Gene expression data of hepatocellular carcinoma (HCC) was compared with that of cirrhosis (C) to identify critical genes in HCC. A total of five gene expression data sets were downloaded from Gene Expression Omnibus. HCC and healthy samples were combined as dataset HCC, whereas cirrhosis samples were included in dataset C. A network was constructed for dataset HCC with the package R for performing Weighted Gene Co‑expression Network Analysis. Modules were identified by cluster analysis with the packages flashClust and dynamicTreeCut. Hub genes were screened out by calculating connectivity. Functional annotations were assigned to the hub genes using the Database for Annotation, Visualization and Integration Discovery, and functional annotation networks were visualized with Cytoscape. Following the exclusion of outlier samples, 394 HCC samples and 47 healthy samples were included in dataset HCC and 233 cirrhosis samples were included in dataset C. A total of 6 modules were identified in the weighted gene co‑expression network of dataset HCC (blue, brown, turquoise, green, red and yellow). Modules blue, brown and turquoise had high preservation whereas module yellow exhibited the lowest preservation. These modules were associated with transcription, mitosis, cation transportation, cation homeostasis, secretion and regulation of cyclase activity. Various hub genes of module yellow were cytokines, including chemokine (C‑C motif) ligand 22 and interleukin‑19, which may be important in the development of HCC. Gene expression profiles of HCC were compared with those of cirrhosis and numerous critical genes were identified, which may contribute to the progression of HCC. Further studies on these genes may improve the understanding of HCC pathogenesis.

The present study aimed to identify the recurrence‑associated genes in colon cancer, which may provide theoretical evidence for the development of novel methods to prevent tumor recurrence. Colon cancer and matched normal samples microarray data (E‑GEOD‑39582) were downloaded from ArrayExpress. Genes with significant variation were identified, followed by the screening of differentially expressed genes (DEGs). Subsequently, the co‑expression network of DEGs was constructed using the weighted correlation network analysis (WGCNA) method, which was verified using the validation dataset. The significant modules associated with recurrence in the network were subsequently screened and verified in another independent dataset E‑GEOD‑33113. Function and pathway enrichment analyses were also conducted to determine the roles of selected genes. Survival analysis was performed to identify the association between these genes and survival. A total of 434 DEGs were identified in the colon samples, and stress‑associated endoplasmic reticulum protein family member 2 (SERP2) and long non‑coding RNA‑0219 (LINC0219) were determined to be the vital DEGs between all the three sub‑type groups with different clinical features. The brown module was identified to be the most significant module in the co‑expression network associated with the recurrence of colon cancer, which was verified in the E‑GEOD‑33113 dataset. Top 10 genes in the brown module, including EGF containing fibulin like extracellular matrix protein 2 (EFEMP2), fibrillin 1 (FBN1) and secreted protein acidic and cysteine rich (SPARC) were also associated with survival time of colon cancer patients. Further analysis revealed that the function of cell adhesion, biological adhesion, extracellular matrix (ECM) organization, pathways of ECM‑receptor interaction and focal adhesion were the significantly changed terms in colon cancer. In conclusion, SERP2, EFEMP2, FBN1, SPARC, and LINC0219 were revealed to be the recurrence‑associated molecular and prognostic indicators in colon cancer by WGCNA co‑expression network analysis.

The present study aimed to elucidate the role of marine collagen peptides (MCPs) in protection of carotid artery vascular endothelial cells (CAVECs) in type 2 diabetes mellitus (T2DM), and the mechanism underlying this process. In an in vivo experiment, diabetic Wistar rats were divided randomly into four groups (n=10/group): Diabetes control, and three diabetes groups administered low, medium and high doses of MCPs (2.25, 4.5 and 9.0 g/kg body weight/day, respectively). Another 10 healthy rats served as the control. In an in vitro experiment, human umbilical‑vein endothelial cells (HUVECs) were incubated in normal and high concentrations of glucose with or without MCPs (3.0, 15.0 and 30.0 mg/ml, respectively) for 24, 48 or 72 h. Blood vessel/endothelial construction, inflammatory exudation and associated molecular biomarkers in CAVECs were detected and analyzed. The results of the present study demonstrated that in rats, MCP treatment for 4 weeks significantly lowered blood glucose and attenuated endothelial thinning and inflammatory exudation in carotid‑artery vascular endothelial cells. In vitro, the high‑glucose intervention significantly increased cell apoptosis in HUVECs, and medium and high doses of MCPs (4.5 and 9.0 g/kg body weight/day, respectively) partially ameliorated this high glucose‑mediated apoptosis and decreased levels of apoptosis biomarkers. In conclusion, a moderate oral MCP dose (≥4.5 g/kg body weight/day) may be a novel therapeutic tool to protect against early cardiovascular complications associated with T2DM by inhibiting apoptosis and reducing the expression of coupling factor 6 and microparticles.

Gene expression profiles of samples taken from patients with acute lung injury (ALI) induced by mechanical ventilation (MV) and lipopolysaccharide (LPS) were analyzed in order to identify key genes, and explore the underlying mechanisms. The GSE2411 microarray data set was downloaded from the Gene Expression Omnibus. This data set contained microarray data from 24 mouse lung samples, which were equally divided into four groups: Control group, MV group, LPS group and MV+LPS group. Differentially expressed genes (DEGs) were identified in the MV, LPS and MV+LPS groups, as compared with the control group, using packages of R software. Hierarchical clustering and between‑group comparisons were performed for each group of DEGs. Overrepresented biological processes were revealed by functional enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery. Unique DEGs in the LPS and MV+LPS groups were selected, and pathway enrichment analyses were performed using the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation system. A total of 32, 264 and 685 DEGs were identified in the MV, LPS and MV+LPS groups, respectively. The MV+LPS group had more DEGs, as compared with the other two treatment groups. Genes associated with the immune and inflammatory responses were significantly overrepresented in both the LPS and MV+LPS groups, suggesting that LPS dominated the progression of ALI. Unique DEGs in the LPS and MV+LPS groups were associated with cytokine‑cytokine receptor interaction. The Janus kinase‑signal transducer and activator of transcription signaling pathway was shown to be enriched in the LPS+MV‑unique DEGs. The results of the present study demonstrated that MV could exaggerate the transcriptional response of the lungs to LPS. Numerous key genes were identified, which may advance knowledge regarding the pathogenesis of ALI.

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, impaired glomerular filtration rate or a combination of the two. The aim of the present study was to identify the potential key genes involved in DKD progression and to subsequently investigate the underlying mechanism involved in DKD development. The array data of GSE30528 including 9 DKD and 13 control samples was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues were compared with normal tissues, and were analyzed using the limma package. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DEGs using the GO Function software in Bioconductor. The protein‑protein interaction (PPI) network was then constructed using Cytoscape software. A total of 426 genes (115 up‑ and 311 downregulated) were differentially expressed between the DKD and normal tissue samples. The PPI network was constructed with 184 nodes and 335 edges. Vascular endothelial growth factor A (VEGFA), α‑actinin‑4 (ACTN4), proto‑oncogene, Src family tyrosine kinase (FYN), collagen, type 1, α2 (COL1A2) and insulin‑like growth factor 1 (IGF1) were hub proteins. Major histocompatibility complex, class II, DP α1 (HLA‑DPA1) was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a GO term enriched in module A. VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA‑DPA1 may be potential key genes associated with the progression of DKD, and immune mechanisms may serve a part in DKD development.

The present study aimed to explore the molecular mechanisms underlying the development of thoracic spinal cord injury (SCI). The gene expression profile of GSE20907, which included 12 thoracic non‑injured spinal cord control samples and 12 thoracic transected spinal cord samples at different stages of SCI, was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package in R/Bioconductor. DEG‑associated pathways were analyzed using the Kyoto encyclopedia of genes and genomes database. A protein‑protein interaction (PPI) network was constructed and transcription factors (TFs) were predicted using cytoscape. Compared with the control samples, there were 1,942, 396, 188 and 396 DEGs identified at day 3 (d3), week 1 (wk1), wk2 and month 1 (m1), respectively. Cluster analysis indicated that the DEGs at m1 were similar to those in the control group. Downregulated DEGs were enriched in nervous system disease pathways, such as Parkinson's disease. Upregulated DEGs were enriched in immune response‑associated pathways, such as Fc γ R‑mediated phagocytosis at early stages (d3 and wk1). Upregulated DEGs were enriched in pathways associated with cancer and pyrimidine metabolism at wk2 and m1, respectively. In the PPI network, nodes including RAC2, CD4, STAT3 and JUN were identified. Furthermore, ATF3, JUN and EGR1 were identified as TFs associated with SCI. In conclusion, the results of the present study showed that the number of DEGs decreased in a time‑dependent manner following SCI. OLIG1, ATF3 and JUN may represent SCI regeneration‑associated genes. Immune-associated inflammation was shown to be important in SCI, and SCI exhibits causal associations with other diseases, including cardiovascular disease and cancers. The present study provided novel insight into the molecular mechanisms of SCI regeneration, which may aid in the development of strategies to enhance recovery following SCI.

Irradiation commonly causes pneumocyte senescence, which may lead to severe fatal lung injury characterized by pulmonary dysfunction and respiratory failure. However, the molecular mechanism underlying the induction of pneumocyte senescence by irradiation remains to be elucidated. In the present study, weighted gene co‑expression network analysis (WGCNA) was used to screen for differentially expressed genes, and to identify the hub genes and gene modules, which may be critical for senescence. A total of 2,916 differentially expressed genes were identified between the senescence and non‑senescence groups following thoracic irradiation. In total, 10 gene modules associated with cell senescence were detected, and six hub genes were identified, including B‑cell scaffold protein with ankyrin repeats 1, translocase of outer mitochondrial membrane 70 homolog A, actin filament‑associated protein 1, Cd84, Nuf2 and nuclear factor erythroid 2. These genes were markedly associated with cell proliferation, cell division and cell cycle arrest. The results of the present study demonstrated that WGCNA of microarray data may provide further insight into the molecular mechanism underlying pneumocyte senescence.

The aim of this study was to identify the molecular events that distinguish serrated colorectal carcinoma (SCRC) from conventional colorectal carcinoma (CCRC) through differential gene expression, pathway and protein-protein interaction (PPI) network analysis. The GSE4045 and GSE8671 microarray datasets were downloaded from the Gene Expression Omnibus database. We identified the genes that are differentially expressed between SCRC and normal colon tissues, CCRC and healthy tissues, and between SCRC and CCRC using Student's t-tests and Benjamini‑Hochberg (BH) multiple testing corrections. The differentially expressed genes (DEGs) were then mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and their enrichment for specific pathways was investigated using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool with a significance threshold of 0.1. Analysis of the potential interactions between the protein products of 220 DEGs (between CCRC and SCRC) was performed by constructing a PPI network using data from the high performance RDF database (P<0.1). The interaction between pathways was also analyzed in CCRC based on the PPI network. Our study identified thousands of genes differentially expressed in SCRC and CCRC compared to healthy tissues. The DEGs in SCRC and CCRC were enriched in cell cycle, DNA replication, and base excision repair pathways. The proteasome pathway was significantly enriched in SCRC but not in CCRC after BH adjustment. The PPI network showed that tumour necrosis factor receptor-associated factor 6 (TRAF6) and atrophin 1 (ATN1) were the most central genes in the network, with respective degrees of node predicted at 90 and 88. In conclusion, the preoteasome pathway was shown to be specifically enriched in SCRC. Furthermore, TRAF6 and ATN1 may be promising biomarkers for the distinction between serrated and conventional CRC.

