Observation of peptide differences between cancer and control in gastric juice

Biomarkers for various diseases have been extensively searched for the past 5 years. Nevertheless, most efforts were focused on the search for protein biomarkers from serum samples. In this work, we tried to look for peptide biomarkers from gastric juice samples with MALDI‐TOF‐MS. More than 200 gastric juice samples from healthy people, gastric ulcer patients, duodenal ulcer patients, and cancer patients were examined. There were clear pattern differences of mass spectra among samples from healthy people and patients with different gastric diseases. We found five peptides for gastric cancer diagnosis with high sensitivity and specificity. Sequences of these five peptides, including two pepsinogen fragments, leucine zipper protein fragment, albumin fragment, and α‐1‐antitrypsin fragment, have been identified by mass spectrometric analysis and immuno‐deplete assay with antibodies.     

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.     

Comparative proteomic analysis of differentially expressed proteins in an in vitro cellular carcinogenesis model of oral squamous cell carcinoma

In vitro cellular model is an important tool to be used to investigate the cellular events related to pathophysiological conditions in humans. We have developed an in vitro cellular carcinogenesis model of oral squamous cell carcinoma (OSCC). In this study, we performed comparative proteomic analysis using 2‐DE and LC‐tandem mass chromatography to separate and identify differentially expressed proteins. Forty‐five proteins were identified, including 24 proteins with decreased expression and 19 proteins with increased expression during carcinogenesis from immortalized oral epithelial cells to squamous cancerous cells. The identified known proteins were classified into three ontologies of cellular component, molecular function, and biological process. Further validation of five identified proteins (ANXA1, ANXA2, CTSB, KRT17, and S100A6) in the cellular carcinogenesis model and cancerous tissues from OSCC patients confirmed the comparative proteomic results. Moreover, Annexin A1 and A2 expression levels correlated with the pathological differentiation grade of cancerous tissues. Thus, this work provides a dynamic protein file of differentially expressed proteins in oral squamous carcinoma cells, which could provide clues to study the mechanisms of OSCC carcinogenesis and possibly be developed as potential biomarkers for clinical diagnosis or prognostic monitoring.     

Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer

Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is described using a Markov-Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To get an accurate visual appearance model, we developed a new approach to automatic selection of most characteristic second-order cliques that describe pairwise interactions in the LDCT data. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.