Supplementary Table 1 – Details of clinical prostate samples used for miRNA microarray analysis. TURP: Transurethral resection of prostate, LRP: laparoscopic.

Slides:



Advertisements
Similar presentations
Most Random Gene Expression Signatures are Significantly Associated with Breast Cancer Outcome Venet, et al. PLoS Computational Biology, 2011 Molly Carroll.
Advertisements

Computational discovery of gene modules and regulatory networks Ziv Bar-Joseph et al (2003) Presented By: Dan Baluta.
Outline Questions from last lecture? P. 40 questions on Pax6 gene Mechanism of Transcription Activation –Transcription Regulatory elements Comparison between.
Prediction of Therapeutic microRNA based on the Human Metabolic Network Ming Wu, Christina Chan Bioinformatics Advance Access Published January 7, 2014.
Improving miRNA Target Genes Prediction Rikky Wenang Purbojati.
Gene Co-expression Network Analysis BMI 730 Kun Huang Department of Biomedical Informatics Ohio State University.
Microarrays and Cancer Segal et al. CS 466 Saurabh Sinha.
Multidimensional Analysis If you are comparing more than two conditions (for example 10 types of cancer) or if you are looking at a time series (cell cycle.
Comprehensive Gene Expression Analysis of Prostate Cancer Reveals Distinct Transcriptional Programs Associated With Metastatic Disease Kevin Paiz-Ramirez.
From motif search to gene expression analysis
Clustering of DNA Microarray Data Michael Slifker CIS 526.
The virochip (UCSF) is a spotted microarray. Hybridization of a clinical RNA (cDNA) sample can identify specific viral expression.
Chapter 13. The Impact of Genomics on Antimicrobial Drug Discovery and Toxicology CBBL - Young-sik Sohn-
Lecture 17 – miRNAs in Plants & Animals
Multidrug resistance (MDR) has been frequently associated with elevated expression of one or more ATP binding cassette (ABC) transporters such as three.
Transcriptomic Analysis of Peripheral Blood Mononuclear Cells in Rapid Progressors in Early HIV Infection Identifies a Signature Closely Correlated with.
Computational biology of cancer cell pathways Modelling of cancer cell function and response to therapy.
吳 華 席 Hua-Hsi Wu, MD OB/GYN, VGH-TPE Aug 12, 2008
Apostolos Zaravinos and Constantinos C Deltas Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological.
Ranjit Ganta, Raj Acharya, Shruthi Prabhakara Department of Computer Science and Engineering, Penn State University DATA WAREHOUSE FOR BIO-GEO HEALTH CARE.
COMPUTATIONAL ANALYSIS OF MULTILEVEL OMICS DATA FOR THE ELUCIDATION OF MOLECULAR MECHANISMS OF CANCER Presented by Azeez Ayomide Fatai Supervisor: Junaid.
While gene expression data is widely available describing mRNA levels in different cancer cells lines, the molecular regulatory mechanisms responsible.
Bioinformatics and Computational Biology
Jin MENG Shen FU (DPD 08) Biology 2 - Head/Neck and CNS Tumors
Case Study: Characterizing Diseased States from Expression/Regulation Data Tuck et al., BMC Bioinformatics, 2006.
Supplemental Table S1 Table S1. Quantitative real-time RT-PCR primer sequences for genes used in the publication. All sequences are listed in the 5’ –
Supplementary Figure 1 Supplementary Figure 1. Kaplan–Meier estimates of recurrence-free survival (RFS) according to the expression of every ten mRNA/lncRNA.
-33 Red bar: tonsil tumour samples, n =14 x2 Green bar: tonsil normal samples, n = 9 x2 A B Supplementary Figure 1. A)Unsupervised HCL analysis was used.
Introduction to Oncomine Xiayu Stacy Huang. Oncomine is a cancer-specific microarray database and has a web-based data-mining platform aimed at facilitating.
EPIGENETIC REGULATION OF MICRORNA EXPRESSION IN PROSTATE CANCER Seodhna M. Lynch 1, Karla M. O’Neill 1, Michael M. McKenna 2, Colum P. Walsh 1, Declan.
Introduction The stem cell derived transcription factors SOX4, POU2F2 and BACH2 are known to be important in B-cell differentiation and B-cell malignancies.
Squeezing out the histone modifications data Wieslawa Mentzen with Matteo Floris and Paolo Uva Connections between epigenetics and microRNAs during embryonic.
? miRNA profiling (primary vs recurrent) Between patients cohort (n=6)
Figure 1 miRNA expression in multiple sclerosis lesions
Volume 12, Issue 12, Pages (September 2015)
Volume 2, Issue 2, Pages (February 2014)
Volume 42, Issue 1, Pages (January 2015)
BET inhibition and depletion repress the expression of BRCA1 and RAD51
Volume 67, Issue 1, Pages 7-10 (January 2015)
Profiling of a panel of radioresistant prostate cancer cells identifies deregulation of key miRNAs  McDermott Niamh , Meunier Armelle , Wong Simon , Buchete.
log fraction of singletons
Volume 7, Issue 1, Pages e7 (July 2018)
Volume 23, Issue 11, Pages (June 2018)
Revealing Global Regulatory Perturbations across Human Cancers
Angela L. Rasmussen, Michael G. Katze  Cell Host & Microbe 
Figure Revised Niemann-Pick disease type C (NP-C) diagnostic algorithm for the use of biomarkers and genetic testing Revised Niemann-Pick disease type.
Peroxisome Proliferator-Activated Receptor-α Is a Functional Target of p63 in Adult Human Keratinocytes  Silvia Pozzi, Michael Boergesen, Satrajit Sinha,
Panos Oikonomou, Hani Goodarzi, Saeed Tavazoie  Cell Reports 
Selective Inhibition of p300 HAT Blocks Cell Cycle Progression, Induces Cellular Senescence, and Inhibits the DNA Damage Response in Melanoma Cells  Gai.
Volume 12, Issue 12, Pages (September 2015)
Revealing Global Regulatory Perturbations across Human Cancers
Validation of RNA transcription profile differential expression using real-time quantitative PCR. Relative gene expression in cases compared with controls.
Neurogenins induce a network of transcription factors that mediate iNGN neurogenesisA network of transcription factors involved in iNGN neurogenesis was.
HDAC1 positively regulates the abundance of miRNAs.
Nat. Rev. Urol. doi: /nrurol
USP2a-dependent miRs deregulation modulates MYC expression.
Genomewide profiling of chromatin accessibility in prostate cancer specimens Genomewide profiling of chromatin accessibility in prostate cancer specimens.
Fig. 3 Conserved genomic association of PRC1 activity in different leukemic cells. Conserved genomic association of PRC1 activity in different leukemic.
Figure 1. Identification of three tumour molecular subtypes in CIT and TCGA cohorts. We used CIT multi-omics data ( Figure 1. Identification of.
CD4+CLA+CD103+ T cells from human blood and skin share a transcriptional profile. CD4+CLA+CD103+ T cells from human blood and skin share a transcriptional.
CREB1 promotes the induction of endogenous ERα target genes.
Single-cell atlas of lincRNA expression during mESC to NPC differentiation. Single-cell atlas of lincRNA expression during mESC to NPC differentiation.
Bcl-2 and bcl-xL Antisense Oligonucleotides Induce Apoptosis in Melanoma Cells of Different Clinical Stages  Robert A. Olie, Christoph Hafner, Renzo Küttel,
SATB2-AS1 repressed Snail transcription depending on SATB2-mediated recruitment of HDAC1. SATB2-AS1 repressed Snail transcription depending on SATB2-mediated.
Diagram of the relationship between the T4 transcriptional pattern and the different mechanisms of DNA replication and recombination. Diagram of the relationship.
RUNX3 depletion induces cellular senescence and inflammatory cytokine expression in cells undergoing TGFβ-mediated EMT. A, Cells were transfected with.
Cross-species computational analyses of adverse treatment response.
Highly metastatic PDAC cells have a unique gene signature, which is not preserved in metastases but predicts poor patient outcome. Highly metastatic PDAC.
Isolation of large soft agar clones from HBEC3p53,KRAS and HBEC3p53,KRAS,MYC identifies tumorigenic and nontumorigenic clones and genome-wide mRNA expression.
Characteristic gene expression patterns distinguish LCH cells from other immune cells present in LCH lesions. Characteristic gene expression patterns distinguish.
Presentation transcript:

