Correlation between miR expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 534 miRs and 6 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

  • 4 miRs correlated to 'Time to Death'.

    • HSA-MIR-222 ,  HSA-MIR-221 ,  HSA-MIR-34A ,  HSA-MIR-148A

  • 4 miRs correlated to 'AGE'.

    • HSA-MIR-210 ,  HSA-MIR-148A ,  HSA-MIR-552 ,  HSA-MIR-29B

  • 6 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-139 ,  HSA-MIR-29C ,  HSA-MIR-433 ,  HSA-MIR-137 ,  HSA-MIR-330 ,  ...

  • No miRs correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=4 shorter survival N=4 longer survival N=0
AGE Spearman correlation test N=4 older N=3 younger N=1
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=6        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
Clinical variable #1: 'Time to Death'

4 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-127.6 (median=9.9)
  censored N = 112
  death N = 448
     
  Significant markers N = 4
  associated with shorter survival 4
  associated with longer survival 0
List of 4 miRs significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 4 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-222 1.27 3.524e-10 1.9e-07 0.564
HSA-MIR-221 1.31 1.807e-07 9.6e-05 0.555
HSA-MIR-34A 1.19 4e-05 0.021 0.547
HSA-MIR-148A 1.18 8.284e-05 0.044 0.554

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-222 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.52e-10 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

4 miRs related to 'AGE'.

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

AGE Mean (SD) 57.91 (14)
  Significant markers N = 4
  pos. correlated 3
  neg. correlated 1
List of 4 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 4 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-210 0.2017 1.491e-06 0.000796
HSA-MIR-148A 0.1947 3.462e-06 0.00184
HSA-MIR-552 -0.1699 5.304e-05 0.0282
HSA-MIR-29B 0.1682 6.366e-05 0.0338

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'AGE'. P value = 1.49e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 218
  MALE 342
     
  Significant markers N = 0
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) 77.37 (15)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

6 miRs related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  GLIOBLASTOMA MULTIFORME (GBM) 8
  TREATED PRIMARY GBM 20
  UNTREATED PRIMARY (DE NOVO) GBM 532
     
  Significant markers N = 6
List of 6 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 6 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-139 9.232e-07 0.000493
HSA-MIR-29C 2.043e-06 0.00109
HSA-MIR-433 5.361e-06 0.00285
HSA-MIR-137 2.015e-05 0.0107
HSA-MIR-330 3.539e-05 0.0188
HSA-MIR-29B 5.565e-05 0.0294

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'HISTOLOGICAL.TYPE'. P value = 9.23e-07 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No miR related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S9.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 389
  YES 171
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = GBM-TP.mirna__h_mirna_8x15k__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt

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

  • Number of patients = 560

  • Number of miRs = 534

  • 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

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

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

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.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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