Correlation between miRseq expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1V1236V
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/C1V1236V
Overview
Introduction

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

Summary

Testing the association between 484 miRs and 11 clinical features across 139 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one miRs.

- 1 miR correlated to 'Time to Death'.

- HSA-MIR-224

- 1 miR correlated to 'AGE'.

- HSA-MIR-1274B

- 6 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

- HSA-MIR-452 , HSA-MIR-224 , HSA-MIR-200B , HSA-MIR-200A , HSA-MIR-217 , ...

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

- HSA-MIR-452 , HSA-MIR-200B , HSA-MIR-224 , HSA-MIR-200A , HSA-MIR-216A , ...

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

- HSA-LET-7F-2 , HSA-LET-7E , HSA-MIR-3607 , HSA-MIR-3647 , HSA-MIR-1245 , ...

- 1 miR correlated to 'GENDER'.

- HSA-MIR-625

- 4 miRs correlated to 'HISTOLOGICAL.TYPE'.

- HSA-MIR-3161 , HSA-MIR-92B , HSA-MIR-1255A , HSA-MIR-24-1

- No miRs correlated to 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.

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=1 | shorter survival | N=1 | longer survival | N=0
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0
NEOPLASM DISEASESTAGE | ANOVA test | N=6 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=7 | higher stage | N=4 | lower stage | N=3
PATHOLOGY N STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY M STAGE | ANOVA test | N=7 |  |  |  |
GENDER | t test | N=1 | male | N=1 | female | N=0
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | t test | N=4 | kidney papillary renal cell carcinoma | N=1 | kidney clear cell renal carcinoma | N=3
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 |  |  |  |
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

One miR related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0-194.8 (median=14.6)
| censored | N = 112
| death | N = 15
| Significant markers | N = 1
| associated with shorter survival | 1
| associated with longer survival | 0

List of one miR significantly associated with 'Time to Death' by Cox regression test

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

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
HSA-MIR-224 | 1.87 | 4.614e-05 | 0.022 | 0.875

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-224 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 4.61e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One miR related to 'AGE'.

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

AGE | Mean (SD) | 59.89 (12)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one miR significantly correlated to 'AGE' by Spearman correlation test

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

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-1274B | 0.3566 | 9.16e-05 | 0.0443

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-1274B to 'AGE'. P value = 9.16e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

6 miRs related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 77
| STAGE II | 11
| STAGE III | 31
| STAGE IV | 10
| Significant markers | N = 6

List of 6 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6. Get Full Table List of 6 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

| ANOVA_P | Q
--- | --- | ---
HSA-MIR-452 | 1.79e-06 | 0.000866
HSA-MIR-224 | 3.106e-06 | 0.0015
HSA-MIR-200B | 6.228e-06 | 0.003
HSA-MIR-200A | 4.432e-05 | 0.0213
HSA-MIR-217 | 4.714e-05 | 0.0226
HSA-MIR-1245 | 8.538e-05 | 0.0409

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-452 to 'NEOPLASM.DISEASESTAGE'. P value = 1.79e-06 with ANOVA analysis.

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

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

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 1.71 (0.9)
|  | N
| 1 | 82
| 2 | 17
| 3 | 39
| 4 | 1
| Significant markers | N = 7
| pos. correlated | 4
| neg. correlated | 3

List of 7 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S8. Get Full Table List of 7 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-452 | 0.391 | 1.949e-06 | 0.000943
HSA-MIR-200B | -0.3834 | 3.186e-06 | 0.00154
HSA-MIR-224 | 0.3756 | 5.207e-06 | 0.00251
HSA-MIR-200A | -0.3753 | 5.316e-06 | 0.00256
HSA-MIR-216A | 0.4804 | 8.54e-06 | 0.0041
HSA-MIR-429 | -0.3659 | 9.456e-06 | 0.00453
HSA-MIR-217 | 0.3425 | 4.191e-05 | 0.02

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-452 to 'PATHOLOGY.T.STAGE'. P value = 1.95e-06 with Spearman correlation analysis.

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

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

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

PATHOLOGY.N.STAGE | Mean (SD) | 0.48 (0.67)
|  | N
| 0 | 26
| 1 | 12
| 2 | 4
| Significant markers | N = 0

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

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

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 58
| M1 | 6
| MX | 62
| Significant markers | N = 7

List of 7 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11. Get Full Table List of 7 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

| ANOVA_P | Q
--- | --- | ---
HSA-LET-7F-2 | 1.427e-08 | 6.91e-06
HSA-LET-7E | 3.033e-06 | 0.00146
HSA-MIR-3607 | 1.007e-05 | 0.00485
HSA-MIR-3647 | 2.803e-05 | 0.0135
HSA-MIR-1245 | 3.082e-05 | 0.0148
HSA-LET-7G | 5.154e-05 | 0.0247
HSA-MIR-126 | 7.102e-05 | 0.0339

Figure S5. Get High-res Image As an example, this figure shows the association of HSA-LET-7F-2 to 'PATHOLOGY.M.STAGE'. P value = 1.43e-08 with ANOVA analysis.

Clinical variable #7: 'GENDER'

One miR related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 42
| MALE | 97
| Significant markers | N = 1
| Higher in MALE | 1
| Higher in FEMALE | 0

List of one miR differentially expressed by 'GENDER'

Table S13. Get Full Table List of one miR differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-625 | 4.18 | 7.113e-05 | 0.0344 | 0.7347

Figure S6. Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'GENDER'. P value = 7.11e-05 with T-test analysis.

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

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

Table S14. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.82 (19)
| Score | N
| 0 | 1
| 40 | 1
| 80 | 3
| 90 | 16
| 100 | 13
| Significant markers | N = 0

Clinical variable #9: 'HISTOLOGICAL.TYPE'

4 miRs related to 'HISTOLOGICAL.TYPE'.

Table S15. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE | Labels | N
| KIDNEY CLEAR CELL RENAL CARCINOMA | 6
| KIDNEY PAPILLARY RENAL CELL CARCINOMA | 133
| Significant markers | N = 4
| Higher in KIDNEY PAPILLARY RENAL CELL CARCINOMA | 1
| Higher in KIDNEY CLEAR CELL RENAL CARCINOMA | 3

List of 4 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S16. Get Full Table List of 4 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

| T(pos if higher in 'KIDNEY PAPILLARY RENAL CELL CARCINOMA') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-3161 | -7.27 | 1.515e-09 | 7.02e-07 | 0.8396
HSA-MIR-92B | -5.85 | 1.783e-06 | 0.000824 | 0.7318
HSA-MIR-1255A | 5.06 | 4.309e-06 | 0.00199 | 0.6667
HSA-MIR-24-1 | -5.18 | 6.771e-06 | 0.00311 | 0.7306

Figure S7. Get High-res Image As an example, this figure shows the association of HSA-MIR-3161 to 'HISTOLOGICAL.TYPE'. P value = 1.52e-09 with T-test analysis.

Clinical variable #10: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

Table S17. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED | Mean (SD) | 45.67 (69)
| Significant markers | N = 0

Clinical variable #11: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S18. Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET | Mean (SD) | 1972 (19)
| Value | N
| 1951 | 1
| 1960 | 1
| 1965 | 1
| 1991 | 1
| 1993 | 1
| Significant markers | N = 0

Methods & Data
Input

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

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

- Number of patients = 139

- Number of miRs = 484

- 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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