Gene expression signatures differentiate ovarian/peritoneal serous carcinoma from breast carcinoma in effusions

Epigenetic study of early breast cancer (EBC) based on DNA methylation and gene integration analysis

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women. The purpose of this study is to identify key molecular markers related to the diagnosis and prognosis of early breast cancer (EBC). The data of mRNA, lncRNA and DNA methylation were downloaded from The Cancer Genome Atlas (TCGA) dataset for identification of differentially expressed mRNAs (DEmRNAs), differentially expressed lncRNAs (DElncRNAs) and DNA methylation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzes were used to identify the biological functions of DEmRNAs. The correlation analysis between DNA methylation and DEmRNAs was carried out. Then, diagnostic analysis and prognostic analysis of identified DEmRNAs and DElncRNAs were also performed in the TCGA database. Subsequently, methylation state verification for identified DEmRNAs was performed in the GSE32393 dataset. In addition, real-time polymerase chain reaction (RT-PCR) in vitro verification of genes was performed. Finally, AC093110.1 was overexpressed in human BC cell line MCF-7 to verify cell proliferation and migration. In this study, a total of 1633 DEmRNAs, 750 DElncRNAs and 8042 differentially methylated sites were obtained, respectively. In the Venn analysis, 11 keys DEmRNAs (ALDH1L1, SPTBN1, MRGPRF, CAV2, HSPB6, PITX1, WDR86, PENK, CACNA1H, ALDH1A2 and MME) were we found. ALDH1A2, ALDH1L1, HSPB6, MME, MRGPRF, PENK, PITX1, SPTBN1, WDR86 and CAV2 may be considered as potential diagnostic gene biomarkers in EBC. Strikingly, CAV2, MME, AC093110.1 and AC120498.6 were significantly actively correlated with survival. Methylation state of identified DEmRNAs in GSE32393 dataset was consistent with the result in TCGA. AC093110.1 can affect the proliferation and migration of MCF-7. ALDH1A2, ALDH1L1, HSPB6, MME, MRGPRF, PENK, PITX1, SPTBN1, WDR86 and CAV2 may be potential diagnostic gene biomarkers of EBC. Strikingly, CAV2, MME, AC093110.1 and AC120498.6 were significantly actively correlated with survival. The identification of these genes can help in the early diagnosis and treatment of EBC. In addition, AC093110.1 can regulate SPTBN1 expression and play an important role in cell proliferation and migration, which provides clues to clarify the regulatory mechanism of EBC.

Identification of Potential Bisphenol A (BPA) Exposure Biomarkers in Ovarian Cancer

Endocrine-disrupting chemicals (EDCs) can exert multiple deleterious effects and have been implicated in carcinogenesis. The xenoestrogen Bisphenol A (BPA) that is found in various consumer products has been involved in the dysregulation of numerous signalling pathways. In this paper, we present the analysis of a set of 94 genes that have been shown to be dysregulated in presence of BPA in ovarian cancer cell lines since we hypothesised that these genes might be of biomarker potential. This study sought to identify biomarkers of disease and biomarkers of disease-associated exposure. In silico analyses took place using gene expression data extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Differential expression was further validated at protein level using immunohistochemistry on an ovarian cancer tissue microarray. We found that 14 out of 94 genes are solely dysregulated in the presence of BPA, while the remaining 80 genes are already dysregulated (p-value < 0.05) in their expression pattern as a consequence of the disease. We also found that seven genes have prognostic power for the overall survival in OC in relation to their expression levels. Out of these seven genes, Keratin 4 (KRT4) appears to be a biomarker of exposure-associated ovarian cancer, whereas Guanylate Binding Protein 5 (GBP5), long intergenic non-protein coding RNA 707 (LINC00707) and Solute Carrier Family 4 Member 11 (SLC4A11) are biomarkers of disease. BPA can exert a plethora of effects that can be tissue- or cancer-specific. Our in silico findings generate a hypothesis around biomarkers of disease and exposure that could potentially inform regulation and policy making.

Protein Corona Gold Nanoparticles Fingerprinting Reveals a Profile of Blood Coagulation Proteins in the Serum of HER2-Overexpressing Breast Cancer Patients

Breast cancer (BC) is a molecularly heterogeneous disease that encompasses five major molecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B HER2 positive (LB+), HER2 positive (HER2+) and triple negative breast cancer (TNBC)). BC treatment mainly depends on the identification of the specific subtype. Despite the correct identification, therapies could fail in some patients. Thus, further insights into the genetic and molecular status of the different BC subtypes could be very useful to improve the response of BC patients to the range of available therapies. In this way, we used gold nanoparticles (AuNPs, 12.96 ± 0.72 nm) as a scavenging tool in combination with Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) to quantitatively analyze the serum proteome alterations in the different breast cancer intrinsic subtypes. The differentially regulated proteins specific of each subtype were further analyzed with the bioinformatic tools STRING and PANTHER to identify the major molecular function, biological processes, cellular origin, protein class and biological pathways altered due to the heterogeneity in proteome of the different BC subtypes. Importantly, a profile of blood coagulation proteins was identified in the serum of HER2-overexpressing BC patients.

The Pathophysiological Significance of Fibulin-3

Fibulin-3 (also known as EGF-containing fibulin extracellular matrix protein 1 (EFEMP1)) is a secreted extracellular matrix glycoprotein, encoded by the EFEMP1 gene that belongs to the eight-membered fibulin protein family. It has emerged as a functionally unique member of this family, with a diverse array of pathophysiological associations predominantly centered on its role as a modulator of extracellular matrix (ECM) biology. Fibulin-3 is widely expressed in the human body, especially in elastic-fibre-rich tissues and ocular structures, and interacts with enzymatic ECM regulators, including tissue inhibitor of metalloproteinase-3 (TIMP-3). A point mutation in EFEMP1 causes an inherited early-onset form of macular degeneration called Malattia Leventinese/Doyne honeycomb retinal dystrophy (ML/DHRD). EFEMP1 genetic variants have also been associated in genome-wide association studies with numerous complex inherited phenotypes, both physiological (namely, developmental anthropometric traits) and pathological (many of which involve abnormalities of connective tissue function). Furthermore, EFEMP1 expression changes are implicated in the progression of numerous types of cancer, an area in which fibulin-3 has putative significance as a therapeutic target. Here we discuss the potential mechanistic roles of fibulin-3 in these pathologies and highlight how it may contribute to the development, structural integrity, and emergent functionality of the ECM and connective tissues across a range of anatomical locations. Its myriad of aetiological roles positions fibulin-3 as a molecule of interest across numerous research fields and may inform our future understanding and therapeutic approach to many human diseases in clinical settings.

Mutation analysis and genomic imbalances of cells found in effusion fluids from patients with ovarian cancer

Ovarian carcinomas and carcinosarcomas often cause malignant effusions, an accumulation within serous cavities of fluid containing cancer cells. Few studies have focused on the molecular alterations and genetic mechanisms behind effusion formation. The present study investigated the mutation status of TP53, PIK3CA, KRAS, HRAS, NRAS and BRAF in effusion fluids from 103 patients with ovarian cancer. In addition, array Comparative Genomic Hybridization (aCGH) analysis was performed on 20 effusions from patients with high-grade serous carcinoma (10 cases positive for TP53 mutation and 10 with TP53 wild-type). TP53 mutations, two of which were novel: c.826_830delCCTGT and c.475_476GC>TT, were identified in 44% of the cases. Mutations in KRAS, HRAS, and PIK3CA were identified in two, two and four cases, respectively. None of the effusions analysed showed NRAS or BRAF mutations. The aCGH analysis revealed highly imbalanced genomes similar to those described in primary ovarian carcinomas. No specific profile was indicated to distinguish tumors with TP53 mutations from those without. The molecular profiling of cells found in effusion fluids from patients with ovarian cancer thus showed considerable molecular heterogeneity. TP53 seems to be the most frequently mutated gene in these cells and may serve a leading role in the metastatic process.

Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Traditional clinical features are not sufficient to accurately judge the prognosis of endometrioid endometrial adenocarcinoma (EEA). Molecular biological characteristics and traditional clinical features are particularly important in the prognosis of EEA. The aim of the present study was to establish a predictive model that considers genes and clinical features for the prognosis of EEA. The clinical and RNA sequencing expression data of EEA were derived from samples from The Cancer Genome Atlas (TCGA) and Peking University People's Hospital (PKUPH; Beijing, China). Samples from TCGA were used as the training set, and samples from the PKUPH were used as the testing set. Variable selection using Random Forests (VSURF) was used to select the genes and clinical features on the basis of TCGA samples. The RF classification method was used to establish the prediction model. Kaplan-Meier curves were tested with the log-rank test. The results from this study demonstrated that on the basis of TCGA samples, 11 genes and the grade were selected as the input features. In the training set, the out-of-bag (OOB) error of RF model-1, which was established using the '11 genes', was 0.15; the OOB error of RF model-2, which was established using the 'grade', was 0.39; and the OOB error of RF model-3, established using the '11 genes and grade', was 0.15. In the testing set, the classification accuracy of RF model-1, model-2 and model-3 was 71.43, 66.67 and 80.95%, respectively. In conclusion, to the best of our knowledge, the VSURF was used to select features relevant to EEA prognosis, and an EEA predictive model combining genes and traditional features was established for the first time in the present study. The prediction accuracy of the RF model on the basis of the 11 genes and grade was markedly higher than that of the RF models established by either the 11 genes or grade alone.

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

The matrix protein Fibulin-3 promotes KISS1R induced triple negative breast cancer cell invasion

Breast cancer is a leading cause of cancer mortality. In particular, triple negative breast cancer (TNBC) comprise a heterogeneous group of basal-like tumors lacking estrogen receptor (ERα), progesterone receptor (PR) and HER2 (ErbB2). TNBC represents 15-20% of all breast cancers and occurs frequently in women under 50 years of age. Unfortunately, these patients lack targeted therapy, are typically high grade and metastatic at time of diagnosis. The mechanisms regulating metastasis remain poorly understood. We have previously shown that the kisspeptin receptor, KISS1R stimulates invasiveness of TNBC cells. In this report, we demonstrate that KISS1R signals via the secreted extracellular matrix protein, fibulin-3, to regulate TNBC invasion. We found that the fibulin-3 gene is amplified in TNBC primary tumors and that plasma fibulin-3 levels are elevated in TNBC patients compared to healthy subjects. In this study, we show that KISS1R activation increases fibulin-3 expression and secretion. We show that fibulin-3 regulates TNBC metastasis in a mouse experimental metastasis xenograft model and signals downstream of KISS1R to stimulate TNBC invasion, by activating matrix metalloproteinase 9 (MMP-9) and the MAPK pathway. These results identify fibulin-3 as a new downstream mediator of KISS1R signaling and as a potential biomarker for TNBC progression and metastasis, thus revealing KISS1R and fibulin-3 as novel drug targets in TNBC.

Mechanisms of oestrogen receptor (ER) gene regulation in breast cancer

Most breast cancers are driven by a transcription factor called oestrogen receptor (ER). Understanding the mechanisms of ER activity in breast cancer has been a major research interest and recent genomic advances have revealed extraordinary insights into how ER mediates gene transcription and what occurs during endocrine resistance. This review discusses our current understanding on ER activity, with an emphasis on several evolving, but important areas of ER biology.

Pre-analytical issues in effusion cytology

Effusions or body cavity fluids are amongst the most commonly submitted samples to the cytology laboratory. Knowledge of proper collection, storage, preservation and processing techniques is essential to ensure proper handling and successful analysis of the sample. This article describes how the effusions should be collected and proper conditions for submission. The different processing techniques to extract the cellular material and prepare slides satisfactory for microscopic evaluation are described such as direct smears, cytospins, liquid based preparations and cell blocks. The article further elaborates on handling the specimens for additional ancillary testing such as immunostaining and molecular tests, including predictive ones, as well as future research approaches.

PITX1, a specificity determinant in the HIF-1α-mediated transcriptional response to hypoxia

Hypoxia is an important developmental cue for multicellular organisms but it is also a contributing factor for several human pathologies, such as stroke, cardiovascular diseases and cancer. In cells, hypoxia activates a major transcriptional program coordinated by the Hypoxia Inducible Factor (HIF) family. HIF can activate more than one hundred targets but not all of them are activated at the same time, and there is considerable cell type variability. In this report we identified the paired-like homeodomain pituitary transcription factor (PITX1), as a transcription factor that helps promote specificity in HIF-1α dependent target gene activation. Mechanistically, PITX1 associates with HIF-1β and it is important for the induction of certain HIF-1 dependent genes but not all. In particular, PITX1 controls the HIF-1α-dependent expression of the histone demethylases; JMJD2B, JMJD2A, JMJD2C and JMJD1B. Functionally, PITX1 is required for the survival and proliferation responses in hypoxia, as PITX1 depleted cells have higher levels of apoptotic markers and reduced proliferation. Overall, our study identified PITX1 as a key specificity factor in HIF-1α dependent responses, suggesting PITX1 as a protein to target in hypoxic cancers.

[EFEMP1 suppresses growth and invasion of lung cancer cells by downregulating matrix metalloproteinase-7 expression]

背景与目的

EFEMP1属于fibulin家族成员，是一种与细胞代谢密切相关的重要的细胞外基质蛋白，其在肿瘤的发生发展中的作用尚不清楚。本研究旨在探讨EFEMP1影响肺癌细胞生长和侵袭转移的生物学作用及其机制。

方法

Western blot方法检测肺癌细胞中EFEMP1表达，甲基化特异性PCR（methylation-specific PCR, MSP）方法检测EFEMP1在肺癌细胞中启动子区甲基化状态。肺癌细胞中转染EFEMP1后，检测细胞克隆形成及侵袭能力变化，并用Western blot及实时定量PCR检测MMP-7表达，Luciferase实验检测EFEMP1对基质金属蛋白酶7（matrix metalloproteinase-7, MMP-7）报告质粒的影响。

结果

Western blot结果显示肺癌细胞中EFEMP1表达下降，MSP分析结果说明A549和H1299中EFEMP1启动子区存在甲基化位点，5-aza-2’-deoxycytidine处理后，EFEMP1表达升高。A549和H1299转染EFEMP1后细胞克隆形成能力以及侵袭活性明显下降，MMP-7蛋白表达下调。Luciferase实验结果显示EFEMP1可以抑制MMP-7报告质粒的表达活性。

结论

EFEMP1是一种肺癌生长和侵袭的抑制因子，由于表观遗传学的改变，其在肺癌细胞中表达下降，通过上调MMP-7的表达促进肺癌细胞的侵袭转移。

HPV16 early gene E5 specifically reduces miRNA-196a in cervical cancer cells

High-risk human papillomavirus (HPV) type 16, which is responsible for greater than 50% of cervical cancer cases, is the most prevalent and lethal HPV type. However, the molecular mechanisms of cervical carcinogenesis remain elusive, particularly the early steps of HPV infection that may transform normal cervical epithelium into a pre-neoplastic state. Here, we report that a group of microRNAs (microRNAs) were aberrantly decreased in HPV16-positive normal cervical tissues, and these groups of microRNAs are further reduced in cervical carcinoma. Among these miRNAs, miR196a expression is the most reduced in HPV16-infected tissues. Interestingly, miR196a expression is low in HPV16-positive cervical cancer cell lines but high in HPV16-negative cervical cancer cell lines. Furthermore, we found that only HPV16 early gene E5 specifically down-regulated miRNA196a in the cervical cancer cell lines. In addition, HoxB8, a known miR196a target gene, is up-regulated in the HPV16 cervical carcinoma cell line but not in HPV18 cervical cancer cell lines. Various doses of miR196a affected cervical cancer cell proliferation and apoptosis. Altogether, these results suggested that HPV16 E5 specifically down-regulates miR196a upon infection of the human cervix and initiates the transformation of normal cervix cells to cervical carcinoma.

