Kidney Renal Papillary Cell Carcinoma: 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 17268 genes and 8 clinical features across 47 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 1 gene correlated to 'GENDER'.

    • NARFL

  • 37 genes correlated to 'PATHOLOGY.T'.

    • INSM1 ,  DLEU2 ,  GNG11 ,  GNASAS ,  CDC42BPG ,  ...

  • 26 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • RFX7 ,  DGKI ,  ELMOD2 ,  MYADML ,  CMTM5 ,  ...

  • 29 genes correlated to 'TUMOR.STAGE'.

    • INSM1 ,  WDR16 ,  GNG11 ,  LYPD3 ,  DLEU2 ,  ...

  • No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.

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=0        
GENDER t test N=1 male N=0 female N=1
KARNOFSKY PERFORMANCE SCORE t test   N=0        
PATHOLOGY T Spearman correlation test N=37 higher pT N=27 lower pT N=10
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=26        
TUMOR STAGE Spearman correlation test N=29 higher stage N=23 lower stage N=6
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) 1-123.6 (median=21.6)
  censored N = 36
  death N = 11
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 61.15 (13)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 16
  MALE 31
     
  Significant markers N = 1
  Higher in MALE 0
  Higher in FEMALE 1
List of one gene differentially expressed by 'GENDER'

Table S4.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
NARFL -6.08 3.996e-07 0.0069 0.8891

Figure S1.  Get High-res Image As an example, this figure shows the association of NARFL to 'GENDER'. P value = 4e-07 with T-test analysis.

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

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Labels N
  class100 4
  class90 3
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

37 genes related to 'PATHOLOGY.T'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.91 (1)
  N
  T1 25
  T2 1
  T3 21
     
  Significant markers N = 37
  pos. correlated 27
  neg. correlated 10
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
INSM1 0.7366 3.58e-09 6.18e-05
DLEU2 -0.7329 4.697e-09 8.11e-05
GNG11 -0.7008 4.128e-08 0.000713
GNASAS -0.6876 9.348e-08 0.00161
CDC42BPG 0.6794 1.519e-07 0.00262
LYPD3 0.6761 1.84e-07 0.00318
KIR2DS4 -0.6725 2.256e-07 0.00389
SDK1 0.6677 2.949e-07 0.00509
CCDC64B 0.6635 3.726e-07 0.00643
NSD1 0.6621 4.005e-07 0.00691

Figure S2.  Get High-res Image As an example, this figure shows the association of INSM1 to 'PATHOLOGY.T'. P value = 3.58e-09 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.54 (0.72)
  N
  N0 14
  N1 7
  N2 3
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

26 genes related to 'PATHOLOGICSPREAD(M)'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 33
  M1 4
  MX 9
     
  Significant markers N = 26
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
RFX7 2.652e-09 4.58e-05
DGKI 1.111e-08 0.000192
ELMOD2 1.321e-07 0.00228
MYADML 1.342e-07 0.00232
CMTM5 1.661e-07 0.00287
FAM183B 2.98e-07 0.00514
GDPD4 4.301e-07 0.00742
ZNF329 4.403e-07 0.0076
C8ORF45 4.503e-07 0.00777
NFE2L1 4.792e-07 0.00827

Figure S3.  Get High-res Image As an example, this figure shows the association of RFX7 to 'PATHOLOGICSPREAD(M)'. P value = 2.65e-09 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

29 genes related to 'TUMOR.STAGE'.

Table S11.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.07 (1.2)
  N
  Stage 1 24
  Stage 2 1
  Stage 3 15
  Stage 4 6
     
  Significant markers N = 29
  pos. correlated 23
  neg. correlated 6
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S12.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
INSM1 0.7593 9.561e-10 1.65e-05
WDR16 0.6959 7.929e-08 0.00137
GNG11 -0.694 8.897e-08 0.00154
LYPD3 0.6937 9.044e-08 0.00156
DLEU2 -0.6711 3.32e-07 0.00573
ZNF177 0.6707 3.395e-07 0.00586
NSD1 0.6691 3.7e-07 0.00639
MYT1L -0.6663 4.322e-07 0.00746
CDC42BPG 0.6649 4.668e-07 0.00806
GNASAS -0.6631 5.133e-07 0.00886

Figure S4.  Get High-res Image As an example, this figure shows the association of INSM1 to 'TUMOR.STAGE'. P value = 9.56e-10 with Spearman correlation analysis.

Methods & Data
Input

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

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

  • Number of patients = 47

  • Number of genes = 17268

  • 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)
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