Ovarian Serous Cystadenocarcinoma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 12742 genes and 7 clinical features across 549 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 144 genes correlated to 'AGE'.

    • LPA ,  GRIK2 ,  FLJ44881 ,  PCDHGB7 ,  CNTD1 ,  ...

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

    • PRH1 ,  BMPR1B ,  TAS2R50 ,  DBN1

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

    • OLFML1

  • 2144 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • NID2 ,  CHRNA9 ,  MLL3 ,  FXR2 ,  EXOC5 ,  ...

  • 19 genes correlated to 'NEOADJUVANT.THERAPY'.

    • NF1 ,  TM7SF3 ,  CCR4 ,  CDCA2 ,  CASP3 ,  ...

  • 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=144 older N=18 younger N=126
PRIMARY SITE OF DISEASE ANOVA test N=4        
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=2144 yes N=2096 no N=48
NEOADJUVANT THERAPY t test N=19 yes N=7 no N=12
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=29)
  censored N = 253
  death N = 290
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

144 genes related to 'AGE'.

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

AGE Mean (SD) 59.73 (12)
  Significant markers N = 144
  pos. correlated 18
  neg. correlated 126
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
LPA 0.3052 4.421e-13 5.63e-09
GRIK2 0.2992 1.308e-12 1.67e-08
FLJ44881 -0.2933 5.384e-12 6.86e-08
PCDHGB7 0.2849 1.604e-11 2.04e-07
CNTD1 -0.27 1.853e-10 2.36e-06
INHBE -0.2667 3.131e-10 3.99e-06
TTLL7 -0.2626 5.995e-10 7.64e-06
SDR16C5 -0.261 7.639e-10 9.73e-06
PRLH -0.2602 8.662e-10 1.1e-05
GPR12 -0.2554 2.297e-09 2.92e-05

Figure S1.  Get High-res Image As an example, this figure shows the association of LPA to 'AGE'. P value = 4.42e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

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

4 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 545
  PERITONEUM (OVARY) 2
     
  Significant markers N = 4
List of 4 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5.  Get Full Table List of 4 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
PRH1 8.359e-10 1.07e-05
BMPR1B 2.951e-08 0.000376
TAS2R50 8.842e-07 0.0113
DBN1 1.046e-06 0.0133

Figure S2.  Get High-res Image As an example, this figure shows the association of PRH1 to 'PRIMARY.SITE.OF.DISEASE'. P value = 8.36e-10 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
OLFML1 0.5794 3.38e-08 0.000431

Figure S3.  Get High-res Image As an example, this figure shows the association of OLFML1 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 3.38e-08 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) 3.05 (0.56)
  N
  Stage 1 16
  Stage 2 24
  Stage 3 421
  Stage 4 83
     
  Significant markers N = 0
Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

2144 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 546
     
  Significant markers N = 2144
  Higher in YES 2096
  Higher in NO 48
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
NID2 27.3 2.31e-100 2.94e-96 0.9829
CHRNA9 28.28 3.239e-94 4.13e-90 0.8765
MLL3 22.58 1.441e-73 1.84e-69 0.8767
FXR2 21.37 6.892e-69 8.78e-65 0.9609
EXOC5 21.03 8.412e-67 1.07e-62 0.9104
DVL2 19.75 6.859e-66 8.74e-62 0.8657
RNF130 19.16 8.573e-63 1.09e-58 0.8663
NPPB 18.98 4.221e-62 5.37e-58 0.8541
FKBP3 18.83 3.703e-61 4.72e-57 0.8712
CDCA7 18.61 1.143e-59 1.46e-55 0.8773

Figure S4.  Get High-res Image As an example, this figure shows the association of NID2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.31e-100 with T-test analysis.

Clinical variable #7: 'NEOADJUVANT.THERAPY'

19 genes related to 'NEOADJUVANT.THERAPY'.

Table S11.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 441
  YES 108
     
  Significant markers N = 19
  Higher in YES 7
  Higher in NO 12
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
NF1 -5.36 1.883e-07 0.0024 0.6315
TM7SF3 -5.25 2.783e-07 0.00355 0.5927
CCR4 5.14 4.881e-07 0.00622 0.6277
CDCA2 -5.11 5.377e-07 0.00685 0.6028
CASP3 5.1 7.371e-07 0.00939 0.6306
KLRA1 5.07 8.368e-07 0.0107 0.6241
DZIP3 -5.01 8.808e-07 0.0112 0.5866
ZEB1 -4.98 1.138e-06 0.0145 0.6002
TLL1 -4.98 1.155e-06 0.0147 0.6037
TFCP2L1 -4.87 1.713e-06 0.0218 0.6147

Figure S5.  Get High-res Image As an example, this figure shows the association of NF1 to 'NEOADJUVANT.THERAPY'. P value = 1.88e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = OV.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 549

  • Number of genes = 12742

  • 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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