Correlation between mRNA expression and clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C16D5R1H
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16D5R1H
Overview
Introduction

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

Summary

Testing the association between 18632 genes and 7 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 192 genes correlated to 'AGE'.

    • STS ,  GREB1 ,  DEPDC6 ,  GNPNAT1 ,  SLCO1A2 ,  ...

  • 2 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • SPINK8 ,  PTBP1

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

    • WDR60

  • 30 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • FLJ22662 ,  WDR62 ,  GLIPR1L2 ,  ATHL1 ,  ST6GALNAC6 ,  ...

  • 1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

    • IL1RAPL2

  • No genes correlated to 'Time to Death', and 'TUMOR.STAGE'.

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=0        
AGE Spearman correlation test N=192 older N=74 younger N=118
PRIMARY SITE OF DISEASE ANOVA test N=2        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=30 yes N=15 no N=15
COMPLETENESS OF RESECTION ANOVA test N=1        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0.3-180.2 (median=28.3)
  censored N = 267
  death N = 290
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

192 genes related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 59.71 (12)
  Significant markers N = 192
  pos. correlated 74
  neg. correlated 118
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
STS -0.3073 1.644e-13 3.06e-09
GREB1 -0.3024 4.055e-13 7.56e-09
DEPDC6 -0.3011 5.244e-13 9.77e-09
GNPNAT1 -0.2964 1.238e-12 2.31e-08
SLCO1A2 0.2854 8.665e-12 1.61e-07
EIF4E3 -0.2846 9.966e-12 1.86e-07
NPAL2 -0.2761 4.274e-11 7.96e-07
NLK 0.275 5.1e-11 9.5e-07
BRCC3 -0.2749 5.214e-11 9.71e-07
APPL2 0.2744 5.623e-11 1.05e-06

Figure S1.  Get High-res Image As an example, this figure shows the association of STS to 'AGE'. P value = 1.64e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

2 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  OMENTUM 2
  OVARY 558
  PERITONEUM OVARY 2
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
SPINK8 1.447e-13 2.7e-09
PTBP1 1.063e-09 1.98e-05

Figure S2.  Get High-res Image As an example, this figure shows the association of SPINK8 to 'PRIMARY.SITE.OF.DISEASE'. P value = 1.45e-13 with ANOVA analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 75.64 (13)
  Score N
  40 2
  60 20
  80 49
  100 7
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S7.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
WDR60 0.504 2.551e-06 0.0475

Figure S3.  Get High-res Image As an example, this figure shows the association of WDR60 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 2.55e-06 with Spearman correlation analysis.

Clinical variable #5: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 3.17 (0.48)
  N
  Stage 1 2
  Stage 2 2
  Stage 3 133
  Stage 4 35
     
  Significant markers N = 0
Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

30 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S9.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 559
     
  Significant markers N = 30
  Higher in YES 15
  Higher in NO 15
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
FLJ22662 -24.23 4.068e-41 7.58e-37 0.867
WDR62 -13.77 2.368e-35 4.41e-31 0.7513
GLIPR1L2 -16.48 6.348e-35 1.18e-30 0.7794
ATHL1 12.49 3.399e-31 6.33e-27 0.6535
ST6GALNAC6 17.15 2.815e-20 5.24e-16 0.799
SDK1 -15 2.746e-15 5.12e-11 0.768
CYP4A11 8.73 5.398e-15 1.01e-10 0.6184
LOC388161 22.94 2.065e-13 3.85e-09 0.9207
TMC5 8.42 5.86e-12 1.09e-07 0.5897
C9ORF114 -11.98 2.006e-11 3.74e-07 0.7358

Figure S4.  Get High-res Image As an example, this figure shows the association of FLJ22662 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.07e-41 with T-test analysis.

Clinical variable #7: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S11.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 14
  R1 27
  R2 1
     
  Significant markers N = 1
List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S12.  Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
IL1RAPL2 6.299e-07 0.0117

Figure S5.  Get High-res Image As an example, this figure shows the association of IL1RAPL2 to 'COMPLETENESS.OF.RESECTION'. P value = 6.3e-07 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 562

  • Number of genes = 18632

  • Number of clinical features = 7

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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