Search

4101 Background: Microsatellite instability-high (MSI-H) status is a unique genomic state with encouraging effects of PD-1-based therapy in patients with advanced cancer. Cholangiocarcinoma is often driven by genetic mutations. However, the genomic characterization and translational medicine of MSI-H are not clear. Methods: In this study, 881 Chinese patients with cholangiocarcinoma (582 intrahepatic cholangiocarcinoma and 299 extrahepatic cholangiocarcinoma) were enrolled and their genomes were investigated using panel sequencing or whole-exome sequencing. Clinicopathological and genomic features as well as PD-1 inhibitor-based immunotherapy were analyzed by MSI status in patients with cholangiocarcinoma. Results: Overall, 47 (5.3%) cholangiocarcinoma patients were identified as MSI-H patients after enrichment enrollment. Intrahepatic cholangiocarcinoma (ICC) accounted for 74.47% (35/47) of MSI-H patients. Clinicopathological parameters showed younger, ICC-dominated, and more positive PD-L1 expression in MSI-H patients. In the genome of MSI-H cholangiocarcinoma, ACVR2A (75.00%), ARID1A (75.0%), KMT2D (72.2%), TGFBR2 (63.9%), PBRM1 (58.3%), RNF43 (55.6%), TP53 (52.8%) ), ARID1B (47.2%) had a higher mutation frequency. Comparing the SNV and INDEL mutation in the genome, the differential genes between MSI-H and MSS were ARID1A, ACVR2A, KMT2D, TGFBR2, PBRM1, RNF43, LRP1B, ARID1B, etc., respectively. In addition, the differential mutation pathways of MSI-H showed that the mutation rate of DDR, SWI/SNF CellCycle, HRD and other pathways was significantly higher than that of MSS samples. The tumor mutation burden (TMB) in MSI-H patients was significantly higher than that in MSS (P

RhoA is a member of the RHO family GTPases and is associated with essential functions in gastric cancer. In this study, we identified a gastric cancer biomarker, termed the "regulation of RhoA activity panel" (RRAP). Patients with gastric cancer from The Cancer Genome Atlas database were divided into training (N=160) and validation (N=155) cohorts. A cohort of 109 Chinese gastric cancer patients was utilized as an independent validation. Patients with mutated RRAP showed significantly better overall survival than patients with wild type RRAP. We also analyzed the association between RRAP and the migration capacity, immune-related signatures, and the tumor microenvironment. RRAP-mutant tumors had a significantly lower degree of lymph node metastasis and lower activities of migration-related pathways. These tumors also showed significantly increased immune cell infiltration and cytotoxic activity. Furthermore, two independent patient cohorts who received immune checkpoint blockade therapy were assessed for RRAP mutant status. As expected, for both immunotherapy cohorts, higher response rates to immune checkpoint blockade therapy were observed in patients with RRAP-mutant tumors than in patients with wild type RRAP tumors. Overall, this study indicates that the RRAP gene set is a potential biomarker for gastric cancer prognosis and therapeutic selection.

Background Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC). Methods dbPHCC focuses on biomarkers which were related to HCC prognosis by traditional experiments rather than high-throughput technology. All of the prognostic biomarkers came from literatures issued during 2002 to 2014 in PubMed and were manually selected. dbPHCC collects comprehensive information of candidate biomarkers and HCC prognosis. Results dbPHCC mainly contains 567 biomarkers: 323 proteins, 154 genes, and 90 microRNAs. For each biomarker, the reference information, experimental conditions, and prognostic information are shown. Based on two available patient cohort data sets, an exemplified prognostic model was constructed using 15 phosphotransferases in dbPHCC. The web interface does not only provide a full range of browsing and searching, but also provides online analysis tools. dbPHCC is available at http://lifecenter.sgst.cn/dbphcc/ Conclusions dbPHCC provides a comprehensive and convenient search and analysis platform for HCC prognosis research. General significance dbPHCC is the first database to focus on experimentally verified individual biomarkers, which are related to HCC prognosis. Prognostic markers in dbPHCC have the potential to be therapeutic drug targets and may help in designing new treatments to improve survival of HCC patients. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

e13514 Background: Hepatoid adenocarcinoma of stomach (HAS) is a rare subtype of gastric cancer (GC) with poor prognosis due to frequent liver metastasis. HAS, recognized as an extrahepatic cancer that resembles hepatocellular carcinoma (HCC), is characterized by solid sheet-like growth structures with hepatoid differentiation and excessive production of alpha-fetoprotein (AFP). This HAS subtype of GC (AFPGC) is genetically distinct compared to other common GC but requires more evidences for better characterization. Methods: Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were collected from 25 HAS and 9 AFPGC patients at Zhongshan Hospital from July 2013 to August 2017 for whole exome sequencing (WES) test. The testing was carried out by a College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory. Results: Our cohort included 25 males and 9 females who had undergone surgical treatment. The 25 HAS patients had a median age of 66.5 years (range 48-82) and the 9 AFPGC patients had a median age of 64 years (range 52-76). Patients with AFP>20 had a significantly better overall survival (OS) compared with patients with AFP≤20. The most frequently mutated genes were TP53 (44%), TTN (44%), and MUC19 (30%) in HAS tumor samples and CDC27 (44%), NKX2-3 (44%), and TTN (44%) in AFPGC samples. Patients with URI1 or POP4 alterations had a significantly poorer OS than patients without those genes mutations (7.6 months vs. 19.8 months, p = 0.016, and 10.9 months vs. 19.7 months, p = 0.038, respectively). However, patients with SPTA1 alterations had a significantly better OS compared to patients without SPTA1 alterations (27.6 months vs. 15.8 months, p = 0.042). Patients with higher tumor mutational burden (TMB) (≥3.9 muts/Mb) had a significantly better OS compared with patients with lower TMB (

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. HCC gene regulatory network (HCC GRN), whose nodes consist of genes, miRNAs or TFs and whose edges consist of interaction relationships of nodes, is one of the important ways to study molecular mechanism of HCC. Based on various experimental data, types of HCC GRNs could be conducted such as TF-miRNA regulatory network. Integrating the studies of HCC GRN, TF-miRNA transcriptional regulatory network performs better in identifying core genes which play important roles in network disturbances. It is a trend that gene variations and transcriptional regulatory networks should be combined, however the corresponding research is almost blank. This review summarizes the source of HCC data sources, the classification, character, and research program of HCC GRN. Finally, according to present analysis and discussion of progress and research status of HCC GRN, we provide a useful reference for researchers.肝癌 (Hepatocellular carcinoma，HCC) 是我国常见的恶性肿瘤之一。肝癌基因调控网络 (HCC regulatory network，HCC GRN) 是研究肝癌分子机制的重要途径之一，其节点包括肝癌相关的分子，如miRNA、TF 等，网络的边由节点间相互作用关系构成。基于不同类型的数据构建的肝癌基因调控网络其类型及特征各有不同。综合近年来肝癌基因调控网络研究发现，由TF 与miRNA 构建的肝癌转录调控网络更能揭露肝癌关键基因，反映关键基因在调控网络中的扰动情况。整合基因变异信息与调控网络成为研究肝癌基因调控网络的趋势，但相应的研究几乎是空白的。本文从HCC GRN 的数据来源、分类及特征，及各类型调控网络的近年研究情况等方面进行综述，并结合相关研究工作对肝癌基因调控网络研究现状进行分析与讨论，对前景进行展望，为这一领域研究工作提供参考。.

MicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA–SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier miR-SF to predict the most recently identified human pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, miR-SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for miR-SF, microPred and miPred, respectively. Thus, miR-SF is effective for identifying pre-miRNAs.

