Correlation between gene methylation status and clinical features
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 17179 genes and 8 clinical features across 48 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • 2 genes correlated to 'PATHOLOGY.N'.

    • GTF2H1 ,  ELAVL1

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

    • C17ORF101 ,  ATXN7 ,  PRNP ,  SIX4 ,  KCNJ3 ,  ...

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

    • C1ORF59 ,  TNFRSF11B ,  CHID1 ,  TUBGCP5 ,  ALG12 ,  ...

  • No genes correlated to 'AGE', 'GENDER', 'PATHOLOGY.T', 'TUMOR.STAGE', and 'NUMBER.OF.LYMPH.NODES'.

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=0        
GENDER t test   N=0        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test N=2 higher pN N=2 lower pN N=0
PATHOLOGICSPREAD(M) ANOVA test N=28        
TUMOR STAGE Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=11        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 63 (12)
  Significant markers N = 0
Clinical variable #2: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 21
  MALE 27
     
  Significant markers N = 0
Clinical variable #3: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S3.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.83 (0.75)
  N
  T1 3
  T2 9
  T3 29
  T4 7
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.7 (0.84)
  N
  N0 25
  N1 10
  N2 11
     
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S5.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
GTF2H1 0.6395 1.72e-06 0.0295
ELAVL1 0.6318 2.489e-06 0.0428

Figure S1.  Get High-res Image As an example, this figure shows the association of GTF2H1 to 'PATHOLOGY.N'. P value = 1.72e-06 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGICSPREAD(M)'

28 genes related to 'PATHOLOGICSPREAD(M)'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 34
  M1 4
  M1A 1
  MX 9
     
  Significant markers N = 28
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
C17ORF101 6.707e-25 1.15e-20
ATXN7 9.753e-23 1.68e-18
PRNP 4.572e-19 7.85e-15
SIX4 8.941e-13 1.54e-08
KCNJ3 3.005e-12 5.16e-08
NUDT19 1.008e-11 1.73e-07
DEAF1 1.409e-11 2.42e-07
KLRC2 1.99e-11 3.42e-07
ZNF501 2.916e-11 5.01e-07
SLC25A22 1.816e-10 3.12e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of C17ORF101 to 'PATHOLOGICSPREAD(M)'. P value = 6.71e-25 with ANOVA analysis.

Clinical variable #6: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.41 (1)
  N
  Stage 1 10
  Stage 2 14
  Stage 3 15
  Stage 4 7
     
  Significant markers N = 0
Clinical variable #7: 'COMPLETENESS.OF.RESECTION'

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

Table S9.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 32
  R1 1
  RX 3
     
  Significant markers N = 11
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
C1ORF59 8.507e-19 1.46e-14
TNFRSF11B 8.3e-18 1.43e-13
CHID1 2.148e-15 3.69e-11
TUBGCP5 4.101e-13 7.04e-09
ALG12 2.399e-10 4.12e-06
NAPEPLD 4.106e-10 7.05e-06
SLC27A5 1.346e-09 2.31e-05
LOC100132707 5.726e-08 0.000983
GGCT 1.612e-07 0.00277
STIP1 7.426e-07 0.0128

Figure S3.  Get High-res Image As an example, this figure shows the association of C1ORF59 to 'COMPLETENESS.OF.RESECTION'. P value = 8.51e-19 with ANOVA analysis.

Clinical variable #8: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S11.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 3.36 (6.2)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 48

  • Number of genes = 17179

  • Number of clinical features = 8

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.

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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