RNA sequencing of blood in coronary artery disease: involvement of regulatory T cell imbalance.

BACKGROUND: Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography. Surprisingly, despite well-established clinical indications, up to 40% of the one million invasive cardiac catheterizations return a result of 'no blockage'. The present studies employed RNA sequencing of whole blood to identify an RNA signature in patients with angiographically confirmed CAD. METHODS: Whole blood RNA was depleted of ribosomal RNA (rRNA) and analyzed by single-molecule sequencing of RNA (RNAseq) to identify transcripts associated with CAD (TRACs) in a discovery group of 96 patients presenting for elective coronary catheterization. The resulting transcript counts were compared between groups to identify differentially expressed genes (DEGs). RESULTS: Surprisingly, 98% of DEGs/TRACs were down-regulated ~ 1.7-fold in patients with mild to severe CAD (> 20% stenosis). The TRACs were independent of comorbid risk factors for CAD, such as sex, hypertension, and smoking. Bioinformatic analysis identified an enrichment in transcripts such as FoxP1, ICOSLG, IKZF4/Eos, SMYD3, TRIM28, and TCF3/E2A that are likely markers of regulatory T cells (Treg), consistent with known reductions in Tregs in CAD. A validation cohort of 80 patients confirmed the overall pattern (92% down-regulation) and supported many of the Treg-related changes. TRACs were enriched for transcripts associated with stress granules, which sequester RNAs, and ciliary and synaptic transcripts, possibly consistent with changes in the immune synapse of developing T cells. CONCLUSIONS: These studies identify a novel mRNA signature of a Treg-like defect in CAD patients and provides a blueprint for a diagnostic test for CAD. The pattern of changes is consistent with stress-related changes in the maturation of T and Treg cells, possibly due to changes in the immune synapse.

Duration of Operative Vaginal Delivery and Adverse Obstetric Outcomes.

OBJECTIVE: This study aimed to evaluate whether the number of vacuum pop-offs, the number of forceps pulls, or the duration of operative vaginal delivery (OVD) is associated with adverse maternal and perinatal outcomes. STUDY DESIGN: This is a secondary analysis of a multicenter observational cohort of women who underwent an attempted OVD. Women were stratified by the duration of OVD and the number of pop-offs (vacuum) or pulls (forceps) attempted. Severe perineal lacerations, failed OVD, and a composite adverse neonatal outcome were compared by the duration of OVD and number of pop-offs or pulls. RESULTS: Of the 115,502 women in the primary cohort, 5,325 (4.6%) underwent an attempt at OVD: 3,594 (67.5%) with vacuum and 1,731 (32.5%) with forceps. After adjusting for potential confounders, an increasing number of pop-offs was associated with an increased odds of the composite adverse neonatal outcome. However, an increasing duration of vacuum exhibited a stronger association with the composite adverse neonatal outcome. Similarly, the number of forceps pulls was less strongly associated with the composite adverse neonatal outcome compared with the duration of forceps application. CONCLUSION: The duration of OVD may be more associated with adverse neonatal outcomes than the number of pop-offs or pulls.

A distance-type measure approach to the analysis of copy number variation in DNA sequencing data.

BACKGROUND: The next generation sequencing technology allows us to obtain a large amount of short DNA sequence (DNA-seq) reads at a genome-wide level. DNA-seq data have been increasingly collected during the recent years. Count-type data analysis is a widely used approach for DNA-seq data. However, the related data pre-processing is based on the moving window method, in which a window size need to be defined in order to obtain count-type data. Furthermore, useful information can be reduced after data pre-processing for count-type data. RESULTS: In this study, we propose to analyze DNA-seq data based on the related distance-type measure. Distances are measured in base pairs (bps) between two adjacent alignments of short reads mapped to a reference genome. Our experimental data based simulation study confirms the advantages of distance-type measure approach in both detection power and detection accuracy. Furthermore, we propose artificial censoring for the distance data so that distances larger than a given value are considered potential outliers. Our purpose is to simplify the pre-processing of DNA-seq data. Statistically, we consider a mixture of right censored geometric distributions to model the distance data. Additionally, to reduce the GC-content bias, we extend the mixture model to a mixture of generalized linear models (GLMs). The estimation of model can be achieved by the Newton-Raphson algorithm as well as the Expectation-Maximization (E-M) algorithm. We have conducted simulations to evaluate the performance of our approach. Based on the rank based inverse normal transformation of distance data, we can obtain the related z-values for a follow-up analysis. For an illustration, an application to the DNA-seq data from a pair of normal and tumor cell lines is presented with a change-point analysis of z-values to detect DNA copy number alterations. CONCLUSION: Our distance-type measure approach is novel. It does not require either a fixed or a sliding window procedure for generating count-type data. Its advantages have been demonstrated by our simulation studies and its practical usefulness has been illustrated by an experimental data application.

