Correlation between RPPA expression and clinical features

Sarcoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FJ2FWD

Overview

Introduction

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

Summary

Testing the association between 192 genes and 5 clinical features across 216 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

PRKAA1|AMPK_ALPHA-R-C , MSH6|MSH6-R-C , CHEK1|CHK1-M-C , EIF4G1|EIF4G-R-C , MSH2|MSH2-M-V , ...

15 genes correlated to 'YEARS_TO_BIRTH'.

SERPINE1|PAI-1-M-E , ANXA1|ANNEXIN-1-M-E , PGR|PR-R-V , BRAF|B-RAF-M-C , PXN|PAXILLIN-R-C , ...

30 genes correlated to 'GENDER'.

YWHAB|14-3-3_BETA-R-V , MSH6|MSH6-R-C , ERBB3|HER3_PY1289-R-C , GAB2|GAB2-R-V , CHEK1|CHK1-M-C , ...

No genes correlated to 'RACE', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
DAYS_TO_DEATH_OR_LAST_FUP	Cox regression test	N=30	shorter survival	N=20	longer survival	N=10
YEARS_TO_BIRTH	Spearman correlation test	N=15	older	N=9	younger	N=6
GENDER	Wilcoxon test	N=30	male	N=30	female	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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


DAYS_TO_DEATH_OR_LAST_FUP	Duration (Months)	0.1-188.2 (median=21.6)
	censored	N = 143
	death	N = 72

	Significant markers	N = 30
	associated with shorter survival	20
	associated with longer survival	10

List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
PRKAA1\|AMPK_ALPHA-R-C	0.19	8.29e-05	0.016	0.362
MSH6\|MSH6-R-C	2.5	0.000319	0.031	0.648
CHEK1\|CHK1-M-C	3.1	0.0006255	0.04	0.595
EIF4G1\|EIF4G-R-C	2.5	0.001058	0.051	0.662
MSH2\|MSH2-M-V	3.2	0.002029	0.078	0.611
RICTOR\|RICTOR_PT1135-R-V	0.22	0.002455	0.079	0.373
EEF2K\|EEF2K-R-V	2.2	0.003248	0.089	0.588
TUBA1B\|ACETYL-A-TUBULIN-LYS40-R-C	1.58	0.007196	0.14	0.601
BAP1\|BAP1-C-4-M-E	2.2	0.007599	0.14	0.628
RAB25\|RAB25-R-V	0.59	0.008331	0.14	0.386

Clinical variable #2: 'YEARS_TO_BIRTH'

15 genes related to 'YEARS_TO_BIRTH'.

Table S3. Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'


YEARS_TO_BIRTH	Mean (SD)	62.25 (14)
	Significant markers	N = 15
	pos. correlated	9
	neg. correlated	6

List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

	SpearmanCorr	corrP	Q
SERPINE1\|PAI-1-M-E	0.2587	0.0001248	0.024
ANXA1\|ANNEXIN-1-M-E	0.2262	0.0008338	0.0578
PGR\|PR-R-V	-0.2248	0.0009035	0.0578
BRAF\|B-RAF-M-C	0.2136	0.001633	0.0784
PXN\|PAXILLIN-R-C	0.197	0.003735	0.137
EEF2\|EEF2-R-C	0.1932	0.004458	0.137
MYH11\|MYH11-R-V	-0.1908	0.004998	0.137
ASNS\|ASNS-R-V	0.185	0.006525	0.151
NF2\|NF2-R-C	0.1832	0.007088	0.151
RAB25\|RAB25-R-V	-0.1775	0.00912	0.175

Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	114
	MALE	102

	Significant markers	N = 30
	Higher in MALE	30
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
YWHAB\|14-3-3_BETA-R-V	7333	0.000928	0.145	0.6306
MSH6\|MSH6-R-C	4364	0.001572	0.145	0.6247
ERBB3\|HER3_PY1289-R-C	7182	0.002862	0.145	0.6176
GAB2\|GAB2-R-V	4516	0.004662	0.145	0.6116
CHEK1\|CHK1-M-C	4522	0.004856	0.145	0.6111
BIRC2 \|CIAP-R-V	7097	0.005161	0.145	0.6103
ANXA1\|ANNEXIN-1-M-E	7064	0.006433	0.145	0.6075
BAX\|BAX-R-V	7044	0.007335	0.145	0.6058
EIF4EBP1\|4E-BP1_PT37_T46-R-V	4588	0.007528	0.145	0.6054
TFRC\|TFRC-R-V	4589	0.007577	0.145	0.6053

Clinical variable #4: 'RACE'

No gene related to 'RACE'.

Table S7. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	6
	BLACK OR AFRICAN AMERICAN	13
	WHITE	169

	Significant markers	N = 0

Clinical variable #5: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S8. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	3
	NOT HISPANIC OR LATINO	165

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = SARC-TP.rppa.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 216
Number of genes = 192
Number of clinical features = 5

Selected clinical features

For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .
Survival time data

Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

if 'vital_status'==0(alive),

if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

if 'vital_status'==NA,excludes this case in survival analysis and report the case.

cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)

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

[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)

[4] 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