The investigation of topological properties of proteins in protein-protein interaction network (PPIN) has great potentials to identify basic protein functions and mechanisms of action. Based on human PPIN, previous study has shown that the topological properties of drug targets are significantly distinguished from those of proteins that are not targeted by drugs (non-drug-targets). MicroRNAs (miRNAs) are known to regulate gene expression at the post-transcriptional level. To determine whether the differences in topological properties between drug targets and non-drug-targets are dominated by the proteins that are regulated by miRNA, we divided the drug targets into two sets: those are regulated by miRNA (mir-drug-targets) and those are not regulated by miRNA (non-mir-drug-targets). We compared the probability of interactions and five topological properties among the three types of proteins in human PPIN. Our results demonstrated that mir-drug-targets preferentially interact with other mir-drug-targets and tend to be hub-bottlenecks. However, there was no bias on topological properties between non-mir-drug-targets and non-drug-targets. The same topological features are observed among non-drug targets. These findings indicate that miRNA regulation has an important role in human PPIN, and may be useful in the development of novel drugs.

Adverse drug reactions (ADRs) are responsible for drug candidate failure during clinical trials. It is crucial to investigate biological pathways contributing to ADRs. Here, we applied a large-scale analysis to identify overrepresented ADR-pathway combinations through merging clinical phenotypic data, biological pathway data, and drug-target relations. Evaluation was performed by scientific literature review and defining a pathway-based ADR-ADR similarity measure. The results showed that our method is efficient for finding the associations between ADRs and pathways. To more systematically understand the mechanisms of ADRs, we constructed an ADR-pathway network and an ADR-ADR network. Through network analysis on biology and pharmacology, it was found that frequent ADRs were associated with more pathways than infrequent and rare ADRs. Moreover, environmental information processing pathways contributed most to the observed ADRs. Integrating the system organ class of ADRs, we found that most classes tended to interact with other classes instead of themselves. ADR classes were distributed promiscuously in all the ADR cliques. These results reflected that drug perturbation to a certain pathway can cause changes in multiple organs, rather than in one specific organ. Our work not only provides a global view of the associations between ADRs and pathways, but also is helpful to understand the mechanisms of ADRs.

Background Traditionally top-down method was used to identify prognostic features in cancer research. That is to say, differentially expressed genes usually in cancer versus normal were identified to see if they possess survival prediction power. The problem is that prognostic features identified from one set of patient samples can rarely be transferred to other datasets. We apply bottom-up approach in this study: survival correlated or clinical stage correlated genes were selected first and prioritized by their network topology additionally, then a small set of features can be used as a prognostic signature. Methods Gene expression profiles of a cohort of 221 hepatocellular carcinoma (HCC) patients were used as a training set, ‘bottom-up’ approach was applied to discover gene-expression signatures associated with survival in both tumor and adjacent non-tumor tissues, and compared with ‘top-down’ approach. The results were validated in a second cohort of 82 patients which was used as a testing set. Results Two sets of gene signatures separately identified in tumor and adjacent non-tumor tissues by bottom-up approach were developed in the training cohort. These two signatures were associated with overall survival times of HCC patients and the robustness of each was validated in the testing set, and each predictive performance was better than gene expression signatures reported previously. Moreover, genes in these two prognosis signature gave some indications for drug-repositioning on HCC. Some approved drugs targeting these markers have the alternative indications on hepatocellular carcinoma. Conclusion Using the bottom-up approach, we have developed two prognostic gene signatures with a limited number of genes that associated with overall survival times of patients with HCC. Furthermore, prognostic markers in these two signatures have the potential to be therapeutic targets.

Although several studies have investigated the essential roles of inflammation in tumor progression, not many have systematically analyzed gene expression patterns across diverse cancers in the context of inflammation. In this study, in order to better understand the inflammatory scenario, we initially constructed the inflammatory timeline (IT) based on two gene expression profiles during inflammatory progression (inflammatory bowel disease and Helicobacter pylori infection). Then, we separately identified the differentially expressed genes (DEGs) from 25 cancer-related microarray data. By comparing the distributions of DEGs in the IT, we identified three novel pan-cancer gene expression patterns. In the first pattern, the up-regulated genes in cancers were over-expressed in the early phase of inflammation, while the down-regulated genes were over-expressed in the late phase of inflammation. The second pattern was the opposite of the first one. The third pattern appeared to be transitional between the first and second patterns. We found that some cancers with different tissue origins have similar gene expression patterns. Finally, we identified two sets of tissue-independent inflammatory signatures that were over-expressed in early and late phases of inflammation, respectively. The dominant biological processes of early inflammatory signatures were cell proliferation, DNA replication, and DNA repair, whereas the late inflammatory signatures were reflective of innate immune response, neutrophil migration, and antigen processing. These inflammatory signatures may be useful to predict gene expression patterns in human cancers. Therefore, the pan-cancer analysis of gene expression patterns in the context of inflammation provides a novel insight into cancers and an unprecedented opportunity to develop new therapies.

