An integrative analysis to distinguish between emphysema (EML) and alpha-1 antitrypsin deficiency-related emphysema (ADL)-A systems biology approach

Effects of Anti-Fibrotic Drugs on Transcriptome of Peripheral Blood Mononuclear Cells in Idiopathic Pulmonary Fibrosis

Two anti-fibrotic drugs, pirfenidone (PFD) and nintedanib (NTD), are currently used to treat idiopathic pulmonary fibrosis (IPF). Peripheral blood mononuclear cells (PBMCs) are immunocompetent cells that could orchestrate cell-cell interactions associated with IPF pathogenesis. We employed RNA sequencing to examine the transcriptome signature in the bulk PBMCs of patients with IPF and the effects of anti-fibrotic drugs on these signatures. Differentially expressed genes (DEGs) between "patients with IPF and healthy controls" and "before and after anti-fibrotic treatment" were analyzed. Enrichment analysis suggested that fatty acid elongation interferes with TGF-β/Smad signaling and the production of oxidative stress since treatment with NTD upregulates the fatty acid elongation enzymes ELOVL6. Treatment with PFD downregulates COL1A1, which produces wound-healing collagens because activated monocyte-derived macrophages participate in the production of collagen, type I, and alpha 1 during tissue damage. Plasminogen activator inhibitor-1 (PAI-1) regulates wound healing by inhibiting plasmin-mediated matrix metalloproteinase activation, and the inhibition of PAI-1 activity attenuates lung fibrosis. DEG analysis suggested that both the PFD and NTD upregulate SERPINE1, which regulates PAI-1 activity. This study embraces a novel approach by using RNA sequencing to examine PBMCs in IPF, potentially revealing systemic biomarkers or pathways that could be targeted for therapy.

Analysis of signaling cascades from myeloma cells treated with pristimerin

Multiple myeloma (MM) is the 2nd most frequently diagnosed blood cancer after non-Hodgkin's lymphoma. The present study aimed to identify the differentially expressed genes (DEGs) between the control and pristimerin-treated MM cell lines. We examined the GSE14011 microarray dataset and screened DEGs with GEO2R statistical tool using the inbuilt limma package. We used a bioinformatics pipeline to identify the differential networks, signaling cascades, and the survival of the hub genes. We implemented two different enrichment analysis including ClueGO and Metacore™, to get accurate annotation for most significant DEGs. We screened the most significant 408 DEGs from the dataset based on p-values and logFC values. Using protein network analysis, we found the genes UBC, HSP90AB1, HSPH1, HSPA1B, HSPA1L, HSPA6, HSPD1, DNAJB1, HSPE1, DNAJC10, BAG3, and DNAJC7 had higher node degree distribution. In contrast, the functional annotation provided that the DEGs were predominantly enriched in B-cell receptor signaling, unfolded protein response, positive regulation of phagocytosis, HSP70, and HSP40-dependent folding, and ubiquitin-proteasomal proteolysis. Using network algorithms, and comparing enrichment analysis, we found the hub genes enriched were INHBE, UBC, HSPA1A, HSP90AB1, IKBKB, and BAG3. These DEGs were further validated with overall survival and gene expression analysis between the tumor and control groups. Finally, pristimerin effects were validated independently in a cell line model consisting of IM9 and U266 MM cells. Pristimerin induced in vitro cytotoxicity in MM cells in a dose-dependent manner. Pristimerin inhibited NF-κB, induced accumulation of ubiquitinated proteins and inhibited HSP60 in the validation of bioinformatics findings, while pristimerin-induced caspase-3 and PARP cleavage confirmed cell death. Taken together, we found that the identified DEGs were strongly associated with the apoptosis induced in MM cell lines due to pristimerin treatment, and combinatorial therapy derived from pristimerin could act as novel anti-myeloma multifunctional agents.

Liver transcriptomics reveals microRNA features of the host response in a mouse model of dengue virus infection

BACKGROUND

Organ dysfunction, especially liver injury, caused by dengue virus (DENV) infection has been associated with fatal cases in dengue patients around the world. However, the pathophysiological mechanisms of liver involvement in dengue remain unclear. There is accumulating evidence that miRNAs are playing an important role in regulating viral pathogenesis, and it can help in diagnostic and anti-viral therapies development.

