Genes In Public Databases Research Articles

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

P4HA1 expression and function in esophageal squamous cell carcinoma.

This study aimed to explore the effect of P4HA1 (prolyl 4-hydroxylase subunit α1) and its ratio on the prognosis of esophageal squamous cell carcinoma. The expression data of P4HA1 in esophageal cancer in The Cancer Genome Atlas and Genotype-Tissue Expression were collected using the public database gene expression profiling interactive analysis. The expression levels of P4HA1 were examined by immunohistochemistry. The relationship between P4HA1 expression and clinicopathological parameters was analyzed the χ2 test. Survival analysis was performed to investigate the effect of P4HA1 and its ratio on prognosis. Compared with normal esophageal mucosal epithelium, there was higher P4HA1 gene mRNA in esophageal cancer tissue. Regarding the expression level, no significant difference was observed in patients with stage I-IV esophageal cancer. Immunohistochemistry showed that P4HA1 was highly expressed in esophageal squamous cell carcinoma (68.7%), while it was negatively expressed in paracancerous tissues. There was a significant difference in expression between cancer and adjacent tissues. The expression of P4HA1 associated with the degree of tumor differentiation, site, lymph node metastasis, and tumor node metastasis stage. The prognostic factors that affected the OS (overall survival) of esophageal cancer patients were the degree of differentiation, lymph node metastasis, and P4HA1 expression. Multivariate analysis of the OS results of patients showed that lymph node metastases and P4HA1 expression were independent prognostic factors that affected the OS of esophageal cancer patients. The prognostic factors affecting the PFS (progression-free survival) of esophageal cancer patients in the univariate survival analysis were as follows: degree of differentiation, lymph node metastasis, and P4HA1 expression. In addition, multivariate analysis of the PFS results of patients showed that lymph node metastasis and P4HA1 expression were independent prognostic factors that affected the PFS of esophageal cancer patients. P4HA1 may be a novel potential biomarker for the early diagnosis, prognosis, and targeted therapy of esophageal cancer.

Genome‑wide identification, organization, and expression profiles of the chicken fibroblast growth factor genes in public databases and Vietnamese indigenous Ri chickens against highly pathogenic avian influenza H5N1 virus infection.

Fibroblast growth factors (FGFs) play critical roles in embryo development, and immune responses to infectious diseases. In this study, to investigate the roles of FGFs, we performed genome-wide identification, expression, and functional analyses of FGF family members in chickens. Chicken FGFs genes were identified and analyzed by using bioinformatics approach. Expression profiles and Hierarchical cluster analysis of the FGFs genes in different chicken tissues were obtained from the genome-wide RNA-seq. A total of 20 FGF genes were identified in the chicken genome, which were classified into seven distinct groups (A-F) in the phylogenetic tree. Gene structure analysis revealed that members of the same clade had the same or similar exon-intron structure. Chromosome mapping suggested that FGF genes were widely dispersed across the chicken genome and were located on chromosomes 1, 4-6, 9-10, 13, 15, 28, and Z. In addition, the interactions among FGF proteins and between FGFs and mitogen‑activated protein kinase (MAPK) proteins are limited, indicating that the remaining functions of FGF proteins should be further investigated in chickens. Kyoto encyclopedia of genes and genomes pathway analysis showed that FGF gene interacts with MAPK genes and are involved in stimulating signaling pathway and regulating immune responses. Furthermore, this study identified 15 differentially expressed genes (DEG) in 21 different growth stages during early chicken embryo development. RNA-sequencing data identified the DEG of FGFs on 1- and 3-days post infection in two indigenous Ri chicken lines infected with the highly pathogenic avian influenza virus H5N1 (HPAIV). Finally, all the genes examined through quantitative real-time polymerase chain reaction and RNA-Seq analyses showed similar responses to HPAIV infection in indigenous Ri chicken lines (R2 = 0.92- 0.95, p<0.01). This study provides significant insights into the potential functions of FGFs in chickens, including the regulation of MAPK signaling pathways and the immune response of chickens to HPAIV infections.

