PANCANCER: Correlation between miR expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between 817 miRs and 6 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

  • 5 miRs correlated to 'AGE'.

    • HSA-MIR-30B* ,  HSA-MIR-30D* ,  HSA-MIR-30B ,  HUR_4 ,  HSA-MIR-30D

  • 14 miRs correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • EBV-MIR-BART6-5P ,  HSA-MIR-96* ,  HSA-MIR-614 ,  KSHV-MIR-K12-9* ,  HSA-MIR-26A-2* ,  ...

  • 6 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-125B-1* ,  HSA-MIR-338-5P ,  EBV-MIR-BART17-5P ,  HSA-MIR-589* ,  HSA-MIR-518D-5P ,  ...

  • No miRs correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', and 'NEOADJUVANT.THERAPY'.

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=0        
AGE Spearman correlation test N=5 older N=1 younger N=4
PRIMARY SITE OF DISEASE ANOVA test N=14        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=6 yes N=6 no N=0
NEOADJUVANT THERAPY t test   N=0        
Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death Duration (Months) 0.3-180.2 (median=28.2)
  censored N = 265
  death N = 292
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

5 miRs related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 59.68 (12)
  Significant markers N = 5
  pos. correlated 1
  neg. correlated 4
List of 5 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 5 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30B* -0.2227 1.28e-07 0.000105
HSA-MIR-30D* -0.2187 2.161e-07 0.000176
HSA-MIR-30B -0.2048 1.254e-06 0.00102
HUR_4 0.1972 3.101e-06 0.00252
HSA-MIR-30D -0.1902 6.917e-06 0.00562

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-30B* to 'AGE'. P value = 1.28e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

14 miRs related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  OMENTUM 2
  OVARY 558
  PERITONEUM (OVARY) 2
     
  Significant markers N = 14
List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5.  Get Full Table List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
EBV-MIR-BART6-5P 5.151e-15 4.21e-12
HSA-MIR-96* 6.953e-13 5.67e-10
HSA-MIR-614 2.311e-12 1.88e-09
KSHV-MIR-K12-9* 2.283e-11 1.86e-08
HSA-MIR-26A-2* 1.475e-10 1.2e-07
EBV-MIR-BART9 2.28e-10 1.85e-07
HSA-MIR-600 6.428e-10 5.21e-07
HSA-MIR-548C-3P 6.817e-10 5.52e-07
HSA-MIR-374A* 4.249e-09 3.44e-06
HCMV-MIR-UL36* 1.328e-08 1.07e-05

Figure S2.  Get High-res Image As an example, this figure shows the association of EBV-MIR-BART6-5P to 'PRIMARY.SITE.OF.DISEASE'. P value = 5.15e-15 with ANOVA analysis.

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

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

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 75.64 (13)
  Score N
  40 2
  60 20
  80 49
  100 7
     
  Significant markers N = 0
Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

6 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 559
     
  Significant markers N = 6
  Higher in YES 6
  Higher in NO 0
List of 6 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of 6 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-125B-1* 12.11 2.011e-17 1.64e-14 0.7895
HSA-MIR-338-5P 12.61 1.412e-15 1.15e-12 0.802
EBV-MIR-BART17-5P 7.93 6.434e-14 5.24e-11 0.6887
HSA-MIR-589* 13.1 1.673e-12 1.36e-09 0.7651
HSA-MIR-518D-5P 6.99 4.74e-10 3.85e-07 0.6786
HSA-MIR-486-5P 15.93 2.182e-08 1.77e-05 0.8253

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-125B-1* to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.01e-17 with T-test analysis.

Clinical variable #6: 'NEOADJUVANT.THERAPY'

No miR related to 'NEOADJUVANT.THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 454
  YES 108
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = PANCANCER.mirna__h_mirna_8x15kv2__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt

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

  • Number of patients = 562

  • Number of miRs = 817

  • Number of clinical features = 6

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

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] 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)