Correlation between miRseq expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1C53J8J
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/C1C53J8J
Overview
Introduction

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

Summary

Testing the association between 456 miRs and 9 clinical features across 482 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one miRs.

  • 66 miRs correlated to 'Time to Death'.

    • HSA-MIR-223 ,  HSA-MIR-130B ,  HSA-MIR-34C ,  HSA-MIR-21 ,  HSA-MIR-10B ,  ...

  • 1 miR correlated to 'AGE'.

    • HSA-MIR-590

  • 38 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-139 ,  HSA-MIR-625 ,  HSA-MIR-486 ,  HSA-MIR-21 ,  HSA-MIR-28 ,  ...

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

    • HSA-MIR-139 ,  HSA-MIR-21 ,  HSA-MIR-486 ,  HSA-MIR-625 ,  HSA-MIR-144 ,  ...

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

    • HSA-MIR-106B ,  HSA-MIR-193A ,  HSA-MIR-625 ,  HSA-MIR-28 ,  HSA-MIR-155 ,  ...

  • 9 miRs correlated to 'GENDER'.

    • HSA-MIR-100 ,  HSA-MIR-708 ,  HSA-MIR-455 ,  HSA-MIR-599 ,  HSA-MIR-30A ,  ...

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

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=66 shorter survival N=56 longer survival N=10
AGE Spearman correlation test N=1 older N=1 younger N=0
NEOPLASM DISEASESTAGE ANOVA test N=38        
PATHOLOGY T STAGE Spearman correlation test N=32 higher stage N=23 lower stage N=9
PATHOLOGY N STAGE t test   N=0        
PATHOLOGY M STAGE t test N=18 m1 N=14 m0 N=4
GENDER t test N=9 male N=3 female N=6
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

66 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-120.6 (median=36.5)
  censored N = 321
  death N = 161
     
  Significant markers N = 66
  associated with shorter survival 56
  associated with longer survival 10
List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-223 1.6 2.042e-12 9.3e-10 0.643
HSA-MIR-130B 2 4.618e-12 2.1e-09 0.647
HSA-MIR-34C 1.28 1.73e-10 7.9e-08 0.64
HSA-MIR-21 2.1 1.018e-09 4.6e-07 0.661
HSA-MIR-10B 0.56 1.228e-08 5.6e-06 0.369
HSA-MIR-365-2 1.67 1.872e-08 8.4e-06 0.622
HSA-MIR-365-1 1.64 3.725e-08 1.7e-05 0.619
HSA-MIR-18A 1.54 4.235e-08 1.9e-05 0.617
HSA-MIR-1248 1.39 7.657e-08 3.4e-05 0.609
HSA-MIR-101-1 0.58 9.034e-08 4e-05 0.404

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-223 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.04e-12 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) 60.58 (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-590 0.1806 6.665e-05 0.0304

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-590 to 'AGE'. P value = 6.66e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

38 miRs related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 228
  STAGE II 52
  STAGE III 124
  STAGE IV 78
     
  Significant markers N = 38
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-139 4.681e-13 2.13e-10
HSA-MIR-625 8.392e-11 3.82e-08
HSA-MIR-486 8.048e-09 3.65e-06
HSA-MIR-21 8.454e-09 3.83e-06
HSA-MIR-28 1.568e-08 7.09e-06
HSA-MIR-144 2.871e-08 1.29e-05
HSA-LET-7I 3.454e-08 1.55e-05
HSA-MIR-155 3.554e-08 1.6e-05
HSA-MIR-130B 6.128e-08 2.75e-05
HSA-MIR-10B 3.356e-07 0.00015

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'NEOPLASM.DISEASESTAGE'. P value = 4.68e-13 with ANOVA analysis.

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

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

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

PATHOLOGY.T.STAGE Mean (SD) 1.93 (0.97)
  N
  1 233
  2 63
  3 175
  4 11
     
  Significant markers N = 32
  pos. correlated 23
  neg. correlated 9
List of top 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-139 -0.3352 4.017e-14 1.83e-11
HSA-MIR-21 0.2754 7.702e-10 3.5e-07
HSA-MIR-486 -0.2724 1.203e-09 5.46e-07
HSA-MIR-625 0.2693 1.879e-09 8.51e-07
HSA-MIR-144 -0.2481 3.392e-08 1.53e-05
HSA-MIR-155 0.2463 4.281e-08 1.93e-05
HSA-MIR-451 -0.2352 1.747e-07 7.86e-05
HSA-MIR-130B 0.2315 2.774e-07 0.000125
HSA-MIR-9-1 0.2304 3.165e-07 0.000142
HSA-MIR-9-2 0.2219 8.669e-07 0.000387

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'PATHOLOGY.T.STAGE'. P value = 4.02e-14 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 Labels N
  class0 222
  class1 18
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 405
  M1 77
     
  Significant markers N = 18
  Higher in M1 14
  Higher in M0 4
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'

T(pos if higher in 'M1') ttestP Q AUC
HSA-MIR-106B 5.67 7.499e-08 3.42e-05 0.6749
HSA-MIR-193A 5.53 1.173e-07 5.34e-05 0.6414
HSA-MIR-625 5.39 4.312e-07 0.000196 0.6892
HSA-MIR-28 5.27 6.543e-07 0.000296 0.6603
HSA-MIR-155 5.26 6.851e-07 0.00031 0.6824
HSA-LET-7I 4.99 1.949e-06 0.000879 0.6613
HSA-MIR-130B 4.98 2.115e-06 0.000952 0.6664
HSA-MIR-144 -4.99 2.395e-06 0.00108 0.676
HSA-MIR-139 -4.82 4.818e-06 0.00216 0.6691
HSA-MIR-27A 4.66 7.415e-06 0.00331 0.6275

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-106B to 'PATHOLOGY.M.STAGE'. P value = 7.5e-08 with T-test analysis.

Clinical variable #7: 'GENDER'

9 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 163
  MALE 319
     
  Significant markers N = 9
  Higher in MALE 3
  Higher in FEMALE 6
List of 9 miRs differentially expressed by 'GENDER'

Table S13.  Get Full Table List of 9 miRs differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-100 8.19 6.655e-15 3.03e-12 0.7304
HSA-MIR-708 5.48 8.984e-08 4.09e-05 0.659
HSA-MIR-455 -5.38 1.342e-07 6.09e-05 0.6599
HSA-MIR-599 -5.16 5.008e-07 0.000227 0.6779
HSA-MIR-30A -4.37 1.585e-05 0.00716 0.6149
HSA-MIR-31 4.37 1.715e-05 0.00773 0.6296
HSA-MIR-30C-2 -4.16 4.06e-05 0.0183 0.6088
HSA-MIR-628 -3.99 7.91e-05 0.0355 0.5945
HSA-MIR-204 -3.96 9.203e-05 0.0412 0.6356

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-100 to 'GENDER'. P value = 6.66e-15 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) 88.33 (23)
  Score N
  0 2
  70 1
  80 3
  90 13
  100 17
     
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 34 (16)
  Value N
  10 1
  40 2
  46 1
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = KIRC-TP.miRseq_RPKM_log2.txt

  • Clinical data file = KIRC-TP.merged_data.txt

  • Number of patients = 482

  • Number of miRs = 456

  • Number of clinical features = 9

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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