Exploring Factors Underlying Poorly-Controlled Asthma in Adults by Integrating Phenotypes and Genotypes Associated with Obesity and Asthma: A Case-Control Study.

Background: Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies. Methods: This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction. Results: General obesity with body mass index (BMI) ≥27 kg/m2 (OR:1.49, 95% confidence interval (CI) 1.09-2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22-2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17-2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways. Conclusion: The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.

Relationship among genetic variants, obesity traits and asthma in the Taiwan Biobank.

BACKGROUND AND OBJECTIVE: Obesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma. METHODS: This is a matched case-control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the 2000-2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model. RESULTS: We found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01-1.13)), waist circumference (OR=1.10 (1.04-1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03-1.15)) and general-central obesity (OR=1.06 (1.00-1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM2.5 exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity. CONCLUSIONS: Obese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.

Using an Event-History with Risk-Free Model to Study the Genetics of Alcoholism.

Case-control genetic association studies typically ignore possible later disease onset in currently healthy subjects and assume that subjects with diseases equally contribute to the likelihood for inference, regardless of their onset age. Therefore, we used an event-history with risk-free model to simultaneously characterize alcoholism susceptibility and onset age in 65 independent non-Hispanic Caucasian males in the Collaborative Study on the Genetics of Alcoholism. Following data quality control, we analysed 22 single nucleotide polymorphisms (SNPs) on 12 candidate genes. The single-SNP analysis showed that the dominant minor allele of rs2134655 on DRD3 increases alcoholism susceptibility; the dominant minor allele of rs1439047 on NTRK2 delays the alcoholism onset age, but the additive minor allele of rs172677 on GRIN2B and the dominant minor allele of rs63319 on ALDH1A1 advance the alcoholism onset age; and the dominant minor allele of rs1079597 on DRD2 shortens the onset age range. Similarly, multiple-SNPs analysis revealed joint effects of rs2134655, rs172677 and rs1079597, with an adjustment for habitual smoking. This study provides a more comprehensive understanding of the genetics of alcoholism than previous case-control studies.

Gene Expression Profiling and Pathway Network Analysis Predicts a Novel Antitumor Function for a Botanical-Derived Drug, PG2.

PG2 is a botanical drug that is mostly composed of Astragalus polysaccharides (APS). Its role in hematopoiesis and relieving cancer-related fatigue has recently been clinically investigated in cancer patients. However, systematic analyses of its functions are still limited. The aim of this study was to use microarray-based expression profiling to evaluate the quality and consistency of PG2 from three different product batches and to study biological mechanisms of PG2. An integrative molecular analysis approach has been designed to examine significant PG2-induced signatures in HL-60 leukemia cells. A quantitative analysis of gene expression signatures was conducted for PG2 by hierarchical clustering of correlation coefficients. The results showed that PG2 product batches were consistent and of high quality. These batches were also functionally equivalent to each other with regard to how they modulated the immune and hematopoietic systems. Within the PG2 signature, there were five genes associated with doxorubicin: IL-8, MDM4, BCL2, PRODH2, and BIRC5. Moreover, the combination of PG2 and doxorubicin had a synergistic effect on induced cell death in HL-60 cells. Together with the bioinformatics-based approach, gene expression profiling provided a quantitative measurement for the quality and consistency of herbal medicines and revealed new roles (e.g., immune modulation) for PG2 in cancer treatment.

Symptom patterns and phenotypic subgrouping of women with polycystic ovary syndrome: association between endocrine characteristics and metabolic aberrations.

STUDY QUESTION: What are the potential endocrine characteristics related to risk and severity of metabolic disturbances in women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Women with PCOS could be subtyped into four subgroups according to heterogeneous endocrine characteristics and the major predictive endocrine factors for metabolic aberrations among different subgroups were free androgen index (FAI) and luteinizing hormone (LH) levels. WHAT IS KNOWN ALREADY: Women diagnosed with PCOS present with highly heterogeneous phenotypes, including endocrine and metabolic aberrations. Different strategies have been proposed to predict the metabolic outcomes but whether the endocrine factors can solely predict the metabolic aberrations is still inconclusive. STUDY DESIGN, SIZE, DURATION: A cross-sectional study including 460 patients recruited from a reproductive endocrinology outpatient clinic of a tertiary medical center. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients with PCOS diagnosed according to the 2003 Rotterdam criteria were studied. Clinical history recorded by questionnaires, anthropometric measurements, biochemistry tests after an overnight fast, and pelvic ultrasonography were collected from all patients. MAIN RESULTS AND THE ROLE OF CHANCE: Applying a matrix visualization and clustering approach (generalized association plots), the patients were divided into four distinct clusters according to the correlation with four endocrine parameters. Each cluster exhibited specific endocrine characteristics and the prevalence of metabolic syndrome (MS) was significantly different among the clusters (P < 0.0001). The high-risk subgroups for MS included one cluster with higher mean (SD) FAI (39.6 (14.7) in cluster 4), and another one with lower mean (SD) FAI (10 (6.4) in cluster 2). A common endocrine characteristic of these two metabolically unhealthy clusters was relatively lower LH level. Contrarily, higher LH level (≧15 mIU/ml) during early follicular phase was found to be the best indicator of the metabolically healthy cluster (cluster 1). While high FAI level did correlate with more severe metabolic aberrations, high LH level showed better predictive value than low FAI level to become a metabolically healthy cluster. LIMITATIONS, REASONS FOR CAUTION: The results should be applied to other populations with caution due to racial or environmental differences. Another limitation is a lack of normal non-PCOS control in our study. WIDER IMPLICATIONS OF THE FINDINGS: Stratifying women with PCOS into meaningful subtypes could provide a better understanding of related risk factors and potentially enable the design and delivery of more effective screening and treatment intervention. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grant NSC 100-2314-B002-027-MY3 from the National Science Council of Taiwan. TRIAL REGISTRATION NUMBER: Nil.

Using an in situ proximity ligation assay to systematically profile endogenous protein-protein interactions in a pathway network.

Signal transduction pathways in the cell require protein-protein interactions (PPIs) to respond to environmental cues. Diverse experimental techniques for detecting PPIs have been developed. However, the huge amount of PPI data accumulated from various sources poses a challenge with respect to data reliability. Herein, we collected ∼ 700 primary antibodies and employed a highly sensitive and specific technique, an in situ proximity ligation assay, to investigate 1204 endogenous PPIs in HeLa cells, and 557 PPIs of them tested positive. To overview the tested PPIs, we mapped them into 13 PPI public databases, which showed 72% of them were annotated in the Human Protein Reference Database (HPRD) and 8 PPIs were new PPIs not in the PubMed database. Moreover, TP53, CTNNB1, AKT1, CDKN1A, and CASP3 were the top 5 proteins prioritized by topology analyses of the 557 PPI network. Integration of the PPI-pathway interaction revealed that 90 PPIs were cross-talk PPIs linking 17 signaling pathways based on Reactome annotations. The top 2 connected cross-talk PPIs are MAPK3-DAPK1 and FAS-PRKCA interactions, which link 9 and 8 pathways, respectively. In summary, we established an open resource for biological modules and signaling pathway profiles, providing a foundation for comprehensive analysis of the human interactome.

Differential network biology reveals a positive correlation between a novel protein-protein interaction and cancer cells migration.

This paper introduces a differential network biology for discovering tumor migration. We applied statistical methods to prioritize PPI candidates and an in situ proximity ligation assay to verify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). Differential network biology analysis was applied to determine the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration. This study demonstrated that functional exploration of a disease network with differential network in interlinked pathways via PPIs can be used to discover tumor migration.

Analysis of protein-protein interactions in cross-talk pathways reveals CRKL protein as a novel prognostic marker in hepatocellular carcinoma.

Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used an in situ proximity ligation assay to identify and quantify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition process. Our immunohistochemical analysis shows high expression levels of the CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples, and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.

Recursive feature selection with significant variables of support vectors.

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

Mixed sequence reader: a program for analyzing DNA sequences with heterozygous base calling.

The direct sequencing of PCR products generates heterozygous base-calling fluorescence chromatograms that are useful for identifying single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), short tandem repeats (STRs), and paralogous genes. Indels and STRs can be easily detected using the currently available Indelligent or ShiftDetector programs, which do not search reference sequences. However, the detection of other genomic variants remains a challenge due to the lack of appropriate tools for heterozygous base-calling fluorescence chromatogram data analysis. In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. The heterozygous sequences are identified as two distinct sequences and aligned with reference sequences. Our results showed that MSR may be used to (i) physically locate indel and STR sequences and determine STR copy number by searching NCBI reference sequences; (ii) predict combinations of microsatellite patterns using the Federal Bureau of Investigation Combined DNA Index System (CODIS); (iii) determine human papilloma virus (HPV) genotypes by searching current viral databases in cases of double infections; (iv) estimate the copy number of paralogous genes, such as ß-defensin 4 (DEFB4) and its paralog HSPDP3.

LOHAS: loss-of-heterozygosity analysis suite.

Detection of loss of heterozygosity (LOH) plays an important role in genetic, genomic and cancer research. We develop computational methods to estimate the proportion of homozygous SNP calls, identify samples with structural alterations and/or unusual genotypic patterns, cluster samples with close LOH structures and map the genomic segments bearing LOH by analyzing data of genome-wide SNP arrays or customized SNP arrays. In addition to cancer genetics/genomics, we also apply the methods to study long contiguous stretches of homozygosity (LCSH) in general populations. The LCSH analysis aids in the identification of samples with complex LCSH patterns indicative of nonrandom mating and/or meiotic recombination cold spots, separation of samples with different genetic backgrounds and sex, and mapping of regions of LCSH. Affymetrix Human Mapping 500K Set SNP data from an acute lymphoblastic leukemia study containing 304 cancer patients and 50 normal controls and from the HapMap Project containing 30 African trios, 30 Caucasian trios and 90 independent Asian samples were analyzed. We identified common gene regions of LOH, e.g., ETV6 and CDKN1B, and identified frequent regions of LCSH, e.g., the region that encompasses the centromeric gene desert region of chromosome 16. Unsupervised analysis separated cancer subtypes and ethnic subpopulations by patterns of LOH/LCSH. Simulation studies considering LOH width, effect size and heterozygous interference fraction were performed, and the results show that the proposed LOH association test has good test power and controls type 1 error well. The developed algorithms are packaged into LOHAS written in R and R GUI.

Comparative metabolomics approach coupled with cell- and gene-based assays for species classification and anti-inflammatory bioactivity validation of Echinacea plants.

Echinacea preparations were the top-selling herbal supplements or medicines in the past decade; however, there is still frequent misidentification or substitution of the Echinacea plant species in the commercial Echinacea products with not well chemically defined compositions in a specific preparation. In this report, a comparative metabolomics study, integrating supercritical fluid extraction, gas chromatography/mass spectrometry and data mining, demonstrates that the three most used medicinal Echinacea species, Echinacea purpurea, E. pallida, and E. angustifolia, can be easily classified by the distribution and relative content of metabolites. A mitogen-induced murine skin inflammation study suggested that alkamides were the active anti-inflammatory components present in Echinacea plants. Mixed alkamides and the major component, dodeca-2E,4E,8Z,10Z(E)-tetraenoic acid isobutylamides, were then isolated from E. purpurea root extracts for further bioactivity elucidation. In macrophages, the alkamides significantly inhibited cyclooxygenase 2 (COX-2) activity and the lipopolysaccharide-induced expression of COX-2, inducible nitric oxide synthase and specific cytokines or chemokines [i.e., TNF-α, interleukin (IL)-1α, IL-6, MCP-1, MIP-1ß] but elevated heme oxygenase-1 protein expression. Cichoric acid, however, exhibited little or no effect. The results of high-performance liquid chromatography/electron spray ionization/mass spectrometry metabolite profiling of alkamides and phenolic compounds in E. purpurea roots showed that specific phytocompound (i.e., alkamides, cichoric acid and rutin) contents were subject to change under certain post-harvest or abiotic treatment. This study provides new insight in using the emerging metabolomics approach coupled with bioactivity assays for medicinal/nutritional plant species classification, quality control and the identification of novel botanical agents for inflammatory disorders.

