Thyroid Adenocarcinoma: Correlation between gene methylation status and clinical features
(primary solid tumor cohort)
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 17081 genes and 5 clinical features across 195 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 80 genes correlated to 'AGE'.

    • C7ORF13 ,  C1ORF59 ,  DLK2 ,  INA ,  ZNF274 ,  ...

  • 8 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  METTL1 ,  FAM35A ,  WBP11P1 ,  ...

  • 1280 genes correlated to 'HISTOLOGICAL.TYPE'.

    • EMP1 ,  PON2 ,  LY6G6C ,  CLCF1 ,  LOC100126784 ,  ...

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

    • KIF15 ,  STX17 ,  MAP3K3 ,  SIK1 ,  RPL32 ,  ...

  • 56 genes correlated to 'RADIATIONEXPOSURE'.

    • ATL2 ,  ALKBH2 ,  C1ORF96 ,  WDR5B ,  ATP5L2 ,  ...

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=80 older N=80 younger N=0
GENDER t test N=8 male N=5 female N=3
HISTOLOGICAL TYPE ANOVA test N=1280        
RADIATIONS RADIATION REGIMENINDICATION t test N=29 yes N=16 no N=13
RADIATIONEXPOSURE t test N=56 yes N=28 no N=28
Clinical variable #1: 'AGE'

80 genes related to 'AGE'.

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

AGE Mean (SD) 46.55 (16)
  Significant markers N = 80
  pos. correlated 80
  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
C7ORF13 0.5246 3.501e-15 5.98e-11
C1ORF59 0.5215 5.38e-15 9.19e-11
DLK2 0.5124 1.907e-14 3.26e-10
INA 0.5114 2.185e-14 3.73e-10
ZNF274 0.4969 1.482e-13 2.53e-09
ANKRD43 0.4967 1.527e-13 2.61e-09
ZNF518B 0.4885 4.341e-13 7.41e-09
SYNGR3 0.4718 3.359e-12 5.74e-08
GNPNAT1 0.471 3.691e-12 6.3e-08
NHLRC1 0.468 5.254e-12 8.97e-08

Figure S1.  Get High-res Image As an example, this figure shows the association of C7ORF13 to 'AGE'. P value = 3.5e-15 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 146
  MALE 49
     
  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 26.79 9.062e-63 1.55e-58 0.999
KIF4B -12.9 7.244e-23 1.24e-18 0.9405
METTL1 8.17 7.481e-12 1.28e-07 0.8707
FAM35A -6.44 1.438e-09 2.46e-05 0.812
WBP11P1 6.56 7.008e-09 0.00012 0.8156
ANKRD20A4 6.1 2.664e-08 0.000455 0.7643
CCDC121 5.84 7.92e-08 0.00135 0.7386
C14ORF33 -5.08 1.893e-06 0.0323 0.7025

Figure S2.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 9.06e-63 with T-test analysis.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

1280 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 113
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 55
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 20
     
  Significant markers N = 1280
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
EMP1 4.828e-26 8.25e-22
PON2 4.85e-25 8.28e-21
LY6G6C 1.263e-24 2.16e-20
CLCF1 1.28e-24 2.19e-20
LOC100126784 3.598e-24 6.14e-20
LAMP3 6.362e-24 1.09e-19
LEPR 9.36e-24 1.6e-19
LEPROT 9.36e-24 1.6e-19
RELL1 1.845e-23 3.15e-19
ZNRF2 3.055e-23 5.21e-19

Figure S3.  Get High-res Image As an example, this figure shows the association of EMP1 to 'HISTOLOGICAL.TYPE'. P value = 4.83e-26 with ANOVA analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 12
  YES 183
     
  Significant markers N = 29
  Higher in YES 16
  Higher in NO 13
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
KIF15 8 1.137e-13 1.94e-09 0.8019
STX17 7.92 2.779e-13 4.75e-09 0.6685
MAP3K3 -9.73 4.039e-13 6.9e-09 0.8547
SIK1 6.61 3.609e-10 6.16e-06 0.7117
RPL32 6.42 5.726e-09 9.78e-05 0.7532
TAF7 6.06 9.459e-09 0.000162 0.698
POMP -6.31 1.076e-08 0.000184 0.8338
MYOM2 6 1.654e-08 0.000282 0.5073
GPR120 6.11 1.994e-08 0.00034 0.6612
TXNIP -6.24 4.907e-08 0.000838 0.7523

Figure S4.  Get High-res Image As an example, this figure shows the association of KIF15 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.14e-13 with T-test analysis.

Clinical variable #5: 'RADIATIONEXPOSURE'

56 genes related to 'RADIATIONEXPOSURE'.

Table S9.  Basic characteristics of clinical feature: 'RADIATIONEXPOSURE'

RADIATIONEXPOSURE Labels N
  NO 160
  YES 8
     
  Significant markers N = 56
  Higher in YES 28
  Higher in NO 28
List of top 10 genes differentially expressed by 'RADIATIONEXPOSURE'

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

T(pos if higher in 'YES') ttestP Q AUC
ATL2 8.35 9.978e-13 1.7e-08 0.7766
ALKBH2 -7.52 3.447e-12 5.89e-08 0.85
C1ORF96 8.01 7.181e-12 1.23e-07 0.7039
WDR5B -7.17 4.638e-11 7.92e-07 0.7773
ATP5L2 6.64 4.411e-10 7.53e-06 0.7469
ATP5I -7.83 5.168e-10 8.83e-06 0.7742
DHX57 6.65 5.975e-10 1.02e-05 0.682
LOC100132707 -6.58 8.041e-10 1.37e-05 0.6961
NKX2-2 -6.61 1.612e-09 2.75e-05 0.7805
LRRC20 -6.54 2.626e-09 4.48e-05 0.7695

Figure S5.  Get High-res Image As an example, this figure shows the association of ATL2 to 'RADIATIONEXPOSURE'. P value = 9.98e-13 with T-test analysis.

Methods & Data
Input

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

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

  • Number of patients = 195

  • Number of genes = 17081

  • 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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