Prostate Adenocarcinoma: Correlation between miRseq expression and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 474 genes and 3 clinical features across 81 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

HSA-MIR-30C-2

5 genes correlated to 'NEOADJUVANT.THERAPY'.

HSA-MIR-200B , HSA-MIR-200C , HSA-MIR-3615 , HSA-MIR-29B-2 , HSA-MIR-3605

No genes correlated to 'AGE'

Results

Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
AGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=1	yes	N=0	no	N=1
NEOADJUVANT THERAPY	t test	N=5	yes	N=1	no	N=4

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	61.11 (6.7)
	Significant markers	N = 0

Clinical variable #2: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S2. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	5
	YES	76

	Significant markers	N = 1
	Higher in YES	0
	Higher in NO	1

List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S3. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-30C-2	-5.51	4.417e-05	0.0194	0.8289

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-30C-2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.42e-05 with T-test analysis.

Clinical variable #3: 'NEOADJUVANT.THERAPY'

5 genes related to 'NEOADJUVANT.THERAPY'.

Table S4. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	3
	YES	78

	Significant markers	N = 5
	Higher in YES	1
	Higher in NO	4

List of 5 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S5. Get Full Table List of 5 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-200B	-7.63	8.53e-10	2.87e-07	0.9145
HSA-MIR-200C	-9.14	1.405e-09	4.72e-07	0.8846
HSA-MIR-3615	-6.5	2.763e-06	0.000926	0.7911
HSA-MIR-29B-2	-9.32	5.163e-05	0.0172	0.9444
HSA-MIR-3605	7.53	0.000125	0.0416	0.8846

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-200B to 'NEOADJUVANT.THERAPY'. P value = 8.53e-10 with T-test analysis.

Methods & Data

Input

Expresson data file = PRAD.miRseq_RPKM_log2.txt
Clinical data file = PRAD.clin.merged.picked.txt
Number of patients = 81
Number of genes = 474
Number of clinical features = 3

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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