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 19802 genes and 13 clinical features across 395 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ABR ,  CARS ,  EARS2 ,  UBFD1 ,  ELOVL2 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • EED ,  DYNC2LI1 ,  FAM114A2__1 ,  MFAP3__1 ,  TAT ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • RAB26 ,  FEM1B ,  SLC16A5 ,  FBXO33 ,  LRRC40 ,  ...

  • 1 gene correlated to 'PATHOLOGY_N_STAGE'.

    • SIN3B

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • FKBP14 ,  DNTTIP1 ,  OPN5 ,  JPH2 ,  USP1 ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  GPX1 ,  CHTF8 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • NEK8 ,  RTN2 ,  CCDC103 ,  EFTUD2 ,  WNT3 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • C3ORF26 ,  FILIP1L ,  FLOT1 ,  IER3 ,  C20ORF151 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • BRWD1 ,  CCDC144A ,  ST13 ,  XPNPEP3 ,  HIST1H1B ,  ...

  • 30 genes correlated to 'RACE'.

    • ZNF48 ,  CRYZ__1 ,  TYW3__1 ,  WDR6 ,  NBPF1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'NUMBER_OF_LYMPH_NODES', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=1 younger N=29
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=11 lower stage N=19
PATHOLOGY_N_STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.1-122.3 (median=14)
  censored N = 245
  death N = 149
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 65.21 (11)
  Significant markers N = 30
  pos. correlated 1
  neg. correlated 29
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
ABR -0.2864 1.009e-08 0.000149
CARS -0.2832 1.5e-08 0.000149
EARS2 -0.2615 1.859e-07 0.000465
UBFD1 -0.2615 1.859e-07 0.000465
ELOVL2 0.2614 1.879e-07 0.000465
KIAA1217 -0.2609 1.998e-07 0.000465
SLC12A7 -0.2594 2.347e-07 0.000465
C2ORF27A -0.2594 2.364e-07 0.000465
CHST9 -0.2597 2.451e-07 0.000465
CGB1 -0.2586 2.582e-07 0.000465
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 3
  STAGE IA 14
  STAGE IB 35
  STAGE II 29
  STAGE IIA 41
  STAGE IIB 56
  STAGE III 3
  STAGE IIIA 77
  STAGE IIIB 63
  STAGE IIIC 38
  STAGE IV 35
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
EED 1.465e-08 0.000121
DYNC2LI1 1.596e-08 0.000121
FAM114A2__1 2.448e-08 0.000121
MFAP3__1 2.448e-08 0.000121
TAT 5.5e-08 0.000218
CCDC93 1.742e-07 0.000493
MRPS36 1.742e-07 0.000493
CCDC109B 2.242e-07 0.000555
MGEA5 2.858e-07 0.000602
MIR197 4.642e-07 0.000602
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.97 (0.83)
  N
  T1 21
  T2 78
  T3 186
  T4 110
     
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
RAB26 0.2933 2.801e-09 5.55e-05
FEM1B -0.2834 9.919e-09 7.06e-05
SLC16A5 0.2828 1.07e-08 7.06e-05
FBXO33 -0.277 2.179e-08 9.06e-05
LRRC40 -0.2747 2.864e-08 9.06e-05
SFRS11 -0.2747 2.864e-08 9.06e-05
CEP152 -0.2738 3.202e-08 9.06e-05
LTA4H -0.2639 1.016e-07 0.000252
RPL24 -0.2627 1.177e-07 0.000259
SRP14 -0.2606 1.493e-07 0.000281
Clinical variable #5: 'PATHOLOGY_N_STAGE'

One gene related to 'PATHOLOGY_N_STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Mean (SD) 1.31 (1.1)
  N
  N0 124
  N1 102
  N2 80
  N3 83
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
SIN3B -0.2269 6.161e-06 0.122
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 353
  class1 23
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
FKBP14 6311 8.311e-06 0.123 0.7773
DNTTIP1 6161 3.182e-05 0.123 0.7588
OPN5 1985 4.016e-05 0.123 0.7555
JPH2 2010 4.97e-05 0.123 0.7524
USP1 6109 4.97e-05 0.123 0.7524
CCDC90B 6100 5.363e-05 0.123 0.7513
LATS1 6076 6.56e-05 0.123 0.7484
PNPT1 6058 7.619e-05 0.123 0.7462
NAPB 6053 7.941e-05 0.123 0.7455
ZMYM6 6048 8.276e-05 0.123 0.7449
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