A method for analyzing censored survival phenotype with gene expression data.

BACKGROUND: Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study. METHOD: We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR). RESULTS: The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate. CONCLUSION: By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.

CGcgh: a tool for molecular karyotyping using DNA microarray-based comparative genomic hybridization (array-CGH).

Microarray-based comparative genomic hybridization (array-CGH) is a technique by which variations in copy numbers between two genomes can be analyzed using DNA microarrays. Array CGH has been used to survey chromosomal amplifications and deletions in fetal aneuploidies or cancer tissues. Herein we report a user-friendly, MATLAB-based, array CGH analyzing program, Chang Gung comparative genomic hybridization (CGcgh), as a standalone PC version. The analyzed chromosomal data are displayed in a graphic interface, and CGcgh allows users to launch a corresponding G-banding ideogram. The abnormal DNA copy numbers (gains and losses) can be identified automatically using a user defined window size (default value is 50 probes) and sequential student t-tests with sliding windows along with chromosomes. CGcgh has been tested in multiple karyotype-confirmed human samples, including five published cases and trisomies 13, 18, 21 and X from our laboratories, and 18 cases of which microarray data are available publicly. CGcgh can be used to detect the copy number changes in small genomic regions, which are commonly encountered by clinical geneticists. CGcgh works well for the data from cDNA microarray, spotted oligonucleotide microarrays, and Affymetrix Human Mapping Arrays (10K, 100K, 500K Array Sets). The program can be freely downloaded from http://www.mcu.edu.tw/department/biotec/en%5Fpage/CGcgh/ .

Single-walled carbon nanotubes can induce pulmonary injury in mouse model.

Carbon nanotubes are a nanomaterial that is extensively used in industry. The potential health risk of chronic carbon nanotubes exposure has been raised as of great public concern. In the present study, we have demonstrated that intratracheal instillation of 0.5 mg of single-walled carbon nanotubes (SWCNT) into male ICR mice (8 weeks old) induced alveolar macrophage activation, various chronic inflammatory responses, and severe pulmonary granuloma formation. We then used Affymetrix microarrays to investigate the molecular effects on the macrophages when exposed to SWCNT. A biological pathway analysis, a literature survey, and experimental validation suggest that the uptake of SWCNT into the macrophages is able to activate various transcription factors such as nuclear factor kappaB (NF-kappaB) and activator protein 1 (AP-1), and this leads to oxidative stress, the release of proinflammatory cytokines, the recruitment of leukocytes, the induction of protective and antiapoptotic gene expression, and the activation of T cells. The resulting innate and adaptive immune responses may explain the chronic pulmonary inflammation and granuloma formation in vivo caused by SWCNT.

Gene selection with multiple ordering criteria.

BACKGROUND: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. RESULTS: We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. CONCLUSION: The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives.

A five-gene signature and clinical outcome in non-small-cell lung cancer.

