Hu-Qin Zhang

CTNNA3 is a tumor suppressor in hepatocellular carcinomas and is inhibited by miR-425

Hepatocellular carcinoma (HCC) is a common and leading cause of death worldwide. Here, we identified that a cell-cell adhesion gene, CTNNA3, is a tumor suppressor in HCC. CTNNA3 inhibited the proliferation, migration and invasion of HCC cell lines. In these cells, CTNNA3 inhibited Akt signal, and in turn decreased the proliferating cell nuclear antigen (PCNA) and the matrix metallopeptidase MMP-9, and increased the cell cycle inhibitor p21Cip1/Waf1. Meanwhile, CTNNA3 is inhibited by miR-425 in HCC. The miR-425 directly bound to the 3′UTR of CTNNA3 and inhibited its expression. The tumor suppressor function of CTNNA3 and the oncogenic function of miR-425 were further confirmed in HCC cell xenograft in nude mice. The miR-425/CTNNA3 axis may provide insights into the mechanisms underlying HCC, and contribute to potential therapeutic strategy of HCC.

Precision medicine for hepatocellular carcinoma: driver mutations and targeted therapy

Hepatocellular carcinoma (HCC) is the third most frequent cause of tumor-related mortality and there are an estimated approximately 850,000 new cases annually. Most HCC patients are diagnosed at middle or advanced stage, losing the opportunity of surgery. The development of HCC is promoted by accumulated diverse genetic mutations, which confer selective growth advantages to tumor cells and are called “driver mutations”. The discovery of driver mutations provides a novel precision medicine strategy for late stage HCC, called targeted therapy. In this review, we summarized currently discovered driver mutations and corresponding signaling pathways, made an overview of identification methods of driver mutations and genes, and classified targeted drugs for HCC. The knowledge of mutational landscape deepen our understanding of carcinogenesis and promise future precision medicine for HCC patients.

Identifying key regulating miRNAs in hepatocellular carcinomas by an omics' method

The miRNAs play important regulating roles in the pathogenesis of hepatocellular carcinoma (HCC). To uncover key regulating miRNAs in HCC that were neglected by traditional analyzing methods of transcriptomics data, we proposed a novel molecular-network-based omics’ (MNBO) method. With this method, we predicted HCC-regulating miRNAs, and confirmed the role of a novel miR-590-3P/EED axis by a clinical study and in vitro, in vivo wet-experiments. The miR-590-3P is significantly down-regulated in HCC patients. And low level of miR-590-3P in HCC is associated with poor prognosis of patients. In HCC cell lines, the miR-590-3P suppressed cell proliferation by inhibiting the transformation G1 phase to S phases of the cell cycle. Moreover, the miR-590-3P inhibited migration and invasion of HCC cells. Further investigations indicated that miR-590-3P play its roles by inhibiting polycomb protein EED. The experiments in animal model implied miR-590-3P could be a potential therapeutic agent for HCC in the future. In conclusion, the discovery of miR-590-3P revealed the MNBO would be a useful strategy to uncover key regulating miRNAs in HCC.

Mass spectrometry-based proteomic analysis of Kashin-Beck disease

Kashin-Beck disease (KBD) is a degenerative osteoarticular disease of unknown etiology. The management of KBD would benefit from the identification of the biomarkers related to this disease. In this study, mass spectrometry (MS)-based proteomic profiling was used to identify potential biomarkers of the disease. One hundred and sixteen serum samples of KBD cases and healthy controls were collected and analyzed. A framework for data analysis was implemented, which included normalization, denoising using undecimated discrete wavelet transforms, baseline subtraction, peak detection and alignment, non-parametric testing and classification by support vector machine. The method identified correlative mass points and obtained a discriminative pattern with 90.91% sensitivity and 82.61% specificity. The results of this study, although preliminary, suggest that further proteomics study may be useful with a larger number of appropriate specimens, careful experiment manipulation and improved MS techniques.

