Colon 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 17130 genes and 10 clinical features across 189 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 3 genes correlated to 'AGE'.

    • USP35 ,  GDNF ,  TAC1

  • 5 genes correlated to 'GENDER'.

    • KIF4B ,  GPX1 ,  POLDIP3 ,  PAFAH1B2 ,  RIMBP3

  • 3 genes correlated to 'PATHOLOGY.N'.

    • APOL1 ,  CASP1 ,  UBE2L6

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

    • FAM86B2 ,  ETV5 ,  DMKN ,  TMOD2 ,  KCNK4 ,  ...

  • 138 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • HSD17B11 ,  SUSD2 ,  SGSM3 ,  IDH3B ,  SEC16A ,  ...

  • 3 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • CASP1 ,  APOL1 ,  UBE2L6

  • No genes correlated to 'Time to Death', 'HISTOLOGICAL.TYPE', 'PATHOLOGY.T', 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=0        
AGE Spearman correlation test N=3 older N=3 younger N=0
GENDER t test N=5 male N=1 female N=4
HISTOLOGICAL TYPE t test   N=0        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test N=3 higher pN N=3 lower pN N=0
PATHOLOGICSPREAD(M) ANOVA test N=236        
TUMOR STAGE Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=138        
NUMBER OF LYMPH NODES Spearman correlation test N=3 higher number.of.lymph.nodes N=3 lower number.of.lymph.nodes N=0
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-129.1 (median=6.3)
  censored N = 148
  death N = 24
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 65.23 (14)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
USP35 0.3754 1.103e-07 0.00189
GDNF 0.3442 1.321e-06 0.0226
TAC1 0.3435 1.391e-06 0.0238

Figure S1.  Get High-res Image As an example, this figure shows the association of USP35 to 'AGE'. P value = 1.1e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 90
  MALE 99
     
  Significant markers N = 5
  Higher in MALE 1
  Higher in FEMALE 4
List of 5 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 5 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -9.84 1.173e-18 2.01e-14 0.8425
GPX1 -9.05 1.929e-16 3.3e-12 0.8345
POLDIP3 -7.58 1.849e-12 3.17e-08 0.7938
PAFAH1B2 -5.5 1.229e-07 0.0021 0.721
RIMBP3 5.43 1.957e-07 0.00335 0.7085

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 1.17e-18 with T-test analysis.

Clinical variable #4: 'HISTOLOGICAL.TYPE'

No gene related to 'HISTOLOGICAL.TYPE'.

Table S6.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 165
  COLON MUCINOUS ADENOCARCINOMA 24
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.9 (0.68)
  N
  T0 1
  T1 5
  T2 32
  T3 124
  T4 27
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.N'

3 genes related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Mean (SD) 0.53 (0.73)
  N
  N0 116
  N1 45
  N2 27
     
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S9.  Get Full Table List of 3 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
APOL1 0.3897 3.257e-08 0.000558
CASP1 0.3756 1.092e-07 0.00187
UBE2L6 0.3521 7.223e-07 0.0124

Figure S3.  Get High-res Image As an example, this figure shows the association of APOL1 to 'PATHOLOGY.N'. P value = 3.26e-08 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

236 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) Labels N
  M0 127
  M1 19
  M1A 5
  M1B 1
  MX 33
     
  Significant markers N = 236
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
FAM86B2 2.889e-155 4.95e-151
ETV5 6.553e-83 1.12e-78
DMKN 2.33e-56 3.99e-52
TMOD2 5.794e-55 9.92e-51
KCNK4 5.352e-54 9.17e-50
MTUS2 2.801e-44 4.8e-40
CLTCL1 6.361e-44 1.09e-39
PDSS2 1.485e-43 2.54e-39
TUBG2 8.325e-40 1.43e-35
TXNRD3IT1 8.372e-36 1.43e-31

Figure S4.  Get High-res Image As an example, this figure shows the association of FAM86B2 to 'PATHOLOGICSPREAD(M)'. P value = 2.89e-155 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

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

TUMOR.STAGE Mean (SD) 2.37 (0.93)
  N
  Stage 1 31
  Stage 2 76
  Stage 3 48
  Stage 4 25
     
  Significant markers N = 0
Clinical variable #9: 'COMPLETENESS.OF.RESECTION'

138 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S13.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 114
  R2 2
  RX 21
     
  Significant markers N = 138
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSD17B11 6.691e-20 1.15e-15
SUSD2 1.457e-19 2.5e-15
SGSM3 1.791e-19 3.07e-15
IDH3B 5.774e-19 9.89e-15
SEC16A 1.507e-18 2.58e-14
KIAA0415 3.8e-18 6.51e-14
FER 4.26e-18 7.3e-14
PFDN6 7.426e-18 1.27e-13
WDR46 7.426e-18 1.27e-13
FA2H 1.081e-17 1.85e-13

Figure S5.  Get High-res Image As an example, this figure shows the association of HSD17B11 to 'COMPLETENESS.OF.RESECTION'. P value = 6.69e-20 with ANOVA analysis.

Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

3 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S15.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2 (5)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S16.  Get Full Table List of 3 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
CASP1 0.3921 1.606e-07 0.00275
APOL1 0.3633 1.394e-06 0.0239
UBE2L6 0.3626 1.473e-06 0.0252

Figure S6.  Get High-res Image As an example, this figure shows the association of CASP1 to 'NUMBER.OF.LYMPH.NODES'. P value = 1.61e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 189

  • Number of genes = 17130

  • 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

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)
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