Targeted drugs have been widely used in the treatment of patients with lung cancer, particularly for those with non‑small cell lung cancer (NSCLC). Plasma cell‑free DNA is an emerging clinical tool for the detection of epidermal growth factor receptor (EGFR) gene mutation in patients with lung cancer. Detection of circulating tumor (ct) DNA by droplet digital PCR (ddPCR) is a highly sensitive and minimally invasive alternative for the assessment and management of cancer. In the present study, four ddPCR systems were developed to detect the 19DELs, L858R, T790M and C797S mutations of the EGFR gene in plasma ctDNA samples, and all exhibited higher sensitivity compared with the amplification refractory mutation system (ARMS)‑PCR assays. The results revealed that the sensitivity of the ddPCR assays for the four major types of EGFR mutant reached 0.04%. In total, 50 plasma ctDNA samples were collected from patients with NSCLC to detect the 19DELs, L858R, T790M and C797S mutations by ddPCR and ARMS‑PCR. All the mutations except for C797S were detected and the concordance rates between ddPCR and ARMS‑PCR were 96% (19DELs), 98% (L858R) and 100% (T790M). The fraction of EGFR mutation ranged from 0.43 to 68.07% using the ddPCR method. Therefore, the present study suggests that the four ddPCR testing systems could be used for early detection of EGFR mutations in plasma samples, so that patients can better select the targeted drugs according to the EGFR mutation.

Spinal cord injury (SCI) is a specific type of damage to the central nervous system causing temporary or permanent changes in its function. The present aimed to identify the genetic changes in neuroplasticity following SCI in rats. The GSE52763 microarray dataset, which included 15 samples [3 sham (1 week), 4 injury only (1 week), 4 injury only (3 weeks), 4 injury + treadmill (3 weeks)] was downloaded from the Gene Expression Omnibus database. An empirical Bayes linear regression model in limma package was used to identify the differentially expressed genes (DEGs) in injury vs. sham and treadmill vs. non‑treadmill comparison groups. Subsequently, time series and enrichment analyses were performed using pheatmap and clusterProfile packages, respectively. Additionally, protein‑protein interaction (PPI) and transcription factor (TF)‑microRNA (miRNA)‑target regulatory networks were constructed using Cytoscape software. In total, 159 and 105 DEGs were identified in injury vs. sham groups and treadmill vs. non‑treadmill groups, respectively. There were 40 genes in cluster 1 that presented increased expression levels in the injury (1 week/3 weeks) groups compared with the sham group, and decreased expression levels in the injury + treadmill group compared with the injury only groups; conversely, 52 genes in cluster 2 exhibited decreased expression levels in the injury (1 week/3 weeks) groups compared with the sham group, and increased expression levels in the injury + treadmill group compared with the injury only groups. Enrichment analysis indicated that clusters 1 and 2 were associated with immune response and signal transduction, respectively. Furthermore, microtubule associated protein 1B, phosphofurin acidic cluster sorting protein 2 and adenosylhomocysteinase‑like 1 exhibited the highest degrees in the regulatory network, and were regulated by miRNAs including miR‑34A, miR‑34B, miR‑34C and miR‑449. These miRNAs and their target genes may serve important roles in neuroplasticity following traumatic SCI in rats. Nevertheless, additional in‑depth studies are required to confirm these data.

Intracranial aneurysm (IA) is a cerebrovascular disease with a high mortality rate. The pathogenesis of IA remains unclear and the treatment limited. The purpose of the present study was to identify the key genes expressed in IAs and provide the basis for further research and treatment. The raw dataset GSE75436 was downloaded from Gene Expression Omnibus, including 15 IA samples and 15 matched superficial temporal artery (STA) samples. Then, differentially expressed genes (DEGs) were identified using the limma package in R software. Hierarchical clustering analysis was performed on the DEGs using the gplot2 package in R. Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tools were used to perform gene ontology (GO) functional enrichment analysis. DAVID and gene set enrichment analysis were separately used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The intersections of the two results were selected as common KEGG pathways. Protein‑protein interaction (PPI) analysis among the DEGs involved in the common KEGG pathways was performed using Search Tool for the Retrieval of Interacting Genes online tools, and visualized with Cytoscape software. A total of 782 DEGs were identified, comprising 392 upregulated and 390 downregulated DEGs. Hierarchical clustering demonstrated that the DEGs could precisely distinguish the IAs from the STAs. The GO enrichment analysis demonstrated that the upregulated DEGs were mainly involved in the inflammatory response and the management of extracellular matrix, and the downregulated DEGs were mainly involved in the process of vascular smooth muscle contraction. The KEGG pathway enrichment analysis demonstrated that the common pathways were 'leishmaniasis', 'Toll‑like receptor signaling pathway' and 'vascular smooth muscle contraction'. In the PPI network, tumor necrosis factor (TNF), interleukin 8 and Toll‑like receptor 4 had the highest degrees; they were associated with the inflammatory response. The Toll‑like receptor signaling pathway and TNF gene may serve as targets for future research and treatment.