Investigation on maternal lineage of a Neolithic group from northern Shaanxi based on ancient DNA

Abstract A magnetic bead purification method was successfully used to extract ancient DNA from the skeletal remains of 10 specimens excavated from Wuzhuangguoliang (Wzhgl) site, which was located in northern Shaanxi. The multidimensional scaling (MDS) and analysis of molecular variance approach (AMOVA) revealed that ancient Wzhgl people bored a very high similarity to southern Han Chinese. By constructing the MJ-network of various modern people including Han Chinese and Japanese, the phylogenetic analysis indicated that the Wzhgl population had close maternal distance with ancient Shandong and Xinjiang people. These findings indicated that Wzhgl contributed to the gene pool of Han Chinese and modern Japanese. In addition, population migration and interflow between Wzhgl people and ancient Shandong or Xinjiang probably occurred in Neolithic period.

Biomarker Discovery from Proteomic Data Based on Wavelet Package Transform and AdaBoost

Advances in proteomics provide a new method for early detection of cancer, it can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. In particular, proteomic pattern of body fluid has attracted attention as an approach to early detection of cancer. Mass spectrometry can provide rapid and precise measurements of the proteins in the body fluid. But the data processing is still a challenge due to noise artifact and high dimensionality of the proteomic data. In this paper, we proposed a scheme that combined wavelet package transform, statistic analysis and AdaBoost to process a public prostate cancer proteomic dataset, the obtained discriminative proteomic pattern can differentiate the cancer form control with high sensitivity and specificity.

Prognostic value of EGFR and KRAS in resected non-small cell lung cancer: a systematic review and meta-analysis

Background The prognostic value of EGFR and KRAS mutations in resected non-small cell lung cancer (NSCLC) has been reported. However, conflicting results were reported in these studies. The effect of mutations in these two genes in resected NSCLC remains controversial. Methods We searched Internet databases for studies reporting disease-free survival (DFS) and overall survival (OS) in resected NSCLC patients with EGFR or KRAS mutations. A meta-analysis calculating the pooled hazard ratio (HR) for DFS and OS was used to measure the association of EGFR or KRAS mutations with the prognosis of patients after surgery. Results A total of 9,635 patients from 32 studies were included in this analysis. The combined HR for EGFR mutations on DFS was 0.77 (95% CI 0.66–0.90, p=0.001) and on OS was 0.72 (95% CI 0.66–0.80, p<0.00001). In addition, the combined HR for KRAS mutations on DFS was 1.5 (95% CI 1.15–1.96, p=0.002) and on OS was 1.49 (95% CI 1.28–1.73, p<0.00001). Sensitivity analysis, subgroup analysis, and bias analysis proved the stability of the results. Conclusion The analysis showed that EGFR mutations were significantly associated with DFS and OS. These findings indicated that surgically treated NSCLC patients with EGFR mutations were inclined to exhibit a prolonged DFS and OS. In addition, the results indicated that KRAS mutations predicted worse DFS and OS in patients with resected NSCLC.

Wavelet Transform and Bagging Predictor Approaches to Cancer Identification from Mass Spectrometry-Based Proteomic Data

The early detection of cancer has the potential to dramatically reduce the mortality of cancer. Recently, using the mass spectrometry based proteomics to develop profiles of patient serum proteins, combined with bioinformatics algorithms has been reported as a promising method to achieve this goal. In this paper, we develop a workflow that combined wavelet transform, statistic analysis and bagging predictor to process a public ovarian cancer proteomic dataset, and ultimately obtained a discriminative proteomic pattern that can differentiate the cancer form control with high sensitivity and specificity. Compared with the previous studies, the results of our study are based on peaks of mass spectrometry and the discovered discriminative pattern is more biologically.

Preprocessing and Classifying of Mass Spectrometry-Based Proteomics Data Using Wavelet Transform and Decision Tree Learning

Advances in mass spectrometry-based proteomics have brought expectations for biomedical researchers. It can be used for identify proteomic patterns in body fluids to discriminate patients from control, the results are inspiring. However, most of the earlier studies are based on the direct application of original MS data, together with dimension reduction or feature selection methods. We deemed that only the peaks of MS data have real biological meaning, so its important to obtain the ultimate proteomic pattern using the real peaks. In this paper, we proposed a workflow that combined wavelet transform, statistical analysis and decision tree learning to process MS data. Especially, the statistical analysis which have not been attached too much importance in most studies was investigated, the possible distribution law of the MS peaks was proposed.