scholarly journals Exploration of the involvement of LncRNA in HIV-associated encephalitis using bioinformatics

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5721 ◽  
Author(s):  
Diangeng Li ◽  
Pengtao Bao ◽  
Zhiwei Yin ◽  
Lei Sun ◽  
Jin Feng ◽  
...  
Keyword(s):  
White Matter ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Function Analysis ◽  
Signal Pathways ◽  
Public Database ◽  
Kegg Pathway Analysis ◽  
Nervous System Function ◽  
Brain Tissues

Background HIV-associated encephalitis (HIVE) is one of the common complications of HIV infection, and the pathogenesis of HIVE remains unclear, while lncRNA might be involved it. In this study, we made re-annotation on the expression profiling from the HIVE study in the public database, identified the lncRNA that might be involved in HIVE, and explored the possible mechanism. Methods In the GEO public database, the microarray expression profile (GSE35864) of three regions of brain tissues (white matter, frontal cortex and basal ganglia brain tissues) was chosen, updated annotation was performed to construct the non-cording-RNA (ncRNA) microarray data. Morpheus was used to identify the differential expressed ncRNA, and Genbank of NCBI was used to identify lncRNAs. The StarBase, PITA and miRDB databases were used to predict the target miRNAs of lncRNA. The TargetScan, PicTar and MiRanda databases were used to predict the target genes of miRNAs. The GO and KEGG pathway analysis were used to make function analysis on the targets genes. Results Seventeen differentially expressed lncRNAs were observed in the white matter of brain tissues, for which 352 target miRNAs and 6,659 target genes were predicted. The GO function analysis indicated that the lncRNAs were mainly involved in the nuclear transcription and translation processes. The KEGG pathway analysis showed that the target genes were significantly enriched in 33 signal pathways, of which 11 were clearly related to the nervous system function. Discussion The brand-new and different microarray results can be obtained through the updated annotation of the chips, and it is feasible to identify lncRNAs from ordinary chips. The results suggest that lncRNA may be involved in the occurrence and development of HIVE, which provides a new direction for further research on the diagnosis and treatment of HIVE.

scholarly journals Identification of Pathways and Key Genes in Venous Remodeling After Arteriovenous Fistula by Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Kong Jie ◽  
Wang Feng ◽  
Zhao Boxiang ◽  
Gong Maofeng ◽  
Zhang Jianbin ◽  
...  
Keyword(s):  
Arteriovenous Fistula ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Function Analysis ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Hub Genes ◽  
Molting Cycle ◽  
Kegg Pathway Analysis ◽  
First Choice

The arteriovenous fistula (AVF) is the first choice for vascular access for hemodialysis of renal failure patients. Venous remodeling after exposure to high fistula flow is important for AVF to mature but the mechanism underlying remodeling is still unknown. The objective of this study is to identify the molecular mechanisms that contribute to venous remodeling after AVF. To screen and identify the differentially expressed genes (DEGs) that may involve venous remodeling after AVF, we used bioinformatics to download the public microarray data (GSE39488) from the Gene Expression Omnibus (GEO) and screen for DEGs. We then performed gene ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) for the functional annotation of DEGs. The protein-protein interaction (PPI) network was constructed and the hub genes were carried out. Finally, we harvested 12 normal vein samples and 12 AVF vein samples which were used to confirm the expressions of the hub genes by immunohistochemistry. A total of 45 DEGs were detected, including 32 upregulated and 13 downregulated DEGs. The biological process (BP) of the GO analysis were enriched in the extrinsic apoptotic signaling pathway, cGMP-mediated pathway signaling, and molting cycle. The KEGG pathway analysis showed that the upregulated DEGs were enriched in glycosaminoglycan biosynthesis and purine metabolism, while the downregulated DEGs were mainly enriched in pathways of glycosaminoglycan biosynthesis, antifolate resistance, and ABC transporters. The GSEA analysis result showed that the top three involved pathways were oxidative phosphorylation, TNFA signaling via NF-K B, and the inflammatory response. The PPI was constructed and the hub genes found through the method of DMNC showed that INHBA and NR4A2 might play an important role in venous remodeling after AVF. The integrated optical density (DOI) examined by immunohistochemistry staining showed that the expression of both INHBA and NR4A2 increased in AVF compared to the control group. Our research contributes to the understanding of the molecular mechanism of venous remodeling after exposure to high fistula flow, which may be useful in treating AVF failure.

scholarly journals Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Wu ◽  
Shui Liu ◽  
Yien Xiang ◽  
Xianzhi Qu ◽  
Yingjun Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Kegg Pathway Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. Three circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. This module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. The relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. The results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

scholarly journals Network Pharmacology Analysis of the active components and anticancer targets of Rhubarb

2021 ◽  
Author(s):  
Hu Junrui ◽  
Duan Yongqiang ◽  
Cui Gongning ◽  
Luo Qiang ◽  
Xi Shanshan ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Drug Targets ◽  
Target Genes ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Components ◽  
Active Compounds ◽  
Cellular Targets ◽  
Kegg Pathway Analysis

AbstractTo investigate the mechanisms and active components governing the anticancer activity of rhubarb.The TCMSP database was screened to identify the active components of rhubarb and Swiss target predictions were generated to predict their cellular targets. TTD and OMIM databases were used to predict tumor-related target genes. "Cytoscape" was used to construct drug targets. PPI network analysis, GO enrichment analysis and KEGG pathway analysis of the key targets were investigated using String and David databases. A total of 33 components and 116 corresponding targets were screened. Amongst them, the key active compounds in rhubarb included emodin, aloe emodin, β-sitosterol, emodin methyl ether and rhein, which were predicted to target TP53, AKT1, STAT3, PIK3CA, HRAS, and VEGFA. GO analysis revealed that the cellular targets clustered into 159 biological processes, including those involved in cellular composition (n=24) and molecular functions (n=42, P<0.01). KEGG pathway analysis revealed 85 (P < 0.01) pathways related to cancer. The active compounds in rhubarb target TP53, AKT1 and PIK3CA. Rhubarb therefore regulates cancer development through an array of biological pathways.

scholarly journals Analysis of Long Noncoding RNA and mRNA Expression Profiles in IL-9-Activated Astrocytes and EAE Mice

2018 ◽  
Vol 45 (5) ◽  
pp. 1986-1998 ◽  
Author(s):  
Xiaomei Liu ◽  
Qing Zhang ◽  
Weixiao Wang ◽  
Dongjiao Zuo ◽  
Jing Wang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Astrocyte Activation ◽  
Kegg Pathway Analysis ◽  
The Brain ◽  
Brain Tissues

Background/Aims: Multiple sclerosis (MS) is an autoimmune disease in the central nervous system associated with demyelination and axonal injury. Astrocyte activation is involved in the pathogenesis of MS and experimental autoimmune encephalomyelitis (EAE), an animal model of MS. This study was designed to find potential lncRNAs in EAE mice and activated astrocytes. Methods: we performed microarray analysis of lncRNAs from the brain tissues of EAE mice and primary mouse astrocytes treated with IL-9(50 ng/ml). 12 lncRNAs were validated through real-time PCR. Gene ontology and KEGG pathway analysis were applied to explore the potential functions of lncRNAs. Results: Differentially expressed 3300 lncRNAs and 3250 mRNAs were in the brain tissues of EAE mice, and 3748 lncRNAs and 3332 mRNAs were in activated astrocytes. Notably, there were 2 co-up-regulated lncRNAs and 3 co-down-regulated lncRNAs both in the brain tissues of EAE mice and in activated astrocytes, including Gm14005, Gm12478, mouselincRNA1117, AK080435, and mouselincRNA0681, which regulate the ER calcium flux kinetics, zinc finger protein and cell apoptosis. Similarly, there were 7 mRNAs co-up-regulated and 2 mRNAs co-down-regulated both in vivo and in vitro. Gene ontology and KEGG pathway analysis showed that the biological functions of differentially expressed mRNAs were associated with metabolism, development and inflammation. The results of realtime PCR validation were consistent with the data from the microarrays. Conclusions: Our data uncovered the expression profiles of lncRNAs and mRNAs in vivo and in vitro, which may help delineate the mechanisms of astrocyte activation during MS/EAE process.

scholarly journals Clinical Significance of Upregulation of mir-196a-5p in Gastric Cancer and Enriched KEGG Pathway Analysis of Target Genes

2015 ◽  
Vol 16 (5) ◽  
pp. 1781-1787 ◽  
Author(s):  
Hai-Long Li ◽  
Shou-Pin Xie ◽  
Ya-Li Yang ◽  
Ying-Xia Cheng ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Clinical Significance ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Kegg Pathway Analysis

scholarly journals POS0399 TMT-BASED QUANTITATIVE PROTEOMICS ANALYSIS OF SYNOVIAL FLUID-DERIVED EXOSOMES IN RHEUMATOID ARTHRITIS, AXIAL SPONDYLOARTHRITIS, GOUT AND OSTEOARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 428.3-429
Author(s):  
Y. Liu ◽  
Y. Huang ◽  
Q. Huang ◽  
Z. Huang ◽  
Z. Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cluster Analysis ◽  
Synovial Fluid ◽  
Pathway Analysis ◽  
Quantitative Proteomics ◽  
Axial Spondyloarthritis ◽  
Kegg Pathway ◽  
Hierarchical Cluster ◽  
Kegg Pathway Analysis ◽  
Differential Proteins