Supplementary Table 1 – Details of clinical prostate samples used for miRNA microarray analysis. TURP: Transurethral resection of prostate, LRP: laparoscopic resection of prostate, ORP: open resection of prostate, and chTURP: channel TURP. N/A: Not Applicable.

High in SCunchangedHigh in CB BPH PCa CRPC Common to all Supplementary Table 2 – Shared miRNA expression in BPH, PCa, and CRPC fractionated populations. Values indicate the total number of miRNAs in each category (Total miRNAs: 835). Note the generally higher number of miRNAs overexpressed in SCs.

Supplementary Table 3 – Composite miRNA signatures of PCa relative to BPH (top panel) and CRPC relative to PCa (bottom panel)

Supplementary Fig. 1 – Validation of the microarray expression pattern with qRT-PCR, performed on the same samples used for microarray profiling (n = 5 BPH and 5 PCa, each sample in triplicate).

Integration of miRNA-mRNA microarray data derived from patient-derived prostate samples revealed that 60 potential miR-548c-3p target genes are repressed in SCs even at RNA level (manuscript in preparation). Literature analyses showed that higher expression of miR-548c-3p is associated with poor survival (1) Analysis of our published work (2) revealed that stimulation/overexpression of several transcription factors (e.g. RXR, VDR, GR, TAZ, SRF, HSF1) can promote prostate stem cell differentiation. Bioinformatic analysis revealed that miR-548c can inhibit expression of all these transcription factors. We hypothesised that higher expression of miR-548c-3p should be necessary for SC maintenance in prostate epithelium Supplementary Fig. 2 – Rationale for selecting miR-548c-3p

Gene expression changes, as assessed by qRT-PCR, of differentiation-associated genes after miR-548c-3p transfection into CB cells for 3 days, relative to control transfection. (n = 2 BPH and 3 PCa, each sample in triplicate). Data are expressed as mean ± s.d. *P < 0.05, **P < 0.01 (Student's t-test). Expression of miR-548c-3p in prostate epithelial sub-populations. (Newly cultured patient samples distinct from those used in Fig 1 or Figure S1a. n = 5 BPH and 5 PCa, each sample in triplicate). Data are expressed as mean ± s.d. *P < 0.05 (Student's t-test). Expression of miR-548c-3p CB cells 3 days after transfection with miR-548c-3p. (n = 2 BPH and 3 PCa, each sample in triplicate). Data are expressed as mean ± s.d. ***P < (Student's t-test). Supplementary Fig. 3

Supplementary Fig. 4 – The potential miR-548c-3p targets were predicted using miRWalk algorithm and gene ontology analysis was performed on them. Note: Such in silico analysis has known shortcomings and further experimental proof is necessary to extend these conclusions.

References: 1.Taylor B, Schultz N, Hieronymus H, et al. Integrative genomic profiling of human prostate cancer. Cancer Cell. 2010;18(1): Rane JK, Droop AP, Pellacani D, et al. Conserved two-step regulatory mechanism of human epithelial differentiation. Stem cell reports. 2014;2(2):180-8.