Guoqing Wang

Gene-expression signatures can distinguish gastric cancer grades and stages.

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages.

Aberrant expression of microRNAs in gastric cancer and biological significance of miR-574-3p

The discovery of microRNAs (miRNAs) provides a new and powerful tool for studying the mechanisms, diagnosis and treatments of cancer. In this study, we employed AFFX miRNA expression chips to search for miRNAs that may be aberrantly expressed in gastric cancer tissues and to investigate the potential roles that miRNAs may play in the development and progression of gastric cancer. 14 miRNAs were found to be down-regulated and 2 miRNAs up-regulated in gastric cancer tissues compared to the normal gastric tissues. Among the aberrantly expressed miRNAs, miR-574-3p was selected to further study its expression features and functional roles. Interestingly, the reduced expression of miR-574-3p occurred mainly in the early stages of gastric cancer or in cancers with high level of differentiation, suggesting that it can be used as a marker for a mild case of gastric cancer. Functional study revealed that cell proliferation, migration and invasion were significantly inhibited in miR-574-3p-transfected gastric cancer SGC7901 cells. Computational prediction and experimental validation suggest that Cullin2 may be one of the targets of miR-574-3p. Overall our study suggests that the aberrantly expressed miRNAs may play regulatory and functional roles in the development and progression of gastric cancer.

Gene expression profile based classification models of psoriasis

Psoriasis is an autoimmune disease, which symptoms can significantly impair the patients life quality. It is mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. Currently no cure for psoriasis is available due to limited knowledge about its pathogenesis and development mechanisms. Previous studies have profiled hundreds of differentially expressed genes related to psoriasis, however with no robust psoriasis prediction model available. This study integrated the knowledge of three feature selection algorithms that revealed 21 features belonging to 18 genes as candidate markers. The final psoriasis classification model was established using the novel Incremental Feature Selection algorithm that utilizes only 3 features from 2 unique genes, IGFL1 and C10orf99. This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three independent validation strategies. The two marker genes, IGFL1 and C10orf99, were revealed as the upstream components of growth signal transduction pathway of psoriatic pathogenesis.

A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection

The electrocardiogram (ECG) is a biophysical electric signal generated by the heart muscle, and is one of the major measurements of how well a heart functions. Automatic ECG analysis algorithms usually extract the geometric or frequency-domain features of the ECG signals and have already significantly facilitated automatic ECG-based cardiac disease diagnosis. We propose a novel ECG feature by fitting a given ECG signal with a 20th order polynomial function, defined as PolyECG-S. The PolyECG-S feature is almost identical to the fitted ECG curve, measured by the Akaike information criterion (AIC), and achieved a 94.4% accuracy in detecting the Myocardial Infarction (MI) on the test dataset. Currently ST segment elongation is one of the major ways to detect MI (ST-elevation myocardial infarction, STEMI). However, many ECG signals have weak or even undetectable ST segments. Since PolyECG-S does not rely on the information of ST waves, it can be used as a complementary MI detection algorithm with the STEMI strategy. Overall, our results suggest that the PolyECG-S feature may satisfactorily reconstruct the fitted ECG curve, and is complementary to the existing ECG features for automatic cardiac function analysis.

Comprehensive characterization of the genomic alterations in human gastric cancer

Gastric cancer is one of the most prevalent and aggressive cancers worldwide, and its molecular mechanism remains largely elusive. Here we report the genomic landscape in primary gastric adenocarcinoma of human, based on the complete genome sequences of five pairs of cancer and matching normal samples. In total, 103,464 somatic point mutations, including 407 nonsynonymous ones, were identified and the most recurrent mutations were harbored by Mucins (MUC3A and MUC12) and transcription factors (ZNF717, ZNF595 and TP53). 679 genomic rearrangements were detected, which affect 355 protein‐coding genes; and 76 genes show copy number changes. Through mapping the boundaries of the rearranged regions to the folded three‐dimensional structure of human chromosomes, we determined that 79.6% of the chromosomal rearrangements happen among DNA fragments in close spatial proximity, especially when two endpoints stay in a similar replication phase. We demonstrated evidences that microhomology‐mediated break‐induced replication was utilized as a mechanism in inducing ∼40.9% of the identified genomic changes in gastric tumor. Our data analyses revealed potential integrations of Helicobacter pylori DNA into the gastric cancer genomes. Overall a large set of novel genomic variations were detected in these gastric cancer genomes, which may be essential to the study of the genetic basis and molecular mechanism of the gastric tumorigenesis.

Altered Expression of Hypoxia-Inducible Factor-1α (HIF-1α) and Its Regulatory Genes in Gastric Cancer Tissues

Tissue hypoxia induces reprogramming of cell metabolism and may result in normal cell transformation and cancer progression. Hypoxia-inducible factor 1-alpha (HIF-1α), the key transcription factor, plays an important role in gastric cancer development and progression. This study aimed to investigate the underlying regulatory signaling pathway in gastric cancer using gastric cancer tissue specimens. The integration of gene expression profile and transcriptional regulatory element database (TRED) was pursued to identify HIF-1α ↔ NFκB1 → BRCA1 → STAT3 ← STAT1 gene pathways and their regulated genes. The data showed that there were 82 differentially expressed genes that could be regulated by these five transcription factors in gastric cancer tissues and these genes formed 95 regulation modes, among which seven genes (MMP1, TIMP1, TLR2, FCGR3A, IRF1, FAS, and TFF3) were hub molecules that are regulated at least by two of these five transcription factors simultaneously and were associated with hypoxia, inflammation, and immune disorder. Real-Time PCR and western blot showed increasing of HIF-1α in mRNA and protein levels as well as TIMP1, TFF3 in mRNA levels in gastric cancer tissues. The data are the first study to demonstrate HIF-1α-regulated transcription factors and their corresponding network genes in gastric cancer. Further study with a larger sample size and more functional experiments is needed to confirm these data and then translate into clinical biomarker discovery and treatment strategy for gastric cancer.

Prediction of pathogenicity islands in Enterohemorrhagic Escherichia coli O157:H7 using genomic barcodes

The genome of lethal animal pathogenic bacterium Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is characterized by the presence of multiple pathogenicity islands (PAIs). Computational methods have been developed to identify PAIs based on the distinguishing G + C levels in some PAI versus non‐PAI regions. We observed that PAIs can have a very similar G + C level to that of the host chromosome, which may have led to false negative predictions using these methods. We have applied a novel method of genomic barcodes to identify PAIs. Using this technique, we have successfully identified both known and novel PAIs in the genomes of three strains of EHEC O157:H7.

McTwo: a two-step feature selection algorithm based on maximal information coefficient

BackgroundHigh-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This “large p, small n” paradigm in the area of biomedical “big data” may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.ResultsThis work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.ConclusionMcTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

The understanding of circular RNAs as special triggers in carcinogenesis.

Circular RNAs (circRNAs) are a large type of noncoding RNAs characterized by their circular shape resulting from covalently closed continuous loops. They are known to regulate gene expression in mammals. These tissue-specific transcripts are largely generated from exonic or intronic sequences of their host genes. Although several models of circRNA biogenesis have been proposed, the understanding of their origin is far from complete. Unlike other noncoding RNAs, circRNAs are widely expressed, highly conserved and stable in cytoplasm, which confer special functionalities to them. They are known to serve as microRNA (miRNA) sponges, regulators of alternative splicing, transcription factors and encode for proteins. The expression of circRNAs is associated with several pathological states and may potentially serve as novel diagnostic or predictive biomarkers. CircRNAs are known to regulate the expression of numerous cancer-related miRNAs. The circRNA-miRNA-mRNA axis is a known regulatory pattern of several cancer-associated pathways, with both agonist and antagonist effects on carcinogenesis. In consideration of their potential clinical relevance, circRNAs are at the center of ongoing research initiatives on cancer prevention and treatment. In this review, we discuss the current understanding of circRNAs and the prospects for their potential clinical application in the management of cancer patients.

Downregulated miR-31 level associates with poor prognosis of gastric cancer and its restoration suppresses tumor cell malignant phenotypes by inhibiting E2F2

The miRNA microarray analysis showed that miR-31 was reduced in gastric cancer. This study further assessed miR-31 expression and role of miR-31 in gastric cancer tissues and cell lines. The data showed that miR-31 expression was down-regulated in 40 cases of gastric cancer tissues compared to the adjacent normal tissues, and low expression of miR-31 was associated with poor tumor differentiation, lymph node metastasis, advanced T stage and worse overall survival of gastric cancer patients. Ectopic expression of miR-31 reduced tumor cell viability, enhanced apoptosis, arrested tumor cells at G1 transition, and reduced tumor cell migration and invasion in SGC-7901 and MGC-803 gastric cell lines in vitro. Enforced expression of miR-31 also inhibited growth of engrafted tumors in vivo. Luciferase reporter assays and western blot revealed that E2F2 is the direct target of miR-31. E2F2 expression was upregulated in gastric cancer tissues, and inversely associated with miR-31 levels, while knockdown of E2F2 expression mimicked miR-31 anti-tumor activity in gastric cancer cells, but the ectopic expression of E2F2 rescued the miR-31-mediated inhibition in gastric cell lines. Taken together, these results demonstrated that miR-31 acts as a crucial tumor suppressive activity by inhibiting E2F2s expression. Thus, miR-31 might be a candidate therapeutic target for gastric cancer patients.