Microarray data analysis of publicly available high grade prostate cancer data, functional annotation (using DAVID web service) and gene regulatory network construction using Prize-collecting Steiner Forest algorithm

1. Analysis of High Grade
Prostate Cancer
Microarray Data
BIN714 Final Project
Gungor Budak
June 4, 2015
Instructor: Assoc. Prof. Dr. Yesim AYDIN SON

2. Outline
❏ Introduction & background
❏ Data description
❏ Experimental design
❏ Methods
❏ Results
❏ Conclusion
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3. Introduction & Background
❏ In males, Below bladder and in front of
rectum
❏ In males, Contains cells that produce semen
Image: www.roboticoncology.com 3

4. Introduction & Background
❏ T2 stage
❏ only in prostate
❏ large enough in DRE
❏ T4 stage
❏ fixed or growing into
nearby structures
❏ N1
❏ spread to lymph nodes
❏ M1
❏ distant metastasis
Image: www.cancerrecovery.org.uk
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5. Data Description
Goal
Identification of diagnostic markers and
targets for novel therapeutic drugs for high
grade prostate cancer (PC) (Shuin et al., 2010)
ID Organism Type Platform Sample
# (Can.)
Sample
# (Nor.)
GSE45016 Homo
sapiens
Expression
profiling by
array
Affymetrix Human
Genome U133 Plus 2.0
Array
10 1
5

6. Experimental Design
❏ 10 frozen specimens with high PSA1
levels
and high Gleason scores (8-9), staged T2
to T4 with or without N1 and M1
❏ Normal prostate (NP) epithelial cells from
five non-prostate cancer (BPH2
) patients
(males & mixed)
1
Prostate-specific antigen
2
Benign prostatic hyperplasia
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7. Methods
❏ Data analyzed with GEO2R tool
❏ Log transformation applied
❏ eBayes feature selection
❏ Results (diff. expressed genes) filtered
❏ p-value < 0.05
❏ LFC > 2
❏ Only characterized genes (No LOC123456789)
❏ 1166 genes collected
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8. Methods
❏ PC related genes collected
❏ KEGG Diseases (12)
❏ The GeneCards Human Gene Database (20)
❏ Dong JT, 2006 (30)
❏ DAVID web service used for functional
annotation (Dennis Jr et al., 2013)
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9. Methods
❏ PCSF network generated (Tuncbag et al., 2013)
❏ LFC as “prize”
❏ Cost per edge, penalty per fail to include a node
❏ iRefWeb ref. interactome used (Wodak et al., 2010)
❏ Down to 549 genes
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10. Results: GO Bio. Proc.
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11. Results: KEGG Pathways
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12. Results: Network Analysis
PC Genes Steiner Nodes Steiner PC Nodes
5/47 98/549 PTEN, TP53, BRCA1, GSTP1, ELAC2
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13. Results: PTEN
❏ Phosphatase and tensin homolog
❏ Many frameshift
deletions
❏ Metastasis
❏ Best studied in PC
Vishwanatha et al., 2012, J Carcinog
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14. Results: TP53
❏ Tumor protein p53
❏ Commonly single
point mutations
❏ Most frequently
mutated in human
cancer
Brosh & Rotter, 2009, Nature Reviews Cancer
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15. Conclusion
❏ Analyses revealed PC related genes
PTEN & TP53
❏ Improvements
❏ More complete interactome
❏ Better experimental design
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16. Thank you