Correlation between miRseq expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1NG4NJB
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Clear Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NG4NJB
Overview
Introduction

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

Summary

Testing the association between 455 genes and 8 clinical features across 481 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 56 genes correlated to 'Time to Death'.

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

  • 1 gene correlated to 'AGE'.

    • HSA-MIR-590

  • 11 genes correlated to 'GENDER'.

    • HSA-MIR-100 ,  HSA-MIR-708 ,  HSA-MIR-455 ,  HSA-MIR-599 ,  HSA-MIR-30C-2 ,  ...

  • 33 genes correlated to 'PATHOLOGY.T'.

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

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

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

  • 34 genes correlated to 'TUMOR.STAGE'.

    • HSA-MIR-139 ,  HSA-MIR-21 ,  HSA-MIR-486 ,  HSA-MIR-155 ,  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=56 shorter survival N=49 longer survival N=7
AGE Spearman correlation test N=1 older N=1 younger N=0
GENDER t test N=11 male N=3 female N=8
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=33 higher pT N=24 lower pT N=9
PATHOLOGY N t test   N=0        
PATHOLOGICSPREAD(M) t test N=21 m1 N=16 m0 N=5
TUMOR STAGE Spearman correlation test N=34 higher stage N=26 lower stage N=8
Clinical variable #1: 'Time to Death'

56 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 = 155
     
  Significant markers N = 56
  associated with shorter survival 49
  associated with longer survival 7
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-223 1.64 2.721e-13 1.2e-10 0.652
HSA-MIR-130B 2 9.949e-12 4.5e-09 0.653
HSA-MIR-34C 1.28 5.06e-10 2.3e-07 0.644
HSA-MIR-21 2.1 1.033e-09 4.7e-07 0.659
HSA-MIR-10B 0.56 6.213e-09 2.8e-06 0.366
HSA-MIR-101-1 0.56 1.314e-08 5.9e-06 0.393
HSA-MIR-1248 1.41 4.348e-08 2e-05 0.619
HSA-MIR-18A 1.53 1.027e-07 4.6e-05 0.618
HSA-MIR-3614 1.61 1.914e-07 8.6e-05 0.616
HSA-MIR-138-2 1.42 2.924e-07 0.00013 0.651

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 = 2.72e-13 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One gene 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 gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-590 0.1846 4.661e-05 0.0212

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-590 to 'AGE'. P value = 4.66e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

11 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 163
  MALE 318
     
  Significant markers N = 11
  Higher in MALE 3
  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 8.53 6.355e-16 2.89e-13 0.7381
HSA-MIR-708 5.36 1.635e-07 7.42e-05 0.6556
HSA-MIR-455 -5.31 1.912e-07 8.66e-05 0.6567
HSA-MIR-599 -5.08 7.306e-07 0.00033 0.6754
HSA-MIR-30C-2 -4.37 1.674e-05 0.00755 0.6153
HSA-MIR-30A -4.35 1.779e-05 0.00801 0.6139
HSA-MIR-500B -4.2 3.386e-05 0.0152 0.6121
HSA-MIR-676 -4.15 4.509e-05 0.0202 0.6145
HSA-MIR-31 4.11 5.142e-05 0.023 0.6222
HSA-MIR-328 -4.07 5.919e-05 0.0264 0.6087

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-100 to 'GENDER'. P value = 6.36e-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'

33 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.93 (0.97)
  N
  T1 233
  T2 62
  T3 175
  T4 11
     
  Significant markers N = 33
  pos. correlated 24
  neg. correlated 9
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 -0.3323 7.3e-14 3.32e-11
HSA-MIR-21 0.2777 5.795e-10 2.63e-07
HSA-MIR-625 0.2742 9.592e-10 4.35e-07
HSA-MIR-486 -0.2685 2.197e-09 9.93e-07
HSA-MIR-144 -0.2539 1.629e-08 7.35e-06
HSA-MIR-155 0.2523 2.014e-08 9.06e-06
HSA-MIR-130B 0.244 6.012e-08 2.7e-05
HSA-MIR-451 -0.2317 2.781e-07 0.000125
HSA-MIR-9-1 0.2312 2.959e-07 0.000132
HSA-LET-7I 0.2244 6.627e-07 0.000296

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'PATHOLOGY.T'. P value = 7.3e-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 18
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

21 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) Labels N
  M0 405
  M1 76
     
  Significant markers N = 21
  Higher in M1 16
  Higher in M0 5
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-106B 5.7 6.381e-08 2.9e-05 0.6738
HSA-MIR-193A 5.57 9.469e-08 4.3e-05 0.6407
HSA-MIR-155 5.4 3.791e-07 0.000172 0.6873
HSA-MIR-625 5.4 4.328e-07 0.000196 0.6887
HSA-MIR-28 5.22 7.987e-07 0.00036 0.659
HSA-LET-7I 5.15 1.024e-06 0.000461 0.6667
HSA-MIR-144 -5.19 1.063e-06 0.000477 0.6849
HSA-MIR-130B 5.06 1.575e-06 0.000706 0.6683
HSA-MIR-27A 4.74 5.384e-06 0.00241 0.6273
HSA-MIR-21 4.69 7.295e-06 0.00325 0.6458

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-106B to 'PATHOLOGICSPREAD(M)'. P value = 6.38e-08 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

34 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 80
     
  Significant markers N = 34
  pos. correlated 26
  neg. correlated 8
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 -0.3433 9.422e-15 4.29e-12
HSA-MIR-21 0.2837 2.352e-10 1.07e-07
HSA-MIR-486 -0.2826 2.763e-10 1.25e-07
HSA-MIR-155 0.28 4.118e-10 1.86e-07
HSA-MIR-144 -0.2797 4.31e-10 1.94e-07
HSA-MIR-625 0.2742 9.583e-10 4.31e-07
HSA-MIR-451 -0.2572 1.052e-08 4.72e-06
HSA-LET-7I 0.2545 1.508e-08 6.75e-06
HSA-MIR-9-1 0.2358 1.667e-07 7.45e-05
HSA-MIR-10B -0.2348 1.895e-07 8.45e-05

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'TUMOR.STAGE'. P value = 9.42e-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 = 481

  • Number of genes = 455

  • 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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