Lasso algorithm and support vector machine strategy to screen pulmonary arterial hypertension gene diagnostic markers.

This study employs machine learning strategy algorithms to screen the optimal gene signature of pulmonary arterial hypertension (PAH) under big data in the medical field. The public database Gene Expression Omnibus (GEO) was used to analyze datasets of 32 normal controls and 37 PAH disease samples. The enrichment analysis was performed after selecting the differentially expressed genes. Two machine learning methods, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM), were used to identify the candidate genes. The external validation data set further tests the expression level and diagnostic value of candidate diagnostic genes. The diagnostic effectiveness was evaluated by obtaining the receiver operating characteristic curve (ROC). The convolution tool CIBERSORT was used to estimate the composition pattern of the immune cell subtypes and to perform correlation analysis based on the combined training dataset. A total of 564 differentially expressed genes (DEGs) were screened in normal control and pulmonary hypertension samples. The enrichment analysis results were found to be closely related to cardiovascular diseases, inflammatory diseases, and immune-related pathways. The LASSO and SVM algorithms in machine learning used 5 × cross-validation to identify 9 and 7 characteristic genes. The two machine learning algorithms shared Caldesmon 1 (CALD1) and Solute Carrier Family 7 Member 11 (SLC7A11) as genetic signals highly correlated with PAH. The results showed that the area under ROC (AUC) of the specific characteristic diagnostic genes were CALD1 (AUC = 0.924) and SLC7A11 (AUC = 0.962), indicating that the two diagnostic genes have high diagnostic value. CALD1 and SLC7A11 can be used as diagnostic markers of PAH to obtain new insights for the further study of the immune mechanism involved in PAH.

Functional Prediction of trans-Prenyltransferases Reveals the Distribution of GFPPSs in Species beyond the Brassicaceae Clade.

Terpenoids are the most diverse class of plant primary and specialized metabolites, and trans-prenyltransferases (trans-PTs) are the first branch point to synthesize precursors of various chain lengths for further metabolism. Whereas the catalytic mechanism of the enzyme is known, there is no reliable method for precisely predicting the functions of trans-PTs. With the exponentially increasing number of available trans-PTs genes in public databases, an in silico functional prediction method for this gene family is urgently needed. Here, we present PTS-Pre, a web tool developed on the basis of the “three floors” model, which shows an overall 86% prediction accuracy for 141 experimentally determined trans-PTs. The method was further validated by in vitro enzyme assays for randomly selected trans-PTs. In addition, using this method, we identified nine new GFPPSs from different plants which are beyond the previously reported Brassicaceae clade, suggesting these genes may have occurred via convergent evolution and are more likely lineage-specific. The high accuracy of our blind prediction validated by enzymatic assays suggests that PTS-Pre provides a convenient and reliable method for genome-wide functional prediction of trans-PTs enzymes and will surely benefit the elucidation and metabolic engineering of terpenoid biosynthetic pathways.

Evaluation of ITGA3 as a Biomarker of Progression and Recurrence in Papillary Thyroid Carcinoma.

ObjectiveTo investigate the expression of ITGA3 and its association with clinical outcomes in papillary thyroid carcinoma (PTC).MethodsThe expression level, association with clinicopathologic characteristics, co-expressed genes, signaling pathways of ITGA3 in thyroid cancer were comprehensively analyzed using bioinformatics analysis through multiple public gene databases. PTC specimens and cell lines were used to verify the results of bioinformatics analysis.ResultsData mining based on the Oncomine database revealed that ITGA3 expression in classical PTC and tall cell variant PTC was much higher than that in normal thyroid tissue except the follicular variant PTC. Analysis based on The Cancer Genome Atlas (TCGA) database showed that the expression of ITGA3 varies greatly in pathological stages, pathological types, tumor invasion stages, and lymph node metastasis stages of thyroid carcinoma. High expression level of ITGA3 was correlated with tumor regional invasion and lymph node metastasis. Multivariate analysis using logistic regression model showed that high expression of ITGA3 was a risk factor that associated with PTC recurrence and lymph node metastasis. Survival analysis showed that patients with high expression of ITGA3 in PTC had a poorer relapse-free survival (RFS) than patients with low expression of ITGA3 (P < 0.05). Immunohistochemistry experiments showed that the expression of ITGA3 in recurrent thyroid cancer tissues was stronger than that in no-recurrent thyroid cancer tissues (P < 0.05). Knockdown of ITGA3 by sh-RNA in PTC cell lines suppresses cell viability and invasive and migrating capacity.ConclusionITGA3 is overexpressed in PTC, especially in those with higher tumor invasion grades and lymph node metastasis, and was associated with recurrence and poor RFS of PTC. High expression of ITGA3 may have the potential role of predicting PTC recurrence and lymph node metastasis.

