ARID1A deficiency weakens BRG1-RAD21 interaction that jeopardizes chromatin compactness and drives liver cancer cell metastasis

ARID1A, encoding a subunit of SWI/SNF chromatin remodeling complex, is widely recognized as a tumor suppressor gene in multiple tumor types including liver cancer. Previous studies have demonstrated that ARID1A deficiency can cause liver cancer metastasis, possibly due to the altered chromatin organization, however the underlying mechanisms remain poorly understood. To address the effect of Arid1a deficiency on chromatin organization, we generated chromatin interaction matrices, and exploited the conformation changes upon Arid1a depletion in hepatocytes. Our results demonstrated that Arid1a deficiency induced A/B compartment switching, topologically associated domain (TAD) remodeling, and decrease of chromatin loops. Further mechanism studies revealed that ATPase BRG1 of SWI/SNF complex could physically interact with RAD21, a structural subunit of chromatin architectural element cohesin; whereas ARID1A deficiency significantly diminished the coupled BRG1-RAD21. Interestingly, the tumor-associated genes within the switched compartments were differentially expressed depending upon Arid1a depletion or not. As a consequence of ARID1A deficiency-induced conformational alteration, the dysregulation of some genes such as PMP22 and GSC, promoted the invasion capacity of liver cancer cells. This study provides an insight into liver cancer tumorigenesis and progression related to ARID1A mutations.Subject terms: Metastasis, Chromatin remodelling     

Development of a novel six-long noncoding RNA signature predicting survival of patients with bladder urothelial carcinoma

Bladder urothelial carcinoma is a malignant tumor with a high incidence in the uropoietic system. Considerable studies have shown that long noncoding RNA (lncRNA) plays an important role in the development and progression of bladder urothelial carcinoma. In this study, the lncRNA expression and clinical data of 377 bladder urothelial carcinoma patients were obtained from The Cancer Genome Atlas database and differentially expressed lncRNAs in cancer and normal groups were evaluated. Univariate COX and multivariate COX regression analyses of prognosis were performed on differentially expressed lncRNAs in the training data sets, six prognosis-related lncRNAs (LINC02195, LINC01484, LINC01468, SMC2-AS1, AC011298.1, and PTPRD-AS1) were assessed, and a six-lncRNA signature was constructed. The predictive capability of this six-lncRNA signature was validated in the testing data sets and entire data sets. The prognostic ability of the six-lncRNA signature was independent of other clinical elements after multivariate COX regression and stratified analyses of with other clinical elements. We performed functional enrichment analysis with the six prognosis-related lncRNAs. Results of functional enrichment revealed that these prognosis-related lncRNAs might promote the development and metastasis of bladder urothelial carcinoma. In summary, the six-lncRNA signature that we developed could effectively predict the prognosis of bladder urothelial carcinoma patients. This six-lncRNA signature might be a novel independent prognostic marker of bladder urothelial carcinoma. Moreover, it also provides novel insights into the mechanism of bladder urothelial carcinoma.     

Inhibition of stearoyl CoA desaturase‐1 activity suppresses tumour progression and improves prognosis in human bladder cancer

Urinary bladder neoplasm is one of the most common cancers worldwide. Cancer stem cells (CSCs) have been proven to be an important cause of cancer progression and poor prognosis. In the present study, we established bladder CSCs and identified the crucial differentially expressed genes (DEGs) between these cells and parental bladder cancer cells. Analyses of bioinformatics data and clinical samples from local hospitals showed that stearoyl CoA desaturase‐1 (SCD) was the key factor among the DEGs. A significant correlation between SCD gene expression and poor prognosis among patients with bladder cancer was observed in our data. Loss‐of‐function experiments further revealed that the SCD inhibitor A939572 and SCD gene interference reduced cell proliferation and invasion. The above data suggest that SCD may serve as a novel marker for the prediction of tumour progression and poor prognosis in patients with bladder cancer.     

Identification of a metabolism-related gene signature predicting overall survival for bladder cancer

Reprogramming of metabolism is becoming a novel hallmark of cancer. This study aims to perform bioinformatics analysis of metabolism-related genes in bladder cancer, and to construct a signature of metabolism-related genes for predicting the prognosis. A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Taking survival time and clinical information into consideration, we constructed a risk score to predict clinical prognosis. Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that risk score was an independent prognostic indicator in bladder cancer. ROC curve also proved that risk score had better ability to predict prognosis than other individual indicators. Nomogram also showed a clinical net benefit to evaluate the prognosis of bladder cancer patients. GSEA revealed several metabolism-related pathways that were differentially enriched in the high-risk and low-risk groups, which might help to explain the underlying mechanisms. This signature was confirmed to be an effective prognostic biomarker in bladder cancer.     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

抑癌基因ARID1A在肿瘤中的研究进展

染色质重塑复合物相关基因在癌症中频繁突变,这种现象逐渐引起研究者的重视。然而,染色质重塑活动如何引起癌症发生,对此机理研究甚少。ARID1A是SWl／SNF（BRG1相关因子）染色质重塑复合物中的一个亚基,具有DNA结合活性,可以与富含AT的DNA序列特异性结合。近来基因组测序发现,ARID1A在卵巢癌、肝癌、胃癌、乳腺癌等肿瘤中频繁发生突变,这些突变导致ARID1A在肿瘤中表达降低,表明ARID1A是个潜在的抑癌基因。该文将针对ARID1A在各种癌症中的缺失及失活机制、ARID1A的生物学功能和潜在抑癌机理以及与,临床预后之间关系等方面做一综述,以期为肿瘤诊断、治疗提供新思路。     

Affymetrix基因表达谱芯片在新疆维吾尔族与汉族胰腺癌组织样本间差异表达基因筛选中的运用

目的:运用基因表达谱芯片筛选并分析新疆维吾尔族与汉族胰腺癌组织样本间的差异表达基因。方法:收集我院2014年1月至2016年6月间行手术切除的维吾尔族与汉族胰腺导管细胞癌组织并提取总RNA,选取经Nanodrop 2000与Agilent 2100仪器质检合格的样本总RNA采用Affymetrix基因表达谱芯片筛选出差异表达基因并绘制统计图,运用基因本体(GO)分析及信号通路(Pathway)分析对这些差异表达基因的生物信息进行汇总分析。结果:通过基因表达谱芯片分析,新疆维吾尔族与汉族胰腺癌组织样本间共检测到1063个基因存在差异表达,在维吾尔族胰腺癌标本中显著上调表达的基因共281个,差异表达倍数最高的为IGLV1-44基因(差异倍数:9.99)下调表达的基因共782个,差异表达倍数最高的为CPB1基因(差异倍数:33.76);在Gene Ontology数据库中共检索到815个上述差异表达基因具有明确的GO分类,差异表达倍数最高的为CPB1基因(差异倍数:33.76);Pathway分析中共检测到30条信号通路包含有上述差异表达基因,共涉及196个基因,其中以FAK信号通路差异表达基因富集程度最高,差异表达倍数最高的基因为COL11A1基因(差异倍数:5.02)。结论:基因表达谱芯片分析结果显示,在新疆维吾尔族与汉族胰腺癌组织样本间存在大量的差异表达基因,这些基因与胰腺癌的增殖分化、侵袭转移及多药耐药等特性密切相关,且参与了多条生物体内重要信号转导通路的调控。     

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.     

