PANCANCER subset with 8 initial disease types: 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 15549 genes and 10 clinical features across 237 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 26 genes correlated to 'Time to Death'.

    • LGALS4 ,  TEX101 ,  ALG3 ,  TIPRL ,  VIL1 ,  ...

  • 5 genes correlated to 'AGE'.

    • KIF15 ,  BOC ,  FAM123C ,  TSPYL5 ,  C7ORF13

  • 4593 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • SYNGR1 ,  INHA ,  ODZ3 ,  MEIS3 ,  VAT1 ,  ...

  • 324 genes correlated to 'GENDER'.

    • CDKL2 ,  ZNF295 ,  UTP14C ,  ATP13A2 ,  GLIPR1L1 ,  ...

  • 1381 genes correlated to 'HISTOLOGICAL.TYPE'.

    • ZNF419 ,  TMEM64 ,  MED12L ,  RHBDL1 ,  OPRK1 ,  ...

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

    • DEPDC4 ,  SLC39A5 ,  SMAD4 ,  BNIP2 ,  PIP4K2C ,  ...

  • 111 genes correlated to 'NEOADJUVANT.THERAPY'.

    • ZNF780B ,  SMAD4 ,  CATSPER2 ,  C18ORF8 ,  SMARCAL1 ,  ...

  • No genes correlated to 'PATHOLOGY.T', 'PATHOLOGY.N', 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=26 shorter survival N=2 longer survival N=24
AGE Spearman correlation test N=5 older N=5 younger N=0
PRIMARY SITE OF DISEASE ANOVA test N=4593        
GENDER t test N=324 male N=68 female N=256
HISTOLOGICAL TYPE ANOVA test N=1381        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=17 yes N=3 no N=14
NEOADJUVANT THERAPY t test N=111 yes N=17 no N=94
Clinical variable #1: 'Time to Death'

26 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-223.4 (median=28.5)
  censored N = 193
  death N = 40
     
  Significant markers N = 26
  associated with shorter survival 2
  associated with longer survival 24
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
LGALS4 0 6.493e-09 1e-04 0.268
TEX101 0 8.686e-08 0.0014 0.318
ALG3 0 9.539e-08 0.0015 0.263
TIPRL 0 1.016e-07 0.0016 0.277
VIL1 0.01 1.098e-07 0.0017 0.336
APOC2 0 1.513e-07 0.0024 0.34
PPY2 0 1.604e-07 0.0025 0.342
REXO4 0 2.93e-07 0.0046 0.301
PTGDS 0 4.715e-07 0.0073 0.342
PTP4A3 0 4.824e-07 0.0075 0.339

Figure S1.  Get High-res Image As an example, this figure shows the association of LGALS4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.49e-09 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

5 genes related to 'AGE'.

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

AGE Mean (SD) 56.53 (13)
  Significant markers N = 5
  pos. correlated 5
  neg. correlated 0
List of 5 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 5 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
KIF15 0.3243 3.71e-07 0.00577
BOC 0.3172 6.833e-07 0.0106
FAM123C 0.3136 9.301e-07 0.0145
TSPYL5 0.3065 1.675e-06 0.026
C7ORF13 0.3018 2.45e-06 0.0381

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF15 to 'AGE'. P value = 3.71e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

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

4593 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  BREAST 214
  KIDNEY 2
  LUNG 20
  RECTUM 1
     
  Significant markers N = 4593
List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
SYNGR1 9.485e-200 1.47e-195
INHA 2.099e-122 3.26e-118
ODZ3 1.129e-114 1.76e-110
MEIS3 1.335e-110 2.07e-106
VAT1 1.311e-99 2.04e-95
BBS10 4.767e-89 7.41e-85
MELK 4.921e-87 7.65e-83
DEPDC1B 5.105e-83 7.93e-79
CRH 3.584e-76 5.57e-72
MED12L 2.022e-75 3.14e-71

Figure S3.  Get High-res Image As an example, this figure shows the association of SYNGR1 to 'PRIMARY.SITE.OF.DISEASE'. P value = 9.49e-200 with ANOVA analysis.

Clinical variable #4: 'GENDER'

324 genes related to 'GENDER'.

