The present study aimed to identify microRNAs (miRNAs) that may be crucial for the mechanism of mesenchymal stem cell (MSC) treatment in cisplatin‑induced acute kidney injury (AKI) and to investigate other potential drugs that may have a similar function. Transcriptomics (GSE85957) and miRNA expression (GSE66761) datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the linear models for microarray data method and mRNA targets of DEMs were predicted using the miRWalk2.0 database. The crucial DEGs were screened by constructing a protein‑protein interaction (PPI) network and module analysis. Functions of target genes were analyzed using the database for annotation, visualization and integrated discovery. Small molecule drugs were predicted using the connectivity map database. As a result, 5 DEMs were identified to be shared and oppositely expressed in comparisons between AKI model and control groups, and between MSC treatment and AKI model groups. The 103 DEGs were overlapped with the target genes of 5 common DEMs, and the resulting list was used for constructing the miRNA‑mRNA regulatory network, including rno‑miR‑210/Serpine1 and rno‑miR‑378/Fos. Serpine1 (degree=17) and Fos (degree=42) were predicted to be hub genes according to the topological characteristic of degree in the PPI network. Function analysis indicated Serpine1 and Fos may be inflammation‑related. Furthermore, gliclazide was suggested to be a potential drug for the treatment of AKI because the enrichment score was the closest to ‑1 (‑0.9). In conclusion, it can be speculated that gliclazide may have a similar mechanism to MSC as a potential therapeutic agent for cisplatin‑induced AKI, by regulating miR‑210/Serpine1 and miR‑378‑/Fos‑mediated inflammation and cell apoptosis.

The purpose of the present study was to identify key genes and investigate the related molecular mechanisms of bladder cancer (BC) progression. From the Gene Expression Omnibus database, the gene expression dataset GSE7476 was downloaded, which contained 43 BC samples and 12 normal bladder tissues. GSE7476 was analyzed to screen the differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for the DEGs using the DAVID database, and a protein‑protein interaction (PPI) network was then constructed using Cytoscape software. The results of the GO analysis showed that the upregulated DEGs were significantly enriched in cell division, nucleoplasm and protein binding, while the downregulated DEGs were significantly enriched in 'extracellular matrix organization', 'proteinaceous extracellular matrix' and 'heparin binding'. The results of the KEGG pathway analysis showed that the upregulated DEGs were significantly enriched in the 'cell cycle', whereas the downregulated DEGs were significantly enriched in 'complement and coagulation cascades'. JUN, cyclin‑dependent kinase 1, FOS, PCNA, TOP2A, CCND1 and CDH1 were found to be hub genes in the PPI network. Sub‑networks revealed that these gene were enriched in significant pathways, including the 'cell cycle' signaling pathway and 'PI3K‑Akt signaling pathway'. In summary, the present study identified DEGs and key target genes in the progression of BC, providing potential molecular targets and diagnostic biomarkers for the treatment of BC.

Multiple symmetric lipomatosis (MSL) is a rare disorder characterized by aberrant multiple and symmetric subcutaneous adipose tissue accumulation in the face, neck, shoulders, back, chest and abdomen, severely affecting the quality of life of patients. At present, precise MSL etiology and pathogenesis remain to be elucidated. The present study first utilized a digital gene expression technique with a next‑generation sequencing platform to profile differentially expressed genes in three cases of MSL vs. normal control tissue. cDNA libraries from these tissue specimens were constructed and DNA sequenced for identification of differentially expressed genes, which underwent bioinformatic analysis using the Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein‑protein interaction (PPI) network analyses. As a result, a total of 859 differentially expressed genes were identified, including 308 upregulated genes (C19orf80, Apelin, C21orf33, FAM166B and HSD11B2 were mostly upregulated 6.984‑, 4.670‑, 4.412‑, 3.693‑ and 3.561‑fold, respectively) and 551 downregulated genes [FosB proto‑oncogene, AP‑1 transcription factor subunit (FOSB), selectin (SEL) E, RAR related orphan receptor (ROR) B, salt inducible kinase (SIK)1 and epidermal growth factor‑like protein (EGFL)6 were mostly downregulated ‑9.845, ‑8.243, ‑8.123, ‑7.702 and ‑7.664 fold, respectively). The GO functional enrichment analysis demonstrated these differentially expressed genes were predominantly involved in biological processes and cellular components, while the KEGG pathway enrichment analysis demonstrated that ribosome, non‑alcoholic fatty liver disease, human T‑lymphotropic virus type 1 (HTLV‑I) infection and Alzheimer's disease pathways were altered in MSL. The PPI network data demonstrated ubiquitin C (UBC), translocator protein (TSPO), Jun Proto‑Oncogene, AP‑1 Transcription Factor (JUN) and FOS were among these differentially expressed genes that participated in regulation of adipocyte differentiation, although no previous study has linked them to MSL. In conclusion, the present study profiled differentially expressed genes in MSL and identified gene pathways that may be associated with MSL development and progression.

Gastric cancer has become a serious disease in the past decade. It has the second highest mortality rate among the four most common cancer types, leading to ~700,000 mortalities annually. Previous studies have attempted to elucidate the underlying biological mechanisms of gastric cancer. The present study aimed to obtain useful biomarkers and to improve the understanding of gastric cancer mechanisms at the genetic level. The present study used bioinformatics analysis to identify 1,829 differentially expressed genes (DEGs) which were obtained from the GSE54129 dataset. Using protein‑protein interaction information from the Search Tool for the Retrieval of Interacting Genes database, disease modules were constructed for gastric cancer using Cytoscape software. In the Gene Ontology analysis of biology processes, upregulated genes were significantly enriched in 'extracellular matrix organization', 'cell adhesion' and 'inflammatory response', whereas downregulated DEGs were significantly enriched in 'xenobiotic metabolic process', 'oxidation‑reduction process' and 'steroid metabolic process'. During Kyoto Encyclopedia of Genes and Genomes analysis, upregulated DEGs were significantly enriched in 'extracellular matrix‑receptor interaction', 'focal adhesion' and 'PI3K‑Akt signaling pathway', whereas the downregulated DEGs were significantly enriched in 'chemical carcinogenesis', 'metabolism of xenobiotics by cytochrome P450' and 'peroxisome'. The present study additionally identified 10 hub genes from the DEGs: Tumor protein p53 (TP53), C‑X‑C motif chemokine ligand 8 (CXCL8), tetraspanin 4 (TSPAN4), lysophosphatidic acid receptor 2 (LPAR2), adenylate cyclase 3 (ADCY3), phosphoinositide‑3‑kinase regulatory subunit 1 (PIK3R1), neuromedin U (NMU), C‑X‑C motif chemokine ligand (CXCL12), fos proto‑oncogene, AP‑1 transcription factor subunit (FOS) and sphingosine‑1‑phosphate receptor 1 (S1PR1), which have high degrees with other DEGs. The survival analysis revealed that the high expression of ADCY3, LPAR2, S1PR1, TP53 and TSPAN4 was associated with a lower survival rate, whereas high expression of CXCL8, FOS, NMU and PIK3R1 was associated with a higher survival rate. No significant association was identified between CXCL12 and survival rate. Additionally, TSPAN1 and TSPAN8 appeared in the top 100 DEGs. Finally, it was observed that 4 hub genes were highly expressed in gastric cancer tissue compared with para‑carcinoma tissue in the 12 patients; the increased TSPAN4 was significant (>5‑fold). Tetraspanin family genes may be novel biomarkers of gastric cancer. The findings of the present study may improve the understanding of the molecular mechanisms underlying the development of gastric cancer.

Osteoarthritis (OA) is a chronic arthropathy that occurs in the middle‑aged and elderly population. The present study aimed to identify gene signature differences between synovial cells from OA synovial membrane with and without inflammation, and to explain the potential mechanisms involved. The differentially expressed genes (DEGs) between 12 synovial membrane with inflammation and 12 synovial membrane without inflammation from the dataset GSE46750 were identified using the Gene Expression Omnibus 2R. The DEGs were subjected to enrichment analysis, protein‑protein interaction (PPI) analysis and module analysis. The analysis results were compared with text‑mining results. A total of 174 DEGs were identified. Gene Ontology enrichment results demonstrated that functional molecules encoded by the DEGs primarily had extracellular location, molecular functions predominantly involving 'chemokine activity' and 'cytokine activity', and were associated with biological processes, including 'inflammatory response' and 'immune response'. The Kyoto Encyclopedia of Genes and Genomes results demonstrated that DEGS may function through pathways associated with 'rheumatoid arthritis', 'chemokine signaling pathway', 'complement and coagulation cascades', 'TNF signaling pathway', 'intestinal immune networks for IgA production', 'cytokine‑cytokine receptor interaction', 'allograft rejection', 'Toll‑like receptor signaling pathway' and 'antigen processing and presentation'. The top 10 hub genes [interleukin (IL)6, IL8, matrix metallopeptidase (MMP)9, colony stimulating factor 1 receptor, FOS proto‑oncogene, AP1 transcription factor subunit, insulin‑like growth factor 1, TYRO protein tyrosine kinase binding protein, MMP3, cluster of differentiation (CD)14 and CD163] and four gene modules were identified from the PPI network using Cytoscape. In addition, text‑mining was used to identify the commonly used drugs and their targets for the treatment of OA. It was initially verified whether the results of the present study were useful for the study of OA treatment targets and pathways. The present study provided insight for the molecular mechanisms of OA synovitis. The hub genes and associated pathways derived from analysis may be targets for OA treatment. IL8 and MMP9, which were validated by text‑mining, may be used as molecular targets for the OA treatment, while other hub genes require further validation.

