Correlation between miRseq expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C19K48MM
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/C19K48MM
Overview
Introduction

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

Summary

Testing the association between 497 miRs and 10 clinical features across 954 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one miRs.

  • 36 miRs correlated to 'AGE'.

    • HSA-MIR-424 ,  HSA-MIR-99A ,  HSA-MIR-31 ,  HSA-MIR-381 ,  HSA-MIR-598 ,  ...

  • 29 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-210 ,  HSA-MIR-143 ,  HSA-LET-7F-2 ,  HSA-MIR-301A ,  HSA-MIR-374C ,  ...

  • 1 miR correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-758

  • 1 miR correlated to 'PATHOLOGY.N.STAGE'.

    • HSA-MIR-10A

  • 49 miRs correlated to 'PATHOLOGY.M.STAGE'.

    • HSA-MIR-16-1 ,  HSA-MIR-628 ,  HSA-MIR-2276 ,  HSA-MIR-550A-2 ,  HSA-MIR-361 ,  ...

  • 363 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-210 ,  HSA-MIR-616 ,  HSA-MIR-1306 ,  HSA-MIR-301B ,  HSA-MIR-328 ,  ...

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

    • HSA-MIR-3605 ,  HSA-MIR-3607 ,  HSA-MIR-1271 ,  HSA-MIR-3677 ,  HSA-MIR-940 ,  ...

  • No miRs correlated to 'Time to Death', 'GENDER', and 'NUMBER.OF.LYMPH.NODES'.

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=36 older N=8 younger N=28
NEOPLASM DISEASESTAGE ANOVA test N=29        
PATHOLOGY T STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY N STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
PATHOLOGY M STAGE ANOVA test N=49        
GENDER t test   N=0        
HISTOLOGICAL TYPE ANOVA test N=363        
RADIATIONS RADIATION REGIMENINDICATION t test N=15 yes N=1 no N=14
NUMBER OF LYMPH NODES Spearman correlation 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) 0-234.2 (median=21.6)
  censored N = 807
  death N = 110
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

36 miRs related to 'AGE'.

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

AGE Mean (SD) 58.56 (13)
  Significant markers N = 36
  pos. correlated 8
  neg. correlated 28
List of top 10 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-424 -0.2179 1.105e-11 5.49e-09
HSA-MIR-99A -0.1996 5.338e-10 2.65e-07
HSA-MIR-31 -0.2031 5.99e-10 2.97e-07
HSA-MIR-381 -0.1869 6.597e-09 3.26e-06
HSA-MIR-598 -0.1858 7.854e-09 3.87e-06
HSA-MIR-652 -0.1847 9.709e-09 4.78e-06
HSA-LET-7C -0.1701 1.31e-07 6.43e-05
HSA-MIR-542 -0.1686 1.707e-07 8.36e-05
HSA-MIR-202 -0.1989 6.515e-07 0.000319
HSA-MIR-125B-1 -0.1589 8.433e-07 0.000412

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-424 to 'AGE'. P value = 1.11e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

29 miRs related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 77
  STAGE IA 75
  STAGE IB 10
  STAGE II 9
  STAGE IIA 321
  STAGE IIB 216
  STAGE III 2
  STAGE IIIA 132
  STAGE IIIB 26
  STAGE IIIC 53
  STAGE IV 14
  STAGE TIS 1
  STAGE X 17
     
  Significant markers N = 29
List of top 10 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of top 10 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-210 1.709e-12 8.5e-10
HSA-MIR-143 8.923e-09 4.43e-06
HSA-LET-7F-2 1.141e-07 5.65e-05
HSA-MIR-301A 3.817e-07 0.000189
HSA-MIR-374C 5.012e-07 0.000247
HSA-MIR-3607 7.6e-07 0.000374
HSA-MIR-3653 2.524e-06 0.00124
HSA-MIR-130B 4.453e-06 0.00218
HSA-MIR-592 4.74e-06 0.00232
HSA-MIR-484 7.227e-06 0.00353