A statistical method for the conservative adjustment of false discovery rate (q-value).

BACKGROUND: q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation. RESULTS: We proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p-value by a permutation procedure. This was also considered in our adjustment method. We used simulation data as well as experimental microarray or sequencing data to illustrate the usefulness of our method. CONCLUSIONS: The conservativeness of our approach has been mathematically confirmed in this study. We have demonstrated the importance of conservative adjustment of q-value, particularly in the situation that the proportion of differentially expressed genes is small or the overall differential expression signal is weak.

An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.

MOTIVATION: We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets. RESULTS: In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer. AVAILABILITY AND IMPLEMENTATION: Additional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/∼ylai/research/Concordance. CONTACT: ylai@gwu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.

BACKGROUND: With the current microarray and RNA-seq technologies, two-sample genome-wide expression data have been widely collected in biological and medical studies. The related differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available. In practice, discordant molecular behaviors among a series of data sets can be of biological and clinical interest. METHODS: In this study, a statistical method is proposed for detecting discordance gene set enrichment. Our method is based on a two-level multivariate normal mixture model. It is statistically efficient with linearly increased parameter space when the number of data sets is increased. The model-based probability of discordance enrichment can be calculated for gene set detection. RESULTS: We apply our method to a microarray expression data set collected from forty-five matched tumor/non-tumor pairs of tissues for studying pancreatic cancer. We divided the data set into a series of non-overlapping subsets according to the tumor/non-tumor paired expression ratio of gene PNLIP (pancreatic lipase, recently shown it association with pancreatic cancer). The log-ratio ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). Our purpose is to understand whether any gene sets are enriched in discordant behaviors among these subsets (when the log-ratio is increased from negative to positive). We focus on KEGG pathways. The detected pathways will be useful for our further understanding of the role of gene PNLIP in pancreatic cancer research. Among the top list of detected pathways, the neuroactive ligand receptor interaction and olfactory transduction pathways are the most significant two. Then, we consider gene TP53 that is well-known for its role as tumor suppressor in cancer research. The log-ratio also ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). We divided the microarray data set again according to the expression ratio of gene TP53. After the discordance enrichment analysis, we observed overall similar results and the above two pathways are still the most significant detections. More interestingly, only these two pathways have been identified for their association with pancreatic cancer in a pathway analysis of genome-wide association study (GWAS) data. CONCLUSIONS: This study illustrates that some disease-related pathways can be enriched in discordant molecular behaviors when an important disease-related gene changes its expression. Our proposed statistical method is useful in the detection of these pathways. Furthermore, our method can also be applied to genome-wide expression data collected by the recent RNA-seq technology.

miR-671-5p inhibits epithelial-to-mesenchymal transition by downregulating FOXM1 expression in breast cancer.

MicroRNA (miRNA) dysfunction is associated with a variety of human diseases, including cancer. Our previous study showed that miR-671-5p was deregulated throughout breast cancer progression. Here, we report for the first time that miR-671-5p is a tumor-suppressor miRNA in breast tumorigenesis. We found that expression of miR-671-5p was decreased significantly in invasive ductal carcinoma (IDC) compared to normal in microdissected formalin-fixed, paraffin-embedded (FFPE) tissues. Forkhead Box M1 (FOXM1), an oncogenic transcription factor, was predicted as one of the direct targets of miR-671-5p, which was subsequently confirmed by luciferase assays. Forced expression of miR-671-5p in breast cancer cell lines downregulated FOXM1 expression, and attenuated the proliferation and invasion in breast cancer cell lines. Notably, overexpression of miR-671-5p resulted in a shift from epithelial-to-mesenchymal transition (EMT) to mesenchymal-to-epithelial transition (MET) phenotypes in MDA-MB-231 breast cancer cells and induced S-phase arrest. Moreover, miR-671-5p sensitized breast cancer cells to cisplatin, 5-fluorouracil (5-FU) and epirubicin exposure. Host cell reactivation (HCR) assays showed that miR-671-5p reduces DNA repair capability in post-drug exposed breast cancer cells. cDNA microarray data revealed that differentially expressed genes when miR-671-5p was transfected are associated with cell proliferation, invasion, cell cycle, and EMT. These data indicate that miR-671-5p functions as a tumor suppressor miRNA in breast cancer by directly targeting FOXM1. Hence, miR-671-5p may serve as a novel therapeutic target for breast cancer management.

