Kidney Renal Clear Cell Carcinoma: Correlation between miRseq expression and clinical features
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 467 genes and 9 clinical features across 463 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 64 genes correlated to 'Time to Death'.

    • HSA-MIR-130B ,  HSA-MIR-223 ,  HSA-MIR-21 ,  HSA-MIR-34C ,  HSA-MIR-365-2 ,  ...

  • 8 genes correlated to 'GENDER'.

    • HSA-MIR-100 ,  HSA-MIR-708 ,  HSA-MIR-599 ,  HSA-MIR-455 ,  HSA-MIR-31 ,  ...

  • 35 genes correlated to 'PATHOLOGY.T'.

    • HSA-MIR-139 ,  HSA-MIR-21 ,  HSA-MIR-625 ,  HSA-MIR-130B ,  HSA-MIR-155 ,  ...

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

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

  • 38 genes correlated to 'TUMOR.STAGE'.

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

  • 4 genes correlated to 'NEOADJUVANT.THERAPY'.

    • HSA-MIR-2116 ,  HSA-MIR-3190 ,  HSA-MIR-133A-2 ,  HSA-MIR-627

  • No genes correlated to 'AGE', '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=64 shorter survival N=58 longer survival N=6
AGE Spearman correlation test   N=0        
GENDER t test N=8 male N=4 female N=4
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=35 higher pT N=26 lower pT N=9
PATHOLOGY N t test   N=0        
PATHOLOGICSPREAD(M) t test N=18 m1 N=14 m0 N=4
TUMOR STAGE Spearman correlation test N=38 higher stage N=27 lower stage N=11
NEOADJUVANT THERAPY t test N=4 yes N=3 no N=1
Clinical variable #1: 'Time to Death'

64 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=33.7)
  censored N = 312
  death N = 148
     
  Significant markers N = 64
  associated with shorter survival 58
  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 2.2 7.438e-15 3.5e-12 0.666
HSA-MIR-223 1.64 4.44e-13 2.1e-10 0.653
HSA-MIR-21 2.1 1.529e-09 7.1e-07 0.659
HSA-MIR-34C 1.27 1.575e-09 7.3e-07 0.638
HSA-MIR-365-2 1.7 1.357e-08 6.3e-06 0.637
HSA-MIR-18A 1.58 4.319e-08 2e-05 0.619
HSA-MIR-10B 0.56 6.785e-08 3.1e-05 0.368
HSA-MIR-1248 1.4 7.101e-08 3.3e-05 0.618
HSA-MIR-365-1 1.64 8.411e-08 3.9e-05 0.63
HSA-MIR-101-1 0.55 1.144e-07 5.2e-05 0.394

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 = 7.44e-15 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 60.74 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

8 genes related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 159
  MALE 304
     
  Significant markers N = 8
  Higher in MALE 4
  Higher in FEMALE 4
List of 8 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 8 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-100 8.44 1.414e-15 6.6e-13 0.7428
HSA-MIR-708 5.17 4.198e-07 0.000196 0.6548
HSA-MIR-599 -5.04 8.81e-07 0.00041 0.6715
HSA-MIR-455 -4.82 2.1e-06 0.000975 0.6465
HSA-MIR-31 4.07 6.19e-05 0.0287 0.6213
HSA-MIR-30A -3.99 7.963e-05 0.0368 0.605
HSA-MIR-500B -3.97 8.751e-05 0.0403 0.612
HSA-MIR-320E 3.93 0.0001083 0.0498 0.6208

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-100 to 'GENDER'. P value = 1.41e-15 with T-test analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 88 (23)
  Score N
  0 2
  70 1
  80 3
  90 13
  100 16
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

35 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.92 (0.97)
  N
  T1 225
  T2 59
  T3 169
  T4 10
     
  Significant markers N = 35
  pos. correlated 26
  neg. correlated 9
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-139 -0.3294 3.547e-13 1.66e-10
HSA-MIR-21 0.275 1.764e-09 8.22e-07
HSA-MIR-625 0.2736 2.153e-09 1e-06
HSA-MIR-130B 0.2653 6.709e-09 3.11e-06
HSA-MIR-155 0.2574 1.926e-08 8.92e-06
HSA-MIR-486 -0.2491 5.558e-08 2.57e-05
HSA-MIR-144 -0.2457 8.535e-08 3.93e-05
HSA-MIR-9-2 0.2281 7.05e-07 0.000324
HSA-MIR-9-1 0.2243 1.088e-06 5e-04
HSA-MIR-451 -0.2227 1.295e-06 0.000593

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'PATHOLOGY.T'. P value = 3.55e-13 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Labels N
  N0 214
  N1 16
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

18 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 391
  M1 72
     
  Significant markers N = 18
  Higher in M1 14
  Higher in M0 4
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  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 5.72 4.428e-08 2.07e-05 0.6466
HSA-MIR-155 5.04 1.979e-06 0.000922 0.6825
HSA-MIR-130B 5.02 1.997e-06 0.000929 0.67
HSA-MIR-28 4.97 2.549e-06 0.00118 0.6563
HSA-MIR-625 4.96 2.845e-06 0.00132 0.673
HSA-MIR-144 -4.97 2.887e-06 0.00133 0.6833
HSA-MIR-27A 4.79 4.513e-06 0.00208 0.6314
HSA-MIR-106B 4.54 1.19e-05 0.00547 0.6431
HSA-MIR-130A 4.53 1.444e-05 0.00663 0.6412
HSA-MIR-454 4.47 1.83e-05 0.00838 0.6437

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-193A to 'PATHOLOGICSPREAD(M)'. P value = 4.43e-08 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

38 genes related to 'TUMOR.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.11 (1.2)
  N
  Stage 1 221
  Stage 2 47
  Stage 3 119
  Stage 4 76
     
  Significant markers N = 38
  pos. correlated 27
  neg. correlated 11
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S13.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-139 -0.3433 2.998e-14 1.4e-11
HSA-MIR-155 0.2819 6.611e-10 3.08e-07
HSA-MIR-21 0.278 1.153e-09 5.36e-07
HSA-MIR-144 -0.2726 2.465e-09 1.14e-06
HSA-MIR-625 0.2667 5.548e-09 2.57e-06
HSA-MIR-486 -0.2633 8.841e-09 4.08e-06
HSA-MIR-130B 0.2494 5.364e-08 2.47e-05
HSA-MIR-451 -0.2489 5.757e-08 2.65e-05
HSA-MIR-142 0.2423 1.31e-07 6.01e-05
HSA-MIR-10B -0.2365 2.624e-07 0.00012

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'TUMOR.STAGE'. P value = 3e-14 with Spearman correlation analysis.

Clinical variable #9: 'NEOADJUVANT.THERAPY'

4 genes related to 'NEOADJUVANT.THERAPY'.

Table S14.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 5
  YES 458
     
  Significant markers N = 4
  Higher in YES 3
  Higher in NO 1
List of 4 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S15.  Get Full Table List of 4 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-2116 9.51 1.313e-09 5.19e-07 0.6887
HSA-MIR-3190 -15.94 2.027e-08 7.99e-06 0.8822
HSA-MIR-133A-2 5.9 1.068e-05 0.0042 0.6483
HSA-MIR-627 12.3 3.566e-05 0.014 0.8612

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-2116 to 'NEOADJUVANT.THERAPY'. P value = 1.31e-09 with T-test analysis.

Methods & Data
Input

  • Expresson data file = KIRC.miRseq_RPKM_log2.txt

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

  • Number of patients = 463

  • Number of genes = 467

  • 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

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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