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Tartrate-resistant acid phosphatase 5a (TRACP5a) is mainly secreted by activated macrophages in chronic inflammation. Serum TRACP5a is associated with symptom distress in lung cancer patients during chemotherapy. Therefore, this study aimed to investigate whether chemotherapy drugs modulate TRACP5a as an inducible marker for symptom distress in lung cancer patients during chemotherapy. In clinical analysis, lung cancer participants completely received the six-cycle chemotherapy process (n = 42). Clinical determinations for TRACP5a, C-reactive protein (CRP), interleukin-6 (IL-6), white blood cells, monocytes, and hemoglobin were analyzed at six time points: BL, C1d8, C2d1, C4d1, C4d8, and Ed28. Meanwhile, five questionnaires for fatigue, sleep disturbance, pain, depression, and confusion were finished before drug treatment. For monocyte-to-macrophage differentiation, THP-1 cells were treated with phorbol 12-myristate 13-acetate (PMA). TRACP5a secretion in THP-1 cells was determined at the following days up to 6 days after 1-day incubation of chemotherapy drugs by dot blotting. Clinical analysis revealed that TRACP5a significantly increased at C1d8 and C4d8, but dropped at C2d1 and Ed28. CRP and IL-6 displayed a broad-range variation, resulting in no significant difference among the assessment time points. In contrast, monocytes decreased at C1d8 and C4d8, but rose again at C2d1 and Ed28. In symptom distress, the changes only in fatigue and sleep disturbance were positively associated with the trend in TRACP5a. In PMA-treated THP-1 cells, TRACP5a significantly increased after stimulation with gemcitabine and paclitaxel. Taken together, induction of TRACP5a by chemotherapy drugs might be generated from monocyte-differentiated macrophages, further causing clinical symptom distress in lung cancer patients.

OBJECTIVE Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) are principal enzymes responsible for the metabolism of ethanol. East Asian populations are unique in that they carry both a prevalent ADH1B*2 and a dominant-negative ALDH2*2 allele. A systematic investigation of ethanol-metabolizing activities in normal livers correlated with the corresponding functional allelic variations and protein contents of the relevant isozymes in respective enzyme families has been lacking. MATERIALS AND METHODS To obtain a reasonable sample size encompassing all possible genetic allelotypes of the ADH1B and ALDH2, 141 surgical liver specimens from adult Han Chinese were studied. Expression patterns and activities of ADH and ALDH were determined with stratification of the genetic phenotypes. Absolute protein contents as well as cellular localization of the activity and protein of ADH/ALDH isozymes were also investigated. RESULTS The activities of ADH1B*1/*2 and ADH1B*2/*2 allelic phenotypes were 5-6-fold those of the ADH1B*1/*1, suggesting that ADH1B*2 allele-encoded subunits are dominant over expression of hepatic ADH activity. The activities of the ALDH2-active phenotype were 90% higher than those of the ALDH2-inactive phenotype. Sex and age did not significantly influence the hepatic ADH and ALDH activities with specified genetic phenotypes. The isozyme protein contents were as follows in decreasing order: ADH1, ADH2, ALDH1A1, ALDH2, and ADH3. Both ADH1, but not ADH2/3, and ALDH1A1/2 showed a preferential expression in perivenular hepatocytes. CONCLUSION Functional correlations of ADH1B*2 and ALDH2*2 variant alleles in the liver provide a biochemical genetic basis suggesting their contribution toward variability in ethanol metabolism as well as susceptibility to alcoholism and alcohol-related diseases in East Asians.

Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.

Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0).Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances.Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes,CA7, SPIB, GUCA2B, AQP8, IL6RandCWH43;oncogenes,SPP1andTCN1. Genes of higher significances showed lower variation in rank ordering by different methods.Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes,CA7,TCN1, andCWH43have not been reported to be related to CRC.

Background It has been well documented that a variant allele of mitochondrial aldehyde dehydrogenase 2 (ALDH2), ALDH2*2, commonly occurs in East Asians but rarely in other ethnic populations. This unique allelic variation significantly influences drinking behavior and susceptibility to development of alcoholism. Previous structural, functional, and cellular studies indicate that the resulting variant polypeptide subunit K (Lys-487) exerts dominance of null activity and shorter half-life over the tetrameric enzyme molecules in distinct manners. However, the in vivo evidence for the proposed dominance mechanisms remains lacking. Methods To address this question, we investigated 33 surgical liver samples identified to be normal homozygous ALDH2*1/*1 (n = 17), heterozygous ALDH2*1/*2 (n = 13), and variant homozygous ALDH2*2/*2 (n = 3). The ALDH2 activity was determined at a sufficient low acetaldehyde concentration (3 μM) and the isozyme protein amount by immunotitration using purified class-specific antibodies. Results The tissue ALDH2 activity in heterozygotes was 17% that of the ALDH2*1/*1 genotype (p