Table S12.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 136
  MALE 259
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
ALG11__1 33217 1.782e-47 1.76e-43 0.943
UTP14C 33217 1.782e-47 1.76e-43 0.943
KIF4B 5748 3.67e-28 2.42e-24 0.8368
GPX1 7342 1.648e-21 8.16e-18 0.7916
CHTF8 27072 1.726e-18 5.7e-15 0.7686
HAS3 27072 1.726e-18 5.7e-15 0.7686
FRG1B 9242 8.302e-15 2.35e-11 0.7376
RIMBP3 23629 2.399e-08 5.94e-05 0.6708
RWDD2B 11620 2.741e-08 6.03e-05 0.6701
DDX43 12519 2.319e-06 0.00459 0.6446
Clinical variable #8: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

Table S14.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 300
  YES 73
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
NEK8 6685 2.851e-07 0.0031 0.6937
RTN2 6787 4.696e-07 0.0031 0.6901
CCDC103 6833 6.272e-07 0.0031 0.688
EFTUD2 6833 6.272e-07 0.0031 0.688
WNT3 6960 1.373e-06 0.00524 0.6822
RNF24 6984 1.588e-06 0.00524 0.6811
KRT13 7054 2.415e-06 0.00683 0.6779
AURKB 7139 3.983e-06 0.00809 0.674
NCF1B 7143 4.077e-06 0.00809 0.6738
KCNG3 7152 4.296e-06 0.00809 0.6734
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S16.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  STOMACH ADENOCARCINOMA DIFFUSE TYPE 67
  STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) 134
  STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) 74
  STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE 78
  STOMACH ADENOCARCINOMA SIGNET RING TYPE 13
  STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE 20
  STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE 8
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
C3ORF26 6.094e-13 4.75e-09
FILIP1L 6.094e-13 4.75e-09
FLOT1 9.602e-13 4.75e-09
IER3 9.602e-13 4.75e-09
C20ORF151 1.072e-11 4.25e-08
KRT23 2.28e-11 6.91e-08
DTX4 2.444e-11 6.91e-08
LCT 3.496e-11 7.48e-08
SHBG__1 3.838e-11 7.48e-08
GPR39 4.266e-11 7.48e-08
Clinical variable #10: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

Table S18.  Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR Labels N
  R0 335
  R1 17
  R2 12
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
BRWD1 2.429e-05 0.207
CCDC144A 8.665e-05 0.207
ST13 0.0001013 0.207
XPNPEP3 0.0001013 0.207
HIST1H1B 0.0001795 0.207
SCP2 0.000189 0.207
FICD 0.0002211 0.207
PPP3R1 0.0002377 0.207
CCDC144NL 0.0002385 0.207
LOC151658 0.000265 0.207
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

Table S20.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 5.6 (8.4)
  Significant markers N = 0
Clinical variable #12: 'RACE'

30 genes related to 'RACE'.

Table S21.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 89
  BLACK OR AFRICAN AMERICAN 13
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 1
  WHITE 253
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S22.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
ZNF48 3.757e-18 7.44e-14
CRYZ__1 1.486e-16 9.81e-13
TYW3__1 1.486e-16 9.81e-13
WDR6 6.461e-15 3.2e-11
NBPF1 8.674e-15 3.21e-11
LOC100271836 9.719e-15 3.21e-11
LOC100131801 1.621e-14 4.01e-11
XAB2 1.621e-14 4.01e-11
NCF1B 1.922e-14 4.23e-11
PLEKHH3 1.88e-13 3.54e-10
Clinical variable #13: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S23.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 293
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = STAD-TP.meth.by_min_clin_corr.data.txt

  • Clinical data file = STAD-TP.merged_data.txt

  • Number of patients = 395

  • Number of genes = 19802

  • Number of clinical features = 13

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] 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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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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