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 17042 genes and 5 clinical features across 170 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 46 genes correlated to 'AGE'.

    • KIAA1143 ,  C1ORF59 ,  INA ,  DLK2 ,  CBLN1 ,  ...

  • 8 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  METTL1 ,  ANKRD20A4 ,  WBP11P1 ,  ...

  • 1117 genes correlated to 'HISTOLOGICAL.TYPE'.

    • LY6G6C ,  PON2 ,  EMP1 ,  CLCF1 ,  LEPR ,  ...

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

    • MAP3K3 ,  STX17 ,  KIF15 ,  PLEKHF2 ,  AHRR ,  ...

  • 316 genes correlated to 'NEOADJUVANT.THERAPY'.

    • PSMD1 ,  FBXO40 ,  MTMR15 ,  RBM39 ,  SLC7A6 ,  ...

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=46 older N=46 younger N=0
GENDER t test N=8 male N=5 female N=3
HISTOLOGICAL TYPE ANOVA test N=1117        
RADIATIONS RADIATION REGIMENINDICATION t test N=24 yes N=12 no N=12
NEOADJUVANT THERAPY t test N=316 yes N=168 no N=148
Clinical variable #1: 'AGE'

46 genes related to 'AGE'.

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

AGE Mean (SD) 47.09 (16)
  Significant markers N = 46
  pos. correlated 46
  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
KIAA1143 0.5207 3.377e-13 5.76e-09
C1ORF59 0.5124 9.101e-13 1.55e-08
INA 0.489 1.322e-11 2.25e-07
DLK2 0.4886 1.384e-11 2.36e-07
CBLN1 0.4872 1.607e-11 2.74e-07
C7ORF13 0.4803 3.39e-11 5.78e-07
ZNF518B 0.4795 3.717e-11 6.33e-07
ZNF274 0.4746 6.246e-11 1.06e-06
ANKRD43 0.4677 1.272e-10 2.17e-06
SYNGR3 0.452 6.112e-10 1.04e-05

Figure S1.  Get High-res Image As an example, this figure shows the association of KIAA1143 to 'AGE'. P value = 3.38e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'GENDER'

8 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 124
  MALE 46
     
  Significant markers N = 8
  Higher in MALE 5
  Higher in FEMALE 3
List of 8 genes differentially expressed by 'GENDER'

Table S4.  Get Full Table List of 8 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
UTP14C 24.59 4.563e-53 7.78e-49 0.9988
KIF4B -11.73 3.222e-20 5.49e-16 0.9313
METTL1 7.68 7.481e-11 1.27e-06 0.8671
ANKRD20A4 6.02 4.244e-08 0.000723 0.7702
WBP11P1 6 8.434e-08 0.00144 0.8124
CCDC121 5.8 1.024e-07 0.00175 0.7437
FAM35A -5.58 1.063e-07 0.00181 0.8029
RAB12 -4.98 2.279e-06 0.0388 0.6974

Figure S2.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 4.56e-53 with T-test analysis.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

1117 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  OTHER 7
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 92
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 51
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 20
     
  Significant markers N = 1117
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
LY6G6C 3.542e-22 6.04e-18
PON2 4.82e-22 8.21e-18
EMP1 8.902e-22 1.52e-17
CLCF1 1.905e-21 3.25e-17
LEPR 3.496e-21 5.96e-17
LEPROT 3.496e-21 5.96e-17
LOC100126784 6.189e-21 1.05e-16
LAMP3 1.918e-20 3.27e-16
ZNRF2 7.387e-20 1.26e-15
CLEC16A 9.221e-20 1.57e-15

Figure S3.  Get High-res Image As an example, this figure shows the association of LY6G6C to 'HISTOLOGICAL.TYPE'. P value = 3.54e-22 with ANOVA analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 12
  YES 158
     
  Significant markers N = 24
  Higher in YES 12
  Higher in NO 12
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 -9.28 6.397e-13 1.09e-08 0.8486
STX17 7.6 2.236e-12 3.81e-08 0.6619
KIF15 6.97 6.774e-11 1.15e-06 0.7853
PLEKHF2 -7.75 5.988e-10 1.02e-05 0.8291
AHRR -6.56 6.53e-10 1.11e-05 0.6487
KIAA1143 6.66 1.863e-09 3.17e-05 0.7231
SIK1 6.22 3.822e-09 6.51e-05 0.6999
FAM180B -6.82 6.579e-09 0.000112 0.759
GPR120 6.1 1.475e-08 0.000251 0.6524
NDUFB8 5.81 4.191e-08 0.000714 0.7083

Figure S4.  Get High-res Image As an example, this figure shows the association of MAP3K3 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 6.4e-13 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

316 genes related to 'NEOADJUVANT.THERAPY'.

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

NEOADJUVANT.THERAPY Labels N
  NO 3
  YES 167
     
  Significant markers N = 316
  Higher in YES 168
  Higher in NO 148
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
PSMD1 -18.75 5.287e-43 9.01e-39 0.9102
FBXO40 -16.49 1.101e-36 1.88e-32 0.8363
MTMR15 16.29 2.322e-36 3.96e-32 0.9361
RBM39 -17.85 5.534e-36 9.43e-32 0.9182
SLC7A6 -16.13 1.709e-33 2.91e-29 0.9122
CCDC86 -13.94 8.217e-29 1.4e-24 0.8862
TSSC1 17.22 8.788e-29 1.5e-24 0.9681
MUC15 13.49 1.939e-28 3.3e-24 0.9222
CCDC159 12.78 6.488e-24 1.11e-19 0.9242
TMEM205 12.78 6.488e-24 1.11e-19 0.9242

Figure S5.  Get High-res Image As an example, this figure shows the association of PSMD1 to 'NEOADJUVANT.THERAPY'. P value = 5.29e-43 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 = 170

  • Number of genes = 17042

  • 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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