Correlation between mRNA expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C100001H
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Breast Invasive Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C100001H
Overview
Introduction

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

Summary

Testing the association between 17814 genes and 8 clinical features across 526 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 2 genes correlated to 'Time to Death'.

    • RPS26 ,  PPP1R14D

  • 409 genes correlated to 'AGE'.

    • ESR1 ,  CNTNAP3 ,  MAGED4B ,  KRT17 ,  FOXD2 ,  ...

  • 6 genes correlated to 'GENDER'.

    • PI3 ,  TMEM16C ,  CACNG1 ,  RP13-36C9.6 ,  MAPK4 ,  ...

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

    • OR13C4

  • 2 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • GUCA2A ,  ASZ1

  • 1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.

    • CSDE1

  • 5 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • PGC ,  BMPER ,  LRP4 ,  GPER ,  PRSS2

  • No genes correlated to 'DISTANT.METASTASIS'

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=2 shorter survival N=2 longer survival N=0
AGE Spearman correlation test N=409 older N=195 younger N=214
GENDER t test N=6 male N=1 female N=5
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=1 no N=0
DISTANT METASTASIS ANOVA test   N=0        
LYMPH NODE METASTASIS ANOVA test N=2        
NUMBER OF LYMPH NODES Spearman correlation test N=1 higher number.of.lymph.nodes N=0 lower number.of.lymph.nodes N=1
NEOPLASM DISEASESTAGE ANOVA test N=5        
Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-223.4 (median=24.2)
  censored N = 429
  death N = 65
     
  Significant markers N = 2
  associated with shorter survival 2
  associated with longer survival 0
List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
RPS26 2.7 9.674e-08 0.0017 0.682
PPP1R14D 2.3 2.933e-07 0.0052 0.592

Figure S1.  Get High-res Image As an example, this figure shows the association of RPS26 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.67e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

409 genes related to 'AGE'.

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

AGE Mean (SD) 57.92 (13)
  Significant markers N = 409
  pos. correlated 195
  neg. correlated 214
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
ESR1 0.4216 4.416e-24 7.87e-20
CNTNAP3 -0.299 2.509e-12 4.47e-08
MAGED4B -0.2963 4.052e-12 7.22e-08
KRT17 -0.2955 4.657e-12 8.29e-08
FOXD2 0.2906 1.082e-11 1.93e-07
KLK6 -0.2902 1.15e-11 2.05e-07
NUDT16 0.289 1.415e-11 2.52e-07
PPP1R14C -0.2866 2.11e-11 3.76e-07
SYT8 -0.2848 2.851e-11 5.08e-07
MGC102966 -0.2847 2.899e-11 5.16e-07

Figure S2.  Get High-res Image As an example, this figure shows the association of ESR1 to 'AGE'. P value = 4.42e-24 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

6 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 520
  MALE 6
     
  Significant markers N = 6
  Higher in MALE 1
  Higher in FEMALE 5
List of 6 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 6 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
PI3 -9.21 6.424e-11 1.14e-06 0.708
TMEM16C -14.25 1.234e-10 2.2e-06 0.9256
CACNG1 18.35 1.806e-08 0.000322 0.9609
RP13-36C9.6 -6.21 9.225e-07 0.0164 0.6635
MAPK4 -9.12 1.37e-06 0.0244 0.8016
PLA2G3 -10.61 1.719e-06 0.0306 0.8301

Figure S3.  Get High-res Image As an example, this figure shows the association of PI3 to 'GENDER'. P value = 6.42e-11 with T-test analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 148
  YES 378
     
  Significant markers N = 1
  Higher in YES 1
  Higher in NO 0
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
OR13C4 5.02 9.688e-07 0.0173 0.6263

Figure S4.  Get High-res Image As an example, this figure shows the association of OR13C4 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.69e-07 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S9.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  CM0 (I+) 2
  M0 369
  M1 8
  MX 10
     
  Significant markers N = 0
Clinical variable #6: 'LYMPH.NODE.METASTASIS'

2 genes related to 'LYMPH.NODE.METASTASIS'.

Table S10.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 118
  N0 (I+) 7
  N0 (I-) 59
  N1 41
  N1A 62
  N1B 14
  N1C 2
  N1MI 11
  N2 24
  N2A 24
  N3 5
  N3A 16
  NX 6
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11.  Get Full Table List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
GUCA2A 6.031e-12 1.07e-07
ASZ1 3.589e-08 0.000639

Figure S5.  Get High-res Image As an example, this figure shows the association of GUCA2A to 'LYMPH.NODE.METASTASIS'. P value = 6.03e-12 with ANOVA analysis.

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

One gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S12.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.83 (3.5)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S13.  Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
CSDE1 -0.2433 6.958e-07 0.0124

Figure S6.  Get High-res Image As an example, this figure shows the association of CSDE1 to 'NUMBER.OF.LYMPH.NODES'. P value = 6.96e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

5 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S14.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 41
  STAGE IA 22
  STAGE IB 2
  STAGE IIA 136
  STAGE IIB 83
  STAGE IIIA 60
  STAGE IIIB 12
  STAGE IIIC 14
  STAGE IV 8
  STAGE X 11
     
  Significant markers N = 5
List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S15.  Get Full Table List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
PGC 4.234e-14 7.54e-10
BMPER 2.903e-09 5.17e-05
LRP4 2.449e-08 0.000436
GPER 1.558e-07 0.00277
PRSS2 5.544e-07 0.00987

Figure S7.  Get High-res Image As an example, this figure shows the association of PGC to 'NEOPLASM.DISEASESTAGE'. P value = 4.23e-14 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = BRCA-TP.medianexp.txt

  • Clinical data file = BRCA-TP.clin.merged.picked.txt

  • Number of patients = 526

  • Number of genes = 17814

  • Number of clinical features = 8

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

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle