Prostate Adenocarcinoma: Correlation between gene methylation status and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17322 genes and 3 clinical features across 99 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

30 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

NAA20 , SCD , ZNF280D , HIST1H2BB , ZKSCAN4 , ...

219 genes correlated to 'NEOADJUVANT.THERAPY'.

METT10D , DSG3 , KLHL33 , ALDH3A2 , KCTD18 , ...

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=30	yes	N=21	no	N=9
NEOADJUVANT THERAPY	t test	N=219	yes	N=130	no	N=89

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	61.08 (6.7)
	Significant markers	N = 0

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

30 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	5
	YES	94

	Significant markers	N = 30
	Higher in YES	21
	Higher in NO	9

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S3. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
NAA20	9.06	7.094e-14	1.23e-09	0.8681
SCD	-8.88	2.439e-12	4.22e-08	0.8511
ZNF280D	7.61	9.97e-11	1.73e-06	0.7638
HIST1H2BB	7.04	2.837e-10	4.91e-06	0.717
ZKSCAN4	7.95	4.126e-10	7.15e-06	0.8383
CLEC4C	-6.74	1.439e-09	2.49e-05	0.7426
ZNF525	8.2	1.965e-09	3.4e-05	0.8638
AASDHPPT	6.39	7.869e-09	0.000136	0.834
VWDE	6.17	1.567e-08	0.000271	0.7787
CGB2	-6.77	1.643e-08	0.000284	0.8468

Figure S1. Get High-res Image As an example, this figure shows the association of NAA20 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.09e-14 with T-test analysis.

Clinical variable #3: 'NEOADJUVANT.THERAPY'

219 genes related to 'NEOADJUVANT.THERAPY'.

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


NEOADJUVANT.THERAPY	Labels	N
	NO	4
	YES	95

	Significant markers	N = 219
	Higher in YES	130
	Higher in NO	89

List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
METT10D	12.14	3.578e-21	6.2e-17	0.9105
DSG3	-12.04	1.097e-20	1.9e-16	0.9895
KLHL33	-11.71	2.916e-20	5.05e-16	0.9132
ALDH3A2	-11.23	3.532e-19	6.12e-15	0.9816
KCTD18	11.69	1.714e-17	2.97e-13	0.9421
HSD17B14	12.69	9.774e-17	1.69e-12	0.9947
ARV1	14.36	2.383e-16	4.13e-12	0.9947
TTC13	14.36	2.383e-16	4.13e-12	0.9947
RRM1	10.5	1.372e-15	2.38e-11	0.8658
PPP3R1	9.81	2.695e-15	4.67e-11	0.8553

Figure S2. Get High-res Image As an example, this figure shows the association of METT10D to 'NEOADJUVANT.THERAPY'. P value = 3.58e-21 with T-test analysis.

Methods & Data

Input

Expresson data file = PRAD.meth.for_correlation.filtered_data.txt
Clinical data file = PRAD.clin.merged.picked.txt
Number of patients = 99
Number of genes = 17322
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)

Made with Nozzle