Identification of a Qualitative Signature for the Diagnosis of Dementia With Lewy Bodies.

Background and purpose: Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. To date, there is no report of qualitative signature for the diagnosis of DLB. We aimed to develop a blood-based qualitative signature for differentiating DLB patients from healthy controls. Methods: The GSE120584 dataset was downloaded from the public database Gene Expression Omnibus (GEO). We combined multiple methods to select features based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs. Specifically, we first quickly selected miRNA pairs related to DLB by identifying reversal stable miRNA pairs. Then, an optimal miRNA pair subset was extracted by random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) methods. Furthermore, we applied logistic regression (LR) and SVM to build several prediction models. The model performance was assessed using the receiver operating characteristic curve (ROC) analysis. Lastly, we conducted bioinformatics analyses to explore the molecular mechanisms of the discovered miRNAs. Results: A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls. The signature is robust against experimental batch effects and applicable at the individual levels. The accuracies of the-signature-based models on the test set are 82.61 and 79.35%, respectively, indicating that the signature has acceptable discrimination performance. Moreover, bioinformatics analyses revealed that predicted target genes were enriched in 11 Go terms and 2 KEGG pathways. Moreover, five potential hub genes were found for DLB, including SRF, MAPK1, YWHAE, RPS6KA3, and KDM7A. Conclusion: This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis.

Analysis of sepsis-related genes through weighted gene co-expression network

To identify the Key genes in the development of sepsis through Weighted Gene Co-Expression Network Analysis (WGCNA). The gene expression dataset GSE154918 was downloaded from the public database Gene Expression Omnibus (GEO) database, which containes data from 105 microarrays of 40 control cases, 12 cases of asymptomatic infection, 39 cases of sepsis, and 14 cases of follow-up sepsis. The R software was used to screen out differentially expressed genes (DEG) in sepsis, and the Distributed Access View Integrated Database (DAVID), SEARCH TOOL FOR RETRIEVAL OF INTERACTING NEIGHBOURING GENES (STRING) and visualization software Cytoscape were used to perform gene function and pathway enrichment analysis, Protein-protein interaction (PPI) network analysis and key gene analysis to screen out the key genes in the development of sepsis. Forty-six candidate genes were obtained by WGCNA and combined with DEG expression analysis, and these 46 genes were analyzed by gene ontology (GO) and Kyoto City Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to obtain gene functions and involved signaling pathways. The PPI network was further constructed using the STRING database, and 5 key genes were selected by the PPI network visualization software Cytoscape, including the mast cell expressed membrane protein 1 gene (MCEMP1), the S100 calcium-binding protein A12 gene (S100A12), the adipokine resistance factor gene (RETN), the c-type lectin structural domain family 4 member gene (CLEC4D), and peroxisome proliferator-activated receptor gene (PPARG), and differential expression analysis of each of these 5 genes showed that the expression levels of the above 5 genes were significantly upregulated in sepsis patients compared with healthy controls. In this study, 5 key genes related to sepsis were screened by constructing WGCNA method, which may be potential candidate targets related to sepsis diagnosis and treatment.

