Identification and characterization of gene and microRNA networks associated with cancer survival and drug abuse

Delfino, Kristin R.

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https://hdl.handle.net/2142/46791 Copy

Description

Title

Identification and characterization of gene and microRNA networks associated with cancer survival and drug abuse

Author(s)

Delfino, Kristin R.

Issue Date

2014-01-16T18:02:38Z

Director of Research (if dissertation) or Advisor (if thesis)

Rodriguez-Zas, Sandra L.

Doctoral Committee Chair(s)

Rodriguez-Zas, Sandra L.

Committee Member(s)

• Bollero, German A.
• Nowak, Romana A.
• Loor, Juan J.

Department of Study

Animal Sciences

Discipline

Animal Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• MicroRNA
• Gene
• Pathways
• Systems Biology
• Biomarkers
• Functional Analysis
• Ribonucleic Acid (RNA)

Abstract

The study of the dysregulation of the transcriptome in diseases like cancer and drug abuse can offer insights into preventive and therapeutical remedies, as well as targets for future basic and applied research. The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs), transcription factors (TFs), and target genes. Previously, there has been limited validation of reported associations between these diseases and miRNAs, TFs, and target mRNA in independent studies. This may be due to several reasons. Few studies simultaneously analyze multiple miRNAs, TFs, and target mRNA. Also, most studies do not consider clinical or cohort-dependent factors when characterizing the associations between the transcriptome and disease. Lastly, most transcriptome studies tend to be small, and the individual analysis has limited statistical power to detect accurate and precise associations between transcripts and diseases. This thesis aims to address the previous limitations and identify replicable biomarkers of cancer and drug abuse. Functional and network analyses were performed to identify and study targets of microRNA biomarkers associated with glioblastoma multiforme survival within and across race, gender, recurrence, and therapy cohorts. A Cox survival model was applied to profiles from 253 individuals, 534 microRNAs, and the results were confirmed using cross-validation, discriminant analyses, and cross-study comparisons. All 45 microRNAs revealed were confirmed in independent cancer studies, and 25 of those were further confirmed in glioblastoma studies. Thirty-nine and six microRNAs were associated with one and multiple glioblastoma survival indicators, respectively. Nineteen and 26 microRNAs exhibited cohort-dependent and independent associations with glioblastoma, respectively. An approach integrating survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value <0.05) with ovarian cancer survival and recurrence, respectively. The simultaneous analysis of co-expression profiles along with consideration of clinical characteristics of patients allowed reliable microRNA-transcription factor-target gene networks associated with ovarian cancer survival to be inferred. Illicit drug exposure brings about changes in the brain transcriptome that result in the dysregulation of pathways. To detect the progression of drug exposure pathways, meta-analysis of five individual microarray experiments measuring gene expression in the brain of mice under acute and chronic drug exposure was performed. Functional analysis and network visualization offered insights into the network changes across drug exposure levels. Meta-analyses uncovered 263 and 2,641 genes differentially expression (FDR-adjusted P-value <0.1) between control and acute and chronic exposure, respectively. Individual genes in these processes have been previously associated with drug exposure and reward-dependent behaviors. The MAPK signaling pathway and the molecular functions of protein dimerization and leucine zipper transcription factor were enriched in response to acute exposure. This study was able to detect the progression of drug exposure pathways using meta, functional, and network analyses.

Graduation Semester

2013-12

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http://hdl.handle.net/2142/46791

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Copyright 2013 Kristin Delfino

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