Correlation between miRseq expression and clinical features
Mesothelioma (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1JQ0ZWH
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JQ0ZWH
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 512 miRs and 7 clinical features across 32 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.

- 3 miRs correlated to 'Time to Death'.

- HSA-MIR-130B , HSA-MIR-671 , HSA-MIR-616

- 1 miR correlated to 'AGE'.

- HSA-MIR-3193

- 1 miR correlated to 'PATHOLOGY.T.STAGE'.

- HSA-MIR-25

- No miRs correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', and 'GENDER'.

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 |
--- | --- | --- | --- | --- | --- | ---
Time to Death | Cox regression test | N=3 | shorter survival | N=3 | longer survival | N=0
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=1 | lower stage | N=0
PATHOLOGY N STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

3 miRs related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'

Time to Death | Duration (Months) | 0.2-51.8 (median=12.8)
| censored | N = 9
| death | N = 23
| Significant markers | N = 3
| associated with shorter survival | 3
| associated with longer survival | 0

List of 3 miRs differentially expressed by 'Time to Death'

Table S2. Get Full Table List of 3 miRs significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
HSA-MIR-130B | 16 | 4.421e-06 | 0.0023 | 0.823
HSA-MIR-671 | 5.8 | 7.28e-05 | 0.037 | 0.773
HSA-MIR-616 | 2.6 | 0.0001436 | 0.073 | 0.775

Clinical variable #2: 'AGE'

One miR related to 'AGE'.

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

AGE | Mean (SD) | 64.53 (8)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one miR differentially expressed by 'AGE'

Table S4. Get Full Table List of one miR significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-3193 | 0.7571 | 0.0004332 | 0.222

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 2
| STAGE IB | 1
| STAGE II | 8
| STAGE III | 17
| STAGE IV | 4
| Significant markers | N = 0

Clinical variable #4: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.34 (0.83)
|  | N
| 1 | 5
| 2 | 13
| 3 | 12
| 4 | 2
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one miR differentially expressed by 'PATHOLOGY.T.STAGE'

Table S7. Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-25 | 0.5907 | 0.0003722 | 0.191

Clinical variable #5: 'PATHOLOGY.N.STAGE'

No miR related to 'PATHOLOGY.N.STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Mean (SD) | 0.52 (0.78)
|  | N
| 0 | 19
| 1 | 5
| 2 | 5
| Significant markers | N = 0

Clinical variable #6: 'PATHOLOGY.M.STAGE'

No miR related to 'PATHOLOGY.M.STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 26
| M1 | 2
| MX | 4
| Significant markers | N = 0

Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 8
| MALE | 24
| Significant markers | N = 0

Methods & Data
Input

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

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

- Number of patients = 32

- Number of miRs = 512

- Number of clinical features = 7

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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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