Genome-wide association study identifies new loci associated with risk of HBV infection and disease progression

BackgroundRecent studies have identified susceptibility genes of HBV clearance, chronic hepatitis B, liver cirrhosis, hepatocellular carcinoma, and showed the host genetic factors play an important role in these HBV-related outcomes.MethodsCollected samples from different outcomes of HBV infection and performed genotyping by Affymetrix 500 k SNP Array. GCTA tool, PLINK, and Bonferroni method were applied for analysis of genotyping and disease progression. ANOVA was used to evaluate the significance of the association between biomarkers and genotypes in healthy controls. PoMo, FST, Vcftools and Rehh package were used for building the racial tree and population analysis. FST statistics accesses 0.15 was used as a threshold to detect the signature of selection.ResultsThere are 1031 participants passed quality control from 1104 participants, including 275 HBV clearance, 92 asymptomatic persistence infection (ASPI), 93 chronic hepatitis B (CHB), 188 HBV-related decompensated cirrhosis (DC), 214 HBV-related hepatocellular carcinoma (HCC) and 169 healthy controls (HC). In the case–control study, one novel locus significantly associated with CHB (SNP: rs1264473, Gene: GRHL2, P = 1.57 × 10−6) and HCC (SNP: rs2833856, Gene: EVA1C, P = 1.62 × 10−6; SNP: rs4661093, Gene: ETV3, P = 2.26 × 10−6). In the trend study across progressive stages post HBV infection, one novel locus (SNP: rs1537862, Gene: LACE1, P = 1.85 × 10−6), and three MHC loci (HLA-DRB1, HLA-DPB1, HLA-DPA2) showed significant increased progressive risk from ASPI to CHB. Underlying the evolutionary study of HBV-related genes in public database, the derived allele of two HBV clearance related loci, rs3077 and rs9277542, are under strong selection in European population.ConclusionsIn this study, we identified several novel candidate genes associated with individual HBV infectious outcomes, progressive stages, and liver enzymes. Two SNPs that show selective significance (HLA-DPA1, HLA-DPB1) in non-East Asian (European, American, South Asian) versus East Asian, indicating that host genetic factors contribute to the ethnic disparities of susceptibility of HBV infection. Taken together, these findings provided a new insight into the role of host genetic factors in HBV related outcomes and progression.

Predicting and Exploring the Mechanisms of Erzhi Pill in Prevention and Treatment of Osteoporosis Based on Network Pharmacology and Zebrafish Experiments.

BackgroundErzhi Pill (EZP), a traditional Chinese medicine (TCM) prescription, has been widely applied to improve bone metabolism and treat osteoporosis (OP) in China. However, its effective constituents and mechanisms remain unclear.MethodsBy combining network pharmacology and zebrafish experiments, an integrative method was employed to address this problem. Firstly, the disease targets of OP were collected from two public gene databases. Secondly, the active compounds and drug targets of EZP were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). Thirdly, a drug-target-disease interaction network was constructed, and the key active components were identified by analyzing the topological characteristics of the network. Finally, these predicted results were tested by zebrafish experiments and compared with those from the literature. Specifically, quercetin as an important representative active component of EZP was applied to wild type and transgenic zebrafish larvae to assess its effects on skull mineralization and osteoplastic differentiation.ResultsOur study identified 72 active compounds, 220 targets and 166 signaling pathways probably involved in the prevention and treatment of OP by EZP, wherein quercetin, apigenin, daidzein, luteolin, ursolic acid and kaempferol could be the key compounds, while PI3K-Akt signaling pathway, TNF signaling pathway and IL-17 signaling pathway could be the key signaling pathways. The experiments indicated that quercetin attenuated both the decrease of skull mineralization and the inhibition of skull osteoplastic differentiation in zebrafish larvae trigged by dexamethasone.ConclusionOur study not only investigated potentially effective constituents and mechanisms of EZP in the prevention and treatment of OP, but also provided a reference for the in-depth research, development and application of TCM.