Identification and Functional Validation of Reciprocal microRNA-mRNA Pairings in African American Prostate Cancer Disparities.

PURPOSE: African Americans (AA) exhibit higher rates of prostate cancer incidence and mortality compared with European American (EA) men. In addition to socioeconomic influences, biologic factors are believed to play a critical role in prostate cancer disparities. We investigated whether population-specific and -enriched miRNA-mRNA interactions might contribute to prostate cancer disparities. EXPERIMENTAL DESIGN: Integrative genomics was used, combining miRNA and mRNA profiling, miRNA target prediction, pathway analysis, and functional validation, to map miRNA-mRNA interactions associated with prostate cancer disparities. RESULTS: We identified 22 AA-specific and 18 EA-specific miRNAs in prostate cancer versus patient-matched normal prostate, and 10 "AA-enriched/-depleted" miRNAs in AA prostate cancer versus EA prostate cancer comparisons. Many of these population-specific/-enriched miRNAs could be paired with target mRNAs that exhibited an inverse pattern of differential expression. Pathway analysis revealed EGFR (or ERBB) signaling as a critical pathway significantly regulated by AA-specific/-enriched mRNAs and miRNA-mRNA pairings. Novel miRNA-mRNA pairings were validated by qRT-PCR, Western blot, and/or IHC analyses in prostate cancer specimens. Loss/gain of function assays performed in population-specific prostate cancer cell lines confirmed miR-133a/MCL1, miR-513c/STAT1, miR-96/FOXO3A, miR-145/ITPR2, and miR-34a/PPP2R2A as critical miRNA-mRNA pairings driving oncogenesis. Manipulating the balance of these pairings resulted in decreased proliferation and invasion, and enhanced sensitization to docetaxel-induced cytotoxicity in AA prostate cancer cells. CONCLUSIONS: Our data suggest that AA-specific/-enriched miRNA-mRNA pairings may play a critical role in the activation of oncogenic pathways in AA prostate cancer. Our findings also suggest that miR-133a/MCL1, miR-513c/STAT1, and miR-96/FOXO3A may have clinical significance in the development of novel strategies for treating aggressive prostate cancer.

Outlier reset CUSUM for the exploration of copy number alteration data.

Copy number alteration (CNA) data have been collected to study disease related chromosomal amplifications and deletions. The CUSUM procedure and related plots have been used to explore CNA data. In practice, it is possible to observe outliers. Then, modifications of the CUSUM procedure may be required. An outlier reset modification of the CUSUM (ORCUSUM) procedure is developed in this paper. The threshold value for detecting outliers or significant CUSUMs can be derived using results for sums of independent truncated normal random variables. Bartel's non-parametric test for autocorrelation is also introduced to the analysis of copy number variation data. Our simulation results indicate that the ORCUSUM procedure can still be used even in the situation where the degree of autocorrelation level is low. Furthermore, the results show the outlier's impact on the traditional CUSUM's performance and illustrate the advantage of the ORCUSUM's outlier reset feature. Additionally, we discuss how the ORCUSUM can be applied to examine CNA data with a simulated data set. To illustrate the procedure, recently collected single nucleotide polymorphism (SNP) based CNA data from The Cancer Genome Atlas (TCGA) Research Network is analyzed. The method is applied to a data set collected in an ovarian cancer study. Three cytogenetic bands (cytobands) are considered to illustrate the method. The cytobands 11q13 and 9p21 have been shown to be related to ovarian cancer. They are presented as positive examples. The cytoband 3q22, which is less likely to be disease related, is presented as a negative example. These results illustrate the usefulness of the ORCUSUM procedure as an exploratory tool for the analysis of SNP based CNA data.

Length of latency with preterm premature rupture of membranes before 32 weeks' gestation.

