Correlation between RPPA expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C10C4TJG
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/C10C4TJG
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 8 clinical features across 254 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 23 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 ,  ...

  • 8 genes correlated to 'AGE'.

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

  • 3 genes correlated to 'GENDER'.

    • STAT5A|STAT5-ALPHA-R-V ,  RAB11A RAB11B|RAB11-R-E ,  TSC1|TSC1-R-C

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

    • IGFBP2|IGFBP2-R-V

  • 46 genes correlated to 'HISTOLOGICAL.TYPE'.

    • SYK|SYK-M-V ,  ANXA1|ANNEXIN-1-M-E ,  AR|AR-R-V ,  BAX|BAX-R-V ,  MAPK14|P38_MAPK-R-V ,  ...

  • No genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION', '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
Time to Death Cox regression test N=23 shorter survival N=11 longer survival N=12
AGE Spearman correlation test N=8 older N=6 younger N=2
GENDER Wilcoxon test N=3 male N=3 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=0 lower score N=1
HISTOLOGICAL TYPE Kruskal-Wallis test N=46        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test   N=0        
RACE Wilcoxon test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

23 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=16.1)
  censored N = 197
  death N = 55
     
  Significant markers N = 23
  associated with shorter survival 11
  associated with longer survival 12
List of top 10 genes differentially expressed by 'Time to Death'

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 2.035e-07 3.8e-05 0.721
MAPK14|P38_MAPK-R-V 3.7 1.425e-05 0.0027 0.69
SERPINE1|PAI-1-M-E 2.3 1.56e-05 0.0029 0.68
PGR|PR-R-V 0 4.765e-05 0.0089 0.3
CHEK2|CHK2_PT68-R-E 0.03 0.0001042 0.019 0.318
SMAD1|SMAD1-R-V 5.3 0.000108 0.02 0.645
INPP4B|INPP4B-G-E 0.13 0.0001131 0.021 0.358
BAX|BAX-R-V 5.1 0.0001661 0.03 0.669
IGFBP2|IGFBP2-R-V 2.1 0.0001831 0.033 0.734
RAB11A RAB11B|RAB11-R-E 0.04 0.0002407 0.043 0.31
Clinical variable #2: 'AGE'

8 genes related to 'AGE'.

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

AGE Mean (SD) 42.58 (13)
  Significant markers N = 8
  pos. correlated 6
  neg. correlated 2
List of 8 genes differentially expressed by 'AGE'

Table S4.  Get Full Table List of 8 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
IGFBP2|IGFBP2-R-V 0.3146 3.062e-07 5.79e-05
PEA15|PEA15-R-V -0.2931 1.999e-06 0.000376
SERPINE1|PAI-1-M-E 0.2747 8.9e-06 0.00166
EGFR|EGFR-R-V 0.214 0.000597 0.111
ASNS|ASNS-R-V 0.2116 0.0006892 0.128
ANXA1|ANNEXIN-1-M-E 0.209 0.0008039 0.148
CDKN1A|P21-R-V 0.2087 0.0008182 0.15
CDH1|E-CADHERIN-R-V -0.2 0.001352 0.246
Clinical variable #3: 'GENDER'

3 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 108
  MALE 146
     
  Significant markers N = 3
  Higher in MALE 3
  Higher in FEMALE 0
List of 3 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 3 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
STAT5A|STAT5-ALPHA-R-V 5649 0.0001133 0.0214 0.6417
RAB11A RAB11B|RAB11-R-E 10021.5 0.0002227 0.0419 0.6356
TSC1|TSC1-R-C 5974 0.0009711 0.182 0.6211
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) 86.88 (12)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'KARNOFSKY.PERFORMANCE.SCORE'

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 -0.3378 0.00027 0.051
Clinical variable #5: 'HISTOLOGICAL.TYPE'

46 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 83
  OLIGOASTROCYTOMA 70
  OLIGODENDROGLIOMA 101
     
  Significant markers N = 46
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 1.613e-15 3.05e-13
ANXA1|ANNEXIN-1-M-E 1.157e-10 2.17e-08
AR|AR-R-V 1.623e-08 3.04e-06
BAX|BAX-R-V 7.397e-08 1.38e-05
MAPK14|P38_MAPK-R-V 8.768e-08 1.62e-05
YAP1|YAP_PS127-R-E 1.262e-07 2.32e-05
ERBB3|HER3_PY1289-R-C 2.152e-07 3.94e-05
KIT|C-KIT-R-V 1.073e-06 0.000195
PTEN|PTEN-R-V 5.358e-06 0.00097
CDKN1B|P27_PT157-R-C 6.585e-06 0.00119
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 170
     
  Significant markers N = 0
Clinical variable #7: 'RACE'

No gene related to 'RACE'.

Table S12.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  BLACK OR AFRICAN AMERICAN 11
  WHITE 242
     
  Significant markers N = 0
Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S13.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 13
  NOT HISPANIC OR LATINO 229
     
  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 = 254

  • Number of genes = 189

  • 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

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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