High expression of COL5A2, a member of COL5 family,indicates the poor survival and facilitates cell migration in gastric cancer

Background: Gastric cancer (GC) metastasis determines the prognosis of patients, and exploring the molecular mechanism of GC metastasis is expected to provide a theoretical basis for clinical treatment. Recent studies have shown that extracellular matrix protein is closely related to GC metastasis. The present study aimed to explore the expression profile and role of COL5A2, as an extracellular matrix protein, in GC.Methods: The expression, overall survival, and progression-free survival data of COL5 family members were extracted from The Cancer Genome Atlas (TCGA) database, respectively. Weighted gene co-expression network analysis of the {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 database was performed out to identify modules and associated genes.Results: COL5A2 was selected as our research target in the TCGA database, and was also verified in the {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 and {"type":"entrez-geo","attrs":{"text":"GSE15459","term_id":"15459"}}GSE15459 datasets. COL5A2 was up-regulated in GC tissues by paraffin immunohistochemistry and RT-qPCR. The prognosis of patients with low COL5A2 expression was better than that of patients with high COL5A2 expression. Scratch and migration experiments showed that knockdown of COL5A2 decreased the migration ability of gastric cancer cells compared with the control group. In vivo, mice with tail vein injection COL5A2 knockdown had fewer and smaller metastatic nodules in liver. GSEA results showed that the TCGA and {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 samples were significantly enriched in several well-known cancer-related pathways, such as the TGF-β, MAPK, and JAK2 signaling pathways.Conclusion: COL5A2 was most closely related to advanced GC among COL5 family members. High COL5A2 expression is associated with a poor prognosis, and may be a novel therapeutic target for GC.     

Tumor microenvironment characterization in stage IV gastric cancer

Immunotherapy is remarkably affected by the immune environment of the principal tumor. Nonetheless, the immune environment’s clinical relevance in stage IV gastric cancer (GC) is largely unknown. The gene expression profiles of 403 stage IV GC patients in the three cohorts: GEO (Gene Expression Omnibus, {"type":"entrez-geo","attrs":{"text":"GSE84437","term_id":"84437"}}GSE84437 (n=292) and {"type":"entrez-geo","attrs":{"text":"GSE62254","term_id":"62254"}}GSE62254 (n=77), and TCGA (The Cancer Genome Atlas, n=34) were used in the present study. Using four publicly available stage IV GC expression datasets, 29 immune signatures were expression profiled, and on this basis, we classified stage IV GC. The classification was conducted using the hierarchical clustering method. Three stage IV GC subtypes L, M, and H were identified representing low, medium, and high immunity, respectively. Immune correlation analysis of these three types revealed that Immune H exhibited a better prognostic outcome as well as a higher immune score compared with other subtypes. There was a noticeable difference in the three subgroups of HLA genes. Further, on comparing with other subtypes, CD86, CD80, CD274, CTLA4, PDCD1, and PDCD1LG2 had higher expression in the Immunity H subtype. In stage IV GC, potentially positive associations between immune and pathway activities were displayed, due to the enrichment of pathways including TNF signaling, Th-17 cell differentiation, and JAK-STAT signaling pathways in Immunity H vs Immunity L subtypes. External cohorts from TCGA cohort ratified these results. The identification of stage IV GC subtypes has potential clinical implications in stage IV GC treatment.     

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.Methods: Microarray datasets from the Gene Expression Omnibus (GEO) ({"type":"entrez-geo","attrs":{"text":"GSE54129","term_id":"54129"}}GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein–protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell–cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8⁺ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.     

Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets ({"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520 and {"type":"entrez-geo","attrs":{"text":"GSE36376","term_id":"36376"}}GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76–3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.     

