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 20232 genes and 8 clinical features across 79 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 51 genes correlated to 'Time to Death'.

    • KCNF1 ,  PER1 ,  ECE2 ,  FBLN5 ,  KCNK13 ,  ...

  • 320 genes correlated to 'PATHOLOGY.T'.

    • PAX6 ,  TRH ,  ALX3 ,  TLX3 ,  OTP ,  ...

  • 1 gene correlated to 'PATHOLOGY.N'.

    • HMX3

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

    • HSD3B2 ,  LOC100132247 ,  KRAS ,  ERCC2 ,  PRR24 ,  ...

  • 384 genes correlated to 'TUMOR.STAGE'.

    • PAX6 ,  GATA4 ,  NKX2-6 ,  TLX3 ,  OTP ,  ...

  • No genes correlated to 'AGE', 'GENDER', and 'KARNOFSKY.PERFORMANCE.SCORE'.

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=51 shorter survival N=51 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=320 higher pT N=306 lower pT N=14
PATHOLOGY N Spearman correlation test N=1 higher pN N=1 lower pN N=0
PATHOLOGICSPREAD(M) ANOVA test N=136        
TUMOR STAGE Spearman correlation test N=384 higher stage N=379 lower stage N=5
Clinical variable #1: 'Time to Death'

51 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-182.7 (median=20.9)
  censored N = 60
  death N = 12
     
  Significant markers N = 51
  associated with shorter survival 51
  associated with longer survival 0
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
KCNF1 6401 8.158e-08 0.0017 0.82
PER1 4501 1.13e-07 0.0023 0.785
ECE2 3801 1.478e-07 0.003 0.867
FBLN5 1301 1.749e-07 0.0035 0.719
KCNK13 7601 1.877e-07 0.0038 0.775
PTCH1 17001 2.101e-07 0.0043 0.862
IGF2AS 1801 3.265e-07 0.0066 0.899
SORBS3 1301 3.62e-07 0.0073 0.768
NRARP 6001 3.881e-07 0.0078 0.709
DDN 1101 3.942e-07 0.008 0.63

Figure S1.  Get High-res Image As an example, this figure shows the association of KCNF1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 8.16e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 60.51 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 24
  MALE 55
     
  Significant markers N = 0
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 Mean (SD) 92.11 (14)
  Score N
  40 1
  90 9
  100 9
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

320 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.78 (0.98)
  N
  T1 47
  T2 3
  T3 28
  T4 1
     
  Significant markers N = 320
  pos. correlated 306
  neg. correlated 14
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
PAX6 0.7441 3.91e-15 7.91e-11
TRH 0.7168 1.097e-13 2.22e-09
ALX3 0.7107 2.192e-13 4.43e-09
TLX3 0.6993 7.603e-13 1.54e-08
OTP 0.695 1.198e-12 2.42e-08
EVX2 0.6759 8.24e-12 1.67e-07
NR5A2 0.6751 8.872e-12 1.79e-07
PHOX2B 0.6751 8.884e-12 1.8e-07
NKX2-6 0.6683 1.714e-11 3.47e-07
PPP1R16B 0.6671 1.906e-11 3.85e-07

Figure S2.  Get High-res Image As an example, this figure shows the association of PAX6 to 'PATHOLOGY.T'. P value = 3.91e-15 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Mean (SD) 0.52 (0.72)
  N
  N0 19
  N1 8
  N2 4
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S9.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
HMX3 0.7418 1.793e-06 0.0363

Figure S3.  Get High-res Image As an example, this figure shows the association of HMX3 to 'PATHOLOGY.N'. P value = 1.79e-06 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

136 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 45
  M1 4
  MX 27
     
  Significant markers N = 136
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSD3B2 8.765e-15 1.77e-10
LOC100132247 8.163e-12 1.65e-07
KRAS 2.545e-11 5.15e-07
ERCC2 2.943e-11 5.95e-07
PRR24 5.299e-11 1.07e-06
RNASE4 5.624e-11 1.14e-06
SPC24 6.758e-11 1.37e-06
ANG 7.035e-11 1.42e-06
SLC26A2 8.433e-11 1.71e-06
TMEM25 2.659e-10 5.38e-06

Figure S4.  Get High-res Image As an example, this figure shows the association of HSD3B2 to 'PATHOLOGICSPREAD(M)'. P value = 8.76e-15 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

384 genes related to 'TUMOR.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.91 (1.1)
  N
  Stage 1 44
  Stage 2 3
  Stage 3 21
  Stage 4 8
     
  Significant markers N = 384
  pos. correlated 379
  neg. correlated 5
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S13.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
PAX6 0.7403 2.147e-14 4.34e-10
GATA4 0.7224 1.752e-13 3.54e-09
NKX2-6 0.7016 1.668e-12 3.38e-08
TLX3 0.7003 1.908e-12 3.86e-08
OTP 0.6911 4.869e-12 9.85e-08
TRH 0.6894 5.752e-12 1.16e-07
FOXE1 0.68 1.427e-11 2.89e-07
NKX2-5 0.6785 1.649e-11 3.34e-07
NHLH2 0.6763 2.021e-11 4.09e-07
PRDM13 0.6761 2.056e-11 4.16e-07

Figure S5.  Get High-res Image As an example, this figure shows the association of PAX6 to 'TUMOR.STAGE'. P value = 2.15e-14 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 = 79

  • Number of genes = 20232

  • 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle