Correlation between RPPA expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1736PJ0
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1736PJ0
Overview
Introduction

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

Summary

Testing the association between 189 genes and 6 clinical features across 248 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 12 genes correlated to 'Time to Death'.

    • ANXA1|ANNEXIN-1-M-E ,  MAPK14|P38_MAPK-R-V ,  SERPINE1|PAI-1-M-E ,  PGR|PR-R-V ,  CHEK2|CHK2_PT68-R-E ,  ...

  • 3 genes correlated to 'AGE'.

    • IGFBP2|IGFBP2-R-V ,  PEA15|PEA15-R-V ,  SERPINE1|PAI-1-M-E

  • 1 gene correlated to 'GENDER'.

    • RAB11A RAB11B|RAB11-R-E

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • IGFBP2|IGFBP2-R-V

  • 27 genes correlated to 'HISTOLOGICAL.TYPE'.

    • SYK|SYK-M-V ,  ANXA1|ANNEXIN-1-M-E ,  BAX|BAX-R-V ,  AR|AR-R-V ,  PTEN|PTEN-R-V ,  ...

  • 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=12 shorter survival N=7 longer survival N=5
AGE Spearman correlation test N=3 older N=2 younger N=1
GENDER t test N=1 male N=1 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=0 lower score N=1
HISTOLOGICAL TYPE ANOVA test N=27        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
Clinical variable #1: 'Time to Death'

12 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-211.2 (median=16.4)
  censored N = 193
  death N = 55
     
  Significant markers N = 12
  associated with shorter survival 7
  associated with longer survival 5
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

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
ANXA1|ANNEXIN-1-M-E 2.1 2.03e-07 3.8e-05 0.721
MAPK14|P38_MAPK-R-V 3.7 1.378e-05 0.0026 0.691
SERPINE1|PAI-1-M-E 2.3 1.509e-05 0.0028 0.68
PGR|PR-R-V 0 4.747e-05 0.0088 0.3
CHEK2|CHK2_PT68-R-E 0.03 0.0001022 0.019 0.317
SMAD1|SMAD1-R-V 5.3 0.0001085 0.02 0.645
INPP4B|INPP4B-G-E 0.13 0.0001132 0.021 0.358
BAX|BAX-R-V 5.1 0.0001653 0.03 0.669
IGFBP2|IGFBP2-R-V 2.1 0.0001816 0.033 0.735
RAB11A RAB11B|RAB11-R-E 0.04 0.0002358 0.042 0.309

Figure S1.  Get High-res Image As an example, this figure shows the association of ANXA1|ANNEXIN-1-M-E to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.03e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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

AGE Mean (SD) 42.79 (13)
  Significant markers N = 3
  pos. correlated 2
  neg. correlated 1
List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
IGFBP2|IGFBP2-R-V 0.3275 1.303e-07 2.46e-05
PEA15|PEA15-R-V -0.2942 2.421e-06 0.000455
SERPINE1|PAI-1-M-E 0.2843 5.417e-06 0.00101

Figure S2.  Get High-res Image As an example, this figure shows the association of IGFBP2|IGFBP2-R-V to 'AGE'. P value = 1.3e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 107
  MALE 141
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one gene differentially expressed by 'GENDER'

Table S6.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
RAB11A RAB11B|RAB11-R-E 3.96 0.0001003 0.019 0.642

Figure S3.  Get High-res Image As an example, this figure shows the association of RAB11A RAB11B|RAB11-R-E to 'GENDER'. P value = 0.0001 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.43 (11)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
IGFBP2|IGFBP2-R-V -0.3524 0.0001709 0.0323

Figure S4.  Get High-res Image As an example, this figure shows the association of IGFBP2|IGFBP2-R-V to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 0.000171 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

27 genes related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 79
  OLIGOASTROCYTOMA 70
  OLIGODENDROGLIOMA 99
     
  Significant markers N = 27
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
SYK|SYK-M-V 6.511e-15 1.23e-12
ANXA1|ANNEXIN-1-M-E 2.047e-09 3.85e-07
BAX|BAX-R-V 4.87e-08 9.11e-06
AR|AR-R-V 1.041e-07 1.94e-05
PTEN|PTEN-R-V 1.955e-07 3.62e-05
MAPK14|P38_MAPK-R-V 2.036e-07 3.75e-05
YAP1|YAP_PS127-R-E 2.194e-07 4.02e-05
ERBB3|HER3_PY1289-R-C 2.118e-06 0.000385
ACACA|ACC1-R-E 4.219e-06 0.000764
KIT|C-KIT-R-V 5.348e-06 0.000963

Figure S5.  Get High-res Image As an example, this figure shows the association of SYK|SYK-M-V to 'HISTOLOGICAL.TYPE'. P value = 6.51e-15 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S11.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 84
  YES 164
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = LGG-TP.rppa.txt

  • Clinical data file = LGG-TP.merged_data.txt

  • Number of patients = 248

  • Number of genes = 189

  • Number of clinical features = 6

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

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] 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)
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