Background: Fatigue has been described as the most frequent and distressing problem of cancer patients undergoing chemotherapy. Objective: The aim of this study is to evaluate the validity and reliability of the Taiwanese version of the General Fatigue Scale (GFS-T) and to evaluate the severity of the fatigue among breast cancer patients in Taiwan. Methods: A cross-sectional research design was used, recruiting breast cancer patients from 2 medical centers in Taiwan. Patients completed the scale exploring their GFS-T, the Brief Fatigue Inventory–Taiwan Form, and the Eastern Cooperative Oncology Group Performance Status. The data were collected between the day before the first chemotherapy (T1) and 1 week after the first chemotherapy (T2). Results: A total of 171 patients participated in this study. Cronbach’s α for the GFS-T at both time points both were .94. Factor analysis generated 1 factor that accounted for 73.7% of variance in participants’ fatigue. The receiver operating characteristic curve analyses suggested that the GFS-T cut-point of 24 had an adequate combination of sensitivity and specificity to distinguish high and low performance status. The receiver operating characteristic curve is 0.67 (95% confidence interval, 0.59-0.75). Conclusions: The GFS-T is a reliable and valid instrument for assessing fatigue among cancer patients. Further research is needed to better understand predictors of cancer-related fatigue. Implications for Practice: The GFS-T can provide clinical nurses with a useful measure to assess fatigue in cancer patients.

Alcohol dehydrogenase (ADH) is the principal enzyme responsible for ethanol metabolism in mammals. Human ADH constitutes a unique complex enzyme family with no equivalent counterpart in experimental rodents. This study was undertaken to quantitatively assess relative contributions of human ADH isozymes and allozymes to hepatic versus gastric metabolism of ethanol in the context of the entire family.Kinetic parameters for ethanol oxidation for recombinant human class I ADH1A, ADH1B1, ADH1B2, ADH1B3, ADH1C1, and ADH1C2; class II ADH2; class III ADH3; and class IV ADH4 were determined in 0.1 M sodium phosphate at pH 7.5 over a wide range of substrate concentrations in the presence of 0.5 mM NAD+. The composite numerical formulations for organ steady-state ethanol clearance were established by summing up the kinetic equations of constituent isozymes/allozymes with the assessed contents in livers and gastric mucosae with different genotypes.In ADH1B*1 individuals, ADH1B1 and ADH1C allozymes were found to be the major contributors to hepatic-alcohol clearance; ADH2 made a significant contribution only at high ethanol levels (20 mM). ADH1B2 was the major hepatic contributor in ADH1B*2 individuals. ADH1C allozymes were the major contributor at low ethanol (2 mM), whereas ADH1B3 the major form at higher levels (10 mM) in ADH1B*3 individuals. For gastric mucosal-alcohol clearance, the relative contributions of ADH1C allozymes and ADH4 were converse as ethanol concentration increased. It was assessed that livers with ADH1B*1 may eliminate approximately 95% or more of single-passed ethanol as inflow sinusoidal alcohol reaches approximately 1 mM and that stomachs with different ADH1C genotypes may remove 20% to 30% of single-passed alcohol at the similar level in mucosal cells.This work provides just a model, but a strong one, for quantitative assessments of ethanol metabolism in the human liver and stomach. The results indicate that the hepatic-alcohol clearance of ADH1B*2 individuals is higher than that of the ADH1B*1 and those of the ADH1B*3 versus the ADH1B*1 vary depending on sinusoidal ethanol levels. The maximal capacity for potential alcohol first-pass metabolism in the liver is greater than in the stomach.

Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated.A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets(GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality.The logistic regression analysis resulted in the selection of five significant genes (P0.05; MDM2, DUSP6, CPEB4, MMD, and EIF2S3), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% accuracy, 66.0% sensitivity, 92.0% specificity, a positive predictive value of 89.2% and a negative predictive value of 73.0%. Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation. Models that provided similar expected and observed event rates in subgroups were termed well calibrated. A model in which MDM2, DUSP6, CPEB4, MMD, and EIF2S3 were selected showed the result in logistic regression analysis (H-L P = 0.460, R2= 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971).A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Background: Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated. Microarray technology has shown a great potential in the colorectal cancer research. Genes significantly associated with cancer in microarray studies, were selected as candidate genes in the study. Pooling Internet public microarray data sets can overcome the limitation by the small number of samples in previous studies. Objective: Using public microarray data sets verifies gene expression profiles for colorectal cancer. Methods: Logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and controls. Public microarray datasets of GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105 included 519 cases of adenocarcinoma and 88 controls of normal mucosa, which were used to verify the candidate genes from logistic models and estimated its external generality. Results: A 7-gene model of CPEB4, EIF2S3, MGC20553, MAS4A1, ANXA3, TNFAIP6 and IL2RB was pairwise selected that showed the best results in logistic regression analysis (H-L p=1.000, R2=0.951, AUC=0.999, accuracy=0.968, specificity=0.966 and sensitivity=0.994). Conclusions: A novel gene expression profile was associated with CRC and can potentially be applied to bloodbased detection assays.

