Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

AIM: To analyze the metastasis-related proteins in hepatocellular carcinoma (HCC) and discover the biomarker candidates for diagnosis and therapeutic intervention of HCC metastasis with bioinformatics tools. METHODS: Metastasis-related proteins were determined by stable isotope labeling and MS analysis and analyzed with bioinformatics resources, including Phobius, Kyoto encyclopedia of genes and genomes (KEGG), online mendelian inheritance in man (OMIM) and human protein reference database (HPRD). RESULTS: All the metastasis-related proteins were linked to 83 pathways in KEGG, including MAPK and p53 signal pathways. Protein-protein interaction network showed that all the metastasis-related proteins were categorized into 19 function groups, including cell cycle, apoptosis and signal transduction. OMIM analysis linked these proteins to 186 OMIM entries. CONCLUSION: Metastasis-related proteins provide HCC cells with biological advantages in cell proliferation, migration and angiogenesis, and facilitate metastasis of HCC cells. The bird's eye view can reveal a global characteristic of metastasis-related proteins and many differentially expressed proteins can be identified as candidates for diagnosis and treatment of HCC.

Cancer-associated fibroblasts from invasive breast cancer have an attenuated capacity to secrete collagens.

Normal fibroblasts produce extracellular matrix (ECM) components that form the structural framework of tissues. Cancer-associated fibroblasts (CAFs) with an activated phenotype mainly contribute to ECM deposition and construction of cancer masses. However, the stroma of breast cancer tissues has been shown to be more complicated, and the mechanisms through which CAFs influence ECM deposition remain elusive. In this study, we found that the activated fibroblast marker α-smooth muscle actin (α-SMA) was only present in the stroma of breast cancer tissue, and the CAFs isolated from invasive breast cancer sample remained to be activated and proliferative in passages. To further assess the difference between CAFs and normal breast fibroblasts (NFs), MALDI TOF/TOF­MS was used to analyze the secretory proteins of primary CAFs and NFs. In total, 2,903 and 3,023 proteins were identified. Mass spectrum quantitative assay and data analysis for extracellular proteins indicated that the CAFs produce less collagens and matrix-degrading enzymes compared with NFs. This finding was confirmed by western blot analysis. Furthermore, we discovered that reduced collagen deposition was present in the stroma of invasive breast cancer. These studies showed that although CAFs from invasive breast cancer possess an activated phenotype, they secreted less collagen and induced less ECM deposition in cancer stroma. In cancer tissue, the remodeling of stromal structure and tumor microenvironment might, therefore, be attributed to the biological changes in CAFs including their protein expression profile.

Quantitative proteomic analysis of a paired human liver healthy versus carcinoma cell lines with the same genetic background to identify potential hepatocellular carcinoma markers.

To comprehensively measure global changes in protein expression associated with human hepatocellular carcinoma (HCC), comparative proteomic analysis of two cell lines derived from the healthy and carcinoma tissue of a same donor respectively was conducted using quantitative amino acid-coded mass tagging /stable isotope labeling with amino acids in cell culture-based LC-MS/MS approach. Among a total of 501 proteins precisely quantified, the expressions of 128 proteins were significantly altered including 70 proteins up-regulated and 58 down-regulated in HCC cells. According to their previously characterized functions, the differentially expressed proteins were found associated with nine functional categories including glycolysis, stress response, cell communication, cell cycle, apoptosis/death, etc. For example, multiple enzymes involving glycolysis pathway were found differentially regulated in HCC cells, illustrating the critical participation of glycolysis in the HCC transformation. The accuracy of certain differentially expressed proteins identified through the amino acid-coded mass tagging-based quantification was validated in the paired cell lines, and later their pathological correlations were examined in multiple clinical pairs of normal versus tumor tissues from HCC specimen by using a variety of biological approaches including Western blotting and in situ immunoassays. These consistencies suggested that multiple proteins such as HSP27, annexin V, glyceraldehyde-3-phosphate dehydrogenase, nucleolin and elongation factor Tu could be the biomarkers candidates for diagnosis of HCC.

Comprehensive profiling of metastasis-related proteins in paired hepatocellular carcinoma cells with different metastasis potentials.

Precise and comprehensive identifications of the proteins associated with metastasis are critical for early diagnosis and therapeutic intervention of hepatocellular carcinoma (HCC). Therefore, we investigated the proteomic differences between a pair of HCC cell lines, originating from the same progenitor, with different metastasis potential using amino acid-coded mass tagging-based LC-MS/MS quantitative proteomic approach. Totally the relative abundance of 336 proteins in these cell lines were quantified, in which 121 proteins were upregulated by >30%, and 64 proteins were downregulated by >23% in the cells with high metastasis potential. Further validation studies by Western blotting in a series of HCC cell types with progressively increasing trend of metastasis showed that peroxiredoxin 4, HSP90ß and HSP27 were positively correlated with increasing metastasis while prohibitin was negatively correlated with metastasis potential. These validation results were also consistent with that obtained from comparative analysis of clinic tissues samples. Function annotations of differentially expressed HCC proteome suggested that the emergence and development of high metastasis involved the dysregulation of cell migration, cell cycle and membrane traffics. Together our results revealed a much more comprehensive profile than that from 2-DE-based method and provided more global insights into the mechanisms of HCC metastasis and potential markers for clinical diagnosis.

Protein secondary structure prediction using dynamic programming.

In the present paper, we describe how a directed graph was constructed and then searched for the optimum path using a dynamic programming approach, based on the secondary structure propensity of the protein short sequence derived from a training data set. The protein secondary structure was thus predicted in this way. The average three-state accuracy of the algorithm used was 76.70%.

Recognition of gene acceptor site based on multi-objective optimization.

A new method for predicting the gene acceptor site based on multi-objective optimization is introduced in this paper. The models for the acceptor, branch and distance between acceptor site and branch site were constructed according to the characteristics of the sequences from the exon-intron database and using common biological knowledge. The acceptor function, branch function and distance function were defined respectively, and the multi-objective optimization model was constructed to recognize the splice site. The test results show that the algorithm used in this study performs better than the SplicePredictor, which is one of the leading acceptor site detectors.