STAT1 is a key gene in a gene regulatory network related to immune phenotypes in bladder cancer: An integrative analysis of multi-omics data

The immunophenotype of bladder cancer plays a pivotal role in the prognosis of cancer, but the effect of different epigenetic factors on different immunophenotypes in bladder tumours remains unclear. This study used multi-omics data analysis to provide molecular basis support for different immune phenotypes. Unsupervised cluster analysis revealed distinct subclusters with higher (subcluster B2) or lower cytotoxic immune phenotypes (subcluster A1) related to PD-L1 and IFNG expression. Mutational landscape analyses showed that the mutation level of TP53 in subcluster B1 was highest than other subclusters, and subcluster B1 had a lower frequency of concurrent mutation than subcluster A2. A total of 2364 differentially expressed genes were identified between subclusters A2 and B1, and the main functions of the up-regulated genes in subcluster B1 were enriched in the activation of T cells and other related pathways. We found that STAT1 was a key gene in a gene regulatory network related to immune phenotypes in bladder cancer. Finally, we constructed a prognostic prediction model by LASSO Cox regression which could distinguish high-risk and low-risk cases significantly. In conclusion, the present study addressed a field synopsis between genetic and epigenetic events in immune phenotypes of bladder cancer.     

Isolation and characterization of a novel human bladder cancer cell line: BK10

Summary Molecular studies of bladder carcinomas have aided in determining causative genetic events and the prognosis of cancers endowed with certain abnormalities. In vitro bladder cancer characterization of key cytogenetic alterations is useful for study of molecular changes that may promote oncogenic events. In our laboratory, a novel human bladder cancer cell line, BK10, has been established in vitro and passaged for more than 20 mo. This new bladder cancer cell line (BK10) was derived from bladder tissue containing grade III-IV/IV transitional cell carcinoma. Bladder cancer tissue was obtained at the time of radical cystoprostatectomy extirpation. Cell cultures derived from this surgical sample exhibited an epithelial morphology and expressed epithelial cytokeratins. Immunostains of BK10 were negative for prostate specific antigen (PSA), fibronectin, smooth muscle actin alpha, and desmin. Karyotypic analysis revealed an aneuploid chromosomal content 〈4n〉 with many numerical and structural abnormalities previously linked to bladder oncogenesis. Translocations occurred in chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 22, X and Y. G-banding analysis revealed rearrangements involving chromosomes 9q and 17p, and the location of the ab11 oncogene and the p53 gene, respectively. The availability of this bladder cancer cell line will provide a useful too for the further study of bladder carcinoma oncogenesis and gene therapy.     

Gene expression signature of human HepG2 cell line

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and is associated with various clinico-pathological characteristics such as genetic mutations and viral infections. Therefore, numerous laboratories look out for identifying always new putative markers for the improvement of HCC diagnosis/prognosis. Many molecular profiling studies investigated gene expression changes related to HCC. HepG2 represents a pure cell line of human liver carcinoma, often used as HCC model due to the absence of viral infection. In this study we compare gene expression profiles associated with HepG2 (as HCC model) and normal hepatocyte cells by microarray technology. Hierarchical cluster analysis of genes evidenced that 2646 genes significantly down-regulated in HepG2 cells compared to hepatocytes whereas a further 3586 genes significantly up-regulated. By using the Ingenuity Pathway Analysis (IPA) program, we have classified the genes that were differently expressed and studied the functional networks correlating these genes in the complete human interactome. Moreover, to confirm the differentially expressed genes as well as the reliability of our microarray data, we performed a quantitative Real time RT-PCR analysis on 9 up-regulated and 11 down-regulated genes, respectively. In conclusion this work i) provides a gene signature of human hepatoma cells showing genes that change their expression as a consequence of liver cancer in the absence of any genetic mutations or viral infection, ii) evidences new differently expressed genes found in our signature compared to previous published studies and iii) suggests some genes on which to focus future studies to understand if they can be used to improve the HCC prognosis/diagnosis.     

ARID3B Directly Regulates Ovarian Cancer Promoting Genes

The DNA-binding protein AT-Rich Interactive Domain 3B (ARID3B) is elevated in ovarian cancer and increases tumor growth in a xenograft model of ovarian cancer. However, relatively little is known about ARID3B''s function. In this study we perform the first genome wide screen for ARID3B direct target genes and ARID3B regulated pathways. We identified and confirmed numerous ARID3B target genes by chromatin immunoprecipitation (ChIP) followed by microarray and quantitative RT-PCR. Using motif-finding algorithms, we characterized a binding site for ARID3B, which is similar to the previously known site for the ARID3B paralogue ARID3A. Functionality of this predicted site was demonstrated by ChIP analysis. We next demonstrated that ARID3B induces expression of its targets in ovarian cancer cell lines. We validated that ARID3B binds to an epidermal growth factor receptor (EGFR) enhancer and increases mRNA expression. ARID3B also binds to the promoter of Wnt5A and its receptor FZD5. FZD5 is highly expressed in ovarian cancer cell lines, and is upregulated by exogenous ARID3B. Both ARID3B and FZD5 expression increase adhesion to extracellular matrix (ECM) components including collagen IV, fibronectin and vitronectin. ARID3B-increased adhesion to collagens II and IV require FZD5. This study directly demonstrates that ARID3B binds target genes in a sequence-specific manner, resulting in increased gene expression. Furthermore, our data indicate that ARID3B regulation of direct target genes in the Wnt pathway promotes adhesion of ovarian cancer cells.     

Comprehensive analysis of microRNA-messenger RNA regulatory network in gemcitabine-resistant bladder cancer cells

Chemotherapy is still a standard treatment of unresectable bladder cancer or distant metastases. The chemotherapy resistance always occurs after a period of treatment indicating poor prognosis. The current study aimed to explore the molecular mechanism of chemoresistance in bladder cancer cells. The gene expression profiles of GSE77883, including three untreated T24 cells samples and three gemcitabine-resistant T24 cells samples, was downloaded from Gene Expression Omnibus database. The screening of differentially expressed genes (DEGs), gene function analysis, and interaction prediction between microRNAs (miRNAs) and DEGs were performed by R software. The protein-protein interaction (PPI) and miRNA-DEGs networks were constructed and visualized by Cytoscape software. Then, the small molecules, with potential synergistic or antagonistic effects to gemcitabine resistance, were identified using the Connectivity Map database. Finally, gemcitabine-resistant T24 cell line was established and key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR). In total, 536 upregulated and 513 downregulated genes were screened and mainly enriched in oxidative stress response and signaling pathways related to extracellular matrix–receptor interaction and focal adhesion. PPI network showed interleukin 6, tumor necrosis factor, kinesin family member 11, and BUB1 mitotic checkpoint serine/threonine kinase B were key genes. The miRNA-DEGs regulatory networks included 18 miRNAs and 185 DEGs, including miR-182-5p, miR-590-3p, miR-320a and serum- and glucocorticoid-regulated kinase 1 (SGK1). Then, the related key genes and miRNAs were confirmed by qRT-PCR. Furthermore, 81 small molecules with antagonistic or synergistic effect to GEM were screened. We have investigated the molecular mechanisms driving GEM-resistance in bladder cancer cells that would contribute to the development of chemotherapy for advanced bladder cancer.     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果：NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的...     

Prognostic value of differentially methylated gene profiles in bladder cancer

DNA methylation can regulate gene expression and is pivotal in the occurrence and development of bladder cancer. In this study, we analyzed whole-genome DNA methylation on the basis of data from The Cancer Genome Atlas to select epigenetic biomarkers predictive of survival and further understand the molecular mechanisms underlying methylation patterns in bladder cancer. We identified 540 differentially methylated genes between tumor and normal tissues, including a number of independent prognostic factors based on univariate analysis. Genes (MIR6732, SOWAHC, SERPINI1, OR10W1, OR7G3, AIM1, and ZFAND5) were integrated to establish a risk model for prognostic assessment based on multivariate Cox analysis. The methylation of SOWAHC was negatively correlated with its messenger RNA expression, and together these were significantly correlated with prognosis. This study took advantage of high-throughput data mining to provide new bioinformatics evidence and ideas for further study into the pathogenesis and prognosis of bladder cancer.     

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.