Correlation between gene methylation status and clinical features
Sarcoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1WQ033Z
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/C1WQ033Z
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 19856 genes and 9 clinical features across 260 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'.

    • RANBP17 ,  PLEKHM3 ,  TSC22D1 ,  TMEM63A ,  SLC29A1 ,  ...

  • 30 genes correlated to 'TUMOR_TISSUE_SITE'.

    • HOXA10 ,  GATA4 ,  MAB21L1 ,  MIR548F5 ,  NBEA ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  FAM35A ,  GLUD1 ,  KIF4B ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • TAGLN2 ,  BCMO1 ,  HCRTR1 ,  LBX2__1 ,  LOC151534__1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • TPM2 ,  PRKAG2 ,  MYLK ,  ACTN1 ,  LIG3 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • ISL1 ,  ST6GALNAC2 ,  NICN1 ,  GRID2 ,  BCL11A ,  ...

  • 1 gene correlated to 'RACE'.

    • ISCA1

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 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=15 younger N=15
TUMOR_TISSUE_SITE Kruskal-Wallis test N=30        
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        
RACE Kruskal-Wallis test N=1        
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) 0.5-188.2 (median=26.4)
  censored N = 164
  death N = 95
     
  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) 60.98 (15)
  Significant markers N = 30
  pos. correlated 15
  neg. correlated 15
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
RANBP17 0.3613 2.107e-09 4.18e-05
PLEKHM3 0.3478 8.866e-09 6.86e-05
TSC22D1 0.3463 1.037e-08 6.86e-05
TMEM63A 0.3415 1.709e-08 8.27e-05
SLC29A1 0.3395 2.082e-08 8.27e-05
ARID5B 0.3343 3.521e-08 0.000117
HLA-DQA2 -0.3262 7.775e-08 0.000191
CCL3 -0.3248 8.88e-08 0.000191
SLC20A2 0.3241 9.517e-08 0.000191
ZIM2__1 -0.3239 9.729e-08 0.000191
Clinical variable #3: 'TUMOR_TISSUE_SITE'

30 genes related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  CHEST - BREAST 1
  CHEST - CHEST WALL 7
  CHEST - LUNG/PLEURA 2
  CHEST - MEDIASTINUM 1
  CHEST - OTHER (PLEASE SPECIFY 2
  GYNECOLOGICAL - OVARY 1
  GYNECOLOGICAL - UTERUS 29
  HEAD AND NECK - HEAD 2
  HEAD AND NECK - NECK 1
  HEAD AND NECK - OTHER (PLEASE SPECIFY 2
  LOWER ABDOMINAL/PELVIC - BLADDER 1
  LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY 2
  LOWER ABDOMINAL/PELVIC - PELVIC 11
  LOWER ABDOMINAL/PELVIC - SPERMATIC CORD 2
  LOWER EXTREMITY - FOOT/ANKLE 4
  LOWER EXTREMITY - GROIN 2
  LOWER EXTREMITY - LOWER LEG/CALF 17
  LOWER EXTREMITY - OTHER (PLEASE SPECIFY 5
  LOWER EXTREMITY - THIGH/KNEE 44
  RETROPERITONEUM/UPPER ABDOMINAL - COLON 4
  RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC 2
  RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL 5
  RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY 8
  RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY 2
  RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS 1
  RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM 74
  RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES 3
  SUPERFICIAL TRUNK - ABDOMINAL WALL 2
  SUPERFICIAL TRUNK - BACK 5
  SUPERFICIAL TRUNK - BUTTOCK 4
  SUPERFICIAL TRUNK - FLANK 1
  UPPER EXTREMITY - SHOULDER/AXILLA 7
  UPPER EXTREMITY - UPPER ARM/ELBOW 5
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'TUMOR_TISSUE_SITE'

