Uterine Corpus Endometrioid Carcinoma: Correlation between mRNAseq expression and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

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

Summary

Testing the association between 18744 genes and 5 clinical features across 266 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

1 gene correlated to 'Time to Death'.

KCNK10|54207

135 genes correlated to 'AGE'.

DIO2|1734 , DACT1|51339 , TNFAIP2|7127 , S100A1|6271 , CYGB|114757 , ...

1392 genes correlated to 'HISTOLOGICAL.TYPE'.

KIAA1324|57535 , L1CAM|3897 , SPDEF|25803 , HIF3A|64344 , CLDN6|9074 , ...

11 genes correlated to 'NEOADJUVANT.THERAPY'.

RANGRF|29098 , WFDC1|58189 , ANAPC4|29945 , NPDC1|56654 , IHH|3549 , ...

No genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'

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
Time to Death	Cox regression test	N=1	shorter survival	N=1	longer survival	N=0
AGE	Spearman correlation test	N=135	older	N=51	younger	N=84
HISTOLOGICAL TYPE	ANOVA test	N=1392
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0
NEOADJUVANT THERAPY	t test	N=11	yes	N=10	no	N=1

Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0-173.6 (median=20.2)
	censored	N = 246
	death	N = 20

	Significant markers	N = 1
	associated with shorter survival	1
	associated with longer survival	0

List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
KCNK10\|54207	2.4	1.168e-07	0.0022	0.763

Figure S1. Get High-res Image As an example, this figure shows the association of KCNK10|54207 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.17e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

135 genes related to 'AGE'.

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


AGE	Mean (SD)	63.13 (11)
	Significant markers	N = 135
	pos. correlated	51
	neg. correlated	84

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

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

	SpearmanCorr	corrP	Q
DIO2\|1734	-0.4108	2.977e-12	5.58e-08
DACT1\|51339	-0.3832	9.863e-11	1.85e-06
TNFAIP2\|7127	0.3737	3.044e-10	5.7e-06
S100A1\|6271	0.372	3.737e-10	7e-06
CYGB\|114757	-0.362	1.172e-09	2.2e-05
PTCH1\|5727	-0.3515	3.729e-09	6.99e-05
DLC1\|10395	-0.3505	4.169e-09	7.81e-05
KIAA1199\|57214	-0.3491	4.877e-09	9.14e-05
PDGFRB\|5159	-0.3486	5.103e-09	9.56e-05
PHLDA1\|22822	-0.3471	6.024e-09	0.000113

Figure S2. Get High-res Image As an example, this figure shows the association of DIO2|1734 to 'AGE'. P value = 2.98e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

1392 genes related to 'HISTOLOGICAL.TYPE'.

Table S5. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	205
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	3
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	4
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	1
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	5
	MIXED SEROUS AND ENDOMETRIOID	7
	SEROUS ENDOMETRIAL ADENOCARCINOMA	41

	Significant markers	N = 1392

List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
KIAA1324\|57535	3.201e-41	6e-37
L1CAM\|3897	6.779e-35	1.27e-30
SPDEF\|25803	8.597e-33	1.61e-28
HIF3A\|64344	9.313e-30	1.75e-25
CLDN6\|9074	1.3e-27	2.44e-23
TFF3\|7033	2.257e-27	4.23e-23
SLC6A12\|6539	5.885e-27	1.1e-22
FOXA2\|3170	2.98e-26	5.58e-22
FIGNL2\|401720	3.16e-26	5.92e-22
IL20RA\|53832	1.441e-25	2.7e-21

Figure S3. Get High-res Image As an example, this figure shows the association of KIAA1324|57535 to 'HISTOLOGICAL.TYPE'. P value = 3.2e-41 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	99
	YES	167

	Significant markers	N = 0

Clinical variable #5: 'NEOADJUVANT.THERAPY'

11 genes related to 'NEOADJUVANT.THERAPY'.

Table S8. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	83
	YES	183

	Significant markers	N = 11
	Higher in YES	10
	Higher in NO	1

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

Table S9. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
RANGRF\|29098	5.61	8.45e-08	0.00158	0.6981
WFDC1\|58189	5.14	8.394e-07	0.0157	0.6877
ANAPC4\|29945	5.03	1.239e-06	0.0232	0.6803
NPDC1\|56654	5.07	1.24e-06	0.0232	0.7089
IHH\|3549	5.03	1.431e-06	0.0268	0.6894
NEIL1\|79661	5.03	1.466e-06	0.0275	0.6854
CEBPB\|1051	-4.99	1.764e-06	0.0331	0.6904
KIAA1324\|57535	4.99	1.838e-06	0.0344	0.6773
C19ORF45\|374877	4.95	2.005e-06	0.0376	0.6815
HSF4\|3299	4.91	2.337e-06	0.0438	0.6865

Figure S4. Get High-res Image As an example, this figure shows the association of RANGRF|29098 to 'NEOADJUVANT.THERAPY'. P value = 8.45e-08 with T-test analysis.

Methods & Data

Input

Expresson data file = UCEC.mRNAseq_RPKM_log2.txt
Clinical data file = UCEC.clin.merged.picked.txt
Number of patients = 266
Number of genes = 18744
Number of clinical features = 5

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' 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] 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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

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

[5] 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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