X-box binding protein 1 (XBP1): a potential role in chemotherapy response,clinical pathologic features,non-inflamed tumour microenvironment for breast cancer

X-box binding protein 1 (XBP1) is mainly expressed in breast cancer (BC) in human cancers. Its tumorigenesis and favourable prognosis are contradictory, and its essential role in chemotherapeutic response and immunosuppression is unknown in BC. The study firstly identified XBP1 who received neoadjuvant chemotherapy (NAC) from {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055 and {"type":"entrez-geo","attrs":{"text":"GSE24460","term_id":"24460"}}GSE24460. Associations between XBP1 expression and clinicopathological characteristics was investigated using Oncomine, TCGA, UALCAN and bc-GenExMiner. The prognostic value of XBP1 was assessed using the Kaplan–Meier Plotter, bc-GenExMiner, {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, and {"type":"entrez-geo","attrs":{"text":"GSE25056","term_id":"25056"}}GSE25056. Furthermore, we systematically correlated XBP1 and immunological characteristics in the BC tumour microenvironment (TME) using TISIDB, TIMER, {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, {"type":"entrez-geo","attrs":{"text":"GSE25056","term_id":"25056"}}GSE25056 and TCGA dataset. Finally, an essential role of XBP1 in chemotherapy response was evaluated based on {"type":"entrez-geo","attrs":{"text":"GSE25055","term_id":"25055"}}GSE25055, {"type":"entrez-geo","attrs":{"text":"GSE25065","term_id":"25065"}}GSE25065, {"type":"entrez-geo","attrs":{"text":"GSE24460","term_id":"24460"}}GSE24460, {"type":"entrez-geo","attrs":{"text":"GSE5846","term_id":"5846"}}GSE5846, ROC Plotter and CELL databases. Furthermore, XBP1 mRNA expression levels were obviously highest in BC among human cancers and were significantly related to a good prognosis. In addition, XBP1 mRNA and protein levels were higher in the luminal subtype than in normal tissues and basal-like subtype, which might be attributed to membrane transport-related processes. Apart from BC, negative immunological correlations of XBP1 were not observed in other malignancies. XBP1 might shape the non-inflamed TME in BC. Finally, XBP1 expression was higher in chemo-resistive than chemo-sensitive cases, it had a predictive value and could independently predict chemotherapy response in BC patients receiving NAC. Our study suggests that the essential role of XBP1 in clinical pathologic features, non-inflamed TME, chemotherapy response in BC.     

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma

Autophagy is involved in cancer initiation and progression but its role in uveal melanoma (UM) was rarely investigated. Herein, we built an autophagy-related gene (ARG) risk model of UM patients by univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression model and filtrated out nine prognostic ARGs in The Cancer Genome Atlas (TCGA) cohort. Survival and Receiver Operating Characteristic (ROC) Curve analysis in the TCGA and other four independent UM cohorts ({"type":"entrez-geo","attrs":{"text":"GSE22138","term_id":"22138"}}GSE22138, {"type":"entrez-geo","attrs":{"text":"GSE27831","term_id":"27831"}}GSE27831, {"type":"entrez-geo","attrs":{"text":"GSE44295","term_id":"44295"}}GSE44295 and {"type":"entrez-geo","attrs":{"text":"GSE84976","term_id":"84976"}}GSE84976) proved that the ARG-signature possessed robust and steady prognosis predictive ability. We calculated risk scores for patients included in our study and patients with higher risk scores showed worse clinical outcomes. We found the expressions of the nine ARGs were significantly associated with clinical and molecular features (including risk score) and overall survival (OS) of UM patients. Furthermore, we utilized univariate and multivariate Cox regression analyses to determine the independent prognostic ability of the ARG-signature. Functional enrichment analysis showed the ARG-signature was correlated with several immune-related processes and pathways like T-cell activation and T-cell receptor signaling pathway. Gene set enrichment analysis (GSEA) found tumor hallmarks including angiogenesis, IL6-JAK-STAT3-signaling, reactive oxygen species pathway and oxidative phosphorylation were enriched in high-risk UM patients. Finally, infiltrations of several immune cells and immune-related scores were found significantly associated with the ARG-signature. In conclusion, the ARG-signature might be a strong predictor for evaluating the prognosis and immune infiltration of UM patients.     

Downregulation of ACE2 expression by SARS-CoV-2 worsens the prognosis of KIRC and KIRP patients via metabolism and immunoregulation

Background: Angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) allow entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into host cells and play essential roles in cancer therapy. However, the functions of ACE2 and TMPRSS2 in kidney cancer remain unclear, especially as kidneys are targets for SARS-CoV-2 infection.Methods: UCSC Xena project, the Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases ({"type":"entrez-geo","attrs":{"text":"GSE30589","term_id":"30589"}}GSE30589 and {"type":"entrez-geo","attrs":{"text":"GSE59185","term_id":"59185"}}GSE59185) were searched for gene expression in human tissues, gene expression data, and clinical information. Several bioinformatics methods were utilized to analyze the correlation between ACE2 and TMPRSS2 with respect to the prognosis of kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP).Results: ACE2 expression was significantly upregulated in tumor tissue, while its downregulation was associated with low survival in KIRC and KIRP patients. TMPRSS2 was downregulated in KIRC and KIRP, and its expression was not correlated with patient survival. According to clinical risk factor-based prediction models, ACE2 exhibits predictive accuracy for kidney cancer prognosis and is correlated with metabolism and immune infiltration. In an animal model, ACE2 expression was remarkably downregulated in SARS-CoV-2-infected cells compared to in the control.Conclusion: ACE2 expression is highly correlated with various metabolic pathways and is involved in immune infiltration.it plays a crucial role than TMPRSS2 in diagnosing and prognosis of kidney cancer patients. The overlap in ACE2 expression between kidney cancer and SARS-CoV-2 infection suggests that patients with KIRC or KIRP are at high risk of developing serious symptoms.     

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood.Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses.Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, {"type":"entrez-geo","attrs":{"text":"GSE30219","term_id":"30219"}}GSE30219, {"type":"entrez-geo","attrs":{"text":"GSE157010","term_id":"157010"}}GSE157010, {"type":"entrez-geo","attrs":{"text":"GSE73403","term_id":"73403"}}GSE73403, and {"type":"entrez-geo","attrs":{"text":"GSE4573","term_id":"4573"}}GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis.Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.     

Early B-cell factors involve in the tumorigenesis and predict the overall survival of gastric cancer

Gastric cancer (GC) is a heavy health burden around the world, which is the fifth most frequent tumor and leads to the third most common cancer-related deaths. It is urgent to identify prognostic markers as the guideline for personalized treatment and follow-up. We accessed the prognostic value of Early B-cell factors (EBFs) in GC. A total of 415 GC tissues and 34 normal tissues from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort, 616 external patients from {"type":"entrez-geo","attrs":{"text":"GSE15459","term_id":"15459"}}GSE15459, {"type":"entrez-geo","attrs":{"text":"GSE22377","term_id":"22377"}}GSE22377, {"type":"entrez-geo","attrs":{"text":"GSE51105","term_id":"51105"}}GSE51105, {"type":"entrez-geo","attrs":{"text":"GSE62245","term_id":"62245"}}GSE62245 were enrolled for analysis. Univariate and multivariate Cox regression analyses were employed to evaluate the sole and integrative prognostic value of EBFs, respectively. Genetic alterations, DNA methylation of EBFs were also evaluated, as well as the involved signaling pathways. We revealed that increased EBFs associated with the poor prognosis of GC patients, the prognostic model was established in TCGA-STAD cohort, and validated in Gene Expression Omnibus (GEO) cohorts, with effectiveness in both HER2 positive and negative patients. DNA methylation was involved in the impact on prognosis. Cell cycle, immune-associated, and MAPK pathways were influenced by EBFs. Anti-CTLA4 immunotherapy is more suitable for EBFs determining high-risk groups, but not anti-PD-1/PD-L1 therapy. 5-Fluorouracil, methotrexate, vorinostat are suitable to inhibit the function of EBFs. Our new findings provide novel insight into the prediction of prognosis and clinical treatment of GC patients based on EBFs.     

