A real-world study and network pharmacology analysis of EGFR-TKIs combined with ZLJT to delay drug resistance in advanced lung adenocarcinoma.

OBJECTIVE: This study aimed to explore the efficacy and safety of combining epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with ZiLongJin Tablet (ZLJT) in delaying acquired resistance in advanced EGFR-mutant lung adenocarcinoma (LUAD) patients. Furthermore, we employed network pharmacology and molecular docking techniques to investigate the underlying mechanisms. METHODS: A retrospective comparative study was conducted on stage IIIc/IV LUAD patients treated with EGFR-TKIs alone or in combination with ZLJT at the Second Affiliated Hospital of the Air Force Medical University between January 1, 2017, and May 1, 2023. The study evaluated the onset of TKI resistance, adverse reaction rates, safety indicators (such as aspartate aminotransferase, alanine aminotransferase, and creatinine), and inflammatory markers (neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio) to investigate the impact of EGFR-TKI combined with ZLJT on acquired resistance and prognostic indicators. Additionally, we utilized the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine, PubChem, UniProt, and Swiss Target Prediction databases to identify the active ingredients and targets of ZLJT. We obtained differentially expressed genes related to EGFR-TKI sensitivity and resistance from the Gene Expression Omnibus database using the GSE34228 dataset, which included sensitive (n = 26) and resistant (n = 26) PC9 cell lines. The "limma" package in R software was employed to detect DEGs. Based on this, we constructed a protein‒protein interaction network, performed gene ontology and KEGG enrichment analyses, and conducted pathway network analysis to elucidate the correlation between the active ingredients in ZLJT and signaling pathways. Finally, molecular docking was performed using AutoDockVina, PYMOL 2.2.0, and Discovery Studio Client v19.1.0 software to simulate spatial and energy matching during the recognition process between predicted targets and their corresponding compounds. RESULTS: (1) A total of 89 patients were included, with 40 patients in the EGFR-TKI combined with ZLJT group (combination group) and 49 patients in the EGFR-TKI alone group (monotherapy group). The baseline characteristics of the two groups were comparable. There was a significant difference in the onset of resistance between the combination group and the monotherapy group (P < 0.01). Compared to the monotherapy group, the combination group showed a prolongation of 3.27 months in delayed acquired resistance. There was also a statistically significant difference in the onset of resistance to first-generation TKIs between the two groups (P < 0.05). (2) In terms of safety analysis, the incidence of adverse reactions related to EGFR-TKIs was 12.5% in the combination group and 14.3% in the monotherapy group, but this difference was not statistically significant (P > 0.05). There were no statistically significant differences in serum AST, ALT, CREA, TBIL, ALB and BUN levels between the two groups after medication (P > 0.05). (3) Regarding inflammatory markers, there were no statistically significant differences in the changes in neutrophil-to-lymphocyte Ratio(NLR) and Platelet-to-lymphocyte Ratio(PLR) values before and after treatment between the two groups (P > 0.05). (4) Network pharmacology analysis identified 112 active ingredients and 290 target genes for ZLJT. From the GEO database, 2035 differentially expressed genes related to resistant LUAD were selected, and 39 target genes were obtained by taking the intersection. A "ZLJT-compound-target-disease" network was successfully constructed using Cytoscape 3.7.0. GO enrichment analysis revealed that ZLJT mainly affected biological processes such as adenylate cyclase-modulating G protein-coupled receptor. In terms of cellular components, ZLJT was associated with the cell projection membrane. The molecular function primarily focused on protein heterodimerization activity. KEGG enrichment analysis indicated that ZLJT exerted its antitumor and anti-drug resistance effects through pathways such as the PI3K-Akt pathway. Molecular docking showed that luteolin had good binding activity with FOS (-9.8 kJ/mol), as did tanshinone IIA with FOS (-9.8 kJ/mol) and quercetin with FOS (-8.7 kJ/mol). CONCLUSION: ZLJT has potential antitumor progression effects. For patients with EGFR gene-mutated non-small cell LUAD, combining ZLJT with EGFR-TKI treatment can delay the occurrence of acquired resistance. The underlying mechanisms may involve altering signal transduction pathways, blocking the tumor cell cycle, inhibiting tumor activity, enhancing cellular vitality, and improving the bioavailability of combination therapy. The combination of EGFR-TKI and ZLJT represents an effective approach for the treatment of tumors using both Chinese and Western medicine.

