BackgroundHigh-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.ResultsWe demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences.ConclusionsThe Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.

Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.

Introduction Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (“phenomapping”) could identify phenotypically distinct HFpEF categories. Methods and Results We prospectively studied 397 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering to define and characterize mutually exclusive groups comprising a novel classification of HFpEF. All phenomapping analyses were performed blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years, 62% were female, 39% were African-American, and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio 4.2, 95% CI 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions Phenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

The relationship between gene expression measured at the mRNA level and the corresponding protein level is not well characterized in human cancer. In this study, we compared mRNA and protein expression for a cohort of genes in the same lung adenocarcinomas. The abundance of 165 protein spots representing 98 individual genes was analyzed in 76 lung adenocarcinomas and nine non-neoplastic lung tissues using two-dimensional polyacrylamide gel electrophoresis. Specific polypeptides were identified using matrix-assisted laser desorption/ ionization mass spectrometry. For the same 85 samples, mRNA levels were determined using oligonucleotide microarrays, allowing a comparative analysis of mRNA and protein expression among the 165 protein spots. Twentyeight of the 165 protein spots (17%) or 21 of 98 genes (21.4%) had a statistically significant correlation between protein and mRNA expression (r > 0.2445; p < 0.05); however, among all 165 proteins the correlation coefficient values (r) ranged from ؊0.467 to 0.442. Correlation coefficient values were not related to protein abundance. Further, no significant correlation between mRNA and protein expression was found (r ‫؍‬ ؊0.025) if the average levels of mRNA or protein among all samples were applied across the 165 protein spots (98 genes). The mRNA/ protein correlation coefficient also varied among proteins with multiple isoforms, indicating potentially separate isoform-specific mechanisms for the regulation of protein abundance. Among the 21 genes with a significant correlation between mRNA and protein, five genes differed significantly between stage I and stage III lung adenocarcinomas. Using a quantitative analysis of mRNA and protein expression within the same lung adenocarcinomas, we showed that only a subset of the proteins exhibited a significant correlation with mRNA abundance.

Steroid receptors in the stromal cells of endometrium and its disease counterpart tissue endometriosis play critical physiologic roles. We found that mRNA and protein levels of estrogen receptor 2 (ESR2) were strikingly higher, whereas levels of estrogen receptor 1 (ESR1), total progesterone receptor (PGR), and progesterone receptor B (PGR B) were significantly lower in endometriotic versus endometrial stromal cells. Because ESR2 displayed the most striking levels of differential expression between endometriotic and endometrial cells, and the mechanisms for this difference are unknown, we tested the hypothesis that alteration in DNA methylation is a mechanism responsible for severely increased ESR2 mRNA levels in endometriotic cells. We identified a CpG island occupying the promoter region (À197/þ359) of the ESR2 gene. Bisulfite sequencing of this region showed significantly higher methylation in primary endometrial cells (n ¼ 8 subjects) versus endometriotic cells (n ¼ 8 subjects). The demethylating agent 5-aza-2 0 -deoxycytidine significantly increased ESR2 mRNA levels in endometrial cells. Mechanistically, we employed serial deletion mutants of the ESR2 promoter fused to the luciferase reporter gene and transiently transfected into both endometriotic and endometrial cells. We demonstrated that the critical region (À197/þ372) that confers promoter activity also bears the CpG island, and the activity of the ESR2 promoter was strongly inactivated by in vitro methylation. Taken together, methylation of a CpG island at the ESR2 promoter region is a primary mechanism responsible for differential expression of ESR2 in endometriosis and endometrium. These findings may be applied to a number of areas ranging from diagnosis to the treatment of endometriosis.

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survivalassociated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P ‫؍‬ 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer.

Heterogeneity in systemic sclerosis/SSc confounds clinical trials. We previously identified ‘intrinsic’ gene expression subsets by analysis of SSc skin. Here we test the hypotheses that skin gene expression signatures including intrinsic subset are associated with skin score/MRSS improvement during mycophenolate mofetil (MMF) treatment. Gene expression and intrinsic subset assignment were measured in 12 SSc patients’ biopsies and ten controls at baseline, and from serial biopsies of one cyclophosphamide-treated patient, and nine MMF-treated patients. Gene expression changes during treatment were determined using paired t-tests corrected for multiple hypothesis testing. MRSS improved in four of seven MMF-treated patients classified as the inflammatory intrinsic subset. Three patients without MRSS improvement were classified as normal-like or fibroproliferative intrinsic subsets. 321 genes (FDR <5%) were differentially expressed at baseline between patients with and without MRSS improvement during treatment. Expression of 571 genes (FDR <10%) changed between pre- and post-MMF treatment biopsies for patients demonstrating MRSS improvement. Gene expression changes in skin are only seen in patients with MRSS improvement. Baseline gene expression in skin, including intrinsic subset assignment, may identify SSc patients whose MRSS will improve during MMF treatment, suggesting that gene expression in skin may allow targeted treatment in SSc.

Wilms tumors (WT) have provided broad insights into the interface between development and tumorigenesis. Further understanding is confounded by their genetic, histologic, and clinical heterogeneity, the basis of which remains largely unknown. We evaluated 224 WT for global gene expression patterns; WT1, CTNNB1, and WTX mutation; and 11p15 copy number and methylation patterns. Five subsets were identified showing distinct differences in their pathologic and clinical features: these findings were validated in 100 additional WT. The gene expression pattern of each subset was compared with published gene expression profiles during normal renal development. A novel subset of epithelial WT in infants lacked WT1, CTNNB1, and WTX mutations and nephrogenic rests and displayed a gene expression pattern of the postinduction nephron, and none recurred. Three subsets were characterized by a low expression of WT1 and intralobar nephrogenic rests. These differed in their frequency of WT1 and CTNNB1 mutations, in their age, in their relapse rate, and in their expression similarities with the intermediate mesoderm versus the metanephric mesenchyme. The largest subset was characterized by biallelic methylation of the imprint control region 1, a gene expression profile of the metanephric mesenchyme, and both interlunar and perilobar nephrogenic rests. These data provide a biologic explanation for the clinical and pathologic heterogeneity seen within WT and enable the future development of subset-specific therapeutic strategies. Further, these data support a revision of the current model of WT ontogeny, which allows for an interplay between the type of initiating event and the developmental stage in which it occurs.