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'NEOPLASM.DISEASESTAGE'. P value = 1.71e-12 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.93 (0.73)
  N
  1 253
  2 550
  3 112
  4 36
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7.  Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-758 -0.1423 1.098e-05 0.00546

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-758 to 'PATHOLOGY.T.STAGE'. P value = 1.1e-05 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

One miR related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.77 (0.9)
  N
  0 450
  1 319
  2 106
  3 63
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

Table S9.  Get Full Table List of one miR significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-10A 0.154 2.154e-06 0.00107

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-10A to 'PATHOLOGY.N.STAGE'. P value = 2.15e-06 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

49 miRs related to 'PATHOLOGY.M.STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  CM0 (I+) 2
  M0 822
  M1 14
  MX 116
     
  Significant markers N = 49
List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
HSA-MIR-16-1 1.44e-10 7.16e-08
HSA-MIR-628 1.333e-09 6.61e-07
HSA-MIR-2276 4.719e-09 2.34e-06
HSA-MIR-550A-2 4.898e-08 2.42e-05
HSA-MIR-361 1.564e-07 7.71e-05
HSA-MIR-345 1.659e-07 8.16e-05
HSA-MIR-3174 2.314e-07 0.000114
HSA-MIR-424 3.099e-07 0.000152
HSA-MIR-23B 3.845e-07 0.000188
HSA-MIR-33A 5.668e-07 0.000277

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-16-1 to 'PATHOLOGY.M.STAGE'. P value = 1.44e-10 with ANOVA analysis.

Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 944
  MALE 10
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL.TYPE'

363 miRs related to 'HISTOLOGICAL.TYPE'.

Table S13.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 709
  INFILTRATING LOBULAR CARCINOMA 153
  MEDULLARY CARCINOMA 5
  MIXED HISTOLOGY (PLEASE SPECIFY) 27
  MUCINOUS CARCINOMA 14
  OTHER SPECIFY 44
     
  Significant markers N = 363
List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S14.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-210 9.712e-53 4.83e-50
HSA-MIR-616 2.188e-37 1.09e-34
HSA-MIR-1306 1.526e-36 7.56e-34
HSA-MIR-301B 5.092e-36 2.52e-33
HSA-MIR-328 1.088e-33 5.37e-31
HSA-MIR-301A 1.925e-33 9.47e-31
HSA-MIR-345 2.508e-33 1.23e-30
HSA-MIR-324 2.65e-33 1.3e-30
HSA-MIR-130B 1.834e-32 8.97e-30
HSA-LET-7D 2.66e-32 1.3e-29

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'HISTOLOGICAL.TYPE'. P value = 9.71e-53 with ANOVA analysis.

Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S15.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 277
  YES 677
     
  Significant markers N = 15
  Higher in YES 1
  Higher in NO 14
List of top 10 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S16.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-3605 -6.29 6.332e-10 3.15e-07 0.6127
HSA-MIR-3607 -5.7 1.902e-08 9.43e-06 0.6108
HSA-MIR-1271 -5 7.767e-07 0.000384 0.5949
HSA-MIR-3677 -4.56 6.215e-06 0.00307 0.5855
HSA-MIR-940 -4.54 7.262e-06 0.00358 0.5931
HSA-MIR-3647 -4.5 8.324e-06 0.0041 0.5818
HSA-MIR-3620 -4.51 1.05e-05 0.00515 0.6386
HSA-MIR-551B -4.42 1.225e-05 0.006 0.5939
HSA-MIR-320E -4.31 2.384e-05 0.0117 0.6302
HSA-MIR-545 -4.23 2.791e-05 0.0136 0.5876

Figure S7.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3605 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 6.33e-10 with T-test analysis.

Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

No miR related to 'NUMBER.OF.LYMPH.NODES'.

Table S17.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.32 (4.6)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 954

  • Number of miRs = 497

  • Number of clinical features = 10

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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