Background Hepatocellular carcinoma (HCC) is one of the most highly malignant and lethal cancers of the world. Its pathogenesis has been reported to be multi-factorial, and the molecular carcinogenesis of HCC can not be attributed to just a few individual genes. Based on the microRNA and mRNA expression profiling of normal liver tissues, pericancerous hepatocellular tissues and hepatocellular carcinoma tissues, we attempted to find prognosis related gene sets for HCC patients. Results We identified differentially expressed genes (DEG) from three comparisons: Cancer/Normal, Cancer/Pericancerous and Pericancerous/Normal. GSEA (gene set enrichment analysis) were performed. Based on the enriched gene sets of GO terms, pathways and transcription factor targets, it was found that the genome instability and cell proliferation increased while the metabolism and differentiation decreased in HCC tissues. The expression profile of DEGs in each enriched gene set was used to correlate to the postoperative survival time of HCC patients. Nine gene sets were found to prognostic correlation. Furthermore, after substituting DEG-targeting-microRNA for DEG members of each gene set, two gene sets with the microRNA expression profiles were obtained that had prognostic potential. Conclusions The malignancy of HCC could be represented by gene sets, and pericancerous liver exhibits important characteristics of liver cancer. The expression level of gene sets not only in HCC but also in the pericancerous liver showed potential for prognosis implying an option for HCC prognosis at an early stage. Additionally, the gene-targeting-microRNA expression profiles also showed prognostic potential, demonstrating that the multi-factorial molecular pathogenesis of HCC is contributed by various genes and microRNAs. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-S1-S13) contains supplementary material, which is available to authorized users.

BACKGROUND: Transcriptional regulatory network (TRN) is used to study conditional regulatory relationships between transcriptional factors and genes. However few studies have tried to integrate genomic variation information such as copy number variation (CNV) with TRN to find causal disturbances in a network. Intrahepatic cholangiocarcinoma (ICC) is the second most common hepatic carcinoma with high malignancy and poor prognosis. Research about ICC is relatively limited comparing to hepatocellular carcinoma, and there are no approved gene therapeutic targets yet. METHOD: We first constructed TRN of ICC (ICC-TRN) using forward-and-reverse combined engineering method, and then integrated copy number variation information with ICC-TRN to select CNV-related modules and constructed CNV-ICC-TRN. We also integrated CNV-ICC-TRN with KEGG signaling pathways to investigate how CNV genes disturb signaling pathways. At last, unsupervised clustering method was applied to classify samples into distinct classes. RESULT: We obtained CNV-ICC-TRN containing 33 modules which were enriched in ICC-related signaling pathways. Integrated analysis of the regulatory network and signaling pathways illustrated that CNV might interrupt signaling through locating on either genomic sites of nodes or regulators of nodes in a signaling pathway. In the end, expression profiles of nodes in CNV-ICC-TRN were used to cluster the ICC patients into two robust groups with distinct biological function features. CONCLUSION: Our work represents a primary effort to construct TRN in ICC, also a primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations (CNV). This kind of approach may bring the traditional studies of TRN based only on expression data one step further to genetic disturbance. Such kind of approach can easily be extended to other disease samples with appropriate data.