OBJECTIVE: The objective of the article is to describe latency for patients with preterm premature membrane rupture (PPROM) between 24(0/7) and 31(6/7) weeks' gestation. STUDY DESIGN: Secondary analysis of data collected prospectively in a multicenter clinical trial of magnesium sulfate for cerebral palsy prevention. Women with PPROM and fewer than six contractions per hour at enrollment who were candidates for expectant management (n = 1,377) were included in this analysis. Length of latency was calculated in days by subtracting the time of delivery from the time of membrane rupture. RESULTS: At each week of gestation, median latency between 24 and 28 weeks was similar at approximately 9 days, but it was significantly shorter with PPROM at 29, 30, and 31 weeks (p < 0.001). In addition, the percentage of patients remaining undelivered at 7 days and 14 days was similar for PPROM between 24 and 28 weeks, but it decreased significantly after that. For each gestational age, the proportion of patients remaining pregnant declined in a fashion similar to an exponential pattern. CONCLUSION: Median latency after PPROM is similar from 24 to 28 weeks' gestation, but it shortens with PPROM at and after 29 weeks.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

BACKGROUND: Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. METHODS: We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. RESULTS: We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. CONCLUSIONS: This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Genomic profiling reveals the potential role of TCL1A and MDR1 deficiency in chemotherapy-induced cardiotoxicity.

BACKGROUND: Anthracyclines, such as doxorubicin (Adriamycin), are highly effective chemotherapeutic agents, but are well known to cause myocardial dysfunction and life-threatening congestive heart failure (CHF) in some patients. METHODS: To generate new hypotheses about its etiology, genome-wide transcript analysis was performed on whole blood RNA from women that received doxorubicin-based chemotherapy and either did, or did not develop CHF, as defined by ejection fractions (EF)≤40%. Women with non-ischemic cardiomyopathy unrelated to chemotherapy were compared to breast cancer patients prior to chemo with normal EF to identify heart failure-related transcripts in women not receiving chemotherapy. Byproducts of oxidative stress in plasma were measured in a subset of patients. RESULTS: The results indicate that patients treated with doxorubicin showed sustained elevations in oxidative byproducts in plasma. At the RNA level, women who exhibited low EFs after chemotherapy had 260 transcripts that differed >2-fold (p<0.05) compared to women who received chemo but maintained normal EFs. Most of these transcripts (201) were not altered in non-chemotherapy patients with low EFs. Pathway analysis of the differentially expressed genes indicated enrichment in apoptosis-related transcripts. Notably, women with chemo-induced low EFs had a 4.8-fold decrease in T-cell leukemia/lymphoma 1A (TCL1A) transcripts. TCL1A is expressed in both cardiac and skeletal muscle, and is a known co-activator for AKT, one of the major pro-survival factors for cardiomyocytes. Further, women who developed low EFs had a 2-fold lower level of ABCB1 transcript, encoding the multidrug resistance protein 1 (MDR1), which is an efflux pump for doxorubicin, potentially leading to higher cardiac levels of drug. In vitro studies confirmed that inhibition of MDR1 by verapamil in rat H9C2 cardiomyocytes increased their susceptibility to doxorubicin-induced toxicity. CONCLUSIONS: It is proposed that chemo-induced cardiomyopathy may be due to a reduction in TCL1A levels, thereby causing increased apoptotic sensitivity, and leading to reduced cardiac MDR1 levels, causing higher cardiac levels of doxorubicin and intracellular free radicals. If so, screening for TCL1A and MDR1 SNPs or expression level in blood, might identify women at greatest risk of chemo-induced heart failure.

Association of polymorphisms in neuroprotection and oxidative stress genes and neurodevelopmental outcomes after preterm birth.