BACKGROUND: Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. METHODS: We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. RESULTS: Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. CONCLUSIONS: Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

Human kallikrein 8 protease confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

The human kallikrein 8 (KLK8) gene, a member of the human tissue kallikrein gene family, encodes a serine protease. The KLK8 protein (hK8) is known to be a favorable prognostic marker in ovarian cancer, but the biological basis of this is not understood. We found that overexpressing the KLK8 gene in highly invasive lung cancer cell lines suppresses their invasiveness. This role in invasiveness was further confirmed by the fact that inhibition of endogenous KLK8 expression with a specific short hairpin RNA reduced cancer cell invasiveness. In situ degradation and cell adhesion assays showed that proteins produced from KLK8 splice variants modify the extracellular microenvironment by cleaving fibronectin. DNA microarray experiments and staining of cells for actin filaments revealed that the degradation of fibronectin by hK8 suppresses integrin signaling and retards cancer cell motility by inhibiting actin polymerization. In addition, studies in a mouse model coupled with the detection of circulating tumor cells by quantitative PCR for the human Alu sequence showed that KLK8 suppresses tumor growth and invasion in vivo. Finally, studies of clinical specimens from patients with non-small cell lung cancer showed that the time to postoperative recurrence was longer for early-stage patients (stages I and II) with high KLK8 expression (mean, 49.9 months) than for patients with low KLK8 expression (mean, 22.9 months). Collectively, these findings show that KLK8 expression confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

Tumor-associated macrophages: the double-edged sword in cancer progression.

PURPOSE: Inflammation plays a critical role in cancer progression. In this study we investigate the pro-tumorigenic activities and gene expression profiles of lung cancer cells after interaction with macrophages. MATERIALS AND METHODS: We measured intratumoral microvessel counts and macrophage density in 41 lung cancer tumor specimens and correlated these with the patients' clinical outcome. The interaction between macrophages and cancer cell lines was assessed using a transwell coculture system. The invasive potential was evaluated by in vitro invasion assay. The matrix-degrading activity was assayed by gelatin zymography. The microarray was applied to a large-scale analysis of the genes involved in the interaction, as well as to monitor the gene expression profiles of lung cancer cells responding to anti-inflammatory drugs in cocultures. RESULTS: The macrophage density positively correlated with microvessel counts and negatively correlated with patient relapse-free survival (P < .05). After coculture with macrophages, lung cancer cell lines exhibited higher invasive potentials and matrix-degrading activities. We identified 50 genes by microarray that were upregulated more than two-fold in cancer cells after coculture. Northern blot analyses confirmed some gene expression such as interleukin-6, interleukin-8, and matrix metalloproteinase 9. The two-dimensional hierarchical clustering also demonstrated that the gene expression profiles of lung cancer cells responding to various anti-inflammatory drugs in cocultures are distinct. CONCLUSION: The interaction of lung cancer cells and macrophages can promote the invasiveness and matrix-degrading activity of cancer cells. Our results also suggest that a great diversity of gene expression occurs in this interaction, which may assist us in understanding the process of cancer metastasis.

Gene selection for sample classifications in microarray experiments.

DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene set becomes an important prestep in sample classification. In this study, we propose a family-wise error (FWE) rate approach to selection of discriminatory genes for two-sample or multiple-sample classification. The FWE approach controls the probability of the number of one or more false positives at a prespecified level. A public colon cancer data set is used to evaluate the performance of the proposed approach for the two classification methods: k nearest neighbors (k-NN) and support vector machine (SVM). The selected gene sets from the proposed procedure appears to perform better than or comparable to several results reported in the literature using the univariate analysis without performing multivariate search. In addition, we apply the FWE approach to a toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV) for a total of 55 samples for a multisample classification. Two gene sets are considered: the gene set omegaF formed by the ANOVA F-test, and a gene set omegaT formed by the union of one-versus-all t-tests. The predicted accuracies are evaluated using the internal and external crossvalidation. Using the SVM classification, the overall accuracies to predict 55 samples into one of the nine treatments are above 80% for internal crossvalidation. OmegaF has slightly higher accuracy rates than omegaT. The overall predicted accuracies are above 70% for the external crossvalidation; the two gene sets omegaT and omegaF performed equally well.