High PD‑L1 expression drives glycolysis via an Akt/mTOR/HIF‑1α axis in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a hematological malignancy derived from immature myeloid cells, which have the characteristics of abnormal proliferation and differentiation. Glycolysis has been a popular topic of research in recent years, with increasing uptake and consumption of glucose. The present study aimed to investigate the glycolysis of tumor cells in patients with AML; in particular, how programmed cell death 1 ligand 1 (PD‑L1) regulates tumor cells glycolysis using real time PCR (RT‑PCR), western blotting and flow cytometry. PD‑L1 high expression predicted poor outcome in patients with AML in the public database Gene Expression Profiling Interactive Analysis. PD‑L1 expression was decreased in the samples from patients with AML with complete remission compared to that in patients with relapsed or refractory AML. In AML cell lines, glycolysis‑associated genes ALDOA, PGK1, LDHA and HK2 were highly expressed in a PD‑L1 high‑expressed cell line. Overexpressed PD‑L1 enhanced glucose consumption and the extracellular acidification rate, accompanied by decreased apoptosis and accumulation of cells in the S phase. In contrast, the apoptosis rate of tumor cells and the percentage of cells in the S phase were significantly increased following PD‑L1 knockdown in the THP1 cell line. HK2 and LDHA expression decreased after AML tumor cells were treated with Akt inhibitor or rapamycin. In addition, the PD‑L1‑overexpressed cell line (PD‑L1‑OV) MOLM‑13 exhibited rapid tumor progression. Glycolysis‑associated genes were highly expressed in tumor tissues of PD‑L1‑OV MOLM‑13, with increased Ki67. Based on these findings, PD‑L1 may be considered as a suitable marker for prognosis and treatment in the clinical setting.

Capreomycin resistance prediction in two species ofMycobacteriumusing a stacked ensemble method

Predicting bacterial resistance provides valuable information that can assist in clinical decisions. With recent advances in whole genome sequencing technology, the detection of antibiotic resistance (AR) proteins directly from genomic data is becoming feasible. AR genes/proteins can be identified using best-hit methods that work by comparing candidate sequences with known AR genes in public databases. However, these approaches may fail to detect resistance genes with sequences that differ significantly from known sequences. Our goal is to develop a machine learning technique to accurately predict capreomycin resistance in Mycobacteria with low false discovery rates. We present a stacked ensemble learning model as an alternative to traditional DNA sequence alignment-based methods using optimal features generated from the physicochemical, evolutionary and secondary structure properties of protein sequences. We train logistic regression, C5.0 and support vector machine (SVM) algorithms as our base classifiers, and our stacked ensemble predictors combine the results from the base classifiers to achieve higher accuracy. Compared with our most accurate base classifier (SVM), our most accurate stacked ensemble predictor increases training accuracy by 2·43%. Our stacked ensemble predictors achieve test accuracy up to 81·25%. We developed a stacked ensemble model to predict capreomycin resistance for Mycobacteria with an accuracy >80% using protein sequences with sequence similarity ranging between 10% and 70%. This performance cannot be achieved with best-hit methods due to differences in sequence similarity. Today an estimated one-half million cases of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) occur annually worldwide at a great cost. Because capreomycin is a second-line drug used to treat drug-resistant TB, the ability to use a machine learning approach to classify capreomycin-resistant TB in a timely manner is crucial for the successful treatment of MDR or XDR TB.

Protective effect of stress-associated endoplasmic reticulum protein 1 on glucose and oxygen deprivation-induced injury in cardiomyocytes

Objective To study the protective effect of stress-associated endoplasmic reticulum protein 1(SERP1)on glucose and oxygen deprivation-induced injury in cardiomyocytes. Methods Gene expression was analyzed in the public database Gene Expression Omnibus(GEO)and screened for any difference in gene expression in myocardial tissues between the control group and the ischemia-reperfusion group(IR group). Rat H9C2 cardiomyocytes were cultured and a myocardial cell injury model was established by oxygen glucose deprivation(OGD). The effect of SERP1 expression on cell viability, apoptosis and the endoplasmic reticulum stress pathway in cardiomyocytes were examined. Results Western blot results showed that the expression of SERP1 in myocardial tissues decreased in the IR group, compared with the control group(t=6.83, P=0.006). Oxygen and glucose deprivation induced decreased levels of SERP1 mRNA and protein expression in H9C2 cardiomyocytes in a time-dependent manner(F=8.50 and 15.70, P=0.007 and 0.001). In addition, oxygen and glucose deprivation led to decreased cell viability and increased apoptosis, while exogenous addition of SERP1 had protective effects in H9C2 cardiomyocytes by promoting cell viability and reduced cell apoptosis.The lncRNA microarray and real-time PCR results showed that SERP1 could inhibit the expression of lncRNA CDKN2B-AS1 and further increase the phosphorylation of JAK2 and STAT3, leading to decreased expression of endoplasmic reticulum stress markers GRP78 and CHOP(all P<0.05). Conclusions SERP1 can inhibit cardiomyocyte injury induced by glucose deprivation, and the underlying molecular mechanism may be related to the inhibition of CDKN2B-AS1 expression, promotion of the JAK2/STAT3 signaling pathway, and suppression of endoplasmic reticulum stress. Key words: Myocardial ischemia; Myocardial infarction; Reperfusion injury; Stress

