RESUMO
Homeobox (HOX) genes encode transcription factors critical to morphogenesis and cell differentiation. Although dysregulation of several HOX genes in ovarian cancer has been reported, little is known about HOXC6 expression in epithelial ovarian cancer. In this report, analysis of laser capture microdissected samples determined HOXC6 expression patterns in normal versus malignant serous ovarian carcinoma tissues. HOXC6 protein was quantified by ELISA in parallel serum samples and further validated in a larger cohort of serum samples collected from women with and without serous ovarian carcinoma. These data demonstrate significant downregulation of HOXC6 in serous ovarian cancer.
Assuntos
Cistadenocarcinoma Seroso/sangue , Proteínas de Homeodomínio/genética , Proteínas de Homeodomínio/metabolismo , Neoplasias Epiteliais e Glandulares/sangue , Neoplasias Ovarianas/sangue , Adulto , Idoso , Idoso de 80 Anos ou mais , Carcinoma Epitelial do Ovário , Linhagem Celular Tumoral , Cistadenocarcinoma Seroso/patologia , Regulação para Baixo , Feminino , Regulação Neoplásica da Expressão Gênica , Células HeLa , Humanos , Pessoa de Meia-Idade , Neoplasias Epiteliais e Glandulares/patologia , Análise de Sequência com Séries de Oligonucleotídeos , Neoplasias Ovarianas/patologiaRESUMO
Pathway enrichment analysis models (PEM) are the premier methods for interpreting gene expression profiles from high-throughput experiments. PEM often use a priori background knowledge to infer the underlying biological functions and mechanisms. A shortcoming of standard PEM is their disregarding of interactions for simplicity, which potentially results in partial and inaccurate inference. In this study, we introduce a graph-based PEM, namely Causal Disturbance Analysis (CADIA), that leverages gene interactions to quantify the topological importance of genes' expression profiles in pathways organizations. In particular, CADIA uses a novel graph centrality model, namely Source/Sink, to measure the topological importance. Source/Sink Centrality quantifies a gene's importance as a receiver and a sender of biological information, which allows for prioritizing the genes that are more likely to disturb a pathways functionality. CADIA infers an enrichment score for a pathway by deriving statistical evidence from Source/Sink centrality of the differentially expressed genes and combines it with classical over-representation analysis. Through real-world experimental and synthetic data evaluations, we show that CADIA can uniquely infer critical pathway enrichments that are not observable through other PEM. Our results indicate that CADIA is sensitive towards topologically central gene-level changes that and provides an informative framework for interpreting high-throughput data.
Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes/genética , Mapas de Interação de Proteínas/genética , Transdução de Sinais/genética , Algoritmos , Perfilação da Expressão Gênica , Humanos , Transcriptoma/genéticaRESUMO
Utilizing microarray gene expression data in cancer research possesses the ability to identify deregulated cellular pathways involved in malignant development. This study investigated the relationships of three gene families, HOX, ErbB and IGFBP, with regard to the development of ovarian cancer. These families were of interest because of similar chromosomal locations and their deregulated expression in ovarian cancer. Higher level statistics were used to differentially analyze microarray data in 65 ovarian samples to assess correlation and relationships among the gene families of interest. Fifteen genes in the three families were found to be significantly deregulated. Thirty-eight significant correlations were found within and between the genes of interest. Our data indicates that the significantly modeled relationships between HOX, ErbB and IGFBP gene pairs could provide insight into the underlying biological mechanisms in ovarian cancer.
Assuntos
Receptores ErbB/genética , Regulação Neoplásica da Expressão Gênica , Proteínas de Homeodomínio/genética , Proteínas de Ligação a Fator de Crescimento Semelhante a Insulina/genética , Neoplasias Ovarianas/genética , Feminino , Humanos , Família Multigênica , Análise de Sequência com Séries de Oligonucleotídeos , RNA Neoplásico/genética , RNA Neoplásico/isolamento & purificação , Reação em Cadeia da Polimerase Via Transcriptase ReversaRESUMO
Screening methods of High-Grade Serous Ovarian Cancer (HGSOC) lack specificity and sensitivity, partly due to benign tumors producing false-positive findings. We utilized a differential expression analysis pipeline on malignant tumor (MT) and normal epithelial (NE) samples, and also filtered the results to discriminate between MT and benign tumor (BT). We report that a panel of 26 dysregulated genes stratifies MT from both BT and NE. We further validated our findings by utilizing unsupervised clustering methods on two independent datasets. We show that the 26-genes panel completely distinguishes HGSOC from NE, and produces a more accurate classification between HGSOC and BT. Pathway analysis reveals that AKT3 is of particular significance, because of its high fold change and appearance in the majority of the dysregulated pathways. mRNA patterns of AKT3 suggest essential connections with tumor growth and metastasis, as well as a strong biomarker potential when used with 3 other genes (PTTG1, MND1, CENPF). Our results show that dysregulation of the 26-mRNA signature panel provides an evidence of malignancy and contribute to the design of a high specificity biomarker panel for detection of HGSOC, potentially in an early more curable stage.