Mechanisms of JinHong Formula on treating sepsis explored by randomized controlled trial combined with network pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: JinHong Formula (JHF) was derived from the famous Rhubarb and Moutan Decoction which was prescribed for appendicitis. It was originally recorded in the classic of "Jingui Yaolve" written by Zhang Zhongjing. It is a kind of traditional Chinese medicine, widely used in the treatment of inflammation. However, the clinical effect of JHF for sepsis and its comprehensive mechanism in sepsis remained largely unknown. RESEARCH PURPOSE: The aim of our study was to evaluate the clinical effect of JHF in the treatment of sepsis, and to explore its mechanism from the perspective of network pharmacology. RESEARCH METHODS: The single-center randomized clinical trial was conducted to assess the effect of JHF in the treatment of sepsis. Additionally, we used the Chinese herbal medicine pharmacology database and analysis platform to identify the active components and therapeutic target of JHF. Numerous well-known disease target databases have been used to screen therapeutic target proteins for sepsis. Furthermore, we have established a Protein-Protein Interaction (PPI) network and carried out Gene Onotology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment analysis. In order to conclude which active compounds from JHF may be responsible for signaling pathway, we performed network analysis. RESEARCH RESULTS: The study included 114 patients. By comparing participants with and without JHF, the results suggested that JHF significantly reduced all-cause mortality on 28 and 60 days after intervention, and improved Sequential Organ Failure Assessment (SOFA) on 7th day after intervention as well as. JHF had an effect of anti-inflammatories and antioxidants (SOD). By using network pharmacological analysis, we identified 72 active components and 426 target genes of JHF, and successfully constructed a "JHF-compound target-sepsis" network. 116 mentioned targets revealed by GO/KEGG enrichment analysis played a significant role in the inflammatory reaction and immunoregulation via interleukin-17 (IL-17) and tumor necrosis factor (TNF) signaling pathway. Moreover, the analysis of "pathway target-active component" revealed that Sennidin A, Rheidin A, Rheidin B, Rheidin C, (E)-4-Phenyl-3-Buten-2-One, Osmanthuside H, Esculetin, and Caffeicacid were responsible for IL-17, TNF signaling pathways. CONCLUSION: JHF contains potential active substance of anti-inflammatory and antioxidant. These active compounds may come into play through IL-17 and TNF signaling pathways. For sepsis, JHF may be a promising and effective treatment strategy.

[Analysis of CDSS Experimental Indexes in 333 Cases of Newly Diagnosed Acute Leukemia].

OBJECTIVE: To analyze the 5 experimental indexes of CDSS in the patients with acute leukemia (AL) so as to provide the laboratorial basis for the early diagnosis and treatment of the secondary DIC in AL. METHODS: Three hundred and thirty three patients with AL were divided into 7 groups, such as AML-M1-M5, other AML and ALL. The experimental indexes and CDSS scores of all AL groups were compared and analyzed in pairs, meanwhile 100 healthy persons were taken in the control group. Clinical events such as early death in all cases were also analyzed. RESULTS: The highest positive rate of Platelet was 59.76%, among the 5 experimental indexes, followed by D-D (30.93%), and the lowest APTT with only 2.70%. Compared with the control group, the differences of remaining indexes were statistically significant (P＜0.01), except APTT in group AML-M3 and FIB in the other AML groups. The score of laboratory index was (1.50±1.51) in all AL patients, and the positive rate of overt DIC ( score≥4) was 14.11% ( 47 cases). The highest score of CDSS was (3.34 ±1.71) in group AML-M3. The difference in the incidence of early death and cerebral (pulmonary) hemorrhage in DIC patients were statistically significant (P＜0.05 and P＜0.01). CONCLUSION: The application of quantitative integral method of experimental indexes in CDSS is objective and feasible, which is of great significance for early diagnosis and early treatment of acute leukemia complicated with DIC.