Changes in the methylation levels of tumor suppressor genes or proto‑oncogenes are involved in the pathogenesis of hepatitis C virus (HCV) infection‑induced hepatocellular carcinoma (HCC). The aim of the present study was to identify novel aberrantly methylated differentially expressed genes by integrating mRNA expression profile (GSE19665 and GSE62232) and methylation profile (GSE60753) microarrays downloaded from the Gene Expression Omnibus database. Functional enrichment analysis of screened genes was performed using the DAVID software and BinGO database. Protein‑protein interaction (PPI) networks were constructed using the STRING database, followed by module analysis with MCODE software. The transcriptional and translational expression levels of crucial genes were confirmed using The Cancer Genome Atlas (TCGA) datasets and Human Protein Atlas database (HPA). A total of 122 downregulated/hypermethylated genes and 63 upregulated/hypomethylated genes were identified. These genes were enriched in the Gene Ontology biological processes terms of 'inflammatory response' [Fos proto‑oncogene, AP‑1 transcription factor subunit (FOS)] and 'cell cycle process' [aurora kinase A (AURKA), cyclin dependent kinase inhibitor 3 (CDKN3) and ubiquitin conjugating enzyme E2 C (UBE2C)]. PPI network and module analysis indicated that human oncogenes FOS, AURKA, CDKN3 and UBE2C may be hub genes. mRNA, protein expression and methylation levels of AURKA and FOS were validated by TCGA and HPA data. In conclusion, aberrantly methylated AURKA and FOS may be potential therapeutic targets for HCV‑positive HCC.

The aim of the present study was to explore the candidate genes, chemicals and mechanisms of congenital obstructive nephropathy (CON). The gene expression profiles of GSE48041, including 24 kidney tissue samples from megabladder (mgb‑/‑) mouse were downloaded from the Gene Expression Omnibus database. Samples were divided into 4 groups: Control, mild, moderate and severe. Differentially expressed genes (DEGs), protein‑protein interaction network, Kyoto Encyclopedia of Genes and Genomes pathways and transcription factor (TF)‑target gene analyses were performed on Set 1 (mild, moderate and severe groups), while Gene Ontology (GO) function enrichment analysis and chemical investigation were performed on Set 2 (severe group). A total of 187 and 139 DEGs were obtained in Set 1 and Set 2, respectively. Chemical carcinogenesis [enriched by genes such as Carbonyl reductase 1 (CBR1)] was one of the most prominent pathways in Set 1. GO analysis for Set 2 revealed that DEGs were mainly assembled in functions such as cellular response to interleukin‑1 and cellular response to tumor necrosis. Furthermore, genes such as Fos Proto‑Oncogene (FOS) were co‑regulated by TFs including RNA polymerase II subunit A (Polr2a) and serum response factor (Srf). Chemical cyclosporine served the most important role in Set 2 by targeting several DEGs in Set 2. DEGs such as CBR1 and FOS, TFs including Polr2a and Srf, and pathways such as chemical carcinogenesis may serve important roles in the process of CON. Interleukin‑1 and tumor necrosis function may be novel targets for CON gene therapy. Furthermore, cyclosporine may be a promising option for future CON therapy.

