Correlation between miRseq expression and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1C24V4T
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C24V4T
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 518 miRs and 7 clinical features across 78 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

  • 33 miRs correlated to 'Time to Death'.

    • HSA-MIR-106B ,  HSA-MIR-29C ,  HSA-MIR-130B ,  HSA-MIR-664 ,  HSA-MIR-3170 ,  ...

  • 3 miRs correlated to 'AGE'.

    • HSA-MIR-505 ,  HSA-MIR-580 ,  HSA-MIR-361

  • 1 miR correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-130B

  • 9 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-615 ,  HSA-MIR-130B ,  HSA-MIR-489 ,  HSA-MIR-130A ,  HSA-MIR-550A-2 ,  ...

  • 2 miRs correlated to 'GENDER'.

    • HSA-MIR-139 ,  HSA-MIR-3074

  • No miRs correlated to 'PATHOLOGY.N.STAGE', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test N=33 shorter survival N=21 longer survival N=12
AGE Spearman correlation test N=3 older N=3 younger N=0
NEOPLASM DISEASESTAGE Kruskal-Wallis test N=1        
PATHOLOGY T STAGE Spearman correlation test N=9 higher stage N=8 lower stage N=1
PATHOLOGY N STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=2 male N=2 female N=0
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

33 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 4.1-153.6 (median=32)
  censored N = 52
  death N = 26
     
  Significant markers N = 33
  associated with shorter survival 21
  associated with longer survival 12
List of top 10 miRs differentially expressed by 'Time to Death'

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-106B 3.6 4.712e-07 0.00024 0.793
HSA-MIR-29C 0.58 5.101e-07 0.00026 0.198
HSA-MIR-130B 2.3 7.514e-07 0.00039 0.798
HSA-MIR-664 0.38 4.508e-06 0.0023 0.228
HSA-MIR-3170 1.97 1.447e-05 0.0074 0.778
HSA-MIR-18A 1.97 1.659e-05 0.0085 0.712
HSA-MIR-301B 1.76 1.76e-05 0.009 0.755
HSA-MIR-1255A 2.7 1.774e-05 0.0091 0.781
HSA-MIR-197 2.1 3.473e-05 0.018 0.758
HSA-MIR-3940 1.97 5.068e-05 0.026 0.788
Clinical variable #2: 'AGE'

3 miRs related to 'AGE'.

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

AGE Mean (SD) 46.44 (16)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 miRs differentially expressed by 'AGE'

Table S4.  Get Full Table List of 3 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-505 0.4496 3.637e-05 0.0188
HSA-MIR-580 0.4959 4.151e-05 0.0215
HSA-MIR-361 0.4308 8.253e-05 0.0426
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

One miR related to 'NEOPLASM.DISEASESTAGE'.

Table S5.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 8
  STAGE II 33
  STAGE III 16
  STAGE IV 16
     
  Significant markers N = 1
List of one miR differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6.  Get Full Table List of one miR differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-130B 0.0005459 0.283
Clinical variable #4: 'PATHOLOGY.T.STAGE'

9 miRs related to 'PATHOLOGY.T.STAGE'.

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

PATHOLOGY.T.STAGE Mean (SD) 2.51 (0.99)
  N
  1 8
  2 38
  3 9
  4 18
     
  Significant markers N = 9
  pos. correlated 8
  neg. correlated 1
List of 9 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S8.  Get Full Table List of 9 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-615 0.5074 8.59e-06 0.00445
HSA-MIR-130B 0.4656 3.319e-05 0.0172
HSA-MIR-489 -0.6525 3.877e-05 0.02
HSA-MIR-130A 0.4484 6.948e-05 0.0358
HSA-MIR-550A-2 0.4375 0.000191 0.0982
HSA-MIR-196A-1 0.4182 0.0002314 0.119
HSA-MIR-16-2 0.4107 0.0003075 0.157
HSA-MIR-3074 0.4041 0.000392 0.2
HSA-MIR-769 0.3933 0.0005779 0.295
Clinical variable #5: 'PATHOLOGY.N.STAGE'

No miR related to 'PATHOLOGY.N.STAGE'.

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

PATHOLOGY.N.STAGE Labels N
  class0 64
  class1 10
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

2 miRs related to 'GENDER'.

Table S10.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 49
  MALE 29
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 miRs differentially expressed by 'GENDER'

Table S11.  Get Full Table List of 2 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
HSA-MIR-139 333 9.706e-05 0.0503 0.7657
HSA-MIR-3074 1062 0.0002845 0.147 0.7474
Clinical variable #7: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S12.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 8
  NOT HISPANIC OR LATINO 28
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = ACC-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 78

  • Number of miRs = 518

  • Number of clinical features = 7

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)
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