Correlation between gene methylation status and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C828QQ
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features. The input file "LUSC-TP.meth.by_min_clin_corr.data.txt" is generated in the pipeline Methylation_Preprocess in stddata run.

Summary

Testing the association between 17190 genes and 15 clinical features across 370 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'.

    • KIF15 ,  C10ORF35 ,  SPRY1 ,  EML4 ,  PON2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SLC12A8 ,  MYH9 ,  SPTBN5 ,  LMF2 ,  MST1P2 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  ATP5J ,  DDX43 ,  C6ORF108 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • DPY19L4 ,  OSBPL9 ,  TTC39C ,  PARS2 ,  RQCD1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

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

  • 30 genes correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • SMARCAL1 ,  REXO2 ,  C18ORF22 ,  CINP ,  GRWD1 ,  ...

  • 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=5 younger N=25
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=29 lower year_of_tobacco_smoking_onset N=1
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=22)
  censored N = 210
  death N = 159
     
  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) 67.56 (8.7)
  Significant markers N = 30
  pos. correlated 5
  neg. correlated 25
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
KIF15 0.3417 2.678e-11 4.6e-07
C10ORF35 0.3034 4.194e-09 3.61e-05
SPRY1 -0.2877 2.73e-08 0.000156
EML4 -0.266 3.025e-07 0.00112
PON2 -0.2653 3.257e-07 0.00112
VGF 0.2632 4.056e-07 0.00116
ABCA17P 0.2614 4.919e-07 0.00121
SELP -0.26 5.663e-07 0.00122
HOXB5 -0.2565 8.106e-07 0.00155
MXRA8 -0.2553 9.16e-07 0.00157
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 60
  STAGE IIB 72
  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 207
  T3 60
  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 236
  N1 99
  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 0.2406 3.418e-06 0.0516
MYH9 0.2347 5.999e-06 0.0516
SPTBN5 0.2269 1.231e-05 0.0705
LMF2 0.2232 1.719e-05 0.0739
MST1P2 0.2174 2.866e-05 0.0773
MLL 0.2158 3.284e-05 0.0773
CTSB 0.214 3.841e-05 0.0773
ZBTB22 0.2116 4.703e-05 0.0773
RNF11 0.2109 4.984e-05 0.0773
PTGDR 0.2108 5.03e-05 0.0773
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 287
  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 96
  MALE 274
     
  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
UTP14C 25119 3.471e-40 5.97e-36 0.9549
KIF4B 4341 1.515e-22 1.3e-18 0.835
ATP5J 20337 1.627e-15 9.32e-12 0.7732
DDX43 6815 2.117e-12 9.1e-09 0.7409
C6ORF108 7181 3.575e-11 1.23e-07 0.727
RIMBP3 18127 3.464e-08 9.92e-05 0.6891
TMEM232 8212 4.316e-08 0.000106 0.6878
SEC61G 8315 8.176e-08 0.000176 0.6839
CRISP2 8620 5.035e-07 0.000962 0.6723
BMS1 8644 5.782e-07 0.000994 0.6714
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 302
  YES 41
     
  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) 65.95 (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
DPY19L4 0.4373 3.058e-07 0.00195
OSBPL9 0.4363 3.272e-07 0.00195
TTC39C 0.4355 3.465e-07 0.00195
PARS2 0.43 5.019e-07 0.00195
RQCD1 0.4255 6.819e-07 0.00195
PMS2L2 0.4255 6.823e-07 0.00195
PDCD6 0.423 8.01e-07 0.00197
ZNF420 0.414 1.443e-06 0.00288
NDUFB9 0.4133 1.508e-06 0.00288
TMEM143 0.4065 2.321e-06 0.00399
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) 352
     
  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 2.59e-05 0.169
EIF3J 6.659e-05 0.169
ITGB8 7.218e-05 0.169
DHX36 8.153e-05 0.169
ZDHHC17 8.19e-05 0.169
TOR3A 8.423e-05 0.169
RFWD2 8.732e-05 0.169
HCN3 9.648e-05 0.169
HS6ST1 0.0001155 0.169
MRRF 0.0001236 0.169
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) 52.21 (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 29
  neg. correlated 1
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 0.000409
REXO2 0.3455 4.763e-08 0.000409
C18ORF22 0.3369 1.061e-07 0.000576
CINP 0.3344 1.34e-07 0.000576
GRWD1 0.3319 1.674e-07 0.000576
SEC23B 0.3243 3.325e-07 0.000743
C10ORF119 0.3231 3.689e-07 0.000743
COX16 0.322 4.061e-07 0.000743
HIGD2A 0.3213 4.317e-07 0.000743
CCDC102B 0.3213 4.323e-07 0.000743
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 279
  R1 9
  R2 2
  RX 18
     
  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 274
     
  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 3.058e-08 0.000526
NARS2 1.039e-07 0.000628
GFPT1 1.096e-07 0.000628
C12ORF10 3.289e-07 0.00141
DSTYK 1.162e-06 0.00308
PM20D1 1.366e-06 0.00308
FAM119B 1.493e-06 0.00308
MTR 1.625e-06 0.00308
GLT8D1 1.868e-06 0.00308
THAP7 1.903e-06 0.00308
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 239
     
  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 = 370

  • Number of genes = 17190

  • 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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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)