Background:The pathogeneses of the joint diseases rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), gout, and osteoarthritis (OA) are still not fully elucidated. Exosomes in synovial fluid (SF) has a critical role in the pathogenesis of arthritis. None of study has compared the proteomics of SF-derived exosomes in RA, axSpA, gout and OA.Objectives:To compare the proteomics of SF-derived exosomes in RA, axSpA, gout and OA based on tandem mass tags (TMT) labeled quantitative proteomics technique.Methods:SF-derived exosomes was isolated from RA, axSpA, gout and OA patients by the Exoquick kit combined ultracentrifugation method. TMT labeled quantitative proteomics technique was used to compare the proteomics of SF-derived exosomes. Volcano plot, hierarchical cluster, Gene Ontologies (GO), Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted.Results:A total of 1678 credible proteins were detected. With the cut off criteria of |log2 (fold-change)| ≥1.2 and p-value <0.05, 267 (140 up-regulated and 127 down-regulated)differential proteins were found in OA vs gout, 291 (179 and 112) in axSpA vs OA, 515 (109 and 406) in RA vs axSpA, 298 (191 and 107) in axSpA vs gout, 462 (160 and 302) in RA vs gout, 536 (170 and 366) in RA vs OA. GO analysis showed that the biological progress of differential proteins were mainly enriched in the “immune response”. Regarding the molecular function, the differential proteins mainly mediated “antigen binding”. GO analysis of the cellular components indicated that most proteins were annotated as “extracellular exosomes”. KEGG pathway analysis demonstrated differential proteins were significantly enriched in “complement and coagulation cascades”. The hierarchical cluster analysis of the differential proteins in the four groups showed that Lysozyme C and Keratin were more abundant in gout, Hemoglobin and Actin-related protein 2/3 complex subunit 3 in OA, Sodium/potassium-transporting ATPase subunit alpha-1 and Immunoglobulin heavy constant delta in axSpA, Pregnancy zone protein and Stromelysin-1 in RA.Conclusion:The protein profiles of SF-derived exosomes in RA, axSpA, gout and OA patients were different. The differential proteins were the potential biomarkers of RA, axSpA, gout and OA.References:[1]Cretu D, Diamandis E P, Chandran V. Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research.[J]. Critical reviews in clinical laboratory sciences, 2013,50(2):51-63.[2]McArdle A J, Menikou S. What is proteomics?[J]. Archives of disease in childhood. Education and practice edition, 2020.Figure 1.The hierarchical cluster analysis of differential proteins in axSpA, OA, Gout and RA.Disclosure of Interests:None declared

scholarly journals Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xia Chen ◽  
Ling Liao ◽  
Yuwei Li ◽  
Hengliu Huang ◽  
Qing Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Metabolic Process ◽  
Hub Genes ◽  
Kegg Pathway Analysis ◽  
B Virus

Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

scholarly journals Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6036 ◽  
Author(s):  
Xin Gao ◽  
Yinyi Chen ◽  
Mei Chen ◽  
Shunlan Wang ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Aurora Kinase ◽  
Smooth Muscle Contraction ◽  
Ppi Network ◽  
Tissue Samples ◽  
Kegg Pathway Analysis ◽  
Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals FKBP-related ncRNA-mRNA axis in breast cancer

2020 ◽  
Author(s):  
Hanchu Xiong ◽  
Zihan Chen ◽  
Wenwen Zheng ◽  
Jing Sun ◽  
Qingshuang Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factors ◽  
Differential Expression ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Prognostic Analysis ◽  
Kegg Pathway Analysis

Abstract Background Breast cancer (BC) is a disease with morbidity ranking the first of women worldwidely. FK506-binding protein (FKBP) family has been demonstrated to possess various functions by interacting with different molecular targets in BC. However, a comprehensive ncRNA-mRNA regulatory axis of FKBP has not yet been reported. Methods FKBP related miRNAs were obtained from miRWalk database. Then, potential lncRNAs, transcription factors as well as mRNAs of screened differentially expressed miRNAs (DE-miRNAs) were analysed by using LncBase v.2, miRGen v3 and miRWalk database. Additionally, differential expression and prognostic analysis of lncRNAs were evaluated using TANRIC database. Next, GO annotation and KEGG pathway analysis were processed using DAVID database. Protein-Protein Interaction (PPI) network was established and hub genes were identified using STRING database. Finally, differential expression and prognostic analysis of hub genes were further conducted using UALCAN and bc-GenExMiner v4.2 database, respectively. Results Eleven DE-miRNAs, consisting of four FKBP4 related DE-miRNAs and seven FKBP5 related DE-miRNAs, were screened. 482 predicted lncRNAs were found for DE-miRNAs. Then, expression and prognostic results of nine of top twenty lncRNAs of BC were significantly identified. LINC00662 and LINC00963 expression were significantly associated with patients’ overall survival (OS). Then, nine potential upstream transcription factors were identified in motifs of DE-miRNAs. 320 target genes were identified for GO annotation and KEGG pathway analysis, which were mainly enriched in cysteine-type endopeptidase activity involved in apoptotic process. Construction and analysis in PPI network showed that RAB7A was selected as a hub gene with the toppest connectivity scores. Differential expression analysis of nine in top ten hub genes of BC were significantly identified. RAB7A and ARRB1 expression were significantly related with BC patients’ OS. Conclusions In current study, we firstly established a predicted FKBP-related ncRNA-mRNA regulatory network, thus exploring a comprehensive interpretation of molecular mechanisms and providing potential clues in seeking novel therapeutics for BC. In the future, much more experiments should be conducted to verify our findings.

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