Zhennan Dong

Serological AFP/Golgi protein 73 could be a new diagnostic parameter of hepatic diseases.

We have investigated the changing rule of serum form of GP73 (sGP73) in different hepato‐pathologic processes and identified the sGP73 role in inflammation, fibrosis and carcinogenesis since sGP73 has been regarded as a candidate tumor marker. Quantitative enzyme‐linked immunosorbent assay detected sGP73 in 535 subjects with hepatocellular carcinoma (HCC), liver cirrhosis (LC), hepatitis, focal nodular hyperplasia (FNH), angioma, intra‐hepatic cholangio‐carcinoma (ICC) and metastatic cancer from adenocarcinomas (MC). Median sGP73 in LC was higher than in HCC and hepatitis (p = 0.001), and sGP73 in all three groups were higher than those in healthy individuals (p < 0.001); sGP73 in LC patients with Child‐Pugh class A was lower than in class B and C (p = 0.001), no significant difference was found between early and advanced HCC groups (110.4 μg/L vs. 102.8 μg/L). AFP/GP73 had a sensitivity of 75.8% and specificity of 79.7% with an area under the receiver operating curve (AUROC) of 0.844 vs. 0.812 for AFP (p = 0.055) with a sensitivity of 95.2% and specificity of 47.1%; in detecting early HCC, AUROC of AFP/GP73 was 0. 804 vs. 0.766 for AFP (p = 0.086). sGP73 correlated with AST, AST/ALT, ALB, A/G and ALP in LC. The positive rate of sGP73 in angioma, FNH, ICC, and MC was 0, 50, 63.3, 53.3%, respectively; AFP/GP73 was 0.796 with the sensitivity of 81.4% and specificity of 70.0% when differentiating MC from AFP‐negative HCC. Increased sGP73 is related to hepatic impairment and chronic fibrosis, and when combined with AFP could improve the differential diagnosis of hepatic diseases.

Development of serum parameters panels for the early detection of pancreatic cancer

Early detection of pancreatic cancer is promising for improving clinical outcome; however, no effective biomarker has yet been identified. Here, we detected 61 clinical serum parameters in 200 healthy controls (Ctrls), 163 pancreatic ductal adenocarcinoma (PDAC) patients and 109 benign pancreatitis patients (Benign) in the training group. A metropolis algorithm with Monte Carlo simulation was used for identifying parameter panels. Sera from 183 Ctrl, 129 PDAC and 95 Benign individuals were used for cross‐validation. Samples from 77 breast, 72 cervical, 101 colorectal, 138 gastric, 108 prostate and 132 lung cancer patients were collected for evaluating cancer selectivity. A panel consisting of carbohydrate antigen (CA)19‐9, albumin (ALB), C‐reactive protein (CRP) and interleukin (IL)−8 had the highest diagnostic value for discriminating between PDAC and Ctrl. The sensitivity (SN) was 99.39% for all‐stage, 96.10% for early‐stage and 98.80% for advanced‐stage PDAC at 90% specificity (SP). In the validation group, the sensitivities were 93.80, 93.10 and 94.40%, respectively, at 90% SP. This panel also identified 80.52% of the breast cancer, 66.67% cervical cancer, 86.14% colorectal cancer, 89.86% gastric cancer, 71.30% prostate cancer and 93.85% lung cancer samples as non‐PDAC. The panel consisting of CA19‐9, carbon dioxide, CRP and IL‐6 panel had the highest diagnostic value for discriminating between PDAC and Benign. The SN was 74.23% for all‐stage, 75.30% for early‐stage and 74.40% for advanced‐stage PDAC at 90% SP. In the validation group, the sensitivities were 72.10, 76.10 and 67.20%, respectively, at 90% SP. Our parameter panels may aid in the early detection of PDAC to improve clinical outcome.

Identification and characterization of novel serum microRNA candidates from deep sequencing in cervical cancer patients.

Small non-coding microRNAs (miRNAs) are involved in cancer development and progression, and serum profiles of cervical cancer patients may be useful for identifying novel miRNAs. We performed deep sequencing on serum pools of cervical cancer patients and healthy controls with 3 replicates and constructed a small RNA library. We used MIREAP to predict novel miRNAs and identified 2 putative novel miRNAs between serum pools of cervical cancer patients and healthy controls after filtering out pseudo-pre-miRNAs using Triplet-SVM analysis. The 2 putative novel miRNAs were validated by real time PCR and were significantly decreased in cervical cancer patients compared with healthy controls. One novel miRNA had an area under curve (AUC) of 0.921 (95% CI: 0.883, 0.959) with a sensitivity of 85.7% and a specificity of 88.2% when discriminating between cervical cancer patients and healthy controls. Our results suggest that characterizing serum profiles of cervical cancers by Solexa sequencing may be a good method for identifying novel miRNAs and that the validated novel miRNAs described here may be cervical cancer-associated biomarkers.