The diagnostic and prognostic values of microRNA-196a in cancer

MicroRNA-196a (miR-196a) was previously reported to be up-regulated in cancers, and it has the diagnostic and prognostic values in cancers. Whereas, the conclusion was still unclear according to the published data. To assess such roles of miR-196a in cancers, the present study was conducted based on published data and online cancer-related databases. To identify the relevant published data, we searched articles in databases and then the relevant data were extracted to evaluate the correlation between miR-196a expression and diagnosis, prognosis for cancer patients. The pooled results showed that miR-196a was a valuable diagnostic biomarker in cancer (area under curve (AUC) = 0.87, 95% CI: 0.84–0.90; sensitivity (SEN) = 0.73, 95% CI: 0.64–0.81; specificity (SPE) = 0.90, 95% CI: 0.81–0.95), which was consistent with the data from databases (breast cancer: miR-196a-3p: AUC = 0.77, 95% CI: 0.74–0.79; miR-196a-5p: AUC = 0.71, 95% CI: 0.66–0.75; pancreatic cancer: miR-196a-3p: AUC = 0.80, 95% CI: 0.73–0.87; miR-196a-5p: AUC = 0.61, 95% CI: 0.51–0.71). In addition, the pooled result revealed that elevated miR-196a expression in tumor tissues (HR = 2.54, 95% CI: 1.79–3.61, P_{Heterogeneity}=0.000, I² = 75.8%) or serum/plasma (HR = 4.06, 95% CI: 2.67–6.18, P_{Heterogeneity}=0.668, I² = 0%) of patients was an unfavorable survival biomarker, which was consistent with the data from databases (adrenocortical carcinoma: HR = 5.70; esophageal carcinoma: HR = 1.93; brain lower grade glioma: HR = 2.91; {"type":"entrez-geo","attrs":{"text":"GSE40267","term_id":"40267"}}GSE40267: HR = 2.47, 95% CI: 1.2–5.07; TCGA: HR = 1.82, 95% CI: 1.21–2.74; {"type":"entrez-geo","attrs":{"text":"GSE19783","term_id":"19783"}}GSE19783: HR = 4.24, 95% CI: 1–18.06). In short, our results demonstrated that miR-196a in tumor tissue or serum/plasma could be used as a prognostic and diagnostic values for cancers.     

Mining the key genes for ventilator-induced lung injury using co-expression network analysis

Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future prevention and treatment. Four microarray datasets from the GEO database were selected in our investigation, and were subjected to the Weighted Gene Co-Expression Network Analysis (WGCNA) to identify the VILI-correlated gene modules. The limma package in R software was used to identify the differentially expressed genes (DEGs) between the VILI and control groups. WGCNA was constructed by merging the {"type":"entrez-geo","attrs":{"text":"GSE9314","term_id":"9314"}}GSE9314, {"type":"entrez-geo","attrs":{"text":"GSE9368","term_id":"9368"}}GSE9368, {"type":"entrez-geo","attrs":{"text":"GSE11434","term_id":"11434"}}GSE11434 and {"type":"entrez-geo","attrs":{"text":"GSE11662","term_id":"11662"}}GSE11662 datasets. A total of 49 co-expression network modules were determined as associated with VILI. The intersected genes between hub genes screened from DEGs for VILI and those identified using WGCNA were as follows: Tlr2, Hmox1, Serpine1, Mmp9, Il6, Il1b, Ptgs2, Fos and Atf3, which were determined to be key genes for VILI. Those key genes were validated by {"type":"entrez-geo","attrs":{"text":"GSE86229","term_id":"86229"}}GSE86229 and quantitative PCR (qPCR) experiment to have significantly statistical difference in their expression between the VILI and control groups. In a nutshell, nine key genes with expression differences in VILI were screened by WGCNA by integrating multiple datasets.     

Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

Background: The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy.Materials and methods: We identified differential genes (DEGs) that were up- and down-regulated in the three datasets ({"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942, {"type":"entrez-geo","attrs":{"text":"GSE13911","term_id":"13911"}}GSE13911, and {"type":"entrez-geo","attrs":{"text":"GSE118916","term_id":"118916"}}GSE118916) and created protein–protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment.Results: Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan–Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy.Conclusion: ECM–receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC patients.     

Bioinformatic analysis of key pathways and genes involved in pediatric atopic dermatitis

The initiation of atopic dermatitis (AD) typically happens very early in life, but most of our understanding of AD is derived from studies on AD patients in adult. The aim of the present study was to identify gene signature speficic to pediatric AD comapred with adult AD. The gene expression profiles of four datasets ({"type":"entrez-geo","attrs":{"text":"GSE32924","term_id":"32924"}}GSE32924, {"type":"entrez-geo","attrs":{"text":"GSE36842","term_id":"36842"}}GSE36842, {"type":"entrez-geo","attrs":{"text":"GSE58558","term_id":"58558"}}GSE58558, and {"type":"entrez-geo","attrs":{"text":"GSE107361","term_id":"107361"}}GSE107361) were downloaded from the GEO database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network was constructed by Cytoscape software. Total 654 differentially expressed genes (DEGs) (394 up-regulated and 260 down-regulated) were identified in pediatric AD samples with adult AD samples as control. The up-regulated DEGs were significantly enriched in the migration and chemotaxis of granulocyte and neutrophil, while down-regulated DEGs were significantly enriched in biological adhesion. KEGG pathway analysis showed that up-regulated DEGs participated in chemokine signaling pathway while down-regulated DEGs participated in adherens junction, focal adhesion, and regulation of actin cytoskeleton. The top 10 hub genes GAPDH, EGFR, ACTB, ESR1, CDK1, CXCL8, CD44, KRAS, PTGS2, and SMC3 were involved in chemokine signaling pathway, cytokine–cytokine receptor interaction, interleukin-17 signaling pathway, and regulation of actin cytoskeleton. In conclusion, we identified DEGs and hub genes involved in pediatric AD, which might be used as therapeutic targets and diagnostic biomarkers for pediatric AD.     

FRZB as a key molecule in abdominal aortic aneurysm progression affecting vascular integrity

Abdominal aortic aneurysm (AAA), when ruptured, results in high mortality. The identification of molecular pathways involved in AAA progression is required to improve AAA prognosis. The aim of the present study was to assess the key genes for the progression of AAA and their functional role. Genomic and clinical data of three independent cohorts were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) ({"type":"entrez-geo","attrs":{"text":"GSE57691","term_id":"57691"}}GSE57691, {"type":"entrez-geo","attrs":{"text":"GSE7084","term_id":"7084"}}GSE7084, and {"type":"entrez-geo","attrs":{"text":"GSE98278","term_id":"98278"}}GSE98278). To develop AAA diagnosis and progression-related differentially expressed genes (DEGs), we used a significance analysis of microarray (SAM). Spearman correlation test and gene set analysis were performed to identify potential enriched pathways for DEGs. Only the Frizzled-related protein (FRZB) gene and chromosome 1 open reading frame 24 (C1orf24) exhibited significant down-regulation in all analyses. With FRZB, the pathways were associated with RHO GTPase and elastin fiber formation. With C1orf24, the pathways were elastic fiber formation, extracellular matrix organization, and cell–cell communication. Since only FRZB was evolutionally conserved in the vertebrates, function of FRZB was validated using zebrafish embryos. Knockdown of frzb remarkably reduced vascular integrity in zebrafish embryos. We believe that FRZB is a key gene involved in AAA initiation and progression affecting vascular integrity.     

Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high‐throughput data

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.Methods: The gene expression profile of {"type":"entrez-geo","attrs":{"text":"GSE12021","term_id":"12021"}}GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in {"type":"entrez-geo","attrs":{"text":"GSE55235","term_id":"55235"}}GSE55235 and {"type":"entrez-geo","attrs":{"text":"GSE100786","term_id":"100786"}}GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.     

Screening and identification of LMNB1 and DLGAP5, two key biomarkers in gliomas

Glioma is the most common primary cancer in the central nervous system. Despite advances in surgery, radiotherapy and chemotherapy over the past decades, the prognosis of glioblastoma patients remains poor. We aim to identify robust gene signatures to better understand the complex molecular mechanisms and to discover potential novel molecular biomarkers for glioma. By exploring {"type":"entrez-geo","attrs":{"text":"GSE16011","term_id":"16011"}}GSE16011, {"type":"entrez-geo","attrs":{"text":"GSE4290","term_id":"4290"}}GSE4290 and {"type":"entrez-geo","attrs":{"text":"GSE50161","term_id":"50161"}}GSE50161 data in Gene Expression Omnibus (GEO) database, we screened out 380 differentially expressed genes between non-tumor and glioma tissues, and further selected 30 hub genes through the Molecular Complex Detection (MCODE) plug-in in Cytoscape. In addition, LMNB1 and DLGAP5 were selected for further analyses due to their high expression in gliomas and were verified by using our cohort. Our study confirmed that LMNB1 and DLGAP5 were up-regulated in gliomas, and patients with high expression of LMNB1 or DLGAP5 had poor survival rate. Furthermore, silence of LMNB1 and DLGAP5 inhibited the proliferation of glioma cells. Together, LMNB1 and DLGAP5 were two potentially novel molecular biomarkers for diagnosis and prognosis of glioma.     

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, {"type":"entrez-geo","attrs":{"text":"GSE1993","term_id":"1993"}}GSE1993 and {"type":"entrez-geo","attrs":{"text":"GSE4422","term_id":"4422"}}GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.     

Bioinformatics Analysis of Key micro-RNAs and mRNAs under the Hand,Foot, and Mouth Disease Virus Infection

To clarify crucial key micro-RNAs and mRNAs associated with hand, foot, and mouth disease (HFMD) virus infection, we conducted this bioinformatics analysis from four GEO datasets. The following datasets were used for the analysis: {"type":"entrez-geo","attrs":{"text":"GSE85829","term_id":"85829"}}GSE85829, {"type":"entrez-geo","attrs":{"text":"GSE94551","term_id":"94551"}}GSE94551, {"type":"entrez-geo","attrs":{"text":"GSE52780","term_id":"52780"}}GSE52780, and {"type":"entrez-geo","attrs":{"text":"GSE45589","term_id":"45589"}}GSE45589. Differentially expressed genes (DEGs) were acquired, and the analysis of functional and pathway enrichment and the relative regulatory network were conducted. After screening common differentially expressed miRNAs (DE-miRNAs), five key miRNAs were acquired: miR-100-3p, miR-125a-3p, miR-1273g-3p, miR-5585-3p, and miR-671-5p. There were three common enriched GO terms between miRNA-derived prediction and mRNA-derived analysis: biosynthetic process, cytosol, and nucleoplasm. There was one common KEGG pathway, i.e., cell cycle shared between miRNA-based and mRNA-based enrichment. Using TarBase V8 in DIANA tools, we acquired 1,520 potential targets (mRNA) from the five key DE-miRNAs, among which the159 DE-mRNAs also included 11 DEGs. These common DEGs showed a PPI network mainly connected by SMC1A, SMARCC1, SF3B3, LIG1, and BRMS1L. Together, changes in five key miRNAs and 11 key mRNAs may play crucial roles in HFMD progression. A combination of these roles may benefit the early diagnosis and treatment of HFMD.Key words: HFMD, micro-RNA, protein-protein interaction, microarray, regulatory network