Correlation between miRseq 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 506 genes and 5 clinical features across 817 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 2 genes correlated to 'Time to Death'.

    • HSA-MIR-874 ,  HSA-MIR-148B

  • 25 genes correlated to 'AGE'.

    • HSA-MIR-424 ,  HSA-MIR-381 ,  HSA-MIR-31 ,  HSA-MIR-99A ,  HSA-MIR-542 ,  ...

  • 3 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-374C ,  HSA-MIR-3607 ,  HSA-MIR-489

  • 7 genes correlated to 'NEOADJUVANT.THERAPY'.

    • HSA-MIR-374C ,  HSA-MIR-3607 ,  HSA-MIR-1180 ,  HSA-MIR-26A-1 ,  HSA-MIR-3676 ,  ...

  • No genes correlated to 'GENDER'

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=2 shorter survival N=2 longer survival N=0
AGE Spearman correlation test N=25 older N=2 younger N=23
GENDER t test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=3 yes N=0 no N=3
NEOADJUVANT THERAPY t test N=7 yes N=3 no N=4
Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-223.4 (median=19)
  censored N = 664
  death N = 94
     
  Significant markers N = 2
  associated with shorter survival 2
  associated with longer survival 0
List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-874 1.57 1.037e-05 0.0052 0.606
HSA-MIR-148B 1.78 6.237e-05 0.031 0.625

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-874 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.04e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

25 genes related to 'AGE'.

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

AGE Mean (SD) 58.26 (13)
  Significant markers N = 25
  pos. correlated 2
  neg. correlated 23
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-424 -0.2216 1.56e-10 7.89e-08
HSA-MIR-381 -0.2139 6.764e-10 3.42e-07
HSA-MIR-31 -0.213 1.308e-09 6.59e-07
HSA-MIR-99A -0.2057 3.019e-09 1.52e-06
HSA-MIR-542 -0.2044 3.778e-09 1.9e-06
HSA-MIR-598 -0.1996 8.862e-09 4.44e-06
HSA-MIR-652 -0.1965 1.505e-08 7.52e-06
HSA-LET-7C -0.1774 3.37e-07 0.000168
HSA-MIR-450B -0.1705 9.763e-07 0.000486
HSA-MIR-125B-1 -0.1652 2.08e-06 0.00103

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-424 to 'AGE'. P value = 1.56e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 808
  MALE 9
     
  Significant markers N = 0
Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 189
  YES 628
     
  Significant markers N = 3
  Higher in YES 0
  Higher in NO 3
List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-374C -4.73 3.893e-06 0.00197 0.6313
HSA-MIR-3607 -4.3 2.14e-05 0.0108 0.5726
HSA-MIR-489 -4.3 2.917e-05 0.0147 0.6304

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-374C to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.89e-06 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

7 genes related to 'NEOADJUVANT.THERAPY'.

Table S8.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 299
  YES 518
     
  Significant markers N = 7
  Higher in YES 3
  Higher in NO 4
List of 7 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S9.  Get Full Table List of 7 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-374C -5.75 1.614e-08 8.17e-06 0.6404
HSA-MIR-3607 -5.21 2.362e-07 0.000119 0.5764
HSA-MIR-1180 4.7 3.18e-06 0.0016 0.5965
HSA-MIR-26A-1 -4.5 7.912e-06 0.00398 0.575
HSA-MIR-3676 4.41 1.232e-05 0.00618 0.5909
HSA-MIR-361 4.29 2.067e-05 0.0104 0.5903
HSA-MIR-320E -3.93 9.988e-05 0.0499 0.5878

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-374C to 'NEOADJUVANT.THERAPY'. P value = 1.61e-08 with T-test analysis.

Methods & Data
Input

  • Expresson data file = BRCA.miRseq_RPKM_log2.txt

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

  • Number of patients = 817

  • Number of genes = 506

  • Number of clinical features = 5

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.

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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