Dapeng Hao

Aberrant regulation of the LIN28A/LIN28B and let-7 loop in human malignant tumors and its effects on the hallmarks of cancer

RNA binding proteins (RBPs) and microRNAs (miRNAs) are two of the most important post-transcriptional regulators of gene expression, and their aberrant expression contributes to the development of human malignancies. Let-7, one of the most well-known tumor suppressors, is frequently down-regulated in a variety of human cancers. The RBP LIN28A/LIN28B, a direct target of the let-7 family of miRNAs, is an inhibitor of let-7 biogenesis and is frequently up-regulated in cancers. Aberrant regulation of the LIN28A/LIN28B and let-7 loop in human malignant tumors is reportedly involved in cancer development, contributing to cellular proliferation, cell death resistance, angiogenesis, metastasis, metabolism reprogramming, tumor-associated inflammation, genome instability, acquiring immortality and evading immune destruction. In this review, we summarized the mechanisms of LIN28A/LIN28B and let-7 loop aberrant regulation in human cancer and discussed the roles and potential mechanisms of the LIN28A/LIN28B and let-7 loop in regulating the hallmarks of cancer. The crosstalk between LIN28A/LIN28B and let-7 loop and certain oncogenes (such as MYC, RAS, PI3K/AKT, NF-κB and β-catenin) in regulating hallmarks of cancer has also been discussed.

The Dichotomy in Degree Correlation of Biological Networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled.

A Genomic Instability Score in Discriminating Nonequivalent Outcomes of BRCA1/2 Mutations and in Predicting Outcomes of Ovarian Cancer Treated with Platinum-Based Chemotherapy

Detecting mutation in BRCA1/2 is a generally accepted strategy for screening ovarian cancers that have impaired homologous recombination (HR) ability and improved sensitivity to PARP inhibitor. However, a substantial subset of BRCA-mutant ovarian cancer patients shows less impaired or unimpaired HR ability, resulting in nonequivalent outcome after ovarian cancer development. We hypothesize that genomic instability provides a lifetime record of DNA repair deficiency and predicts ovarian cancer outcome. Based on the multi-dimensional TCGA ovarian cancer data, we developed a biological rationale-driven genomic instability score integrating somatic mutation and copy number change in a tumor genome. The score successfully divided BRCA-mutant ovarian tumors into cases of significantly improved outcome and cases of unimproved outcome. The score was also capable of discriminating HR-deficiency indicated by BRCA1 epigenetically silencing, EMSY amplification and homozygous deletion of core HR genes. We further found that the score was positively correlated with the complete response rate of chemotherapy and the rate of platinum-sensitivity, and predicted improved outcome of ovarian cancer, regardless of BRCA-mutation status. The score may have important value in outcome prediction and clinical trial design.

Allele-Specific Behavior of Molecular Networks: Understanding Small-Molecule Drug Response in Yeast

The study of systems genetics is changing the way the genetic and molecular basis of phenotypic variation, such as disease susceptibility and drug response, is being analyzed. Moreover, systems genetics aids in the translation of insights from systems biology into genetics. The use of systems genetics enables greater attention to be focused on the potential impact of genetic perturbations on the molecular states of networks that in turn affects complex traits. In this study, we developed models to detect allele-specific perturbations on interactions, in which a genetic locus with alternative alleles exerted a differing influence on an interaction. We utilized the models to investigate the dynamic behavior of an integrated molecular network undergoing genetic perturbations in yeast. Our results revealed the complexity of regulatory relationships between genetic loci and networks, in which different genetic loci perturb specific network modules. In addition, significant within-module functional coherence was found. We then used the network perturbation model to elucidate the underlying molecular mechanisms of individual differences in response to 100 diverse small molecule drugs. As a result, we identified sub-networks in the integrated network that responded to variations in DNA associated with response to diverse compounds and were significantly enriched for known drug targets. Literature mining results provided strong independent evidence for the effectiveness of these genetic perturbing networks in the elucidation of small-molecule responses in yeast.

Cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer.

Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.

Network-based analysis of genotype-phenotype correlations between different inheritance modes

MOTIVATION Recent studies on human disease have revealed that aberrant interaction between proteins probably underlies a substantial number of human genetic diseases. This suggests a need to investigate disease inheritance mode using interaction, and based on which to refresh our conceptual understanding of a series of properties regarding inheritance mode of human disease. RESULTS We observed a strong correlation between the number of protein interactions and the likelihood of a gene causing any dominant diseases or multiple dominant diseases, whereas no correlation was observed between protein interaction and the likelihood of a gene causing recessive diseases. We found that dominant diseases are more likely to be associated with disruption of important interactions. These suggest inheritance mode should be understood using protein interaction. We therefore reviewed the previous studies and refined an interaction model of inheritance mode, and then confirmed that this model is largely reasonable using new evidences. With these findings, we found that the inheritance mode of human genetic diseases can be predicted using protein interaction. By integrating the systems biology perspectives with the classical disease genetics paradigm, our study provides some new insights into genotype-phenotype correlations. CONTACT haodapeng@ems.hrbmu.edu.cn or biofomeng@hotmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Genome-wide characterization of essential, toxicity-modulating and no-phenotype genes in S. cerevisiae.

Based on the requirements for an organisms viability, genes can be classified into essential genes and non-essential genes. Non-essential genes can be further classified into toxicity-modulating genes and no-phenotype genes based on the fitness phenotype of yeast cells when the gene is deleted under DNA-damaging conditions. In this study, graph theoretical approaches were used to characterize essential, toxicity-modulating and no-phenotype genes for S. cerevisiae in the physical interaction (PI) network and the perturbation sensitivity (PS) network. We also gained previously published biological datasets to gain a more complete understanding of the differences and relationships between essential, toxicity-modulating genes and no-phenotype genes. The analysis results indicate that toxicity-modulating genes have similar properties as essential genes, and toxicity-modulating genes might represent a middle ground between essential genes and no-phenotype genes, suggesting that cells initiate highly coordinated responses to damage that are similar to those needed for vital cellular functions. These findings may elucidate the mechanisms for understanding toxicity-modulating processes relevant to certain diseases.

A novel method for expanding current annotations in gene ontology

Since the gap between the amount of protein sequence data and the reliable function annotations in public databases is growing, characterizing protein functions becomes a major task in the post genomic era. Some current ways to predict functions of a protein are based on the relationships between the protein and other proteins in databases. As a large fraction of annotated proteins are not fully characterized, annotating novel proteins is limited. Therefore, it is of high demand to develop efficient computation methods to push the current broad function annotations of the partially known proteins toward more detailed and specific knowledge. In this study, we explore the capability of a rule-based method for expanding the current annotations per some function categorization system such as Gene Ontology. Applications of the proposed method to predict human and yeast protein functions demonstrate its efficiency in expanding the knowledge space of the partially known proteins.