Kidney Renal Clear Cell Carcinoma: Correlation between miRseq expression and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 471 genes and 8 clinical features across 480 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 60 genes correlated to 'Time to Death'.

    • HSA-MIR-130B ,  HSA-MIR-223 ,  HSA-MIR-21 ,  HSA-MIR-34C ,  HSA-MIR-138-1 ,  ...

  • 2 genes correlated to 'AGE'.

    • HSA-MIR-148A ,  HSA-MIR-590

  • 12 genes correlated to 'GENDER'.

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

  • 35 genes correlated to 'PATHOLOGY.T'.

    • HSA-MIR-139 ,  HSA-MIR-625 ,  HSA-MIR-21 ,  HSA-MIR-130B ,  HSA-MIR-486 ,  ...

  • 18 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-193A ,  HSA-MIR-625 ,  HSA-MIR-155 ,  HSA-MIR-28 ,  HSA-MIR-130B ,  ...

  • 41 genes correlated to 'TUMOR.STAGE'.

    • HSA-MIR-139 ,  HSA-MIR-21 ,  HSA-MIR-155 ,  HSA-MIR-625 ,  HSA-MIR-144 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.

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 genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=60 shorter survival N=54 longer survival N=6
AGE Spearman correlation test N=2 older N=2 younger N=0
GENDER t test N=12 male N=4 female N=8
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=35 higher pT N=27 lower pT N=8
PATHOLOGY N t test   N=0        
PATHOLOGICSPREAD(M) t test N=18 m1 N=14 m0 N=4
TUMOR STAGE Spearman correlation test N=41 higher stage N=32 lower stage N=9
Clinical variable #1: 'Time to Death'

60 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-111 (median=35.2)
  censored N = 323
  death N = 154
     
  Significant markers N = 60
  associated with shorter survival 54
  associated with longer survival 6
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

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

HazardRatio Wald_P Q C_index
HSA-MIR-130B 2.1 7.949e-14 3.7e-11 0.665
HSA-MIR-223 1.63 5.21e-13 2.4e-10 0.649
HSA-MIR-21 2 1.109e-09 5.2e-07 0.657
HSA-MIR-34C 1.27 2.752e-09 1.3e-06 0.633
HSA-MIR-138-1 1.38 2.552e-08 1.2e-05 0.644
HSA-MIR-1248 1.4 3.509e-08 1.6e-05 0.62
HSA-MIR-365-2 1.66 4.286e-08 2e-05 0.628
HSA-MIR-18A 1.55 5.002e-08 2.3e-05 0.622
HSA-MIR-10B 0.57 9.608e-08 4.4e-05 0.373
HSA-MIR-101-1 0.55 1.142e-07 5.3e-05 0.395

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-130B to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 7.95e-14 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 60.58 (12)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-148A 0.1852 4.477e-05 0.0211
HSA-MIR-590 0.1823 5.91e-05 0.0278

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-148A to 'AGE'. P value = 4.48e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

12 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 163
  MALE 317
     
  Significant markers N = 12
  Higher in MALE 4
  Higher in FEMALE 8
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-100 8.54 6.562e-16 3.09e-13 0.7423
HSA-MIR-455 -5.26 2.457e-07 0.000115 0.6499
HSA-MIR-708 5.24 3.051e-07 0.000143 0.6535
HSA-MIR-599 -5.25 3.079e-07 0.000144 0.6756
HSA-MIR-30A -4.34 1.875e-05 0.00875 0.6129
HSA-MIR-500B -4.32 2.054e-05 0.00957 0.6203
HSA-MIR-30C-2 -4.32 2.063e-05 0.00959 0.6125
HSA-MIR-31 4.17 3.961e-05 0.0184 0.6225
HSA-MIR-3934 4.06 6.878e-05 0.0318 0.6286
HSA-MIR-204 -4.02 7.103e-05 0.0328 0.6349

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-100 to 'GENDER'. P value = 6.56e-16 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S7.  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 #5: 'PATHOLOGY.T'

35 genes related to 'PATHOLOGY.T'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.92 (0.97)
  N
  T1 233
  T2 62
  T3 174
  T4 11
     
  Significant markers N = 35
  pos. correlated 27
  neg. correlated 8
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-139 -0.3342 5.503e-14 2.59e-11
HSA-MIR-625 0.2793 4.732e-10 2.22e-07
HSA-MIR-21 0.2765 7.164e-10 3.36e-07
HSA-MIR-130B 0.2577 1.017e-08 4.76e-06
HSA-MIR-486 -0.253 1.903e-08 8.88e-06
HSA-MIR-155 0.2529 1.925e-08 8.97e-06
HSA-MIR-144 -0.2463 4.571e-08 2.13e-05
HSA-MIR-9-2 0.2257 5.838e-07 0.000271
HSA-MIR-451 -0.2239 7.202e-07 0.000333
HSA-MIR-9-1 0.2216 9.423e-07 0.000435

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'PATHOLOGY.T'. P value = 5.5e-14 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Labels N
  N0 222
  N1 17
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

18 genes related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 405
  M1 75
     
  Significant markers N = 18
  Higher in M1 14
  Higher in M0 4
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

T(pos if higher in 'M1') ttestP Q AUC
HSA-MIR-193A 5.78 3.13e-08 1.47e-05 0.6467
HSA-MIR-625 5.36 4.97e-07 0.000234 0.6849
HSA-MIR-155 5.17 1.072e-06 0.000503 0.6815
HSA-MIR-28 5.16 1.113e-06 0.000521 0.6609
HSA-MIR-130B 5.12 1.218e-06 0.000569 0.6671
HSA-MIR-144 -5.01 2.339e-06 0.00109 0.6793
HSA-MIR-27A 4.93 2.378e-06 0.00111 0.6328
HSA-MIR-106B 4.86 3.123e-06 0.00145 0.6525
HSA-MIR-139 -4.62 1.123e-05 0.0052 0.6657
HSA-MIR-21 4.5 1.605e-05 0.00741 0.6393

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-193A to 'PATHOLOGICSPREAD(M)'. P value = 3.13e-08 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

41 genes related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.11 (1.2)
  N
  Stage 1 229
  Stage 2 50
  Stage 3 122
  Stage 4 79
     
  Significant markers N = 41
  pos. correlated 32
  neg. correlated 9
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S14.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-139 -0.3474 4.64e-15 2.19e-12
HSA-MIR-21 0.2797 4.449e-10 2.09e-07
HSA-MIR-155 0.278 5.723e-10 2.68e-07
HSA-MIR-625 0.2763 7.378e-10 3.45e-07
HSA-MIR-144 -0.272 1.381e-09 6.45e-07
HSA-MIR-486 -0.2666 2.975e-09 1.39e-06
HSA-MIR-451 -0.2491 3.201e-08 1.49e-05
HSA-MIR-130B 0.2456 5.006e-08 2.32e-05
HSA-MIR-10B -0.2324 2.617e-07 0.000121
HSA-MIR-9-2 0.232 2.746e-07 0.000127

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'TUMOR.STAGE'. P value = 4.64e-15 with Spearman correlation analysis.

Methods & Data
Input

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

  • Clinical data file = KIRC-TP.clin.merged.picked.txt

  • Number of patients = 480

  • Number of genes = 471

  • Number of clinical features = 8

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] 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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