Correlation between gene methylation status and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C13R0S4W
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C13R0S4W
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 20222 genes and 15 clinical features across 368 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • KIAA1143 ,  KIF15 ,  C10ORF35 ,  SPRY1 ,  EML4 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SLC12A8 ,  MYH9 ,  SPTBN5 ,  LMF2__1 ,  PTGDR ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C ,  KIF4B ,  ATP5J ,  GABPA__1 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • PMS2L2 ,  STAG3L1__1 ,  DPY19L4 ,  TTC39C ,  RQCD1__1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • SRP9 ,  ITGB8 ,  EIF3J ,  ZDHHC17 ,  DHX36 ,  ...

  • 30 genes correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • SMARCAL1 ,  REXO2 ,  C18ORF22 ,  CINP ,  TECPR2 ,  ...

  • 30 genes correlated to 'RACE'.

    • SCAMP5 ,  NARS2 ,  GFPT1 ,  C12ORF10 ,  DSTYK ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', 'RESIDUAL_TUMOR', 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=8 younger N=22
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=28 lower stage N=2
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=30 lower score N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test N=30 higher year_of_tobacco_smoking_onset N=28 lower year_of_tobacco_smoking_onset N=2
RESIDUAL_TUMOR Kruskal-Wallis 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) 0-173.8 (median=21)
  censored N = 211
  death N = 156
     
  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) 67.6 (8.7)
  Significant markers N = 30
  pos. correlated 8
  neg. correlated 22
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
KIAA1143 0.3369 5.586e-11 5.65e-07
KIF15 0.3369 5.586e-11 5.65e-07
C10ORF35 0.2986 7.909e-09 5.33e-05
SPRY1 -0.2825 5.198e-08 0.000263
EML4 -0.2607 5.479e-07 0.00159
PON2 -0.2601 5.822e-07 0.00159
VGF 0.2579 7.288e-07 0.00159
ABCA17P 0.2572 7.799e-07 0.00159
ABCA3 0.2572 7.799e-07 0.00159
SELP -0.2572 7.839e-07 0.00159
Clinical variable #3: 'PATHOLOGIC_STAGE'

No gene related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 3
  STAGE IA 71
  STAGE IB 98
  STAGE II 3
  STAGE IIA 61
  STAGE IIB 70
  STAGE III 3
  STAGE IIIA 47
  STAGE IIIB 6
  STAGE IV 4
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 1.99 (0.74)
  N
  T1 90
  T2 206
  T3 59
  T4 13
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.43 (0.64)
  N
  N0 235
  N1 98
  N2 29
     
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

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

SpearmanCorr corrP Q
SLC12A8 0.2483 1.721e-06 0.0348
MYH9 0.2325 7.826e-06 0.0601
SPTBN5 0.2311 8.917e-06 0.0601
LMF2__1 0.2274 1.252e-05 0.0633
PTGDR 0.2166 3.242e-05 0.0748
MLL 0.2165 3.249e-05 0.0748
TAPBP 0.2161 3.382e-05 0.0748
ZBTB22 0.2161 3.382e-05 0.0748
MST1P2 0.2155 3.547e-05 0.0748
VPS36 0.2122 4.688e-05 0.0748
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 283
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 95
  MALE 273
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S10.  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__2 24759 8.389e-40 8.48e-36 0.9547
UTP14C 24759 8.389e-40 8.48e-36 0.9547
KIF4B 4301 2.898e-22 1.95e-18 0.8342
ATP5J 20012 3.077e-15 1.24e-11 0.7716
GABPA__1 20012 3.077e-15 1.24e-11 0.7716
DDX43 6724 2.733e-12 9.21e-09 0.7407
C6ORF108 6965 1.806e-11 5.22e-08 0.7314
RIMBP3 18018 1.559e-08 3.94e-05 0.6947
TMEM232 8081 4.468e-08 1e-04 0.6884
SEC61G 8257 1.333e-07 0.00027 0.6816
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

Table S11.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 291
  YES 39
     
  Significant markers N = 0
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 66.02 (39)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S13.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
PMS2L2 0.4623 7.331e-08 0.000741
STAG3L1__1 0.4623 7.331e-08 0.000741
DPY19L4 0.4537 1.366e-07 0.000821
TTC39C 0.4509 1.658e-07 0.000821
RQCD1__1 0.4413 3.231e-07 0.000821
USP37__1 0.4413 3.231e-07 0.000821
NDUFB9__1 0.4413 3.248e-07 0.000821
TATDN1__1 0.4413 3.248e-07 0.000821
OSBPL9 0.4387 3.864e-07 0.000868
PARS2 0.4361 4.593e-07 0.000929
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 12
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 5
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 350
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
SRP9 2.418e-05 0.178
ITGB8 6.46e-05 0.178
EIF3J 6.611e-05 0.178
ZDHHC17 8.102e-05 0.178
DHX36 8.158e-05 0.178
TOR3A 8.82e-05 0.178
RFWD2 8.864e-05 0.178
HCN3 0.0001002 0.178
MRRF 0.0001177 0.178
RBM18 0.0001177 0.178
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 52.3 (29)
  Significant markers N = 0
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

30 genes related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

Table S17.  Basic characteristics of clinical feature: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1961.18 (12)
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
List of top 10 genes differentially expressed by 'YEAR_OF_TOBACCO_SMOKING_ONSET'

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET' by Spearman correlation test

SpearmanCorr corrP Q
SMARCAL1 0.3491 3.367e-08 0.000482
REXO2 0.3455 4.763e-08 0.000482
C18ORF22 0.3369 1.061e-07 0.000542
CINP 0.3344 1.34e-07 0.000542
TECPR2 0.3344 1.34e-07 0.000542
GRWD1 0.3319 1.674e-07 0.000564
SEC23B 0.3243 3.325e-07 0.000659
C10ORF119 0.3231 3.689e-07 0.000659
COX16 0.322 4.061e-07 0.000659
HIGD2A 0.3213 4.317e-07 0.000659
Clinical variable #13: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

Table S19.  Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR Labels N
  R0 278
  R1 9
  R2 2
  RX 17
     
  Significant markers N = 0
Clinical variable #14: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 7
  BLACK OR AFRICAN AMERICAN 24
  WHITE 273
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SCAMP5 3.18e-08 0.000643
NARS2 1.053e-07 0.000775
GFPT1 1.15e-07 0.000775
C12ORF10 3.479e-07 0.00176
DSTYK 1.075e-06 0.00302
FAM119B 1.414e-06 0.00302
METTL1 1.414e-06 0.00302
PM20D1 1.475e-06 0.00302
MTR 1.66e-06 0.00302
FLJ39582__1 1.757e-06 0.00302
Clinical variable #15: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S22.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 238
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 368

  • Number of genes = 20222

  • Number of clinical features = 15

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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