Evidence map›Paper›PMID 38845779›Full record

ArticleFrontiers in molecular biosciences2024

Bioinformatics study of the potential therapeutic effects of ginsenoside Rh3 in reversing insulin resistance.

Yayun Wang, Dongming Wu, Yongxin Wang, Jingwen Sun, Xiaona Wang, Yanqin Huang, Mingliang Sun

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2024. 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. Review
  2. 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

7 authors.

Yayun WangDepartment of Neurology, Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, Shandong, China.
Dongming WuDepartment of Geriatric Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Yongxin WangIntensive Care Unit II, Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, Shandong, China.
Jingwen SunCollege of Acupuncture and Massage, Beijing University of Chinese Medicine, Beijing, China.
Xiaona WangDepartment of Nephrology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Yanqin HuangDepartment of Endocrinology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Mingliang SunDepartment of Endocrinology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In recent years, the incidence of insulin resistance is increasing, and it can cause a variety of Metabolic syndrome. Ginsenosides have been clinically proven to improve fat metabolism and reduce insulin resistance, but their components and mechanism of action are still unclear. Objective: Ginsenoside, a bioactive compound derived from ginseng, exhibits significant potential in treating obesity, diabetes, and metabolic disorders. Despite evidence supporting its efficacy in ameliorating insulin resistance (IR) in obesity, the specific bioactive components and underlying mechanisms remain obscure. In this study, we endeavored to elucidate the potential molecular targets and pathways influenced by ginsenoside Rh3 (GRh3) to ameliorate IR in liver tissue. We employed a comprehensive approach that integrates system pharmacology and bioinformatics analysis. Materials and methods: Our methodology involved the identification of candidate targets for GRh3 and the profiling of differentially expressed genes (DEGs) related to IR in individuals with insulin resistance. The coalescence of candidate targets and DEGs facilitated the construction of a "GRh3-targets-disease" network for each tissue type, ultimately yielding 38 shared target genes. Subsequently, we conducted pathway enrichment analysis, established protein-protein interaction (PPI) networks, and identified hub targets among the GRh3 targets and IR-related DEGs. Additionally, we conducted animal experiments to corroborate the role of these hub targets in the context of GRh3. Results: Our investigation identified a total of 38 overlapping targets as potential candidates. Notably, our analysis revealed crucial hub targets such as EGFR, SRC, ESR1, MAPK1, and CASP3, alongside implicated signaling pathways, including those related to insulin resistance, the FoxO signaling pathway, the PPAR signaling pathway, and the IL-17 signaling pathway. This study establishes a robust foundation for the mechanisms underlying GRh3's efficacy in mitigating IR. Furthermore, these results suggest that GRh3 may serve as a representative compound within the ginsenoside family. Conclusion: This study elucidates the potential molecular targets and associated pathways through which GRh3 ameliorates IR, showcasing its multifaceted nature, spanning multiple targets, pathways, and mechanisms. These findings establish a robust foundation for subsequent experimental inquiries and clinical applications.

Indexed as

bioinformatics analysisginsenoside Rh3insulin resistancemetabolic syndromemolecular docking

Identifiers

PMID38845779
PMCPMC11153663

What Socratic holds

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LicenceCC BY
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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 Socratic graph.