METHODS

We collected liver tissues of DENV-infected for small RNA sequencing to identify significantly different express miRNAs during dengue virus infection, and the identified target genes of these miRNAs were annotated by biological function and pathway enrichment.

RESULTS

31 significantly altered miRNAs were identified, including 16 up-regulated and 15 down-regulated miRNAs. By performing a series of miRNA prediction and signaling pathway enrichment analyses, the down-regulated miRNAs of mmu-miR-484, mmu-miR-1247-5p and mmu-miR-6538 were identified to be the crucial miRNAs. Further analysis revealed that the inflammation and immune responses involving Hippo, PI3K-Akt, MAPK, Wnt, mTOR, TGF-beta, Tight junction, and Platelet activation were modulated collectively by these three key miRNAs during DENV infection. These pathways are considered to be closely associated with the pathogenic mechanism and treatment strategy of dengue patients.

CONCLUSION

The miRNAs identified by sequencing, especially miR-484 may be the potential therapeutic targets for liver involvement in dengue patients which involves the regulation of vascular permeability and expression of inflammatory cytokines.

Investigation of differentially expressed genes and dysregulated pathways involved in multiple sclerosis

Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.

Integrative ontology and pathway-based approach identifies distinct molecular signatures in transcriptomes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) remains a serious concern globally due to many factors that including late diagnosis, lack of an ideal biomarker for diagnosis and prognosis, and high rate of mortality. In this study, we aimed to identify the essential dysregulated genes and molecular signatures associated with the progression and development of ESCC. The dataset with 15 ESCCs and the 15 adjacent normal tissue samples from the surrounding histopathologically tumor-free mucosa was selected. We applied bioinformatics pipelines including various topological parameters from MCODE, CytoNCA, and cytoHubba to prioritize the most significantly associated DEGs with ESCC. We performed functional enrichment annotation for the identified DEGs using DAVID and MetaCore™ GeneGo platforms. Furthermore, we validated the essential core genes in TCGA and GTEx datasets between the normal mucosa and ESCC for their expression levels. These DEGs were primarily enriched in positive regulation of transferase activity, negative regulation of organelle organization, cell cycle mitosis/S-phase transition, spindle organization/assembly, development, and regulation of angiogenesis. Subsequently, the DEGs were associated with the pathways such as oocyte meiosis, cell cycle, and DNA replication. Our study identified the eight-core genes (AURKA, AURKB, MCM2, CDC20, TPX2, PLK1, FOXM1, and MCM7) that are highly expressed among the ESCC, and TCGA dataset. The multigene comparison and principal component analysis resulted in elevated signals for the AURKA, MCM2, CDC20, TPX2, PLK1, and FOXM1. Overall, our study reported GO profiles and molecular signatures that might help researchers to grasp the pathological mechanisms underlying ESCC development and eventually provide novel therapeutic and diagnostic strategies.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Functional deficiency of succinate dehydrogenase promotes tumorigenesis and development of clear cell renal cell carcinoma through weakening of ferroptosis

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinomas, with high mortality and poor prognoses worldwide. Succinate dehydrogenase (SDH) consists of four nuclear-encoded subunits and it is the only complex involved in both the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). Previous studies have shown decreased SDH activity in ccRCC. However, the role and underlying molecular mechanisms of SDH in ccRCC initiation and development remain unclear. In the present study, pan-cancer analysis of SDH gene expression was analyzed and the relationship between SDH gene expression and clinicopathological parameters was assessed using different databases. cBioPortal, UACLAN, and Tumor Immune Estimation Resource (TIMER) were subsequently utilized to analyze genetic alterations, methylation, and immune cell infiltration of SDH genes in ccRCC patients. We found SDHs were significantly downregulated in ccRCC tissues and correlated with ccRCC progression. Increased methylation and high SDH promoter mutation rates may be the cause of reduced expression of SDHs in ccRCC. Moreover, the interaction network showed that SDH genes were correlated with ferroptosis-related genes. We further demonstrated that SDH inhibition dampened oxidative phosphorylation, reduced ferroptotic events, and restored ferroptotic cell death, characterized by eliminated mitochondrial ROS levels, decreased cellular ROS and diminished peroxide accumulation. Collectively, this study provides new insights into the regulatory role of SDH in the carcinogenesis and progression of ccRCC, introducing a potential target for advanced antitumor therapy through ferroptosis.

