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Gene-set reduction for analysis of major and minor Gleason scores based on differential gene expressions of biological pathways in prostate cancer

  • Author / Creator
    Poudel, Surya P
  • Introduction: Prostate cancer is a heterogeneous disease, and in spite of recent advances regarding understanding its biology, further discovery of the molecular events underlying prostate cancer is still needed. Gleason grading is an important predictor of prostate cancer outcomes. In current practices, patients with a total GS ≥7 are at greater risk but it is still unclear how prostate cancer outcomes differ for various distributions of the total GS between its major and minor components. Objectives: Our goal is to identify genes and biological pathways differentiating between patients with various combinations of GS, while moving from a less aggressive combination (3,3) to a more aggressive combination (4,4). Methods: The Swedish Watchful Waiting Cohort (n=255) consisting of mRNA expression of 6,100 genes in prostate tumor tissue has been used. Significance Analysis of Microarray for Gene Sets (SAM-GS) has been used to screen gene sets from C2 catalog of Molecular Signature Database (MSigDB) to identify those sets differentiating between patients who died from prostate cancer during follow-up (lethal prostate cancer) versus patients who survived at least 10 years after diagnosis (indolent prostate cancer). Those pathways not associated with both major and minor GS ≤ 3 versus both major and minor GS ≥ 4, based on SAM-GS method, has been discarded. Moving from a less aggressive GS combination of (3,3) to a more aggressive one of (4,4) via grey areas of (3,4) and (4,3), the reduced gene sets to their core subsets of genes contributing most to the association with the GS combinations has been obtained by using Significance Analysis of Gene Sets Reduction (SAM-GSR) method. Finally, these results to the gene sets and cores differentiating between GS of (3,4) vs (4,3) were compared. Results: 1351 gene sets out of 1,892 MSigDB gene sets were found to be differentially expressed between 149 lethal and 106 indolent prostate cancer patients, using SAM-GS. Furthermore, 1,246 gene sets were found to be differentially expressed between 80 patients with major and minor GS ≤ 3 versus 68 patients with major and minor GS ≥ 4. SAM-GSR achieved a 91% reduction, averaged over the four GS combinations, starting from (3,3) and ending with (4,4). The numbers of significant gene sets and core set sizes decrease considerably when comparing patients with larger total GS, indicating a challenge in discriminating between higher risk groups of patients. Eight gene sets are differentially expressed between GS of (3,4) vs (4,4), and only one gene set differentiates between (4,3) and (4,4). At the gene level, none of the 13 core genes from comparing (3,4) vs (4,4) are represented among the 332 core genes comparing (3,3) vs (3,4), or among the 323 core genes comparing (3,3) vs (4,3). The set consisting of the 13 genes shows a marginal association with GS of (3,4) vs (4,3), with a SAM-GS p-value of 0.059. Conclusions and Implications: Our comprehensive analysis of combinations of major and minor Gleason scores brings additional insights to the current practice based on the sum of the two components, especially for values of the total GS of 7 or 8, indicating patients at greater risk. Further studies are needed to validate our results at the gene and pathway levels.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3XW48438
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Public Health Sciences
  • Specialization
    • Epidemiology
  • Supervisor / co-supervisor and their department(s)
    • Dinu, Irina (Department of Public Health)
    • Pyne, Saumyadipta (CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus)
  • Examining committee members and their departments
    • Senthilselvan, Ambikaipakan ( Department of Public Health)
    • Dinu, Irina ( Department of Public Health)
    • Pyne, Saumyadipta (CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus)