Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research.

The tumor suppressor ING4 has been shown to be reduced in human HCC. The alteration of ING4 contributes to HCC progression. However, its effect in HCC and the potential mechanism is largely unclear. Herein, we found that downregulation of ING4 in HCC tumor tissues was closely associated with cancer staging, tumor size and vascular invasion. Lentivirus-mediated ING4 overexpression significantly inhibited proliferation, migration and invasion, and induced cell cycle G1 phase arrest and apoptosis in MHCC97H human HCC cells. Moreover, overexpression of ING4 dramatically suppressed MHCC97H tumor cell growth and metastasis to lung in vivo in athymic BALB/c nude mice. Mechanistic studies revealed that overexpression of ING4 markedly increased expression of FOXO3a both at the mRNA and protein level as well as enhanced nuclear level and transcriptional activity of FOXO3a in MHCC97H tumor cells. In addition, ING4 repressed transcriptional activity of NF-κB and expression of miR-155 targeting FOXO3a. Knockdown of ING4 exhibited opposing effects in MHCC97L human HCC cells. Interestingly, knockdown of FOXO3a attenuated not only ING4-elicited tumor suppression but also ING4-mediated regulatory effect on FOXO3a downstream targets, confirming that FOXO3a is involved in ING4-directed tumor-inhibitory effect in HCC. Overexpression of miR-155 attenuated ING4-induced upregulation of FOXO3a, whereas inhibition of miR-155 blunted ING4 knockdown-induced reduction of FOXO3a. Furthermore, inhibition of NF-κB markedly impaired ING4 knockdown-induced upregulation of miR-155 and downregulation of FOXO3a. Taken together, our study provided the first compelling evidence that ING4 can suppress human HCC growth and metastasis to a great extent via a NF-κB/miR-155/FOXO3a pathway.

Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation. Then the future opportunities and challenges about database or knowledge base building, novel model developing and molecular mechanism exploring as well as the intelligent decision supporting system construction for the precision diagnosis, prognosis and treatment of cholangiocarcinoma are discussed.

Background Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker. Results In the present work, we applied a rational computer‐aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC‐associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR‐378a‐3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and β‐catenin, affecting the β‐catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR‐378a‐3p and the impact of its abnormal expression on the cell growth and invasion of HCC. Conclusions A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR‐378a‐3p was a putative biomarker for diagnosis and prognosis of HCC.