The present study aimed to identify the involvement of critical genes in systemic vasculitis, to gain an improved understanding of the molecular circuity and to investigate novel potential gene targets for systemic vasculitis treatment. The dual‑color cDNA microarray data of GSE16945, consisting of peripheral mononuclear blood cell specimens from 13 patients with systemic vasculitis and 16 healthy controls, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in systemic vasculitis compared with controls using BRB ArrayTools, followed by the construction of a protein‑protein interaction (PPI) network using the clusterProfiler package, and significant functional interaction (FI) module selection. Furthermore, transcriptional factors (TFs) among the identified DEGs were predicted and a transcriptional regulation network was constructed. A total of 173 up- and 93 downregulated genes were identified, which were mainly associated with immune response pathways. FBJ murine osteosarcoma viral oncogene homolog (FOS), ubiquitin B (UBB), signal transducer and activator of transcription 1 (STAT1) and MX dynamin‑like GTPase 1 (MX1) were identified as hub proteins in the PPI network. Furthermore, UBB, FOS, and STAT1 were hub proteins in the three identified FI modules, respectively. In total, nine TFs were predicted among the DEGs. Of the DEGs that were predicted to be TFs, STAT1, v‑maf avian musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) and tyrosine 3‑monooxygenase/tryptophan 5‑monooxygenase activation protein Z (YWHAZ), which interacted with each other, were identified to regulate further DEGs as target genes. Various genes, including FOS, UBB, MX1, STAT1, MAFB, and YWHAZ may be potential targets useful for the treatment of systemic vasculitis.

Accumulating evidence has indicated that noncoding RNAs are involved in intervertebral disc degeneration (IDD); however, the competing endogenous RNA (ceRNA)‑mediated regulatory mechanisms in IDD remain rarely reported. The present study aimed to comprehensively investigate the alterations in expression levels of circular RNA (circRNA), long noncoding RNA (lncRNA), microRNA (miRNA/miR) and mRNA in the nucleus pulposus (NP) of patients with IDD. In addition, crucial lncRNA/circRNA‑miRNA‑mRNA ceRNA interaction axes were screened using the GSE67567 microarray dataset obtained from the Gene Expression Omnibus database. After data preprocessing, differentially expressed circRNAs (DECs), lncRNAs (DELs), miRNAs (DEMs) or genes (DEGs) between IDD and normal controls were identified using the Linear Models for Microarray data method. A protein‑protein interaction (PPI) network was constructed for DEGs based on protein databases, followed by module analysis. The ceRNA network was constructed based on the interaction between miRNAs and mRNAs, and lncRNAs/circRNAs and miRNAs. The underlying functions of mRNAs were predicted using the Database for Annotation, Visualization and Integrated Discovery database. The present study identified 636 DECs, 115 DELs, 84 DEMs and 1,040 DEGs between patients with IDD and control individuals. PPI network analysis demonstrated that Fos proto‑oncogene, AP‑1 transcription factor subunit (FOS), mitogen‑activated protein kinase 1 (MAPK1), hypoxia inducible factor 1 subunit α (HIF1A) and transforming growth factor β1 (TGFB1) were hub genes and enriched in modules. Metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1)/hsa_circRNA_102348‑hsa‑​miR‑185‑5p‑TGFB1/FOS, MALAT1‑hsa‑miR‑155‑5p‑HIF1A, hsa_circRNA_102399‑hsa‑miR‑302a‑3p‑HIF1A, MALAT1‑hsa‑​miR‑519d‑3p‑MAPK1 and hsa_circRNA_100086‑hsa‑miR‑509‑3p‑MAPK1 ceRNA axes were obtained by constructing the ceRNA networks. In conclusion, these identified ceRNA interaction axes may be crucial targets for the treatment of IDD.

The present study aimed to investigate the modular mechanisms underlying breast cancer and identify potential targets for breast cancer treatment. The differentially expressed genes (DEGs) between breast cancer and normal cells were assessed using microarray data obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions of these DEGs. Subsequently, the protein-protein interaction (PPI) network was constructed using the Cytoscape software. The identified subnetworks were further analyzed using the Molecular Complex Detection plugin. In total, 571 genes (241 upregulated and 330 downregulated genes) were found to be differentially expressed between breast cancer and normal cells. The GO terms significantly enriched by DEGs included cell adhesion, immune response and extracellular region, while the most significant pathways included focal adhesion and complement and coagulation cascade pathways. The PPI network was established with 273 nodes and 718 edges, while fibronectin 1 (FN1, degrees score, 39), interleukin 6 (IL6; degree score, 96) and c-Fos protein (degree score, 32) were identified as the hub proteins in subnetwork 2. These dysregulated genes were found to be involved in the development of breast cancer. The FN1, IL6 and FOS genes may therefore be potential targets in the treatment of breast cancer.