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

kruskal_wallis_P Q
HOXA10 1.457e-11 2.89e-07
GATA4 4.622e-11 4.59e-07
MAB21L1 1.395e-10 5.54e-07
MIR548F5 1.395e-10 5.54e-07
NBEA 1.395e-10 5.54e-07
HOXA2 1.662e-09 5.5e-06
GDF7 2.633e-09 7.47e-06
DOCK1 4.322e-09 9.53e-06
FAM196A 4.322e-09 9.53e-06
DPPA4 5.129e-09 1.02e-05
Clinical variable #4: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 142
  MALE 118
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S7.  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 15298 2.048e-30 2.03e-26 0.913
UTP14C 15298 2.048e-30 2.03e-26 0.913
FAM35A 4069 9.544e-13 4.74e-09 0.7572
GLUD1 4069 9.544e-13 4.74e-09 0.7572
KIF4B 4968 1.626e-08 5.59e-05 0.7035
FRG1B 4972 1.689e-08 5.59e-05 0.7033
HFE2 5089 5.113e-08 0.000145 0.6963
LOC339524 5276 2.783e-07 0.000507 0.6851
FBLN2 5281 2.909e-07 0.000507 0.6848
MIR548H4__1 5290 3.148e-07 0.000507 0.6843
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 182
  YES 71
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
TAGLN2 3528 2.056e-08 0.000157 0.727
BCMO1 3542 2.399e-08 0.000157 0.7259
HCRTR1 3569 3.224e-08 0.000157 0.7238
LBX2__1 3588 3.963e-08 0.000157 0.7223
LOC151534__1 3588 3.963e-08 0.000157 0.7223
AZU1 3618 5.476e-08 0.000166 0.72
SLC22A7 3646 7.383e-08 0.000166 0.7178
FLJ43860 3652 7.869e-08 0.000166 0.7174
GPR157 3661 8.655e-08 0.000166 0.7167
C6ORF25__1 3672 9.721e-08 0.000166 0.7158
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  DEDIFFERENTIATED LIPOSARCOMA 59
  DESMOID TUMOR 2
  GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS 1
  LEIOMYOSARCOMA (LMS) 105
  MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) 9
  MYXOFIBROSARCOMA 24
  PLEOMORPHIC 'MFH'/ UNDIFFERENTIATED PLEOMORPHIC SARCOMA 29
  SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED 2
  SYNOVIAL SARCOMA - BIPHASIC 2
  SYNOVIAL SARCOMA - MONOPHASIC 6
  UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS) 21
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
TPM2 2.052e-27 4.07e-23
PRKAG2 1.083e-25 1.07e-21
MYLK 1.205e-23 7.98e-20
ACTN1 7.935e-23 3.52e-19
LIG3 8.866e-23 3.52e-19
MYH11 1.282e-22 4.24e-19
SLC20A2 1.651e-22 4.68e-19
LOC728264 1.899e-22 4.71e-19
FBXO32 6.93e-22 1.53e-18
A2M 3.701e-21 6.75e-18
Clinical variable #7: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 155
  R1 69
  R2 9
  RX 26
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
ISL1 1.635e-05 0.149
ST6GALNAC2 2.755e-05 0.149
NICN1 4.472e-05 0.149
GRID2 4.924e-05 0.149
BCL11A 6.471e-05 0.149
TAF6L 7.508e-05 0.149
TMEM179B__1 7.508e-05 0.149
TULP4 7.822e-05 0.149
FAM57A 8.988e-05 0.149
B3GNT5 9.769e-05 0.149
Clinical variable #8: 'RACE'

One gene related to 'RACE'.

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

RACE Labels N
  ASIAN 6
  BLACK OR AFRICAN AMERICAN 18
  WHITE 227
     
  Significant markers N = 1
List of one gene differentially expressed by 'RACE'

Table S15.  Get Full Table List of one gene differentially expressed by 'RACE'

kruskal_wallis_P Q
ISCA1 2.998e-06 0.0595
Clinical variable #9: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 223
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 260

  • Number of genes = 19856

  • Number of clinical features = 9

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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