ArticleJournal of traditional Chinese medicine = Chung i tsa chih ying wen pan2022
Mechanism underlying the effect of Liujunzi decoction on advanced-stage non-small cell lung cancer in patients after first-line chemotherapy.
Article in Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Therapeutic potential of modified Yukgunja-tang (Liujunzi Decoction, Rikkunshito) as an adjuvant treatment for lung cancer: a systematic review and meta-analysis.Frontiers in pharmacology · 2025Pooled it
- Dujieqing decoction suppresses multiple myeloma growth by inhibiting the Wnt/β-catenin pathway.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- Combination of inflammatory proteins in serum can be used to diagnose papillary thyroid carcinoma with lymph node metastasis.Discover oncology · 2025Article
- Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.BMC medical genomics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo further clarify the anticancer mechanisms of Liujunzi decoction and provide possible targets for the treatment of advanced-stage nonsmall cell lung cancer (NSCLC) by re-analyzing differential gene expression profile of peripheral blood mononuclear cells (PBMCs) from Liujunzi decoctiontreated NSCLC patients receiving first-line chemotherapy.
methodsThe PBMC gene expression microarray data set GSE61926 was retrieved from a high throughput gene expression database. Differentially expressed genes (DEGs) were screened by paired sample t-test and the multiple ratio method. Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed using the DAVID database. The protein-protein interaction (PPI) network was constructed using interaction gene library retrieval tools and Cytoscape software.
resultsA total of 162 DEGs were identified, with 67 upregulated genes and 95 downregulated genes. The functional distribution of Gene Oncology (GO) genes showed that DEGs were mostly concentrated in extracellular regions, calcium ion binding, and transcriptase activity. KEGG pathway analysis showed that cytokine-cytokine receptor interactions were significantly enriched. PPI network analysis screened out the top 10 central protein-coding genes with the highest nodal degree: IL2, PIWIL4, DICER1, PIWIL2, SAA1, XCL1, IL22RA1, ARHGAP11A, DCP1A, and GDNF. Among them, the central protein-coding gene with the highest node degree was IL2. In addition, the central protein-coding genes with high node degrees and high molecular complex detection (MCODE) scores were PIWIL4, DICER1, PIWIL2, and DCP1A, all of which are related to tumor development.
conclusionsOne signaling pathway and 10 central protein-coding genes related to anticancer mechanisms were screened by re-analysis of GSE61926 data. IL2, PIWIL4, DICER1, PIWIL2, and DCP1A may have important roles in the mechanism of Liujunzi decoction treatment against NSCLC. Our results suggest that the anticancer mechanism of Liujunzi decoction may be related to gene silencing by RNA and the biological processes of piwi-interacting RNA and other small RNAs.
Indexed as
Identifiers
35294130PMC10164627W4220818144What OpenQuestion holds
Registered trials
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.