Protective effect and mechanism of chemokine C-C motif ligand 6 on glucose and oxygen deprivation induced injury of cardiomyocytes

Objective To study the protective effect of chemokine C-C motif ligand 6 (CCL6) on glucose-oxygen deprivation induced injury in cardiomyocytes and its possible molecular mechanism. Methods Gene expression was analyzed in the public database Gene Expression Omnibus (GEO) database and gene expression of analyzed for myocardial tissue was analyzed gene expression in the sham group and the ischemia-reperfusion group (IR group). Rat H9C2 cardiomyocytes were cultured in vitro, and myocardial cell injury model was established by oxygen glucose deprivation (OGD). Cell viability was detected by MTT assay; apoptosis was determined by Annex V/PI double staining; the expression of related genes was detected by real-time PCR and Western blot. Results Compared with the sham group, transcriptome analysis and real-time PCR showed that the expression of CCL6 in the myocardial tissue of the IR group was significantly decreased (P<0.01). Oxygen glucose deprivation induced a decrease in CCL6 expression levels in H9C2 cardiomyocytes in a time-dependent manner. In addition, oxygen glucose deprivation leads to decreased cell viability and increased apoptosis; while addition of CCL6 promotes cell viability and reduces apoptosis. The lncRNA microarray and real-time PCR showed that CCL6 treatment of cardiomyocytes resulted in a significant decrease in the expression of lncRNA IGF2-AS and further increased the phosphorylation of Akt and GSK-3β. Conclusion CCL6 can inhibit cardiomyocyte injury induced by glucose deprivation, and its molecular mechanism may be related to inhibition of IGF2-AS and enhancement of Akt/GSK-3β signaling pathway. Key words: Cardiomyocytes; Chemokine; Apoptosis

Differentially expressed genes ASPN, COL1A1, FN1, VCAN and MUC5AC are potential prognostic biomarkers for gastric cancer.

Gastric cancer (GC) is one of the most common malignancies worldwide. To the best of our knowledge, no biomarkers have been widely accepted for the early diagnosis and prognostic prediction of GC. This study aimed to identify potential novel prognostic biomarkers for GC. The dataset GSE29272, which originates from the public database Gene Expression Omnibus, was employed in the present study. The online tool GEO2R was used to calculate the differentially expressed genes (DEGs) in GSE29272 between tumour tissues and adjacent tissues. CytoHubba and MCODE plugins of Cytoscape software were used to obtain hub genes and modules of DEGs. The online tools Database for Annotation, Visualisation and Integrated Discovery and Search Tool for the Retrieval of Interacting Genes were employed to conduct Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and to construct protein-protein interaction networks. A total of 117 DEGs were extracted from GSE29272. In addition, 15 hub genes and seven modules were identified in the 117 DEGs. The enrichment analysis revealed that they were mainly enriched in GO biological process and cellular component domains, and the ‘ECM-receptor interaction’, ‘focal adhesion’, ‘metabolism of xenobiotics by cytochrome P450’ and ‘drug metabolism’ pathways. The hub genes asporin (ASPN), collagen type I α1 chain (COL1A1), fibronectin 1 (FN1), versican (VCAN) and mucin 5AC (MUC5AC) were demonstrated to have prognostic value for patients with GC. The ASPN and VCAN genes were significantly associated with overall survival and disease-free survival (log-rank P=0.025, 0.038, 0.0014 and 0.015, respectively). COL1A1 and FN1 were significantly associated with overall survival (log-rank P=0.013 and 0.05, respectively), and MUC5AC was significantly associated with disease-free survival (log-rank P=0.027). Results from the present study suggested that ASPN, COL1A1, FN1, VCAN and MUC5AC may represent novel prognostic biomarkers for GC.

Unveiling new sequestrate Cortinarius species from northern Patagonian Nothofagaceae forests based on molecular and morphological data

ABSTRACTBecause of systematic sampling campaigns in the northern Patagonian Nothofagaceae forests of Argentina, several specimens of sequestrate fungi were collected. Some of those collections showed phylogenetic affinities and morphological similarities to members of the formerly recognized sequestrate genus Thaxterogaster, currently a synonym of Cortinarius on the basis of molecular data. Comparisons of macro- and micromorphological features and sequences of nuc rDNA internal transcribed spacer (ITS) regions have revealed that these collections belong to formerly undescribed species. The sequences of the four new taxa presented here, Cortinarius flavopurpureus, C. translucidus, C. nahuelhuapensis, and C. infrequens, were combined into a data set including additional sequences generated from herbarium collections and retrieved from public gene databases and analyzed by maximum likelihood and Bayesian inference methods. The four new species were resolved as distinct clades with strong support; at the same time, they showed unique morphological characteristics (hypogeous to subhypogeous habit, complete gasteromycetation, and spore shape and ornamentation) that separate them from previously described Cortinarius species. In addition, several undescribed and/or not previously sequenced species from these forests were detected through phylogenetic analysis of ectomycorrhizal root tip sequences. A key of characters to identify the sequestrate Cortinarius from Patagonia is provided.

Co-expression Network Analysis of Biomarkers for Adrenocortical Carcinoma.

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. And currently, there are no specific diagnostic biomarkers for ACC. In our study, we aimed to screen biomarkers for disease diagnosis, progression and prognosis. We firstly used the microarray data from public database Gene Expression Omnibus database to construct a weighted gene co-expression network, and then to identify gene modules associated with clinical features of ACC. Though this algorithm, a significant module with R2 = 0.64 (P = 9 × 10-5) was identified. Co-expression network and protein–protein interaction network were performed for screen the candidate hub genes. Checked by The Cancer Genome Atlas (TCGA) database, another independent dataset GSE19750, and GEPIA database, using one-way ANOVA, Pearson’s correlation, survival analysis, diagnostic capacity (ROC curve) and expression level revalidation, a total 12 real hub genes were identified. Gene ontology and KEGG pathway analysis of genes in the significant module revealed that the hub genes are significantly enriched in cell cycle regulation. Moreover, gene set enrichment analysis suggests that the samples with highly expressed hub genes are correlated with cell cycle. Taken together, our integrated analysis has identified 12 hub genes that are associated with the progression and prognosis of ACC; these hub genes might lead to poor outcomes by regulating the cell cycle.

The first draft genome of Lophophorus: A step forward for Phasianidae genomic diversity and conservation

The monal genus (Lophophorus) is a branch of Phasianidae and its species inhabit the high-altitude mountains of the Qinghai-Tibet Plateau. The Chinese monal, L. lhuysii, is a threatened endemic bird of China that possesses high-altitude adaptability, diversity of plumage color and potentially low reproductive life history. This is the first study to describe the monal genome using next generation sequencing technology. The Chinese monal genome size is 1.01 Gb, with 16,940 protein-coding genes. Gene annotation yielded 100.93 Mb (9.97%) repeat elements, 785 ncRNA, 5,465,549 bp (0.54%) SSR and 15,550 (92%) genes in public databases. Compared to other birds and mammals, the genome evolution analysis showed numerous expanded gene families and positive selected genes involved in high-altitude adaptation, especially related to the adaptation of low temperature and hypoxia. Consequently, this gene data can be used to investigate the molecular evolution of high-altitude adaptation in future bird research. Our first published genome of the genus Lophophorus will be integral for the study of monal population genetic diversity and conservation, genomic evolution and Galliformes species differentiation in the Qinghai-Tibetan Plateau.