Prognostic value of combined serum biomarkers in predicting outcomes in cervical cancer patients

BACKGROUND We evaluated the prognostic value of pretreatment serum biomarkers in predicting outcomes in cervical cancer patients subjected to treatment. METHODS Serum samples collected from 60 cervical cancer patients and 60 age-matched healthy individuals were used for the detection of 22 biomarkers, prior to therapy. Cox multivariate analysis and classification and regression tree analysis (CART) were performed to evaluate the prognostic factors. RESULTS Cox multivariate analysis disclosed that carbohydrate antigen 153 (CA153), squamous cell carcinoma antigen (SCC) and tumor necrosis factor-α (TNF-α) are associated with prognosis in cervical cancer. CART analysis led to the stratification of patients into 3 groups: (1) serum concentrations of CA153 ≥17.60 μg/l, (2) serum concentrations of CA153 <17.60 μg/l and TNF-α ≥10.60 pg/ml, and (3) serum concentrations of CA153 <17.60 μg/l and TNF-α <10.60 pg/ml. The 2-y overall survival rates for Groups 1, 2 and 3 were 33.3%, 60.0% and 93.9%, respectively. CONCLUSIONS Higher serum concentrations of TNF-α, SCC and CA153 before therapy are independently associated with poor prognosis in patients with stage I and II disease. Combined usage of these three biomarkers allows efficient evaluation of outcomes in cervical cancer patients.

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

BackgroundLong-distance ocean voyages may have substantial impacts on seamens health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen.MethodsWe present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure.ResultsOur analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamens health.ConclusionsThe identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamens health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships.

Identification of potential predictors for subtype IgA nephropathy through analyses of blood biochemical indicators

BACKGROUND Immunoglobulin A nephropathy (IgAN), a dominant glomerulonephritis in China, has presented challenges in its early non-invasive diagnosis and accordingly has drawn considerable attention regarding the need to develop effective easy-to-conduct methods. METHODS In this retrospective study, a support vector machine-based classifier was trained to obtain a minimum subset with the highest discerning power between IgAN and non-IgAN cases in China based on 36 biochemical indicators connected with a feature-selection procedure. RESULTS Our analyses indicated 19 biochemical indicators with differential distributions between IgAN and non-IgAN cases, indicating their potential as classifiers. Further examination for the discerning power of all k-feature combinations indicated a 5-feature combination, ALB+CK+Cr+HDL+CA125+TB, which gave the best accuracy, 79.71%, in classifying all training data into the 2 subtypes of nephropathy. Moreover, two combinations, ALB+CK+AFP+AST and TP+Glu+DB+CH, were gender-specific, giving the best classification accuracies of 81.90% and 80.22% for male and female patients, respectively. These 3 classifiers achieved classification accuracies of 75.36%, 72.00% and 84.09% in the entire, the male and the female independently validated datasets, respectively. CONCLUSIONS Blood biochemical indicators could distinguish between IgAN and non-IgAN cases with a bioinformatic algorithm, providing a promising method to diagnose the subtypes of nephropathy.

A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy

BackgroundImmunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters.MethodsTo establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases.ResultsOf the 57 biological parameters, there were 16 parameters that were significantly different (P < 0.05) between IgAN and non-IgAN. The combination of fibrinogen, serum immunoglobulin A level, and manifestation was found to be significant in predicting IgAN. The validation accuracies of the logistic regression and discriminant analysis models were 77.5 and 77.0%, respectively at a predictive probability cut-off of 0.5, and 81.1 and 79.9%, respectively, at a predictive probability cut-off of 0.40. When the predicted probability of the equation containing the combination of fibrinogen, serum IgA level, and manifestation was more than 0.59, a patient had at least an 85.0% probability of having IgAN. When the predicted probability was lower than 0.26, a patient had at least an 88.5% probability of having non-IgAN. The results of the net reclassification improvement certificated serum Immunoglobulin A and fibrinogen had classification power for discriminating IgAN from non-IgAN.ConclusionsThese models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.

A survey on the distribution of healthy people with different anti-tumour ability.

Introduction The aim of the study was to explore the distribution of healthy people with different anti-tumour ability. Material and methods Leukocytes were separated by the Ficoll-Hypaque density gradient centrifugal method. Then they were mixed with A549, MCF-7 and Hela cells at different ratios. The survival rate for target cells was observed and counted by Fluoroskan. Immune function for 200 healthy people was analysed by flow cytometry. Results The results obtained by confocal microscopy revealed that human blood leukocytes possessed direct anti-tumour activity. The survival rate for tumour cells was the lowest in the condition of 20:1 ratio of effector cells to target cells. We speculated that in 200 healthy people the leukocyte capacity for killing MCF-7 cells is stronger than the leukocyte capacity for killing A549 cells and Hela cells. We also found that the distribution for 200 healthy people with different anti-tumour ability was different for different tumour cells. The number of healthy people with the strongest anti-tumour ability was highest when the target cells were MCF-7 cells. Moreover, the survival of A549, MCF-7 and Hela cells was correlated with T, B and NK lymphocytes. Conclusions From the above, we can select healthy individuals with strong anti-tumour ability as anti-tumour donors according to their distribution with different anti-tumour ability, which opened up a new direction for fighting human cancer.