Thyroid Adenocarcinoma: 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 17057 genes and 5 clinical features across 114 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 23 genes correlated to 'AGE'.

    • INA ,  GPR37 ,  ANKRD43 ,  ABCC6P2 ,  GNPNAT1 ,  ...

  • 4 genes correlated to 'GENDER'.

    • KIF4B ,  ACSM1 ,  WBP11P1 ,  METTL1

  • 642 genes correlated to 'HISTOLOGICAL.TYPE'.

    • KLK2 ,  KCNJ1 ,  FITM1 ,  PON2 ,  KIAA1217 ,  ...

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

    • MAP3K3 ,  STX17 ,  KIF15 ,  TAF4B ,  PLEKHF2 ,  ...

  • 307 genes correlated to 'NEOADJUVANT.THERAPY'.

    • PTGS2 ,  MAGEL2 ,  PLSCR1 ,  MTMR15 ,  CYP17A1 ,  ...

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
AGE Spearman correlation test N=23 older N=23 younger N=0
GENDER t test N=4 male N=2 female N=2
HISTOLOGICAL TYPE ANOVA test N=642        
RADIATIONS RADIATION REGIMENINDICATION t test N=13 yes N=8 no N=5
NEOADJUVANT THERAPY t test N=307 yes N=166 no N=141
Clinical variable #1: 'AGE'

23 genes related to 'AGE'.

Table S1.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 47.58 (16)
  Significant markers N = 23
  pos. correlated 23
  neg. correlated 0
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
INA 0.528 1.574e-09 2.69e-05
GPR37 0.5099 6.831e-09 0.000117
ANKRD43 0.4879 3.675e-08 0.000627
ABCC6P2 0.4871 3.883e-08 0.000662
GNPNAT1 0.4765 8.39e-08 0.00143
GPC5 0.4696 1.357e-07 0.00231
SYNGR3 0.4655 1.798e-07 0.00307
CBLN1 0.4635 2.064e-07 0.00352
CDC5L 0.4628 2.162e-07 0.00369
ZNF518B 0.458 2.996e-07 0.00511

Figure S1.  Get High-res Image As an example, this figure shows the association of INA to 'AGE'. P value = 1.57e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 82
  MALE 32
     
  Significant markers N = 4
  Higher in MALE 2
  Higher in FEMALE 2
List of 4 genes differentially expressed by 'GENDER'

Table S4.  Get Full Table List of 4 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -8.53 7.774e-12 1.33e-07 0.9131
ACSM1 -6.87 5.441e-09 9.28e-05 0.8483
WBP11P1 5.71 5.446e-07 0.00929 0.846
METTL1 5.45 1.984e-06 0.0338 0.8422

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 7.77e-12 with T-test analysis.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

642 genes related to 'HISTOLOGICAL.TYPE'.

Table S5.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  OTHER 3
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 65
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 30
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 16
     
  Significant markers N = 642
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
KLK2 7.521e-19 1.28e-14
KCNJ1 2.391e-18 4.08e-14
FITM1 4.817e-16 8.22e-12
PON2 1.25e-15 2.13e-11
KIAA1217 1.282e-15 2.19e-11
EMP1 1.001e-14 1.71e-10
LEPROT 1.314e-14 2.24e-10
RHOA 1.365e-14 2.33e-10
RELL1 2.409e-14 4.11e-10
C5ORF62 5.428e-14 9.25e-10

Figure S3.  Get High-res Image As an example, this figure shows the association of KLK2 to 'HISTOLOGICAL.TYPE'. P value = 7.52e-19 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 11
  YES 103
     
  Significant markers N = 13
  Higher in YES 8
  Higher in NO 5
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
MAP3K3 -8.07 5.979e-11 1.02e-06 0.835
STX17 6.29 6.476e-09 0.00011 0.6858
KIF15 6.16 1.171e-08 2e-04 0.7643
TAF4B 6.13 4.489e-08 0.000766 0.7732
PLEKHF2 -6.22 7.922e-08 0.00135 0.7899
STEAP1 5.42 3.812e-07 0.0065 0.7432
FAT4 5.52 4.328e-07 0.00738 0.7043
AARS2 5.32 5.77e-07 0.00984 0.7361
CDC42 -5.57 6.629e-07 0.0113 0.7546
BMP8B 5.24 7.564e-07 0.0129 0.7061

Figure S4.  Get High-res Image As an example, this figure shows the association of MAP3K3 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 5.98e-11 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

307 genes related to 'NEOADJUVANT.THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 3
  YES 111
     
  Significant markers N = 307
  Higher in YES 166
  Higher in NO 141
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
PTGS2 -16.49 1.352e-29 2.31e-25 0.961
MAGEL2 17.09 6.205e-28 1.06e-23 0.982
PLSCR1 -13.74 1.027e-25 1.75e-21 0.997
MTMR15 12.78 1.34e-23 2.29e-19 0.9309
CYP17A1 12.33 5.144e-21 8.77e-17 0.973
ZNF605 11.48 1.238e-20 2.11e-16 0.976
ABAT 11.57 2.001e-20 3.41e-16 0.955
CCDC86 -11.07 1.321e-19 2.25e-15 0.8709
CCDC159 11.21 2.098e-19 3.58e-15 0.9279
MLX 11.45 2.319e-19 3.95e-15 0.9249

Figure S5.  Get High-res Image As an example, this figure shows the association of PTGS2 to 'NEOADJUVANT.THERAPY'. P value = 1.35e-29 with T-test analysis.

Methods & Data
Input

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

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

  • Number of patients = 114

  • Number of genes = 17057

  • Number of clinical features = 5

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] 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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