LncRNA LINC01116 competes with miR-145 for the regulation of ESR1 expression in breast cancer.

To investigate the biological role and clinical significance of long non-coding RNAs (lncRNA) LINC01116 in breast cancer. In the public database Gene Expression Omnibus (GEO), the breast cancer data set GSE54002 was screened for differentially expressed lncRNA LINC01116 in breast cancer tissues and paracancerous tissues. Quantitative Real-time polymerase chain reaction (qRT-PCR) was used to detect the expression of LINC01116 in 64 breast cancer tissues and 30 normal breast tissues. Level of LINC01116 and clinicopathological parameters of breast cancer were statistically analyzed. The effect of LINC01116 in breast cancer cells was investigated after knockdown of LINC01116. Luciferase reporter gene was further used to investigate the mechanism of endogenous RNA (ceRNA). Results of GSE54002 showed that the expression of LINC01116 in breast cancer tissues was significantly increased. In clinical samples, the level of LINC01116 in patients with breast cancer was significantly increased, which was correlated with the overall survival, tumor size and tumor node metastasis (TNM) stage in patients, but not correlated with the age, sex and lymph node metastasis (p>0.05). LINC01116 can act as an endogenous sponge and bind directly to miR-145, resulting in the up-regulation of estrogen receptor 1 (ESR1), a target gene of miR-145. LncRNA LINC01116 is highly expressed in breast cancer and is a new prognostic biomarker in breast cancer. Our study establishes a new link between LINC01116, miR-145 and ESR1.

IGF2BP2 Overexpression Indicates Poor Survival in Patients with Acute Myelocytic Leukemia

Background/Aims: IGF2BP2 has been reported to serve as an oncogene in various solid cancers. However, the role of IGF2BP2 in acute myelocytic leukemia (AML) is still unknown. Methods: Public databases Gene Omnibus was used to evaluate the expression of IGF2BP2 in AML patients and healthy controls. In addition, primary cells from these two populations were prepared by Ficoll density centrifugation. Rt-qPCR and western blot were used to detect IGF2BP2 expression in the primary cells from these two populations. Meta-analysis was performed to evaluate the association of IGF2BP2 and prognosis. Lentivirus-based shRNAs were used to knock down IGF2BP2 in AML cell lines KG-1a and Kasumi. Results: We searched the public database Gene Omnibus and analyzed IGF2BP2 expression in both AML and healthy populations. The results showed that IGF2BP2 was overexpressed in AML patients. To verify this phenomenon in fresh human samples, we compared the expression of IGF2BP2 in primary cells from 10 AML patients and 10 healthy controls and found that the expression of IGF2BP2 was upregulated in AML primary cells. More importantly, we observed that IGF2BP2 expression was negatively correlated with the CEBPA mutation status, which is an indicator of good prognosis (RR=0.648, p=0.0001). In addition, IGF2BP2 expression was positively associated with poor prognostic factors, such as the FLT3-ITD mutation (RR=1.198, p=0.0009) and IDH1 mutation (RR=1.354, p=0.0003), as well as intermediate and poor cytogenetic risk (RR=1.214, p=0.0026). To evaluate the prognostic value of IGF2BP2 in AML, we further performed a meta-analysis of 8 studies consisting of 1731 patients and found that IGF2BP2 overexpression was correlated with worse overall survival in AML patients [HR=1.31(1.16-1.49); p = 0.00]. Furthermore, we performed Gene Omnibus and Gene Set Enrichment analyses and found that the genes regulated by IGF2BP2 were mainly enriched in cell proliferation. IGF2BP2 knockdown by 4 different shRNA vectors significantly inhibited the growth of two AML cell lines, KG-1a and Kasumi. Conclusion: Thus, IGF2BP2 may serve as a biomarker to predict the prognosis of AML and as a potential target in AML.