Traumatic temporomandibular joint ankylosis (TMJA) is a common disease and disorder of the temporomandibular joint (TMJ); however, its pathogenesis has yet to be completely elucidated. In the authors' previous studies, the lateral pterygoid muscle (LPM) was confirmed to exert a function in distraction osteogenesis (DO) during the healing of a condylar fracture, which resulted in the formation of excess bone. The aim of the present study was to investigate alterations in the expression of any associated genes via an Affymetrix GeneChip method. The traumatic TMJA model was fabricated by a condylar fracture in the TMJ area of sheep with either a dissected LPM (LPD) or normal (LPN). The untreated sheep served as a control. At 4‑ and 12 weeks post‑surgery, the condylar zone was isolated to perform the gene chip analysis, which was performed according to a standard Affymetrix protocol. The validated genes were further evaluated by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). The gene chip analysis indicated that the LPN gene expression pattern was similar compared with the DO process, while LPD was similar to that of normal bone fracture healing. The validated genes were collagen type II α1 chain, C‑type lectin domain family 3 member A, interleukin 1A, cartilage oligomeric matrix protein, chondromodulin (LECT1), calcitonin receptor (CALCR), transforming growth factor (TGF)‑β1, Fos proto‑oncogene (FOS), bone γ‑carboxyglutamate protein and bone morphogenic protein (BMP)7, among which, BMP7, LECT1, CALCR and FOS were confirmed by RT‑qPCR. In conclusion, the present study demonstrated that LPM exerts a DO effect during the pathogenesis of traumatic TMJA, which may provide a novel target for preventing TMJA.

The present study aimed to explore the underlying molecular mechanisms of hepatocellular carcinoma (HCC). RNA‑sequencing profiles GSM629264 and GSM629265, from the GSE25599 data set, were downloaded from the Gene Expression Omnibus database and processed by quality evaluation. GSM629264 and GSM629265 were from HCC and adjacent non‑cancerous tissues, respectively. TopHat software was used for alignment analysis, followed by the detection of novel splicing sites. In addition, the Cufflinks software package was used to analyze gene expressions, and the Cuffdiff program was used to screen for differently expressed genes (DEGs) and differentially expressed splicing variants. Gene ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were also performed. Transcription factors (TFs) and microRNAs (miRNAs) that regulate DEGs were identified, and a protein‑protein interaction (PPI) network was constructed. The hub node in the PPI network was obtained, and the TFs and miRNAs that regulated the hub node were further predicted. The quality of the sequencing data met the standards for analysis, and the clean reads were ~65%. Most sequencing reads mapped into coding sequence exons (CDS_exons), whereas other reads mapped into exon 3' untranslated regions (UTR_Exons), 5'UTR_Exons and Introns. Upregulated and downregulated DEGs between HCC and adjacent non‑cancerous tissues were screened. Genes of differentially expressed splicing variants were identified, including vesicle‑associated membrane protein 4, phosphatidylinositol glycan anchor biosynthesis class C, protein disulfide isomerase family A member 4 and growth arrest specific 5. Screened DEGs were enriched in the complement pathway. In the PPI network, ubiquitin C (UBC) was the hub node. UBC was predicted to be regulated by several TFs, including specificity protein 1 (SP1), FBJ murine osteosarcoma viral oncogene homolog (FOS), proto‑oncogene c‑JUN (JUN), FOS‑like antigen 2 (FOSL2) and SWI/SNF‑related, matrix‑associated, actin‑dependent regulator of chromatin, subfamily A, member 4 (SMARCA4), and several miRNAs, including miR‑30 and miR‑181. Results from the present study demonstrated that UBC, SP1, FOS, JUN, FOSL2, SMARCA4, miR‑30 and miR‑181 may participate in the development of HCC.