Table S7.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 221
  MALE 16
     
  Significant markers N = 324
  Higher in MALE 68
  Higher in FEMALE 256
List of top 10 genes differentially expressed by 'GENDER'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
CDKL2 -16.21 3.922e-40 6.1e-36 0.8702
ZNF295 -13.28 4.152e-28 6.46e-24 0.8773
UTP14C 18.03 2.962e-26 4.61e-22 0.9426
ATP13A2 11.27 2.694e-23 4.19e-19 0.8422
GLIPR1L1 -10.39 5.823e-21 9.05e-17 0.8773
TBC1D24 11.55 6.496e-20 1.01e-15 0.8708
LRIG3 -9.78 3.938e-19 6.12e-15 0.8224
IRF9 11.2 5.169e-19 8.03e-15 0.8119
TMED3 10.39 2.068e-18 3.21e-14 0.7859
TFAP2C 9.15 3.402e-17 5.29e-13 0.7681

Figure S4.  Get High-res Image As an example, this figure shows the association of CDKL2 to 'GENDER'. P value = 3.92e-40 with T-test analysis.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

1381 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  KIDNEY CLEAR CELL RENAL CARCINOMA 2
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 2
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 18
  RECTAL ADENOCARCINOMA 1
     
  Significant markers N = 1381
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
ZNF419 5.29e-40 8.23e-36
TMEM64 1.105e-36 1.72e-32
MED12L 5.295e-36 8.23e-32
RHBDL1 6.429e-33 9.99e-29
OPRK1 1.493e-32 2.32e-28
DFNA5 6.526e-32 1.01e-27
NRCAM 1.616e-31 2.51e-27
KCNC4 1.533e-30 2.38e-26
CAV1 1.619e-28 2.52e-24
SELV 1.378e-27 2.14e-23

Figure S5.  Get High-res Image As an example, this figure shows the association of ZNF419 to 'HISTOLOGICAL.TYPE'. P value = 5.29e-40 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.91 (0.85)
  N
  T1 7
  T2 13
  T3 1
  T4 2
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.48 (0.68)
  N
  N0 13
  N1 6
  N2 2
     
  Significant markers N = 0
Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.74 (0.86)
  N
  Stage 1 11
  Stage 2 8
  Stage 3 3
  Stage 4 1
     
  Significant markers N = 0
Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 79
  YES 158
     
  Significant markers N = 17
  Higher in YES 3
  Higher in NO 14
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
DEPDC4 -5.87 2.496e-08 0.000388 0.7246
SLC39A5 -5.43 1.511e-07 0.00235 0.6914
SMAD4 -5.38 2.647e-07 0.00411 0.7003
BNIP2 -5.3 3.51e-07 0.00546 0.6962
PIP4K2C -5.23 4.965e-07 0.00772 0.7125
ITIH1 -5.12 6.272e-07 0.00975 0.6636
C18ORF8 -5.19 6.969e-07 0.0108 0.7085
CDC27 -5.02 1.353e-06 0.021 0.6939
MMAA -5.05 1.616e-06 0.0251 0.7017
ZNF235 4.98 1.782e-06 0.0277 0.674

Figure S6.  Get High-res Image As an example, this figure shows the association of DEPDC4 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.5e-08 with T-test analysis.

Clinical variable #10: 'NEOADJUVANT.THERAPY'

111 genes related to 'NEOADJUVANT.THERAPY'.

Table S16.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 109
  YES 128
     
  Significant markers N = 111
  Higher in YES 17
  Higher in NO 94
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
ZNF780B -6.66 2.597e-10 4.04e-06 0.74
SMAD4 -6.55 3.69e-10 5.74e-06 0.7318
CATSPER2 -6.3 1.494e-09 2.32e-05 0.7232
C18ORF8 -6.13 4.051e-09 6.3e-05 0.7302
SMARCAL1 6.07 5.117e-09 7.95e-05 0.7144
C14ORF106 6.08 5.279e-09 8.21e-05 0.717
NIPBL 6.05 6.237e-09 9.69e-05 0.7181
PLA2G1B -6.09 6.409e-09 9.96e-05 0.7402
MRPL50 -6.04 7.454e-09 0.000116 0.7177
RNF113B -5.93 1.076e-08 0.000167 0.7126

Figure S7.  Get High-res Image As an example, this figure shows the association of ZNF780B to 'NEOADJUVANT.THERAPY'. P value = 2.6e-10 with T-test analysis.

Methods & Data
Input

  • Expresson data file = PANCAN8.meth.for_correlation.filtered_data.txt

  • Clinical data file = PANCAN8.clin.merged.picked.txt

  • Number of patients = 237

  • Number of genes = 15549

  • Number of clinical features = 10

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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