It is known that chromatin features such as histone modifications and the binding of transcription factors exert a significant impact on the “openness” of chromatin. In this study, we present a quantitative analysis of the genome-wide relationship between chromatin features and chromatin accessibility in DNase I hypersensitive sites. We found that these features show distinct preference to localize in open chromatin. In order to elucidate the exact impact, we derived quantitative models to directly predict the “openness” of chromatin using histone modification features and transcription factor binding features, respectively. We show that these two types of features are highly predictive for chromatin accessibility in a statistical viewpoint. Moreover, our results indicate that these features are highly redundant and only a small number of features are needed to achieve a very high predictive power. Our study provides new insights into the true biological phenomena and the combinatorial effects of chromatin features to differential DNase I hypersensitivity.

Tyrosine nitration is a covalent post-translational modification, which regulates protein functions such as hindering tyrosine phosphorylation and affecting essential signal transductions in cells. Based on up-to-date proteomics data, tyrosine nitration appears to be a highly selective process since not all tyrosine residues in proteins or all proteins are nitrated in vivo. Quite a few investigations included the protein structural information from the RCSB PDB database, where near 100 000 high-quality three-dimensional structures are available. In this work, we analyzed the local protein structures and amino acid topological networks of the nitrated and non-nitrated tyrosine sites in nitrated proteins, including neighboring atomic distribution, amino acid pair (AAP) and amino acid triangle (AAT). It has been found that aromatic and aliphatic residues, particularly with large volume, aromatic, aliphatic, or acidic side chains, are disfavored for the nitration. After integrating these structural features and topological network features with traditional sequence features, the predictive model achieves a sensitivity of 63.30% and a specificity of 92.24%, resulting in a much better accuracy compared to the previous models with only protein sequence information. Our investigation implies that the site selectivity may stem from a more open, hydrophilic and high-pH chemical environment around the tyrosine residue.

Previously, the different mechanisms of HBV infection and HCV infection were studied experimentally. Multiple studies also compared the differential network between HBV induced HCC and HCV induced HCC based on gene expression data. However network level comparison combining viral–human interaction network and dysfunctional protein interaction network for HBV and HCV–HCC has rarely been done before. In this work we did some pioneer job in construction of HBV/HCV viral dysfunctional network in HCC, in hope of investigating viral infection impact on the change of genome expression and eventually, the development of HCC. We found that HBx, the main HBV viral protein, directly acted on the gene groups of cell cycle, which could perfectly explain the dominant cell proliferation effect shown in the dysfunctional network of HBV–HCC. On the other hand, multiple important HCV viral proteins including CORE, NS3 and NS5A were found to target very important cancer related proteins such as TP53 and SMAD3, but no direct targeting to major immune response or inflammation related proteins. Therefore the dominant activation of immune response and inflammation related pathways shown in dysfunctional network of HCV–HCC might not be a direct effect of HCV infection. They might have been an indirect demonstration of activated cancer promoting pathways. Similar approaches may as well be applied to other important virus infection caused human diseases to help elucidate the mechanisms of virus–host interaction, and even help with investigations on anti-virus based therapies. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.

The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.

The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.

MicroRNAs (miRNAs) are a class of small non-coding RNA genes that post-transcriptionally regulate gene expression. With the development of high-throughput miRNA detection technology, researchers have begun to investigate the relationships between miRNA expression and its functions. In this study, we systematically analyzed the underlying molecular mechanisms and biological functions of co-expressed miRNAs. By integrating miRNA expression profiles, miRNA genome locations, transcriptional factors (TFs) of miRNAs and their target genes, we concluded that co-expressed miRNAs are more likely to be located in the same miRNA cluster (p = 6.05 x 10(-30)), are regulated by more common TFs (p = 9.17 x 10(-17)) and have consistent functions (p = 1.01 x 10(-6)). Moreover, among the top 10% (84) co-expressed miRNA pairs that are located on the same chromosomes, 37 miRNA pairs are located in the same cluster. Of the remaining 47 pairs, 36 miRNA pairs share more common TFs (7). They account for 73/84 (86.9%) of total miRNA pairs. Finally, we further analyzed the top 10 co-expressed miRNA pairs. Almost all of these miRNA pairs are located in the same cluster, are regulated by many common TFs and have highly consistent functions, in agreement with previous reports. Thus, our study may provide an important reference for miRNA regulations and functions.