Correlation between miRseq expression and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1K64GG0
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/C1K64GG0
Overview
Introduction

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

Summary

Testing the association between 576 miRs and 11 clinical features across 185 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

- 6 miRs correlated to 'PATHOLOGY.T.STAGE'.

- HSA-MIR-99A , HSA-MIR-199B , HSA-MIR-199A-2 , HSA-MIR-1307 , HSA-MIR-378 , ...

- 5 miRs correlated to 'PATHOLOGY.M.STAGE'.

- HSA-MIR-216B , HSA-MIR-1179 , HSA-MIR-151 , HSA-MIR-223 , HSA-MIR-199A-1

- 1 miR correlated to 'GENDER'.

- HSA-MIR-519C

- No miRs correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', 'NUMBER.OF.LYMPH.NODES', and 'GLEASON_SCORE'.

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 Q value < 0.05.

Clinical feature | Statistical test | Significant miRs | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
Time to Death | Cox regression test | N=0 |  |  |  |
AGE | Spearman correlation test | N=0 |  |  |  |
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=6 | higher stage | N=4 | lower stage | N=2
PATHOLOGY N STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY M STAGE | ANOVA test | N=5 |  |  |  |
GENDER | t test | N=1 | male | N=1 | female | N=0
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 |  |  |  |
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |  |  |  |
GLEASON_SCORE | Spearman correlation test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0.1-140.8 (median=7.7)
| censored | N = 122
| death | N = 56
| Significant markers | N = 0

Clinical variable #2: 'AGE'

No miR related to 'AGE'.

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

AGE | Mean (SD) | 67.67 (11)
| Significant markers | N = 0

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S3. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 2
| STAGE II | 59
| STAGE III | 64
| STAGE IV | 56
| Significant markers | N = 0

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

6 miRs related to 'PATHOLOGY.T.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.82 (0.71)
|  | N
| 0 | 1
| 1 | 1
| 2 | 51
| 3 | 88
| 4 | 26
| Significant markers | N = 6
| pos. correlated | 4
| neg. correlated | 2

List of 6 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S5. Get Full Table List of 6 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-99A | 0.3305 | 1.288e-05 | 0.00742
HSA-MIR-199B | 0.3198 | 2.525e-05 | 0.0145
HSA-MIR-199A-2 | 0.3163 | 3.124e-05 | 0.0179
HSA-MIR-1307 | -0.3104 | 4.456e-05 | 0.0255
HSA-MIR-378 | -0.3072 | 5.401e-05 | 0.0309
HSA-MIR-199A-1 | 0.3067 | 5.536e-05 | 0.0316

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-99A to 'PATHOLOGY.T.STAGE'. P value = 1.29e-05 with Spearman correlation analysis.

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

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

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Mean (SD) | 0.6 (0.92)
|  | N
| 0 | 113
| 1 | 17
| 2 | 33
| 3 | 6
| Significant markers | N = 0

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

5 miRs related to 'PATHOLOGY.M.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 100
| M1 | 5
| MX | 79
| Significant markers | N = 5

List of 5 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S8. Get Full Table List of 5 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

| ANOVA_P | Q
--- | --- | ---
HSA-MIR-216B | 1.261e-08 | 7.27e-06
HSA-MIR-1179 | 6.183e-08 | 3.56e-05
HSA-MIR-151 | 2.299e-05 | 0.0132
HSA-MIR-223 | 3.346e-05 | 0.0192
HSA-MIR-199A-1 | 7.746e-05 | 0.0443

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-216B to 'PATHOLOGY.M.STAGE'. P value = 1.26e-08 with ANOVA analysis.

Clinical variable #7: 'GENDER'

One miR related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 46
| MALE | 139
| Significant markers | N = 1
| Higher in MALE | 1
| Higher in FEMALE | 0

List of one miR differentially expressed by 'GENDER'

Table S10. Get Full Table List of one miR differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-519C | 4.24 | 5.916e-05 | 0.0341 | 0.7274

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-519C to 'GENDER'. P value = 5.92e-05 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S11. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 77.8 (16)
| Significant markers | N = 0

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED | Mean (SD) | 38.27 (26)
| Significant markers | N = 0

Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

No miR related to 'NUMBER.OF.LYMPH.NODES'.

Table S13. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 1.58 (3.4)
| Significant markers | N = 0

Clinical variable #11: 'GLEASON_SCORE'

No miR related to 'GLEASON_SCORE'.

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

GLEASON_SCORE | Mean (SD) | 6.44 (0.68)
| Score | N
| 6 | 25
| 7 | 12
| 8 | 1
| 9 | 1
| Significant markers | N = 0

Methods & Data
Input

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

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

- Number of patients = 185

- Number of miRs = 576

- Number of clinical features = 11

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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