Gene Expression Profiling of Ovarian Tissues for Determination of Molecular Pathways Reflective of Tumorigenesis

ceRNA networks in gynecological cancers progression and resistance

Gynecological cancers are the second most common types of cancer in women. Clinical diagnosis of these cancers is often delayed or misdiagnosed due to lack of insight into their tumorigenesis mechanism and specific diagnostic biomarkers. Many studies have demonstrated that competing endogenous RNAs (ceRNAs) modulate the progression and resistance of gynecological cancer through microRNA (miRNA)-mediated mechanisms, which affect gene expression in multiple cancer-related pathways. Here we review studies on the involvement of the ceRNA hypothesis in the progression and resistance of gynaecological cancers to validate some ceRNAs as therapeutic targets and predictive biomarkers.

Quantitative global lipidomics analysis of patients with ovarian cancer versus benign adnexal mass

Altered lipid metabolism has emerged as an important feature of ovarian cancer (OC), yet the translational potential of lipid metabolites to aid in diagnosis and triage remains unproven. We conducted a multi-level interrogation of lipid metabolic phenotypes in patients with adnexal masses, integrating quantitative lipidomics profiling of plasma and ascites with publicly-available tumor transcriptome data. Using Sciex Lipidyzer, we assessed concentrations of > 500 plasma lipids in two patient cohorts-(i) a pilot set of 100 women with OC (50) or benign tumor (50), and (ii) an independent set of 118 women with malignant (60) or benign (58) adnexal mass. 249 lipid species and several lipid classes were significantly reduced in cases versus controls in both cohorts (FDR < 0.05). 23 metabolites-triacylglycerols, phosphatidylcholines, cholesterol esters-were validated at Bonferroni significance (P < 9.16 × 10-5). Certain lipids exhibited greater alterations in early- (diacylglycerols) or late-stage (lysophospholipids) cases, and multiple lipids in plasma and ascites were positively correlated. Lipoprotein receptor gene expression differed markedly in OC versus benign tumors. Importantly, several plasma lipid species, such as DAG(16:1/18:1), improved the accuracy of CA125 in differentiating early-stage OC cases from benign controls, and conferred a 15-20% increase in specificity at 90% sensitivity in multivariate models adjusted for age and BMI. This study provides novel insight into systemic and local lipid metabolic differences between OC and benign disease, further implicating altered lipid uptake in OC biology, and advancing plasma lipid metabolites as a complementary class of circulating biomarkers for OC diagnosis and triage.

Evaluation of MT Family Isoforms as Potential Biomarker for Predicting Progression and Prognosis in Gastric Cancer

Background

Metallothioneins (MTs) family comprises many isoforms, most of which are frequently dysregulated in a wide range of cancers. However, the expression pattern and exact role of each distinct MT family isoform which contributes to tumorigenesis, progression, and drug resistance of gastric cancer (GC) are still unclear.

Methods

Publicly available databases including Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, SurvExpress, MethHC, cBioportal, and GeneMANIA were accessed to perform an integrated bioinformatic analysis and try to detect fundamental relationships between each MT family member and GC.

Results

Bioinformatic data indicated that the mRNA expression of all MT family members was almost lowly expressed in GC compared with normal gastric tissue (P<0.05), and patients with reduced mRNA expression of each individual MT member had inconsistent prognostic value (OS, FP, PPS), which depended on the individual isoform of MT. A negative correlation between the methylation in promoter region of majority of MT members and their mRNA expression was detected from MethHC database (p<0.001). Data downloaded from TCGA revealed that MTs were rarely mutated in GC patients and MT2A was frequently regulated by other three genes (FOS, JUN, SP1) in GC patients.

Conclusion

MTs were nearly downregulated, and distinct type of MT harbored different prognostic role in GC patients. Methylation in gene promoter region of MTs partially contributed to their reduced expression in GC. Our comprehensive analyses from multiple independent databases may further lead researches to explore MT-targeting reagents or potential diagnostic and prognostic markers for GC patients.

Robust Score Tests With Missing Data in Genomics Studies

Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.

