Data mining of gene arrays for biomarkers of survival in ovarian cancer

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two care fully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy

Bibliometric analysis of emerging technologies in the field of computer science helping in ovarian cancer research

This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them

Repression of Esophageal Neoplasia and Inflammatory Signaling by Anti-miR-31 Delivery In Vivo.

BACKGROUND: Overexpression of microRNA-31 (miR-31) is implicated in the pathogenesis of esophageal squamous cell carcinoma (ESCC), a deadly disease associated with dietary zinc deficiency. Using a rat model that recapitulates features of human ESCC, the mechanism whereby Zn regulates miR-31 expression to promote ESCC is examined. METHODS: To inhibit in vivo esophageal miR-31 overexpression in Zn-deficient rats (n = 12-20 per group), locked nucleic acid-modified anti-miR-31 oligonucleotides were administered over five weeks. miR-31 expression was determined by northern blotting, quantitative polymerase chain reaction, and in situ hybridization. Physiological miR-31 targets were identified by microarray analysis and verified by luciferase reporter assay. Cellular proliferation, apoptosis, and expression of inflammation genes were determined by immunoblotting, caspase assays, and immunohistochemistry. The miR-31 promoter in Zn-deficient esophagus was identified by ChIP-seq using an antibody for histone mark H3K4me3. Data were analyzed with t test and analysis of variance. All statistical tests were two-sided. RESULTS: In vivo, anti-miR-31 reduced miR-31 overexpression (P = .002) and suppressed the esophageal preneoplasia in Zn-deficient rats. At the same time, the miR-31 target Stk40 was derepressed, thereby inhibiting the STK40-NF-κΒ-controlled inflammatory pathway, with resultant decreased cellular proliferation and activated apoptosis (caspase 3/7 activities, fold change = 10.7, P = .005). This same connection between miR-31 overexpression and STK40/NF-κΒ expression was also documented in human ESCC cell lines. In Zn-deficient esophagus, the miR-31 promoter region and NF-κΒ binding site were activated. Zn replenishment restored the regulation of this genomic region and a normal esophageal phenotype. CONCLUSIONS: The data define the in vivo signaling pathway underlying interaction of Zn deficiency and miR-31 overexpression in esophageal neoplasia and provide a mechanistic rationale for miR-31 as a therapeutic target for ESCC

Data mining the serous ovarian tumor transcriptome

Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate is 94%, late stage survival rates drop to 28%. It is because most cases are caught in late stages that high mortality is seen. Correct diagnosis is dependent on the presence of symptoms: ~90% of diagnosed ovar- ian cancers are symptomatic. These symptoms tend to be unfocused and not acute. The goal of this project is to develop a transcript-level data set measuring ovarian tumor expression and associated paracrine signaling for later biomarker research. To this end, laser capture microdissection was used with exon based oligonucleotide ar- rays to measure the transcriptome of benign and malignant (Type II) serous ovarian surface epithelial-stromal tumors. In addition to profiling tumor, surrounding stro- mal tissue expression was measured to examine potential paracrine signaling. In total, ~270 million measurements were performed using 50 microarrays. An initial analysis was performed to measure quality, and to compare our measurements against known ovarian cancer properties as established in the molecular genetics literature. Using ontological annotation and de novo pathway generation methods, major trends were defined in the data set including the following: apical surface and tight junction ac- tivity, mitotic activity, tumor suppression in benign tumors, epithelial-mesenchymal transitioning, known ovarian tumor oncogene activity, and evidence of paracrine sig- naling. A list of differentially expressed transcripts was defined which may be explored as biomarkers. The potential for meaningful future analysis is diverse. This data set will contribute to the capacity of the cancer genetics community to perform high resolution exploration of serous ovarian epithelial-stromal surface tumors, aiding in developing better diagnostics and therapeutics

Changes in protein expression in two cholangiocarcinoma cell lines undergoing formation of multicellular tumor spheroids In vitro

Epithelial-to-Mesenchymal Transition (EMT) is relevant in malignant growth and frequently correlates with worsening disease progression due to its implications in metastases and re- sistance to therapeutic interventions. Although EMT is known to occur in several types of solid tumors, the information concerning tumors arising from the epithelia of the bile tract is still limited. In order to approach the problem of EMT in cholangiocarcinoma, we decided to investigate the changes in protein expression occurring in two cell lines under conditions leading to growth as adherent monolayers or to formation of multicellular tumor spheroids (MCTS), which are considered culture models that better mimic the growth characteristics of in-vivo solid tumors. In our system, changes in phenotypes occur with only a decrease in transmembrane E-cadherin and vimentin expression, minor changes in the transglutami- nase protein/activity but with significant differences in the proteome profiles, with declining and increasing expression in 6 and in 16 proteins identified by mass spectrometry. The aris- ing protein patterns were analyzed based on canonical pathways and network analysis. These results suggest that significant metabolic rearrangements occur during the conver- sion of cholangiocarcinomas cells to the MCTS phenotype, which most likely affect the car- bohydrate metabolism, protein folding, cytoskeletal activity, and tissue sensitivity to oxygen

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).Peer reviewe

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics