Correlation between miRseq expression and clinical features
Uveal Melanoma (Primary solid tumor)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1SX6CR6
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1SX6CR6
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " UVM-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 536 miRs and 7 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one miRs.

- 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- HSA-MIR-508 , HSA-MIR-506 , HSA-MIR-514-3 , HSA-MIR-514-1 , HSA-MIR-181A-1 , ...

- 30 miRs correlated to 'YEARS_TO_BIRTH'.

- HSA-MIR-15A , HSA-MIR-181A-2 , HSA-LET-7D , HSA-MIR-197 , HSA-MIR-20A , ...

- No miRs correlated to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RADIATION_THERAPY'.

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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant miRs | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 |  | N=NA |  | N=NA
YEARS_TO_BIRTH | Spearman correlation test | N=30 | older | N=10 | younger | N=20
PATHOLOGIC_STAGE | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY_M_STAGE | Wilcoxon test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=0 |  |  |  |
RADIATION_THERAPY | Wilcoxon test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs 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) | 0.1-85.5 (median=25.4)
| censored | N = 56
| death | N = 23
| Significant markers | N = 30
| associated with shorter survival | NA
| associated with longer survival | NA

List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

| logrank_P | Q | C_index
--- | --- | --- | ---
HSA-MIR-508 | 6.92e-08 | 1.6e-05 | 0.133
HSA-MIR-506 | 1.02e-07 | 1.6e-05 | 0.135
HSA-MIR-514-3 | 1.17e-07 | 1.6e-05 | 0.137
HSA-MIR-514-1 | 1.24e-07 | 1.6e-05 | 0.131
HSA-MIR-181A-1 | 1.52e-07 | 1.6e-05 | 0.173
HSA-MIR-212 | 2.67e-07 | 2.4e-05 | 0.796
HSA-MIR-509-1 | 5.15e-07 | 3.4e-05 | 0.133
HSA-MIR-509-3 | 5.15e-07 | 3.4e-05 | 0.131
HSA-MIR-513C | 2.46e-06 | 0.00015 | 0.141
HSA-MIR-148B | 3.77e-06 | 2e-04 | 0.731

Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH | Mean (SD) | 61.65 (14)
| Significant markers | N = 30
| pos. correlated | 10
| neg. correlated | 20

List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-15A | 0.4398 | 4.475e-05 | 0.024
HSA-MIR-181A-2 | -0.408 | 0.0001724 | 0.0328
HSA-LET-7D | -0.3973 | 0.0002637 | 0.0328
HSA-MIR-197 | -0.3958 | 0.0002791 | 0.0328
HSA-MIR-20A | 0.3908 | 0.0003387 | 0.0328
HSA-MIR-191 | -0.384 | 0.000438 | 0.0328
HSA-MIR-18A | 0.3807 | 0.000494 | 0.0328
HSA-MIR-744 | -0.3791 | 0.0005242 | 0.0328
HSA-MIR-30C-1 | -0.3777 | 0.0005516 | 0.0328
HSA-MIR-7-1 | -0.3567 | 0.001163 | 0.0623

Clinical variable #3: 'PATHOLOGIC_STAGE'

No miR related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE | Labels | N
| STAGE IIA | 12
| STAGE IIB | 27
| STAGE IIIA | 25
| STAGE IIIB | 10
| STAGE IIIC | 1
| STAGE IV | 4
| Significant markers | N = 0

Clinical variable #4: 'PATHOLOGY_T_STAGE'

No miR related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE | Mean (SD) | 3.25 (0.74)
|  | N
| T2 | 14
| T3 | 32
| T4 | 34
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE | Labels | N
| class0 | 51
| class1 | 4
| Significant markers | N = 0

Clinical variable #6: 'GENDER'

No miR related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 35
| MALE | 45
| Significant markers | N = 0

Clinical variable #7: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY | Labels | N
| NO | 76
| YES | 3
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = UVM-TP.miRseq_RPKM_log2.txt

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

- Number of patients = 80

- Number of miRs = 536

- Number of clinical features = 7

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.

- There are also useful links about clinical features.

- CDE Browser

- Data dictionary for clinical data

- NCI wiki - Biospecimen and Clinical XSD Files Specification

- 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.

- 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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