Evidence map›Paper›PMID 34512330›Full record

ArticleFrontiers in pharmacology2021

Network Pharmacology-Based Analysis on the Potential Biological Mechanisms of Sinisan Against Non-Alcoholic Fatty Liver Disease.

Xiaoyi Wei, Weixin Hou, Jiajun Liang, Peng Fang, Bo Dou, Zisong Wang, Jiayang Sai, Tian Xu, Chongyang Ma, Qiuyun Zhang and 3 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  5. Review
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  7. Anti-Atopic Effect ofPharmaceuticals (Basel, Switzerland) · 2024
    Article
  8. Review
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  11. Review
  12. Article
  13. The therapeutic mechanism ofFrontiers in veterinary science · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Systematic Analysis Strategy Based on Network Pharmacology to Investigate the Potential Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2022
    Article
  19. 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.

Xiaoyi WeiSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Weixin HouSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Jiajun LiangSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Peng FangSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Bo DouSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Zisong WangDepartment of Traditional Chinese Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Jiayang SaiDepartment of Oncology, The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China.
Tian XuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Chongyang MaSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Qiuyun ZhangSchool of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Fafeng ChengSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Xueqian WangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Qingguo WangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-alcoholic fatty liver disease (NAFLD) has become the most prevalent liver disease in China. Sinisan (SNS) is a traditional Chinese medicine formula that has been widely used in treating chronic liver diseases, including NAFLD. However, its underlying biological mechanisms are still unclear. In this study, we employed a network pharmacology approach consisting of overlapped terms- (genes or pathway terms-) based analysis, protein-protein interaction (PPI) network-based analysis, and PPI clusters identification. Unlike the previous network pharmacology study, we used the shortest path length-based network proximity algorithm to evaluate the efficacy of SNS against NAFLD. And we also used random walk with restart (RWR) algorithm and Community Cluster (Glay) algorithm to identify important targets and clusters. The screening results showed that the mean shortest path length between genes of SNS and NAFLD was significantly smaller than degree-matched random ones. Six PPI clusters were identified and ten hub targets were obtained, including STAT3, CTNNB1, MAPK1, MAPK3, AGT, NQO1, TOP2A, FDFT1, ALDH4A1, and KCNH2. The experimental study indicated that SNS reduced hyperlipidemia, liver steatosis, and inflammation. Most importantly, JAK2/STAT3 signal was inhibited by SNS treatment and was recognized as the most important signal considering the network pharmacology part. This study provides a systems perspective to study the relationship between Chinese medicines and diseases and helps to discover potential mechanisms by which SNS ameliorates NAFLD.

Indexed as

JAK2/STAT3NAFLDnetwork pharmacologyprotein-protein interaction (PPI) networkSinisantopological analysis

Identifiers

PMID34512330
PMCPMC8430321

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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.