Osteoporosis is a systemic skeletal disease characterized by reduced bone mineral density (BMD), which results in an increased risk of fracture. Melandrium firmum (Siebold & Zucc.) Rohrbach (MFR), 'Wangbulryuhaeng' in Korean, is the dried aerial portion of Melandrii Herba Rohrbach, which is a member of the Caryophyllaceae family and has been used to treat several gynecological conditions as a traditional medicine. However, to the best of our knowledge, the effect of MFR on osteoclast differentiation and osteoporosis has not been assessed. To evaluate the effects of MFR on osteoclast differentiation, tartrate‑resistant acid phosphatase staining, actin ring formation and bone resorption assays were used. Additionally, receptor activator of nuclear factor‑κB ligand‑induced expression of nuclear factor of activated T cell, cytoplasmic 1 (NFATc1) and c‑Fos were measured using western blotting and reverse transcription‑PCR. The expression levels of osteoclast‑related genes were also examined. To further investigate the anti‑osteoporotic effects of MFR in vivo, an ovariectomized (OVX) rat model of menopausal osteoporosis was established. Subsequently, the femoral head was scanned using micro‑computed tomography. The results revealed that MFR suppressed osteoclast differentiation, formation and function. Specifically, MFR reduced the expression levels of osteoclast‑related genes by downregulating transcription factors, such as NFATc1 and c‑Fos. Consistent with the in vitro results, administration of MFR water extract to OVX rats reduced BMD loss, and reduced the expression levels of NFATc1 and cathepsin K in the femoral head. In conclusion, MFR may contribute to alleviate osteoporosis‑like symptoms. These results suggested that MFR may exhibit potential for the prevention and treatment of postmenopausal osteoporosis.

Streptococcus pyogenes (GAS) is a clinically significant bacterial strain that causes bacterial arthritis, osteomyelitis and implant infections. Infection complications can lead to serious bone destruction. Osteoclasts, the only type of cell with bone resorption function, participate in this process. Streptolysin O (SLO) is produced by almost all clinical Streptococcus pyogenes isolates. However, the role of SLO in bone infection caused by GAS had not been previously examined. The current study was performed to define the effects of SLO on receptor activator of NF‑κB ligand‑stimulated osteoclast differentiation in vitro. Results demonstrated that SLO decreased the phosphorylation of p65 and NF‑κB inhibitor α, suppressed c‑FOS and nuclear factor of activated T‑cells cytoplasmic 1, and downregulated the expression of osteoclast marker genes. SLO also induced apoptosis of mature osteoclasts. The results suggested that SLO blocked osteoclast activation during GAS infection. These findings may prove useful in the development of novel strategies for treating GAS‑associated bone infectious diseases.

To provide insight into molecular diagnosis and individualized treatment of ischemic stroke (IS), several available datasets in IS were analyzed to identify the differentially expressed genes and microRNAs (miRNAs). Series matrix files from GSE22255 and GSE16561 (mRNA profiles), a well as GSE110993 (miRNA profile) were downloaded from the Gene Expression Omnibus database. System‑level clustering was performed with GeneCluster 3.0 software, and gene annotation and pathway enrichment were performed with gene ontology analysis and Database for Annotation, Visualization and Integrated Discovery software. For a protein‑protein interaction (PPI) network, Biological General Repository for Interaction Datasets and IntAct interaction information were integrated to determine the interaction of differentially expressed genes. The selected miRNA candidates were imported into the TargetScan, miRDB and miRecords databases for the prediction of target genes. The present study identified 128 upregulated and 231 downregulated genes in female stroke patients, and 604 upregulated and 337 downregulated genes in male stroke patients compared with sex‑ and age‑matched controls. The construction of a PPI network demonstrated that male stroke patients exhibited YWHAE, CUL3 and JUN as network center nodes, and in female patients CYLD, FOS and PIK3R1 interactions were the strongest. Notably, these interactions are mainly involved in immune inflammatory response, apoptosis and other biological pathways, such as blood coagulation. Female and male upregulated genes were cross‑validated with another set of Illumina HumanRef‑8 v3.0 expression beadchip (GSE16561). Functional item association networks, gene function networks and transcriptional regulatory networks were successfully constructed, and the relationships between miRNAs and target genes were successfully predicted. The present study identified a number of transcription factors, including DEFA1, PDK4, SDPR, TCN1 and MMP9, and miRNAs, including miRNA (miR)‑21, miR‑143/145, miR‑125‑5p and miR‑122, which may serve important roles in the development of cerebral stroke and may be important molecular indicators for the treatment of IS.

