Recent progress in research of heterogeneous nuclear ribonucleoprotein K related with tumor pathogenesis and progression / 中国比较医学杂志

Over the past few decades, the classification of oncogenes or tumor suppressor genes has been an important topic in cancer biology. However,it is difficult to classify some genes. Heterogeneous nuclear ribonucleoprotein K(HNRNPK)is a nucleic acid-binding protein,which is involved in the regulation of gene expression,signal transduction and many other cellular processes. In recent years, it has been found that HNRNPK is overexpressed in many types of tumors,and its overexpression is negatively correlated with the prognosis of patients,suggesting that HNRNPK may play a role as an oncogene in tumorigenesis. In contrast,however,HNRNPK has also been considered as a tumor suppressor gene in acute myeloid leukemia(AML). Therefore, in this article we summarize and discuss the recent progress in the molecular functions and regulatory mechanisms of HNRNPK in tumorigenesis and progression.

Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selectedmolecular functions include several known breast cancer- related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.