The roles of metallothioneins in carcinogenesis

Metallothioneins (MTs) are small cysteine-rich proteins that play important roles in metal homeostasis and protection against heavy metal toxicity, DNA damage, and oxidative stress. In humans, MTs have four main isoforms (MT1, MT2, MT3, and MT4) that are encoded by genes located on chromosome 16q13. MT1 comprises eight known functional (sub)isoforms (MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M, and MT1X). Emerging evidence shows that MTs play a pivotal role in tumor formation, progression, and drug resistance. However, the expression of MTs is not universal in all human tumors and may depend on the type and differentiation status of tumors, as well as other environmental stimuli or gene mutations. More importantly, the differential expression of particular MT isoforms can be utilized for tumor diagnosis and therapy. This review summarizes the recent knowledge on the functions and mechanisms of MTs in carcinogenesis and describes the differential expression and regulation of MT isoforms in various malignant tumors. The roles of MTs in tumor growth, differentiation, angiogenesis, metastasis, microenvironment remodeling, immune escape, and drug resistance are also discussed. Finally, this review highlights the potential of MTs as biomarkers for cancer diagnosis and prognosis and introduces some current applications of targeting MT isoforms in cancer therapy. The knowledge on the MTs may provide new insights for treating cancer and bring hope for the elimination of cancer.

An insight into the complex roles of metallothioneins in malignant diseases with emphasis on (sub)isoforms/isoforms and epigenetics phenomena

Metallothioneins (MTs) belong to a group of small cysteine-rich proteins that are ubiquitous throughout all kingdoms. The main function of MTs is scavenging of free radicals and detoxification and homeostating of heavy metals. In humans, 16 genes localized on chromosome 16 have been identified to encode four MT isoforms labelled by numbers (MT-1-MT-4). MT-2, MT-3 and MT-4 proteins are encoded by a single gene. MT-1 comprises many (sub)isoforms. The known active MT-1 genes are MT-1A, -1B, -1E, -1F, -1G, -1H, -1M and -1X. The rest of the MT-1 genes (MT-1C, -1D, -1I, -1J and -1L) are pseudogenes. The expression and localization of individual MT (sub)isoforms and pseudogenes vary at intra-cellular level and in individual tissues. Changes in MT expression are associated with the process of carcinogenesis of various types of human malignancies, or with a more aggressive phenotype and therapeutic resistance. Hence, MT (sub)isoform profiling status could be utilized for diagnostics and therapy of tumour diseases. This review aims on a comprehensive summary of methods for analysis of MTs at (sub)isoforms levels, their expression in single tumour diseases and strategies how this knowledge can be utilized in anticancer therapy.

Transcriptional signature of lymphoblastoid cell lines of BRCA1, BRCA2 and non-BRCA1/2 high risk breast cancer families

Approximately 25% of hereditary breast cancer cases are associated with a strong familial history which can be explained by mutations in BRCA1 or BRCA2 and other lower penetrance genes. The remaining high-risk families could be classified as BRCAX (non-BRCA1/2) families.

Gene expression involving alternative splicing represents a well-known mechanism regulating the expression of multiple transcripts, which could be involved in cancer development. Thus using RNA-seq methodology, the analysis of transcriptome was undertaken to potentially reveal transcripts implicated in breast cancer susceptibility and development.

RNA was extracted from immortalized lymphoblastoid cell lines of 117 women (affected and unaffected) coming from BRCA1, BRCA2 and BRCAX families. Anova analysis revealed a total of 95 transcripts corresponding to 85 different genes differentially expressed (Bonferroni corrected p-value <0.01) between those groups. Hierarchical clustering allowed distinctive subgrouping of BRCA1/2 subgroups from BRCAX individuals. We found 67 transcripts, which could discriminate BRCAX from BRCA1/BRCA2 individuals while 28 transcripts discriminate affected from unaffected BRCAX individuals.

To our knowledge, this represents the first study identifying transcripts differentially expressed in lymphoblastoid cell lines from major classes of mutation-related breast cancer subgroups, namely BRCA1, BRCA2 and BRCAX. Moreover, some transcripts could discriminate affected from unaffected BRCAX individuals, which could represent potential therapeutic targets for breast cancer treatment.

Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples

Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology subtypes and survival. The aim of our study was to validate previous candidate signatures in an independent setting and to identify single genes capable to serve as biomarkers for ovarian cancer progression. As several datasets are available in the GEO today, we were able to perform a true meta-analysis. First, 829 samples (11 datasets) were downloaded, and the predictive power of 16 previously published gene sets was assessed. Of these, eight were capable to discriminate histology subtypes, and none was capable to predict survival. To overcome the differences in previous studies, we used the 829 samples to identify new predictors. Then, we collected 64 ovarian cancer samples (median relapse-free survival 24.5 months) and performed TaqMan Real Time Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes associated with histology subtypes and survival. Over 90% of subtype-associated genes were confirmed. Overall survival was effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8. Relapse-free survival was predicted by MAPT and SNCG. In summary, we successfully validated several gene sets in a meta-analysis in large datasets of ovarian samples. Additionally, several individual genes identified were validated in a clinical cohort.

A snapshot of microarray-generated gene expression signatures associated with ovarian carcinoma

It was hypothesized that analysis of global gene expression in ovarian carcinoma can identify dysregulated genes that can serve as molecular markers and provide further insight into carcinogenesis and provide the basis for development of new diagnostic tools as well as new targeted therapy protocols. By applying bioinformatics tools for screening of biomedical databases, a gene expression profile databank, specific for ovarian carcinoma, was constructed with utilizable data sets published in 28 studies that applied different array technology platforms. The data sets were divided into four compartments: (i) genes associated with carcinogenesis: in 14 studies, 1881 genes were extracted, 75 genes were identified in more than one study, and only 4 genes (PRKCBP1, SPON1, TACSTD1, and PTPRM) were identified in three studies. (ii) Genes associated with histologic subtypes: in four studies, 463 genes could be identified, but none of them was identified in more than a single study. (iii) Genes associated with therapy response: in seven studies, 606 genes were identified from which 38 were differentially regulated in at least two studies, 3 genes (TMSB4X, GRN, and TJP1) in three studies, and 1 gene (IFITM1) in four studies. (iv) Genes associated with prognosis and progression: 254 genes were found in seven studies. From these genes, merely three were identified in at least two different studies. This snapshot of available gene expression data not only provides independently described potential diagnostic and therapeutic targets for ovarian carcinoma but also emphasizes the drawbacks of the current state of global gene expression analyses in ovarian cancer.

Profiling studies in ovarian cancer: a review

Ovarian cancer is a heterogeneous disease with respect to histopathology, molecular biology, and clinical outcome. In advanced stages, surgery and chemotherapy result in an approximately 25% overall 5-year survival rate, pointing to a strong need to identify subgroups of patients that may benefit from targeted innovative molecular therapy. This review summarizes: (a) microarray research identifying gene-expression profiles in ovarian cancer; (b) the methodological flaws in the available microarray studies; and (c) applications of pathway analysis to define new molecular subgroups. Microarray technology now permits the analysis of expression levels of thousands of genes. So far seven studies have aimed to identify a genetic profile that can predict survival/clinical outcome and/or response to platinum-based therapy. To date, the clinical evidence of prognostic microarray studies has only reached the level of small retrospective studies, and there are other issues that may explain the nonreproducibility among the reported prognostic profiles, such as overfitting, technical platform differences, and accuracy of measurements. We consider pathway analysis a promising new strategy. The accumulation of small differential expressions within a meaningful molecular regulatory network might lead to a critical threshold level, resulting in ovarian cancer. Microarray technologies have already provided valuable expression data for classifying ovarian cancer and the first clues about which molecular changes in ovarian cancer could be exploited in new treatment strategies. Further improvements in technology as well as in study design, combined with pathway analysis, will allow us to detect even more subtle tumor expression differences among subgroups of ovarian cancer patients. Disclosure of potential conflicts of interest is found at the end of this article.

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.