Bone homeostasis is maintained by osteoclasts that absorb bone and osteoblasts that form bone tissue. Menopausal osteoporosis is a disease associated with aging and hormonal changes due to menopause causing abnormal activation of osteoclasts, resulting in a decrease in bone density. Existing treatments for osteoporosis have been reported to have serious side effects, such as jawbone necrosis and breast and uterine cancer; therefore, their use by patients is decreasing, whilst studies focusing on alternative treatments are increasingly popular. Solanum nigrum Line (SL) has been used as a medicinal plant that possesses several pharmacological effects, such as anti‑inflammatory and hepatotoxic protective effects. To the best of our knowledge, however, its effects on osteoporosis and osteoclasts have not been demonstrated previously. In the present study, the anti‑osteoporotic effect of SL was investigated using a postmenopausal model of osteoporosis in which Sprague‑Dawley rat ovaries were extracted. In addition, the inhibitory effects on osteoclast differentiation and function of SL was confirmed using an osteoclast model treated with receptor activator of NF‑κB ligand (RANKL) on murine RAW 264.7 macrophages. In vivo experiments showed that SL reduced the decrease in bone mineral density and improved changes in the morphological index of bone microstructure, such as trabecular number and separation. In addition, the number of tartrate resistant acid phosphatase‑positive cells in the femur and the expression levels of nuclear factor of activated T‑cells cytoplasmic 1 (NFATc1) and cathepsin K protein were inhibited. In vitro, SL suppressed RANKL‑induced osteoclast differentiation and bone resorption ability; this was mediated by NFATc1/c‑Fos, a key transcription factor involved in osteoclast differentiation, ultimately inhibiting expression of various osteoclast‑associated genes. These experimental results show that SL may be an alternative treatment for osteoporosis caused by abnormal activation of osteoclasts in the future.

Although low‑intensity ultrasound (LIUS) is a clinically established procedure, the early cellular effect of LIUS on a genetic level has not yet been studied. The current study investigated the early response genes elicited by LIUS in bone marrow stromal cells (BMSCs) using global‑scale microarrays and computational gene expression analysis tools. Mouse ST2 BMSCs were treated with LIUS [ISATA, 25 mW/cm2 for 20 min with a frequency of 1.11 MHz in a pulsed‑wave mode (0.2‑s burst sine waves repeated at 1 kHz)], then cultured for 0.5, 1 and 3 h at 37˚C. The time course of changes in gene expression was evaluated using GeneChip® high‑density oligonucleotide microarrays and Ingenuity® Pathway Analysis tools. The results were verified by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). A single exposure of LIUS did not affect cell morphology, cell growth or alkaline phosphatase activity. However, 61 upregulated and 103 downregulated genes were identified from 0.5 to 3 h after LIUS treatment. Two significant gene networks, labeled E and H, were identified from the upregulated genes, while a third network, labeled T, was identified from the downregulated genes. Gene network E or H containing the immediate‑early genes FBJ osteosarcoma oncogene and early growth response 1 or the heat shock proteins heat shock protein 1a/b was associated mainly with the biological functions of bone physiology and protein folding or apoptosis, respectively. Gene network T containing transcription factors fos‑like antigen 1 and serum response factor was also associated with the biological functions of the gene expression. RT‑qPCR indicated that the expression of several genes in the gene networks E and H were elevated in LIUS‑treated cells. LIUS was demonstrated to induce gene expression after short application in mouse ST2 BMSCs. The results of the present study provide a basis for the elucidation of the detailed molecular mechanisms underlying the cellular effects of LIUS.

Pneumonia caused by Mycoplasma pneumoniae (M. pneumoniae) is a major cause of community‑acquired pneumonia in children. In some cases, M. pneumoniae pneumonia (MPP) can develop into refractory MPP (RMPP), which shows no clinical or radiological response to macrolides, and can progress to severe and complicated pneumonia. However, the pathogenesis of RMPP remains poorly understood. The present study aimed to identify target genes that could be used as biomarkers for the clinical diagnosis of early‑stage RMPP through high‑throughput sequencing technology. The differences in long non‑coding (lnc)RNAs, mRNAs and circular (circ)RNAs were examined between whole‑blood samples from two patients with non‑refractory MPP (NRMPP), two patients with RMPP and three healthy children using ribosomal (r)RNA‑depleted RNA‑sequencing techniques and an integrated mRNA/circRNA analysis. A total of 17 lncRNAs (four upregulated and 13 downregulated), 18 mRNAs (six upregulated and 12 downregulated) and 24 circRNAs (12 upregulated and 12 downregulated) were the most significantly differentially expressed (P<0.05) between the NRMPP and RMPP groups. Upon functional analysis, the significantly differentially expressed genes encoded by the targeting mRNAs (prostaglandin‑endoperoxide synthase 2, IL‑8 and fos‑like antigen 1) were screened and identified to be enriched in the 'IL‑17 signaling pathway'. Furthermore, the key circRNAs in the NRMPP and RMPP comparative groups were primarily enriched in 'herpes simplex virus 1 infection', 'viral carcinogenesis' and 'RNA transport'. In the present study, a comprehensive analysis of the differences between the NRMPP and RMPP cases was performed based on rRNA‑depleted RNA‑sequencing techniques, and the selected genes and circRNAs may be closely associated with the complex pathogenesis of RMPP.