HOX genes and their role in the development of human cancers

In this review, we summarize published findings on the involvement of HOX genes in oncogenesis. HOX genes are developmental genes--they code for proteins that function as critical master regulatory transcription factors during embryogenesis. Many reports have shown that the protein products of HOX genes also play key roles in the development of cancers. Based on our review of the literature, we found that the expression of HOX genes is not only up- or downregulated in most solid tumors but also that the expression of specific HOX genes in cancers tends to differ based on tissue type and tumor site. It was also observed that HOXC family gene expression is upregulated in most solid tumor types, including colon, lung, and prostate cancer. The two HOX genes that were reported to be most commonly altered in solid tumors were HOXA9 and HOXB13. HOXA were often reported to have altered expression in breast and ovarian cancers, HOXB genes in colon cancers, HOXC genes in prostate and lung cancers, and HOXD genes in colon and breast cancers. It was found that HOX genes are also regulated at the nuclear-cytoplasmic transport level in carcinomas. Tumors arising from tissue having similar embryonic origin (endodermal), including colon, prostate, and lung, showed relatively similar HOXA and HOXB family gene expression patterns compared to breast tumors arising from mammary tissue, which originates from the ectoderm. The differential expression of HOX genes in various solid tumors thus provides an opportunity to advance our understanding of cancer development and to develop new therapeutic agents.

Decreased expression of the GATA3 gene is associated with poor prognosis in primary gastric adenocarcinoma

Background

GATA binding protein 3 (GATA3) was recently proposed to function as a tumor suppressor gene in some types of human cancer. This study aims to investigate GATA3 expression and its prognostic significance in primary gastric adenocarcinoma.

Methodology/Principal Findings

Using real-time quantitative PCR (RT-qPCR) and immunohistochemical staining methods, GATA3 expression was analyzed in tissue samples from a consecutive series of 402 gastric adenocarcinoma patients who underwent resections between 2003 and 2006. The relationship between GATA3 expression, clinicopathological factors, and patient survival was investigated. The expression status of GATA3 was shown to be clearly reduced in the tumor tissue samples compared with that in the matched adjacent non-tumor tissue samples by RT-qPCR (P = 0.0014). Immunohistochemistry analysis indicated that GATA3 expression was significantly decreased in 225 of the 402 (56%) gastric adenocarcinoma cases. Reduced GATA3 expression was also observed in patients with large tumors (P = 0.017), signet ring cell carcinoma or mucinous carcinoma (P = 0.005) and tumors with lymphatic or venous invasion (P = 0.040). Additionally, reduced expression of GATA3 was more commonly observed in tumors that were staged as T4a/b (P<0.001), N3 (P<0.001), or M1 (P<0.001). Kaplan-Meier survival curves revealed that reduced expression of GATA3 was associated with poor prognosis in gastric adenocarcinoma patients (P<0.001). Multivariate Cox analysis identified GATA3 expression as an independent prognostic factor for overall survival (HR = 5.375, 95% CI = 3.647–7.921, P<0.001). To investigate the predictive ability of the models with and without containing GATA3 gene expression, Harrell's c-index was calculated as a measure of predictive accuracy of survival outcome. The c-index values revealed that model containing GATA3 expression (c-index = 0.897) had superior discrimination ability to the model without containg it (c-index = 0.811).

Conclusions/Significance

Our data suggest that GATA3 plays an important role in tumor progression and that reduced GATA3 expression independently predicts an unfavorable prognosis in primary gastric adenocarcinoma patients.

Experimental validation of 5 in-silico predicted glioma biomarkers

BACKGROUND

Glioblastoma multiforme (GBM) is a high-grade glioma with poor prognosis. Identification of new biomarkers specific to GBM could help in disease diagnosis. We have developed and validated a bioinformatics method to predict proteins likely to be suitable as glioma biomarkers via a global microarray meta-analysis to identify uncharacterized genes consistently coexpressed with known glioma-associated genes.

METHODS

A novel bioinformatics method was implemented called global microarray meta-analysis, using approximately 16,000 microarray experiments to identify uncharacterized genes consistently coexpressed with known glioma-associated genes. These novel biomarkers were validated as proteins highly expressed in human gliomas varying in tumor grades using immunohistochemistry. Glioma gene databases were used to assess delineation of expression of these markers in varying glioma grades and subtypes of GBM.

RESULTS

We have identified 5 potential biomarkers-spondin1, Plexin-B2, SLIT3, fibulin-1, and LINGO1-that were validated as proteins highly expressed on the surface of human gliomas using immunohistochemistry. Expression of spondin1, Plexin-B2, and SLIT3 was significantly higher (P < .01) in high-grade gliomas than in low-grade gliomas. These biomarkers were significant discriminators in grade IV gliomas compared with either grade III or II tumors and also distinguished between GBM subclasses.