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.

Long non-coding RNAs PGM5-AS1 upregulates Decorin (DCN) to inhibit cervical cancer progression by sponging miR-4284

Long non-coding RNAs (lncRNAs) have been widely studied and play crucial roles in cervical cancer (CC) progression. Here, we investigated the function and mechanism of lncRNA PGM5-AS1 action in CC cells. Using real-time quantitative polymerase chain reaction or western blotting, PGM5-AS1 and decorin (DCN) were downregulated in CC tissues and cells, whereas miR-4284 was upregulated. Luciferase assay, RNA pull-down assay, and western blotting showed that PGM5-AS1 could sponge miR-4284 to upregulate DCN expression in CC cells. Additionally, cell functional experiments showed that PGM5-AS1 overexpression led to decreased proliferation, migration, and invasion of CC cells. However, the inhibitory effect of PGM5-AS1 overexpression on CC cells was partly relieved by DCN knockdown because of the targeting interaction between PGM5-AS1, miR-4284, and DCN. In summary, this study identified that PGM5-AS1 negatively regulates CC cell malignancy by targeting miR-4284/DCN.

Utilizing network pharmacology and experimental validation to explore the potential molecular mechanisms of BanXia-YiYiRen in treating insomnia

BanXia-YiYiRen (Pinellia Ternata and Coix Seed, BX-YYR) has been clinically proven to be an effective Chinese medicine compatible with the treatment of insomnia. However, the underlying mechanism of BX-YYR against insomnia remains unclear. This study aimed to explore the pharmacological mechanisms of BX-YYR in treating insomnia based on network pharmacology and experimental validation. The drug-disease targets were obtained using publicly available databases. The analysis revealed 21 active compounds and 101 potential targets of BX-YYR from the pharmacological database of Chinese medicine system and analysis platform (TCMSP) and 1020 related targets of insomnia from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Furthermore, 38 common targets of BX-YYR against insomnia were identified, and these common targets were used to construct a protein–protein interaction (PPI) network. The visual PPI network was constructed by Cytoscape software. The top three genes from PPI according to degree value are FOS, AKT1, and CASP3. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were applied to reveal the potential targets and signaling pathways involved in BX-YYR against insomnia, especially the serotonergic pathway. In addition, molecular docking revealed that baicalein, beta-sitosterol, and stigmasterol displayed strong binding to AKT1, FOS, PRKCA, and VEGFA. Experimental study found that BX-YYR against insomnia might play a role in improving sleep by modulating the serotonergic pathway. In summary, our findings revealed the underlying mechanism of BX-YYR against insomnia and provided an objective basis for further experimental study and clinical application.

Development and Validation of Ten-RNA Binding Protein Signature Predicts Overall Survival in Osteosarcoma

Osteosarcoma is a malignant tumor that originates in the bones with the characteristics of high malignancy, predisposition to metastasis, and poor prognosis. RNA binding proteins (RBPs) are closely related to various tumors, but their relationship with osteosarcoma remains unclear. Based on GTEx and TARGET RNA sequencing data, we applied differential analysis to obtain RBP genes that are differentially expressed in osteosarcoma, and analyzed the functions of these RBPs. After applying univariate and LASSO Cox regression analysis, 10 key prognostic RBPs (TDRD6, TLR8, NXT2, EIF4E3, RPS27L, CPEB3, RBM34, TERT, RPS29, and ZC3HAV1) were screened, and an RBP prognostic risk assessment model for patients with osteosarcoma was established. The independent cohort GSE21257 was used for external verification, and the results showed that the signature has an excellent ability to predict prognosis. In addition, a nomogram that can be used for clinical evaluation was constructed. Finally, the expression levels of 10 prognostic RBPs in osteosarcoma cells and tissues were confirmed through experiments. Our study identified a ten-gene prognostic marker related to RBP, which is of great significance for adjusting the treatment strategy of patients with osteosarcoma and exploring prognostic markers.

