Correlation between miRseq expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1GT5KSX
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/C1GT5KSX
Overview
Introduction

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

Summary

Testing the association between 480 miRs and 10 clinical features across 163 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one miRs.

  • 6 miRs correlated to 'Time to Death'.

    • HSA-MIR-224 ,  HSA-MIR-452 ,  HSA-MIR-1293 ,  HSA-MIR-30E ,  HSA-MIR-34A ,  ...

  • 6 miRs correlated to 'AGE'.

    • HSA-MIR-34A ,  HSA-MIR-486 ,  HSA-MIR-29B-1 ,  HSA-MIR-26A-1 ,  HSA-MIR-29B-2 ,  ...

  • 16 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

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

  • 10 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-200B ,  HSA-MIR-200A ,  HSA-MIR-452 ,  HSA-MIR-429 ,  HSA-MIR-216A ,  ...

  • 11 miRs correlated to 'PATHOLOGY.M.STAGE'.

    • HSA-LET-7F-2 ,  HSA-LET-7E ,  HSA-MIR-33A ,  HSA-MIR-3607 ,  HSA-MIR-143 ,  ...

  • No miRs correlated to 'PATHOLOGY.N.STAGE', 'GENDER', '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=6 shorter survival N=3 longer survival N=3
AGE Spearman correlation test N=6 older N=6 younger N=0
NEOPLASM DISEASESTAGE ANOVA test N=16        
PATHOLOGY T STAGE Spearman correlation test N=10 higher stage N=7 lower stage N=3
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=11        
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

6 miRs 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)
  censored N = 133
  death N = 17
     
  Significant markers N = 6
  associated with shorter survival 3
  associated with longer survival 3
List of 6 miRs significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 6 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-224 2.1 7.652e-06 0.0037 0.885
HSA-MIR-452 2.5 1.043e-05 0.005 0.877
HSA-MIR-1293 2.3 1.5e-05 0.0072 0.833
HSA-MIR-30E 0.15 1.611e-05 0.0077 0.249
HSA-MIR-34A 0.54 4.456e-05 0.021 0.208
HSA-MIR-1468 0.35 7.585e-05 0.036 0.234

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 = 7.65e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

6 miRs related to 'AGE'.

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

AGE Mean (SD) 59.72 (13)
  Significant markers N = 6
  pos. correlated 6
  neg. correlated 0
List of 6 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 6 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-34A 0.3247 3.343e-05 0.016
HSA-MIR-486 0.318 4.95e-05 0.0237
HSA-MIR-29B-1 0.3157 5.628e-05 0.0269
HSA-MIR-26A-1 0.3166 5.976e-05 0.0285
HSA-MIR-29B-2 0.311 7.358e-05 0.035
HSA-MIR-451 0.311 8.179e-05 0.0389

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-34A to 'AGE'. P value = 3.34e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

16 miRs related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 95
  STAGE II 11
  STAGE III 37
  STAGE IV 10
     
  Significant markers N = 16
List of top 10 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-200B 7.32e-09 3.51e-06
HSA-MIR-452 1.031e-08 4.94e-06
HSA-MIR-224 1.827e-08 8.73e-06
HSA-MIR-217 3.261e-07 0.000156
HSA-MIR-200A 3.699e-07 0.000176
HSA-MIR-429 2.969e-06 0.00141
HSA-MIR-216A 5.316e-06 0.00252
HSA-MIR-143 1.48e-05 0.007
HSA-MIR-379 4.444e-05 0.021
HSA-MIR-1-2 5.46e-05 0.0257

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-200B to 'NEOPLASM.DISEASESTAGE'. P value = 7.32e-09 with ANOVA analysis.

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

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

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

PATHOLOGY.T.STAGE Mean (SD) 1.65 (0.89)
  N
  1 102
  2 17
  3 43
  4 1
     
  Significant markers N = 10
  pos. correlated 7
  neg. correlated 3
List of 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-200B -0.4292 1.089e-08 5.23e-06
HSA-MIR-200A -0.4085 6.211e-08 2.98e-05
HSA-MIR-452 0.4046 8.489e-08 4.06e-05
HSA-MIR-429 -0.4036 9.172e-08 4.37e-05
HSA-MIR-216A 0.5179 2.365e-07 0.000113
HSA-MIR-217 0.3826 5.938e-07 0.000282
HSA-MIR-224 0.3759 7.624e-07 0.000361
HSA-MIR-100 0.3187 3.384e-05 0.016
HSA-MIR-143 0.3144 4.377e-05 0.0207
HSA-MIR-3189 0.3451 8.676e-05 0.0409

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-200B to 'PATHOLOGY.T.STAGE'. P value = 1.09e-08 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) 0.51 (0.66)
  N
  0 27
  1 16
  2 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 62
  M1 6
  MX 82
     
  Significant markers N = 11
List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
HSA-LET-7F-2 1.649e-08 7.91e-06
HSA-LET-7E 4.045e-07 0.000194
HSA-MIR-33A 1.517e-06 0.000725
HSA-MIR-3607 4.585e-06 0.00219
HSA-MIR-143 6.437e-06 0.00306
HSA-LET-7A-1 2.632e-05 0.0125
HSA-LET-7A-2 2.8e-05 0.0133
HSA-LET-7A-3 3.013e-05 0.0142
HSA-MIR-126 4.668e-05 0.022
HSA-MIR-424 4.774e-05 0.0225

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 = 1.65e-08 with ANOVA analysis.

Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 49
  MALE 114
     
  Significant markers N = 0
Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S13.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 88.72 (18)
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 36.78 (56)
  Significant markers N = 0
Clinical variable #10: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S15.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1976.33 (20)
  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 = 163

  • Number of miRs = 480

  • Number of clinical features = 10

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.

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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