CONCLUSIONS

This study strongly suggests that this type of bioinformatics approach has high translational potential to rapidly discern which poorly characterized proteins may be of clinical relevance.

Loss of NAC1 expression is associated with defective bony patterning in the murine vertebral axis

NAC1 encoded by NACC1 is a member of the BTB/POZ family of proteins and participates in several pathobiological processes. However, its function during tissue development has not been elucidated. In this study, we compared homozygous null mutant Nacc1^-/- and wild type Nacc1^+/+ mice to determine the consequences of diminished NAC1 expression. The most remarkable change in Nacc1^-/- mice was a vertebral patterning defect in which most knockout animals exhibited a morphological transformation of the sixth lumbar vertebra (L6) into a sacral identity; thus, the total number of pre-sacral vertebrae was decreased by one (to 25) in Nacc1^-/- mice. Heterozygous Nacc1^+/- mice had an increased tendency to adopt an intermediate phenotype in which L6 underwent partial sacralization. Nacc1^-/- mice also exhibited non-closure of the dorsal aspects of thoracic vertebrae T10-T12. Chondrocytes from Nacc1^+/+ mice expressed abundant NAC1 while Nacc1^-/- chondrocytes had undetectable levels. Loss of NAC1 in Nacc1^-/- mice was associated with significantly reduced chondrocyte migratory potential as well as decreased expression of matrilin-3 and matrilin-4, two cartilage-associated extracellular matrix proteins with roles in the development and homeostasis of cartilage and bone. These data suggest that NAC1 participates in the motility and differentiation of developing chondrocytes and cartilaginous tissues, and its expression is necessary to maintain normal axial patterning of murine skeleton.

AZGP1 and SPDEF mRNA expression differentiates breast carcinoma from ovarian serous carcinoma

The ANPEP, AZGP1, and SPDEF genes were previously found to be overexpressed in breast compared to ovarian carcinoma effusions. The present study validated this finding in a larger cohort consisting of both primary and metastatic tumors. ANPEP, AZGP1, and SPDEF mRNA expression was investigated in 83 breast carcinomas (57 primary carcinomas and 26 effusions) and 40 ovarian carcinomas (20 primary carcinomas and 20 effusions) using qPCR. ANPEP protein expression was immunohistochemically analyzed in 53 breast carcinoma effusions and patient-matched primary carcinomas (n = 25) and lymph node metastases (n = 16). mRNA and protein levels were studied for association with tumor type and anatomic site, and for clinical role in breast carcinoma. AZGP1 and SPDEF mRNA was overexpressed in breast compared to ovarian carcinoma (both p < 0.001). AZGP1 mRNA was overexpressed in primary breast carcinoma compared to effusions (p < 0.001), with opposite findings for ANPEP (p = 0.044). AZGP1 mRNA expression correlated with positive ER status (p = 0.032) and grade 1 histology (p = 0.011), whereas SPDEF mRNA levels were associated with positive ER (p = 0.002) and PR (p = 0.013) status and tamoxifen treatment (p = 0.004). ANPEP protein expression was higher in breast carcinoma effusions compared to primary tumors and lymph node metastases (both p = 0.001). ANPEP, AZGP1, and SPDEF levels were unrelated to disease-free or overall survival. This is the first study documenting ANPEP, AZGP1, and SPDEF expression in breast carcinoma effusions. AZGP1 and SPDEF may be novel molecular markers for the differentiation of breast from ovarian carcinoma. ANPEP may be involved in breast carcinoma progression in view of its overexpression in effusions compared to solid specimens.

Fibulin-3 as a blood and effusion biomarker for pleural mesothelioma

BACKGROUND

New biomarkers are needed to detect pleural mesothelioma at an earlier stage and to individualize treatment strategies. We investigated whether fibulin-3 in plasma and pleural effusions could meet sensitivity and specificity criteria for a robust biomarker.

