Gao

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Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

6 authors.

Jiapei ShenDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Weijie SunDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jiaying LiuDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jiali LiDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Ying LiDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yufeng GaoDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolism is an ordered series of biological processes that occur in an organism. Altered cellular metabolism is often closely associated with the development of cancer. The aim of this research was to construct a model by multiple metabolism-related molecules to diagnose and assess the prognosis of patients. Method: WGCNA analysis was used to screen out differential genes. GO, KEGG are used to explore potential pathways and mechanisms. The lasso regression model was used to filter out the best indicators to construct the model. Single-sample GSEA (ssGSEA) assess immune cells abundance, immune terms in different Metabolism Index (MBI) groups. Human tissues and cells were used to verify the expression of key genes. Result: WGCNA clustering grouped genes into 5 modules, of which 90 genes from the MEbrown module were selected for subsequent analysis. GO analysis was found that BP mainly has mitotic nuclear division, while KEGG pathway is enriched to Cell cycle, Cellular senescence. Mutation analysis revealed that the frequency of TP53 mutations was much higher in samples from the high MBI group than in the low MBI group. Immunoassay revealed that patients with higher MBI have higher macrophage and Regulatory T cells (Treg) abundance, while NK cells were lowly expressed in the high MBI group. RT-qPCR and immunohistochemistry (IHC) revealed that the hub genes expression is higher in cancer tissues. The expression in hepatocellular carcinoma cells was also much higher than that in normal hepatocytes. Conclusion: In conclusion, a metabolism-related model was constructed that can be used to estimate the prognosis of hepatocellular carcinoma, and the clinical treatment of different hepatocellular carcinoma patients with medications was guided.

Indexed as

chemotherapyhepatocellular carcinomaimmune infiltrationmetabolismprognostic model

Identifiers

PMID36860325
PMCPMC9969091

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