To verify gene expression profiles for colorectal cancer using 12 internet public microarray datasets.Logistic regression analysis was performed, and odds ratios for each gene were determined between colorectal cancer (CRC) and controls. Twelve public microarray datasets of GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105, which included 519 cases of adenocarcinoma and 88 normal mucosa controls, were pooled and used to verify 17 selective genes from 3 published studies and estimate the external generality.We validated the 17 CRC-associated genes from studies by Chang et al (Model 1: 5 genes), Marshall et al (Model 2: 7 genes) and Han et al (Model 3: 5 genes) and performed the multivariate logistic regression analysis using the pooled 12 public microarray datasets as well as the external validation. The goodness-of-fit test of Hosmer-Lemeshow (H-L) showed statistical significance (P = 0.044) for Model 2 of Marshall et al in which observed event rates did not match expected event rates in subgroups of the model population. Expected and observed event rates in subgroups were similar, which are called well calibrated, in Models 1, 3 and 4 with non-significant P values of 0.460, 0.194 and 1.000 for H-L tests, respectively. A 7-gene model of CPEB4, EIF2S3, MGC20553, MS4A1, ANXA3, TNFAIP6 and IL2RB was pairwise selected, which showed the best results in logistic regression analysis (H-L P = 1.000, R (2) = 0.951, areas under the curve = 0.999, accuracy = 0.968, specificity = 0.966 and sensitivity = 0.994).A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Background Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. Results The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… Conclusions The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

Alcohol dehydrogenase (ADH) catalyzes oxidation of ingested ethanol to acetaldehyde, the first step in hepatic metabolism. The purpose of this study was to establish an ex vivo rat liver perfusion system under defined and verified steady states with respect to the metabolites and the metabolic rates, and to quantitatively correlate the observed rates with simulations based on the kinetic mechanism-based rate equations of rat liver ADH. Class I ADH1 was isolated from male Sprague-Dawley rats and characterized by steady-state kinetics in the Krebs-Ringer perfusion buffer with supplements. Nonrecirculating liver perfusion with constant input of ethanol at near physiological hepatic blood flow rate was performed in situ. Ethanol and the related metabolites acetaldehyde, acetate, lactate, and pyruvate in perfusates were determined. Results of the initial velocity, product, and dead-end inhibition studies showed that rat ADH1 conformed to the Theorell-Chance Ordered Bi Bi mechanism. Steady-state metabolism of ethanol in the perfused liver maintained up to 3h as evidenced by the steady-state levels of ethanol and metabolites in the effluent, and the steady-state ethanol disappearance rates and acetate production rates. The changes of the metabolic rates were qualitatively and in general quantitatively correlated to the results from simulations with the kinetic rate equations of ADH1 under a wide range of ethanol, in the presence of competitive inhibitor 4-methylpyrazole and of uncompetitive inhibitor isobutyramide. Preliminary flux control analysis estimated that apparent C(ADH)(J) in the perfused liver may approximate 0.7 at constant infusion with 1-2 mM ethanol, suggesting that ADH plays a major but not the exclusive role in governing hepatic ethanol metabolism. The reported steady-state rat liver perfusion system may potentially be applicable to other drug or drug-ethanol interaction studies.

The genes encoding the enzymes for metabolising alcohol dehydrogenase 1B (ADH1B) and aldehyde dehydrogenase 2 (ALDH2) -- exhibit genetic polymorphism and ethnic variations. Although the ALDH2*2 variant allele has been widely accepted as protecting against the development of alcoholism in Asians, the association of the ADH1B*2 variant allele with drinking behaviour remains inconclusive. The goal of this study was to determine whether the polymorphic ADH1B and ALDH2 genes are associated with stroke in male Han Chinese with high alcohol consumption. Sixty-five stroke patients with a history of heavy drinking (HDS) and 83 stroke patients without such a history (NHDS) were recruited for analysis of the ADH1B and ALDH2 genotypes from the stroke registry in the Tri-Service General Hospital, Taipei, Taiwan, between January 2000 and December 2001. The allelotypes of ADH1B and ALDH2 were determined using the polymerase chain reaction-restriction fragment length polymorphism method. The HDS patients (3 per cent) showed a significantly lower ALDH2*2 allele frequency than NHDS patients (27 per cent) (p < 0.001). After controlling for age, patients with HDS were associated with a significantly higher occurrence of cigarette smoking (p < 0.01) and liver dysfunction (p < 0.01). Multiple logistic regression analyses revealed that the ALDH2*2 variant allele was an independent variable exhibiting strong protection (odds ratio 0.072; 95 per cent confidence interval 0.02-0.26) against HDS after adjustment for hypertension, diabetes mellitus, smoking status and liver dysfunction. By contrast, allelic variations in ADH1B exerted no significant effect on HDS. The present study indicated that, unlike ALDH2*2, ADH1B*2 appears not to be a significant negative risk factor for high alcohol consumption in male Han Chinese with stroke.