Identification of novel biomarkers in breast cancer via integrated bioinformatics analysis and experimental validation

Breast cancer (BC), an extremely aggressive malignant tumor, causes a large number of deaths worldwide. In this study, we pooled profile datasets from three cohorts to illuminate the underlying key genes and pathways of BC. Expression profiles GSE42568, GSE45827, and GSE124646, including 244 BC tissues and 28 normal breast tissues, were integrated and analyzed. Differentially expressed genes (DEGs) were screened out based on these three datasets. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plugin were utilized to visualize protein protein interaction (PPI) of these DEGs. The module with the highest connectivity of gene interactions was selected for further analysis. All of these hub genes had a significantly worse prognosis in BC by survival analysis. Additionally, four genes (CDK1, CDC20, AURKA, and MCM4) dramatically were enriched in oocyte meiosis and cell cycle pathways through re-analysis of DAVID. Moreover, the mRNA and protein levels of CDK1, CDC20, AURKA, and MCM4 were significantly increased in BC patients. In addition, knockdown of CDK1 and CDC20 by small interfering RNA remarkably suppressed cell migration and invasion in MCF-7 and MDA-MB-231 cells. In conclusion, our results suggested that CDK1, CDC20, AURKA, and MCM4 were reliable biomarkers of BC via bioinformatics analysis and experimental validation and may act as prospective targets for BC diagnosis and treatment.

Identification of key molecular markers of acute coronary syndrome using peripheral blood transcriptome sequencing analysis and mRNA-lncRNA co-expression network construction

Acute coronary syndrome (ACS) is a term used to describe major cardiovascular diseases, and treatment of in-stent restenosis in patients with ACS remains a major clinical challenge. Further investigation into molecular markers of ACS may aid early diagnosis, and the treatment of ACS and post-treatment recurrence. In the present study, total RNA was extracted from the peripheral blood samples of 3 patients with ACS, 3 patients with percutaneous coronary intervention (PCI)_non-restenosis, 3 patients with PCI_restenosis and 3 healthy controls. Subsequently, RNA library construction and high-throughput sequencing were performed. DESeq2 package in R was used to screen genes that were differentially expressed between the different samples. Moreover, the intersection of the differentially expressed mRNAs (DEmRNAs) and differentially expressed long noncoding RNAs (DElncRNAs) obtained. GeneCodis4.0 was used to perform function enrichment for DEmRNAs, and lncRNA-mRNA co-expression network was constructed. The GSE60993 dataset was utilized for diagnostic analysis, and the aforementioned investigations were verified using in vitro studies. Results of the present study revealed a large number of DEmRNAs and DElncRNAs in the different groups. We selected genes in the top 10 of differential expression and also involved in the co-expression of lncRNA-mRNA for diagnostic analysis in the GSE60993 dataset. The area under curve (AUC) of PDZK1IP1 (0.747), PROK2 (0.769) and LAMP3 (0.725) were all >0.7. These results indicated that the identified mRNAs and lncRNAs may act as potential clinical biomarkers, and more specifically, PDZK1IP1, PROK2 and LAMP3 may act as potential biomarkers for the diagnosis of ACS.

Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Hepatocellular Carcinoma

With the increasing prevalence of Hepatocellular carcinoma (HCC) and the poor prognosis of immunotherapy, reliable immune-related gene pairs (IRGPs) prognostic signature is required for personalized management and treatment of patients. Gene expression profiles and clinical information of HCC patients were obtained from the TCGA and ICGC databases. The IRGPs are constructed using immune-related genes (IRGs) with large variations. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct IRGPs signature. The IRGPs signature was verified through the ICGC cohort. 1,309 IRGPs were constructed from 90 IRGs with high variability. We obtained 50 IRGPs that were significantly connected to the prognosis and constructed a signature that included 17 IRGPs. In the TCGA and ICGC cohorts, patients were divided into high and low-risk patients by the IRGPs signature. The overall survival time of low-risk patients is longer than that of high-risk patients. After adjustment for clinical and pathological factors, multivariate analysis showed that the IRGPs signature is an independent prognostic factor. The Receiver operating characteristic (ROC) curve confirmed the accuracy of the signature. Besides, gene set enrichment analysis (GSEA) revealed that the signature is related to immune biological processes, and the immune microenvironment status is distinct in different risk patients. The proposed IRGPs signature can effectively assess the overall survival of HCC, and provide the relationship between the signature and the reactivity of immune checkpoint therapy and the sensitivity of targeted drugs, thereby providing new ideas for the diagnosis and treatment of the disease.

Expression Profiles and Prognostic Value of FABPs in Colorectal Adenocarcinomas

Colorectal cancer (CRC) is one of the world's leading causes of cancer-related deaths; thus, it is important to detect it as early as possible. Obesity is thought to be linked to a large rise in the CRC incidence as a result of bad dietary choices, such as a high intake of animal fats. Fatty acid-binding proteins (FABPs) are a set of molecules that coordinate intracellular lipid responses and are highly associated with metabolism and inflammatory pathways. There are nine types of FABP genes that have been found in mammals, which are FABP1-7, FABP9, and FABP12. Each FABP gene has its own roles in different organs of the body; hence, each one has different expression levels in different cancers. The roles of FABP family genes in the development of CRC are still poorly understood. We used a bioinformatics approach to examine FABP family gene expression profiles using the Oncomine, GEPIA, PrognoScan, STRING, cBioPortal, MetaCore, and TIMER platforms. Results showed that the FABP6 messenger (m)RNA level is overexpressed in CRC cells compared to normal cells. The overexpression of FABP6 was found to be related to poor prognosis in CRC patients' overall survival. The immunohistochemical results in the Human Protein Atlas showed that FABP1 and FABP6 exhibited strong staining in CRC tissues. An enrichment analysis showed that high expression of FABP6 was significantly correlated with the role of microRNAs in cell proliferation in the development of CRC through the insulin-like growth factor (IGF) signaling pathway. FABP6 functions as an intracellular bile-acid transporter in the ileal epithelium. We looked at FABP6 expression in CRC since bile acids are important in the carcinogenesis of CRC. In conclusion, high FABP6 expression is expected to be a potential biomarker for detecting CRC at the early stage.

Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Bio-informatics and in Vitro Experiments Reveal the Mechanism of Schisandrin A Against MDA-MB-231 cells

Schisandrin A (SchA) has been reported to have good anti-cancer effects. However, its anti-cancer mechanism in breast cancer remains unknown. This study aimed to explore the mechanism of SchA in breast cancer treatment using bio-informatics analysis and in vitro experiments. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Cards, and PharmMapper databases were used to screen the candidate targets of SchA against MDA-MB-231 cells selected as the tested cell line through MTT analysis. The functions and pathways of the targets were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and further analyzed using DAVID 6.8.1 database. Network pharmacology analysis revealed 77 candidate targets, 31 signal pathways, and 208 GO entries (P < 0.05). The targets regulated serine-type endopeptidase and protein tyrosine kinase activities, thereby promoting the migration and inhibiting the apoptosis of MDA-MB-231 cells. Comprehensive analysis of the ‘Protein–Protein Interaction’ (PPI) and ‘Component-Targets-Pathways’ (C-T-P) networks constructed using Cytoscape 3.7.1 software revealed four core targets: EGFR, PIK3R1, MMP9 and Caspase 3. Their docking scores with SchA were subsequently investigated through molecular docking. The wound healing, Hoechst 33342/PI, and western blot assays confirmed that SchA significantly down-regulated EGFR, PIK3R1, and MMP9, but up-regulated cleaved-caspase 3, thus inhibiting the migration and promoting the apoptosis of MDA-MB-231 cells. Reckoning the findings of the study, SchA is a potential adjuvant treatment for breast cancer.