In the present study, gene expression profiles of cisplatin-sensitive ovarian cancer (OC) cells were compared with those of cisplatin-resistant OC cells to identify key genes and pathways contributing to cisplatin resistance in ovarian cancer cells. The GSE15372 gene expression data set was downloaded from Gene Expression Omnibus, and included five biological replicates of cisplatin-sensitive OC cells and five biological replicates of cisplatin-resistant OC cells. Differentially expressed genes (DEGs) were screened using the limma package in R, based on the cut-off values of P<0.05 and |log2 (fold change)|>1. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology enrichment analysis were performed on the DEGs using the Database for Annotation, Visualization and Integration Discovery. The protein-protein interaction (PPI) network was constructed for the DEGs using STRING, and sub-networks were analyzed by Clustering with Overlapping Neighborhood Expansion. A total of 556 DEGs were identified in the cisplatin-sensitive OC cells, of which 246 were upregulated and 310 were downregulated. Functional enrichment analysis revealed metabolism-associated pathways, DNA replication and cell cycle were significantly enriched in the downregulated genes, while cell growth and differentiation, response to stimulus, and apoptosis were significantly enriched in the upregulated genes. A PPI network, including 342 nodes was constructed for the DEGs and four subnetworks were extracted from the entire network. A total of 34 DEGs interacting with enhancer of zeste homolog 2 (EZH2) were identified, which were associated with DNA replication, pyrimidine metabolism and cell cycle. In conclusion, a number of key genes and pathways associated with the cisplatin-resistance of OC were revealed, particularly EZH2. These findings assist in the development of therapy for OC.

The present study aimed to identify the differentially expressed genes (DEGs) between lung adenocarcinoma and normal lung tissues, and between lung squamous cell carcinoma and normal lung tissues, with the purpose of identifying potential biomarkers for the treatment of lung cancer. The gene expression profile (GSE6044) was downloaded from the Gene Expression Omnibus database, which included data from 10 lung adenocarcinoma samples, 10 lung squamous cell carcinoma samples, and five matched normal lung tissue samples. After data processing, DEGs were identified using the Student's t‑test adjusted via the Benjamini‑Hochberg method. Subsequently, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery, and a global network was constructed. A total of 95 upregulated and 241 downregulated DEGs were detected in lung adenocarcinoma samples, and 204 upregulated and 285 downregulated DEGs were detected in lung squamous cell carcinoma samples, as compared with the normal lung tissue samples. The DEGs in the lung squamous cell carcinoma group were enriched in the following three pathways: Hsa04110, Cell cycle; hsa03030, DNA replication; and hsa03430, mismatch repair. However, the DEGs in the lung adenocarcinoma group were not significantly enriched in any specific pathway. Subsequently, a global network of lung cancer was constructed, which consisted of 341 genes and 1,569 edges, of which the top five genes were HSP90AA1, BCL2, CDK2, KIT and HDAC2. The expression trends of the above genes were different in lung adenocarcinoma and lung squamous cell carcinoma when compared with normal tissues. Therefore, these genes were suggested to be crucial genes for differentiating lung adenocarcinoma and lung squamous cell carcinoma.