OBJECTIVE: To estimate the associations between polymorphisms in neuronal homeostasis, neuroprotection, and oxidative stress candidate genes and neurodevelopmental disability. METHODS: This was a nested case-control analysis of a randomized trial of magnesium sulfate administered to women at imminent risk for early (before 32 weeks) preterm birth for the prevention of death or cerebral palsy in their offspring. We evaluated 21 single-nucleotide polymorphisms (SNPs) in 17 genes associated with neuronal homeostasis, neuroprotection, or oxidative stress in umbilical cord blood. Cases included infant deaths (n=43) and children with cerebral palsy (n=24), mental delay (Bayley Mental Developmental Index less than 70; n=109), or psychomotor delay (Bayley Psychomotor Developmental Index less than 70; n=91) diagnosed. Controls were race-matched and sex-matched children with normal neurodevelopment. Associations between each SNP and each outcome were assessed in logistic regression models assuming an additive genetic pattern, conditional on maternal race and infant sex, and adjusting for study drug assignment, gestational age at birth, and maternal education. RESULTS: The odds of cerebral palsy were increased more than 2.5 times for each copy of the minor allele of vasoactive intestinal polypeptipe (VIP, rs17083008) (adjusted odds ratio 2.67, 95% confidence interval 1.09-6.55, P=.03) and 4.5 times for each copy of the minor allele of N-methyl-D-aspartate receptor subunit 3A (GRIN3A, rs3739722) (adjusted odds ratio 4.67, 95% CI 1.36-16.01, P=.01). The association between the advanced glycosylation end product-specific receptor (AGER, rs3134945) SNP and mental delay was modulated by study drug allocation (P=.02). CONCLUSION: Vasoactive intestinal polypeptipe and GRIN3A SNPs may be associated with cerebral palsy at age 2 in children born preterm.

Change-point analysis of paired allele-specific copy number variation data.

The recent genome-wide allele-specific copy number variation data enable us to explore two types of genomic information including chromosomal genotype variations as well as DNA copy number variations. For a cancer study, it is common to collect data for paired normal and tumor samples. Then, two types of paired data can be obtained to study a disease subject. However, there is a lack of methods for a simultaneous analysis of these four sequences of data. In this study, we propose a statistical framework based on the change-point analysis approach. The validity and usefulness of our proposed statistical framework are demonstrated through the simulation studies and applications based on an experimental data set.

Influence of maternal asthma and asthma severity on newborn morphometry.

OBJECTIVE: To determine if maternal asthma or asthma severity affects newborn morphometry. STUDY DESIGN: A secondary analysis was performed on data collected in a multicenter prospective observational cohort study of asthma in pregnancy. Patients enrolled included women with asthma stratified by severity of disease and controls. Asthma severity was defined according to the classification proposed by the National Asthma Education Program (NAEP) Report of the Working Group on Asthma and Pregnancy, modified to include medication requirements. Newborn morphometry measurements included birth weight (BW) and multiples of the median birth weight (BW-MOM), head circumference (HC), length (L), HC:BW ratio, and ponderal index (PI). RESULTS: Of 2480 patients there were 828 nonasthmatic controls, 828 with mild, 775 with moderate, and 49 with severe disease. Comparing all groups, there were statistically significant differences in maternal age (p < .001), race (p = .005), parity (p = .006), prepregnancy weight (p = .028), and medical care source (p = .001), with the severe asthma group having the highest mean maternal age (25.7 years), and proportion of African Americans (71.4%), proportion of multiparous patients (63.3%), and proportion of patients receiving government assistance (85.7%). When the control group was excluded from the comparisons, differences in prepregnancy weight and medical care source were no longer significant. BW-MOM and L did not differ between groups. The HC:BW ratio increased with asthma severity (p = .029) and was increased compared to controls (p = .010). This remained significant after controlling for confounding variables (both p <.001). HC was statistically significantly different between all groups (p = .032), as well as among women with varying degrees of asthma severity (p = .013), which was not clinically significant. After covariates adjustment, HC was not significantly different among all groups (p = .228), nor the asthma groups (p = .144). CONCLUSION: Asthma severity is associated with an increased HC:BW ratio. Severity was not found to impact HC, BW-MOM, L, or PI independently. However, the magnitudes of the effects were too small to suggest a clinically significant effect of asthma on neonatal morphometry in this large prospectively studied sample.

The effect of maternal body mass index on neonatal outcome in women receiving a single course of antenatal corticosteroids.

OBJECTIVE: The aim of this study was to determine the effect of maternal body mass index on the incidence of neonatal prematurity morbidities in those who receive corticosteroids. STUDY DESIGN: This was a secondary analysis of a trial of corticosteroids in women at risk for preterm birth. Women receiving a single course of corticosteroids were classified by their prepregnancy body mass index (<25 and > or = 25) and compared on a composite outcome comprised of several neonatal morbidities and on each individual outcome. RESULTS: Of 183 eligible women, 96 (52.5%) had a body mass index of <25 and 87 (47.5%) had a body mass index of > or = 25. The composite outcome occurred more frequently in the body mass index of > or = 2 5 group (28.7%), compared with those with a body mass index of <25 (18.8%), although this was not statistically significant (odds ratio, 1.75; 95% confidence interval, 0.83-3.72). Body mass index was not associated with outcomes after adjusting for confounding. CONCLUSION: Maternal body mass index did not affect neonatal prematurity morbidities in those receiving corticosteroids.

