Role and molecular mechanisms of HuangQiSiJunZi decoction for treating triple-negative breast cancer as explored via network pharmacology and bioinformatics analyses.
In this study, we evaluated the molecular mechanisms of HuangQiSiJunZi Decoction (HQSJZD) for treating triple-negative breast cancer (TNBC) using network pharmacology and bioinformatics analyses.
Effective chemical components together with action targets of HQSJZD were selected based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Meanwhile, differentially expressed genes (DEGs) were extracted from TNBC sample data in The Cancer Genome Atlas (TCGA) database. Additionally, we built a protein-protein interaction (PPI) network and acquired hub genes. Gene Expression Omnibus(GEO) datasets were utilized to verify the accuracy of hub gene expression. Additionally, enrichment analyses were conducted on key genes. Furthermore, TNBC severity-related high-risk factors were screened through univariate together with multivariate Cox regressions; next, the logistic regression prediction model was built. Moreover, differential levels of 22 immune cell types in TNBC tissues compared with normal tissues were analyzed. The hub gene levels within pan-cancer and the human body were subsequently visualized and analyzed. Finally, quantitative PCR (RT-qPCR) was used to validate the correlation of the hub genes in TNBC cells.
The study predicted 256 targets of active ingredients and 1791 DEGs in TNBC, and obtained 16 hub genes against TNBC. The prognostic signature based on FOS, MMP9, and PGR was independent in predicting survival. A total of seven types of immune cells, such as CD4 + memory T cells, showed a significant difference in infiltration (p < 0.05), and immune cells were related to the hub genes. The HPA database was adopted for hub gene analyses, and as determined, FOS was highly expressed in most human organs. The results of RT-qPCR validation for the FOS hub gene were consistent with those of bioinformatic analyses.
HQSJZD might regulate the interleukin-17 and aging pathways via FOS genes to increase immune cell infiltration in TNBC tissues, and thus, may treat TNBC and improve the prognosis. The FOS genes are likely to be a new marker for TNBC.
Wang D
，Xie D
，Meng S
，Mi J
，Wang H
，Li L
，Zhang Y
，Cui Y
... -
《BMC CANCER》
Virtual screening of the multi-gene regulatory molecular mechanism of Si-Wu-tang against non-triple-negative breast cancer based on network pharmacology combined with experimental validation.
Si-Wu-Tang (SWT), a prestigious herbal formula from China, has been extensively used for centuries for female-related diseases. It has been documented that SWT has a significant inhibitory effect on non-triple-negative breast cancer (non-TNBC) cells. However, there has been limited comprehensive analysis of the targeted effects of the anticancer components of SWT and its exact biological mechanism.
This study aims to uncover the mechanism by which SWT treats non-TNBC by applying a network pharmacological method combined with experimental validation.
First, SWT compounds were collected from the Traditional Chinese Medicines Systems Pharmacology database (TCMSP) and The Encyclopedia of Traditional Chinese Medicine (ETCM), and then the targets related to SWT were obtained from the TCMSP and SwissTarget databases. Second, a target data set of non-TNBC proteins was established by using the Online Mendelian Inheritance in Man (OMIM), GeneCards and Gene Expression Omnibus (GEO) databases. Third, based on the overlap of targets between SWT and non-TNBC, a protein-protein interaction (PPI) network was built to analyse the interactions among these targets, which focused on screening for hub targets by topology. On these hub genes, we conducted a meta-analysis and survival analysis to screen the best match targets, ESR1, PPARG, CAT, and PTGS2, which had a strong correlation with the ingredients of SWT in our verification by molecular docking. In vitro experiments further proved the reliability of the network pharmacology findings. Finally, FunRich software and the ClusterProfiler package were utilized for the enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) data.
A total of 141 active ingredients and 116 targets of SWT were selected. GO enrichment analysis showed that the biological processes through which SWT acted against non-TNBC (FDR<0.01) mainly involved modulating energy metabolism and apoptosis. According to RT-qPCR and Western blotting, the mRNA and protein expression of ESR1, PPARG and PTGS2 were upregulated (P < 0.01), and the mRNA and protein levels of CAT were downregulated (P < 0.01), suggesting a multi-gene regulatory molecular mechanism of SWT against non-triple-negative breast cancer.
This research explored the multi-gene pharmacological mechanism of action of SWT against non-TNBC through network pharmacology and in vitro experiments. The findings provide new ideas for research on the mechanism of action of Chinese medicine against breast cancer.
Zhang Z
，Liu J
，Liu Y
，Shi D
，He Y
，Zhao P
... -
《-》
Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.
Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.
Ma J
，Chen C
，Liu S
，Ji J
，Wu D
，Huang P
，Wei D
，Fan Z
，Ren L
... -
《-》
Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect and potential mechanism of nomilin against triple-negative breast cancer.
Nomilin is a limonoid compound known for its multiple biological activities, but its role in triple negative breast cancer (TNBC) remains unclear. This study aims to uncover the potential therapeutic effect of nomilin on TNBC and elucidate the specific mechanism of its action.
We employed weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the GeneCards database to identify potential targets for TNBC. Simultaneously, we utilized the Swiss Target Prediction, ChEMBL, and STITCH databases to identify potential targets of nomilin. The core targets and mechanisms of nomilin against TNBC were predicted through protein-protein interaction (PPI) network analysis, molecular docking, and enrichment analysis. The results of the network pharmacology were corroborated by conducting experiments.
A total of 17,204 TNBC targets were screened, and 301 potential targets of nomilin were identified. Through the PPI network, eight core targets of nomilin against TNBC were pinpointed, namely BCL2, Caspase3, CyclinD1, EGFR, HSP90AA1, KRAS, PARP1, and TNF. Molecular docking, molecular dynamics simulation and proteome microarray revealed that nomilin exhibits strong binding activity to these core proteins. Enrichment analysis results indicated that the anti-TNBC effect of nomilin is associated with PI3K/Akt pathway. In vitro and in vivo experiments have demonstrated that nomilin inhibits TNBC cell proliferation and migration while promoting cell apoptosis through the PI3K/Akt pathway.
For the first time, the research effectively discovered the objectives and mechanisms of nomilin in combating TNBC using network pharmacology, molecular docking, molecular dynamics simulation, proteome microarray and experimental confirmation, presenting a hopeful approach for treating TNBC.
Wu Z
，Xiang H
，Wang X
，Zhang R
，Guo Y
，Qu L
，Zhou J
，Xiao Y
... -
《-》
ResearchGate
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钛学术
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