Due to the rising abuse of ketamine usage in recent years, ketamine‑induced urinary tract syndrome has received increasing attention. The present study aimed to investigate the molecular mechanism underlying ketamine‑associated cystitis in a mouse model. Female C57BL/6 mice were randomly divided into two groups: One group was treated with ketamine (100 mg/kg/day of ketamine for 20 weeks), whereas, the control group was treated with saline solution. In each group, micturition frequency and urine volume were examined to assess urinary voiding functions. Mouse bladders were extracted and samples were examined for pathological and morphological alterations using hematoxylin and eosin staining, Masson's trichrome staining and scanning electron microscopy. A cDNA microarray was conducted to investigate the differentially expressed genes following treatment with ketamine. The results suggested that bladder hyperactivity increased in the mice treated with ketamine. Furthermore, treatment with ketamine resulted in a smooth apical epithelial surface, subepithelial vascular congestion and lymphoplasmacytic aggregation. Microarray analysis identified a number of genes involved in extracellular matrix accumulation, which is associated with connective tissue fibrosis progression, and in calcium signaling regulation, that was associated with urinary bladder smooth muscle contraction. Collectively, the present results suggested that these differentially expressed genes may serve critical roles in ketamine‑induced alterations of micturition patterns and urothelial pathogenesis. Furthermore, the present findings may provide a theoretical basis for the development of effective therapies to treat ketamine‑induced urinary tract syndrome.

In order to better understand the etiology of obese type 2 diabetes (T2D) at the molecular level, the present study investigated the gene expression and DNA methylation profiles associated with T2D via systemic analysis. Gene expression (GSE64998) and DNA methylation profiles (GSE65057) from liver tissues of healthy controls and obese patients with T2D were downloaded from the Gene Expression Omnibus database. Differentially‑expressed genes (DEGs) and differentially‑methylated genes (DMGs) were identified using the Limma package, and their overlapping genes were additionally determined. Enrichment analysis was performed using the BioCloud platform on the DEGs and the overlapping genes. Using Cytoscape software, protein‑protein interaction (PPI), transcription factor target networks and microRNA (miRNA) target networks were then constructed in order to determine associated hub genes. In addition, a further GSE15653 dataset was utilized in order to validate the DEGs identified in the GSE64998 dataset analyses. A total of 251 DEGs, including 124 upregulated and 127 downregulated genes, were detected, and a total of 9,698 genes were demonstrated to be differentially methylated in obese patients with T2D compared with non‑obese healthy controls. A total of 103 overlapping genes between the two datasets were revealed, including 47 upregulated genes and 56 downregulated genes. The identified overlapping genes were revealed to be strongly associated with fatty acid and glucose metabolic pathways, in addition to oxidation/reduction. The overlapping genes cyclin D1 (CCND1), PPARG coactivator α (PPARGC1A), fatty acid synthase (FASN), glucokinase (GCK), steraroyl‑coA desaturase (SCD) and tyrosine aminotransferase (TAT) had higher degrees in the PPI, transcription target networks and miRNA target networks. In addition, among the 251 DEGs, a total of 35 DEGs were validated to be being shared genes between the datasets, which included a number of key genes in the PPI network, including CCND1, FASN and TAT. Abnormal gene expression and DNA methylation patterns that were implicated in fatty acid and glucose metabolic pathways and oxidation/reduction reactions were detected in obese patients with T2D. Furthermore, the CCND1, PPARGC1A, FANS, GCK, SCD and TAT genes may serve a role in the development of obesity‑associated T2D.

Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up‑ and down‑regulated genes. The up‑ and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein‑protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up‑ and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE‑miRs), lncRNAs (DE‑lncRs) and mRNAs (DEGs). miRNA‑mRNA and lncRNA‑miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA‑mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA‑mRNA network, consisting of 32 optimal feature‑coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE‑lncRs, 507 DEGs and 55 DE‑miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)‑miR‑33b, HOX transcript antisense RNA (HOTAIR)‑miR‑1‑MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)‑miR‑124‑calcitonin related polypeptide α (CALCA) and EMX2OS‑miR‑124‑γ‑aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR‑1, miR‑33b, miR‑124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4‑miR‑33b, HOTAIR‑miR‑1‑MAGEA2 and EMX2OS‑miR‑124‑CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.