Diagnostic markers for early detection of ovarian cancer.

PURPOSE: Early detection would significantly decrease the mortality rate of ovarian cancer. In this study, we characterize and validate the combination of six serum biomarkers that discriminate between disease-free and ovarian cancer patients with high efficiency. EXPERIMENTAL DESIGN: We analyzed 362 healthy controls and 156 newly diagnosed ovarian cancer patients. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a multiplex, bead-based, immunoassay system. All six markers were evaluated in a training set (181 samples from the control group and 113 samples from OC patients) and a test set (181 sample control group and 43 ovarian cancer). RESULTS: Multiplex and ELISA exhibited the same pattern of expression for all the biomarkers. None of the biomarkers by themselves were good enough to differentiate healthy versus cancer cells. However, the combination of the six markers provided a better differentiation than CA-125. Four models with <2% classification error in training sets all had significant improvement (sensitivity 84%-98% at specificity 95%) over CA-125 (sensitivity 72% at specificity 95%) in the test set. The chosen model correctly classified 221 out of 224 specimens in the test set, with a classification accuracy of 98.7%. CONCLUSIONS: We describe the first blood biomarker test with a sensitivity of 95.3% and a specificity of 99.4% for the detection of ovarian cancer. Six markers provided a significant improvement over CA-125 alone for ovarian cancer detection. Validation was performed with a blinded cohort. This novel multiplex platform has the potential for efficient screening in patients who are at high risk for ovarian cancer.

Genome-wide co-expression based prediction of differential expressions.

MOTIVATION: Microarrays have been widely used for medical studies to detect novel disease-related genes. They enable us to study differential gene expressions at a genomic level. They also provide us with informative genome-wide co-expressions. Although many statistical methods have been proposed for identifying differentially expressed genes, genome-wide co-expressions have not been well considered for this issue. Incorporating genome-wide co-expression information in the differential expression analysis may improve the detection of disease-related genes. RESULTS: In this study, we proposed a statistical method for predicting differential expressions through the local regression between differential expression and co-expression measures. The smoother span parameter was determined by optimizing the rank correlation between the observed and predicted differential expression measures. A mixture normal quantile-based method was used to transform data. We used the gene-specific permutation procedure to evaluate the significance of a prediction. Two published microarray data sets were analyzed for applications. For the data set collected for a prostate cancer study, the proposed method identified many genes with weak differential expressions. Several of these genes have been shown in literature to be associated with the disease. For the data set collected for a type 2 diabetes study, no significant genes could be identified by the traditional methods. However, the proposed method identified many genes with significantly low false discovery rates. AVAILABILITY: The R codes are freely available at http://home.gwu.edu/~ylai/research/CoDiff, where the gene lists ranked by our method are also provided as the Supplementary Material.

Macrophage migration inhibitory factor expression in ovarian cancer.

OBJECTIVE: We evaluated the hypothesis that ovarian cancer patients have significantly higher levels of serum macrophage migration inhibitory factor (MIF). STUDY DESIGN: MIF levels were determined by enzyme-linked immunosorbent assay (ELISA) in epithelial ovarian cancer cell lines and immortalized normal ovarian surface epithelial cells and in serum of ovarian cancer patients (n = 54) and age-matched healthy women (n = 60). To determine the impact of Toll-like receptor-4 ligation on MIF levels, cells were treated for 48 hours with lipopolysaccharide. RESULTS: Cancer cells, but not normal cells, secrete significant amounts of MIF. This correlates in vivo, where serum MIF levels are significantly higher in ovarian cancer patients. Treatment of cancer cells with lipopolysaccharide induced a significant increase in MIF secretion. CONCLUSION: MIF may be relevant in the process of ovarian cancer formation and progression. The events leading to the induction of MIF expression and its contribution to ovarian cancer progression may open new venues for targeted therapy.

A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups.

MOTIVATION: Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance. METHODS: Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation. RESULTS: To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms. AVAILABILITY: The R codes are freely available at http://home.gwu.edu/~ylai/research/Concordance.