Correlation between gene methylation status and clinical features
Mesothelioma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1V123VC
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/C1V123VC
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 20103 genes and 8 clinical features across 77 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

- 2 genes correlated to 'GENDER'.

- ALG11__2 , UTP14C

- 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

- ABCA11P__1 , ABCC5 , ABHD3 , ABI2 , ACER1 , ...

- No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', and 'PATHOLOGY_M_STAGE'.

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=0 |  |  |  |
NEOPLASM_DISEASESTAGE | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY_N_STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY_M_STAGE | Wilcoxon test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=2 | male | N=2 | female | N=0
KARNOFSKY_PERFORMANCE_SCORE | Spearman correlation test | N=30 | higher score | N=19 | lower score | N=11

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) | 0.7-91.7 (median=15)
| censored | N = 12
| death | N = 64
| Significant markers | N = 0

Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH | Mean (SD) | 62.99 (9.4)
| Significant markers | N = 0

Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

No gene related to 'NEOPLASM_DISEASESTAGE'.

Table S3. Basic characteristics of clinical feature: 'NEOPLASM_DISEASESTAGE'

NEOPLASM_DISEASESTAGE | Labels | N
| STAGE I | 7
| STAGE IA | 2
| STAGE IB | 1
| STAGE II | 16
| STAGE III | 40
| STAGE IV | 11
| Significant markers | N = 0

Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE | Mean (SD) | 2.4 (0.92)
|  | N
| T1 | 14
| T2 | 25
| T3 | 28
| T4 | 8
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE | Mean (SD) | 0.74 (0.94)
|  | N
| N0 | 42
| N1 | 10
| N2 | 19
| N3 | 2
| Significant markers | N = 0

Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE | Labels | N
| class0 | 49
| class1 | 2
| Significant markers | N = 0

Clinical variable #7: 'GENDER'

2 genes related to 'GENDER'.

Table S7. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 15
| MALE | 62
| Significant markers | N = 2
| Higher in MALE | 2
| Higher in FEMALE | 0

List of 2 genes differentially expressed by 'GENDER'

Table S8. Get Full Table List of 2 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 | 893 | 3.832e-08 | 0.000385 | 0.9602
UTP14C | 893 | 3.832e-08 | 0.000385 | 0.9602

Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE | Mean (SD) | 75 (38)
| Score | N
| 0 | 1
| 80 | 2
| 90 | 1
| 100 | 2
| Significant markers | N = 30
| pos. correlated | 19
| neg. correlated | 11

List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S10. Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
ABCA11P__1 | -0.971 | 0.001249 | 0.0528
ABCC5 | 0.971 | 0.001249 | 0.0528
ABHD3 | -0.971 | 0.001249 | 0.0528
ABI2 | -0.971 | 0.001249 | 0.0528
ACER1 | 0.971 | 0.001249 | 0.0528
ACSL1 | 0.971 | 0.001249 | 0.0528
ACSS2 | 0.971 | 0.001249 | 0.0528
ADAMTS16 | 0.971 | 0.001249 | 0.0528
ADARB2 | 0.971 | 0.001249 | 0.0528
ADCK5 | 0.971 | 0.001249 | 0.0528

Methods & Data
Input

- Expresson data file = MESO-TP.meth.by_min_clin_corr.data.txt

- Clinical data file = MESO-TP.merged_data.txt

- Number of patients = 77

- Number of genes = 20103

- Number of clinical features = 8

Selected clinical features

- For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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