Evidence map›Paper›PMID 35190740›Full record

ArticleJournal of oncology2022

Targets and Potential Mechanism of

Defu Liu, Zhengjun Wang, Li Zhong, Caoyu Xie, Xiaonan Huang, Yaofeng Zhi, Yuzhuo Zhang, Jiaying Liang, Zhenni Shi, Jin Huang and 3 more

Abstract read
In one paragraph

Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Recent advances inHeliyon · 2024
    Review
  5. Article
  6. 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

13 authors.

Defu LiuGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Zhengjun WangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Li ZhongShenzhen Hospital, Guangzhou University of Chinese Medicine (Futian), Guangzhou, China.
Caoyu XieGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Xiaonan HuangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Yaofeng ZhiGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Yuzhuo ZhangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Jiaying LiangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Zhenni ShiGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Jin HuangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Shuhe ZhangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.
Jin ZhangGuangzhou University of Chinese Medicine, School of Basic Medical Sciences, Guangzhou, China.ORCID https://orcid.org/0000-0003-1224-9455
Fuping DingGuangzhou University of Chinese Medicine, School of Nursing, Guangzhou, China.ORCID https://orcid.org/0000-0002-3445-8912

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze the target and potential mechanism of

methodsThe regulated gene targets of SB were screened by gene expression pattern clustering and differential analysis of gene expression data of HepG2 cells treated with SB at 0 h, 1 h, 3 h, 6 h, 12 h, and 24 h. The module genes related to HCC were identified by the weighted gene coexpression network analysis (WGCNA). KEGG and GO enrichment were used to analyze the molecular function and structure of the module, and GSEA was used to evaluate the different functional pathways between normal people and patients with HCC. Then, the module gene was used for univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to build a prognostic model. The protein-protein interaction network (PPI) was used to analyze the core genes regulated by SB (CGRSB) of the module, and the survival curve revealed the CGRSB impact on patient survival. The CIBERSORT algorithm combined with correlation analysis to explore the relationship between CGRSB and immune infiltration. Finally, the single-cell sequencing technique was used to analyze the distribution of CGRSB at the cellular level.

resultsSB could regulate 903 genes, of which 234 were related to the occurrence of HCC. The prognosis model constructed by these genes has a good effect in evaluating the survival of patients. KEGG and GO enrichment analysis showed that the regulation of SB on HCC mainly focused on some cell proliferation, apoptosis, and immune-related functions. GSEA enrichment analysis showed that these functions are related to the occurrence of HCC. A total of 24 CGRSB were obtained after screening, of which 13 were significantly related to survival, and most of them were unfavorable factors for patient survival. The correlation analysis of gene expression showed that most of CGRSB was significantly correlated with T cells, macrophages, and other functions. The results of single-cell analysis showed that the distribution of CGRSB in macrophages was the most.

conclusionSB has high credibility in the treatment of HCC, such as CDK2, AURKB, RRM2, CENPE, ESR1, and PRIM2. These targets can be used as potential biomarkers for clinical diagnosis. The research also shows that the p53 signal pathway, MAPK signal pathway, apoptosis pathway, T cell receptor pathway, and macrophage-mediated tumor immunity play the most important role in the mechanism of SB in treating HCC.

Identifiers

PMID35190740
PMCPMC8858046

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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.