METHODS

We measured fibulin-3 levels in plasma (from 92 patients with mesothelioma, 136 asbestos-exposed persons without cancer, 93 patients with effusions not due to mesothelioma, and 43 healthy controls), effusions (from 74 patients with mesothelioma, 39 with benign effusions, and 54 with malignant effusions not due to mesothelioma), or both. A blinded validation was subsequently performed. Tumor tissue was examined for fibulin-3 by immunohistochemical analysis, and levels of fibulin-3 in plasma and effusions were measured with an enzyme-linked immunosorbent assay.

RESULTS

Plasma fibulin-3 levels did not vary according to age, sex, duration of asbestos exposure, or degree of radiographic changes and were significantly higher in patients with pleural mesothelioma (105±7 ng per milliliter in the Detroit cohort and 113±8 ng per milliliter in the New York cohort) than in asbestos-exposed persons without mesothelioma (14±1 ng per milliliter and 24±1 ng per milliliter, respectively; P<0.001). Effusion fibulin-3 levels were significantly higher in patients with pleural mesothelioma (694±37 ng per milliliter in the Detroit cohort and 636±92 ng per milliliter in the New York cohort) than in patients with effusions not due to mesothelioma (212±25 and 151±23 ng per milliliter, respectively; P<0.001). Fibulin-3 preferentially stained tumor cells in 26 of 26 samples. In an overall comparison of patients with and those without mesothelioma, the receiver-operating-characteristic curve for plasma fibulin-3 levels had a sensitivity of 96.7% and a specificity of 95.5% at a cutoff value of 52.8 ng of fibulin-3 per milliliter. In a comparison of patients with early-stage mesothelioma with asbestos-exposed persons, the sensitivity was 100% and the specificity was 94.1% at a cutoff value of 46.0 ng of fibulin-3 per milliliter. Blinded validation revealed an area under the curve of 0.87 for plasma specimens from 96 asbestos-exposed persons as compared with 48 patients with mesothelioma.

CONCLUSIONS

Plasma fibulin-3 levels can distinguish healthy persons with exposure to asbestos from patients with mesothelioma. In conjunction with effusion fibulin-3 levels, plasma fibulin-3 levels can further differentiate mesothelioma effusions from other malignant and benign effusions. (Funded by the Early Detection Research Network, National Institutes of Health, and others.).

EFEMP1 suppresses malignant glioma growth and exerts its action within the tumor extracellular compartment

Purpose

There are conflicting reports regarding the function of EFEMP1 in different cancer types. In this study, we sought to evaluate the role of EFEMP1 in malignant glioma biology.

Experimental Design

Real-time qRT-PCR was used to quantify EFEMP1 expression in 95 glioblastoma multiforme (GBM). Human high-grade glioma cell lines and primary cultures were engineered to express ectopic EFEMP1, a small hairpin RNA of EFEMP1, or treated with exogenous recombinant EFEMP1 protein. Following treatment, growth was assayed both in vitro and in vivo (subcutaneous (s.c.) and intracranial (i.c.) xenograft model systems).

Results

Cox regression revealed that EFEMP1 is a favorable prognostic marker for patients with GBM. Over-expression of EFEMP1 eliminated tumor development and suppressed angiogenesis, cell proliferation, and VEGFA expression, while the converse was true with knock-down of endogenous EFEMP1 expression. The EFEMP1 suppression of tumor onset time was nearly restored by ectopic VEGFA expression; however, overall tumor growth rate remained suppressed. This suggested that inhibition of angiogenesis was only partly responsible for EFEMP1's impact on glioma development. In glioma cells that were treated by exogenous EFEMP1 protein or over-expressed endogenous EFEMP1, the EGFR level was reduced and AKT signaling activity attenuated. Mixing of EFEMP1 protein with cells prior to s.c. and i.c. implantations or injection of the protein around the established s.c. xenografts, both significantly suppressed tumorigenicity.

Conclusions

Overall, our data reveals that EEFEMP1 suppresses glioma growth in vivo, both by modulating the tumor extracellular microenvironment and by altering critical intracellular oncogenic signaling pathways.

Prediction of epigenetically regulated genes in breast cancer cell lines

BACKGROUND

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.

RESULTS

Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes.

CONCLUSIONS

Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.