In order to identify the potential pathogenesis of hepatocellular carcinoma (HCC) developing from cirrhosis, a microarray‑based transcriptome profile was analyzed. The GSE63898 expression profile was downloaded from the Gene Expression Omnibus database, which included data from 228 HCC tissue samples and 168 cirrhotic tissue samples. The Robust Multi‑array Average in the Affy package of R was used for raw data processing and Student's t‑test was used to screen differentially expressed genes (DEGs). An enrichment analysis was then conducted using the Database for Annotation, Visualization and Integrated Discovery online tool, and the protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Furthermore, the MCODE plug‑in of Cytoscape was used to conduct a sub‑module analysis. A total of 634 DEGs were identified between HCC and cirrhosis, of which 165 were upregulated and 469 were downregulated. According to the cut‑off criteria, the PPI network was constructed and Jun proto‑oncogene, AP‑1 transcription factor subunit (degree, 39), Fos proto‑oncogene, AP‑1 transcription factor subunit (degree, 34) and v‑myc avian myelocytomatosis viral oncogene homolog (degree, 32) were identified as the hub nodes of the PPI network. Based on the sub‑module analysis, four specific modules were identified. In particular, module 1 was significantly enriched in the chemokine signaling pathway, and C‑X‑C motif chemokine ligand 12, C‑C motif chemokine receptor 7 (CCR7) and C‑C motif chemokine ligand 5 (CCL5) were three important proteins in this module. Module 4 was significantly enriched in chemical carcinogenesis, and cytochrome P450 family 2 subfamily E member 1, cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily A member 6 (CYP2A6) were three important proteins in this module. In conclusion, the present study revealed that CCR7, CCL5, CYP2C9 and CYP2A6 are novel genes identified in the development of HCC; however, the actual functions of these genes require verification.

Exosomal micro (mi)RNAs have been suggested to have important roles in abdominal obesity, and to be associated with metabolic alterations via posttranscriptional regulation of target genes. However, exosomal miRNA profiles in subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) have rarely been investigated. In the present study, microarray data were obtained from the Gene Expression Omnibus database with the following accession numbers: GSE68885 (exosomal miRNAs in SAT obtained from seven patients with obesity and five lean patients), GSE50574 (exosomal miRNAs in VAT obtained from seven patients with obesity and five lean patients) and GSE29718 [mRNAs in SAT (obtained from seven patients with obesity and eight lean patients) and VAT (obtained from three patients with obesity and two lean patients)]. Differentially expressed (DE)‑miRNAs and differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data method, and mRNA targets of DE‑miRNAs were predicted using the miRWalk2.0 database. Potential functions of DE‑miRNA target genes were determined using the Database for Annotation, Visualization and Integrated Discovery. As a result, 10 exosomal DE‑miRNAs were identified in SAT between patients with obesity and lean patients, while 58 DE‑miRNAs were identified in VAT between patients with obesity and lean patients. miRNA (miR)‑4517 was revealed to be a downregulated exosomal miRNA between SAT and VAT, while the other DE‑miRNAs were SAT‑(e.g. hsa‑miR‑3156‑5p and hsa‑miR‑4460) or VAT‑(e.g. hsa‑miR‑582‑5p, hsa‑miR‑566 and miR‑548) specific. Following overlapping with the target genes of DE‑miRNAs, only one DEG [cluster of differentiation 86 (CD86)] was identified in SAT samples, whereas 25 DEGs (e.g. fibroblast growth factor 2 (FGF2), FOS like 2, AP‑1 transcription factor subunit (FOSL2); and adenosine monophosphate deaminase 3 (AMPD3)] were identified in VAT samples. CD86 was revealed to be regulated by hsa‑miR‑3156‑5p; whereas FGF2, FOSL2 and AMPD3 were revealed to be regulated by hsa‑miR‑582‑5p, hsa‑miR‑566 and miR‑548, respectively. Functional enrichment analysis demonstrated that these target genes may be associated with inflammation. In conclusion, exosomal miRNAs may represent underlying therapeutic targets for the treatment of abdominal obesity and metabolic disorders via regulation of inflammatory genes.