Correlation between miRseq expression and clinical features
Esophageal Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1BV7FCR
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/C1BV7FCR
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 521 miRs and 9 clinical features across 124 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

  • 2 miRs correlated to 'Time to Death'.

    • HSA-MIR-505 ,  HSA-MIR-103-2

  • 10 miRs correlated to 'AGE'.

    • HSA-MIR-149 ,  HSA-MIR-944 ,  HSA-MIR-29A ,  HSA-MIR-708 ,  HSA-MIR-375 ,  ...

  • 6 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  HSA-MIR-192 ,  HSA-MIR-708 ,  HSA-MIR-944 ,  ...

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

    • HSA-MIR-125B-1 ,  HSA-MIR-206 ,  HSA-MIR-190 ,  HSA-MIR-199A-1 ,  HSA-MIR-378 ,  ...

  • 57 miRs correlated to 'RACE'.

    • HSA-MIR-944 ,  HSA-MIR-708 ,  HSA-MIR-194-1 ,  HSA-MIR-149 ,  HSA-MIR-205 ,  ...

  • No miRs correlated to 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', and 'NUMBERPACKYEARSSMOKED'.

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=2 shorter survival N=2 longer survival N=0
AGE Spearman correlation test N=10 older N=6 younger N=4
NEOPLASM DISEASESTAGE Kruskal-Wallis test N=6        
PATHOLOGY T STAGE Spearman correlation test N=16 higher stage N=9 lower stage N=7
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=57        
Clinical variable #1: 'Time to Death'

2 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0-122.1 (median=5.8)
  censored N = 79
  death N = 37
     
  Significant markers N = 2
  associated with shorter survival 2
  associated with longer survival 0
List of 2 miRs differentially expressed by 'Time to Death'

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

HazardRatio Wald_P Q C_index
HSA-MIR-505 2.4 0.0001525 0.079 0.649
HSA-MIR-103-2 2.4 0.000224 0.12 0.707
Clinical variable #2: 'AGE'

10 miRs related to 'AGE'.

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

AGE Mean (SD) 63.76 (13)
  Significant markers N = 10
  pos. correlated 6
  neg. correlated 4
List of 10 miRs differentially expressed by 'AGE'

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

SpearmanCorr corrP Q
HSA-MIR-149 -0.3873 8.82e-06 0.0046
HSA-MIR-944 -0.3817 1.562e-05 0.00812
HSA-MIR-29A 0.3732 1.968e-05 0.0102
HSA-MIR-708 -0.3607 3.865e-05 0.02
HSA-MIR-375 0.3484 7.327e-05 0.0379
HSA-MIR-147B 0.3507 0.0001219 0.0629
HSA-MIR-29B-2 0.3372 0.0001282 0.066
HSA-MIR-193B -0.3355 0.0001397 0.0718
HSA-MIR-29B-1 0.3292 0.0001886 0.0967
HSA-MIR-338 0.3157 0.0003552 0.182
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

6 miRs related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 7
  STAGE IA 4
  STAGE IB 5
  STAGE II 1
  STAGE IIA 33
  STAGE IIB 22
  STAGE III 16
  STAGE IIIA 8
  STAGE IIIB 6
  STAGE IIIC 4
  STAGE IV 2
  STAGE IVA 1
     
  Significant markers N = 6
List of 6 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6.  Get Full Table List of 6 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-194-1 0.0001592 0.0829
HSA-MIR-194-2 0.0002082 0.108
HSA-MIR-192 0.0003358 0.174
HSA-MIR-708 0.0003402 0.176
HSA-MIR-944 0.0004679 0.242
HSA-MIR-190 0.0005119 0.264
Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

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

PATHOLOGY.T.STAGE Mean (SD) 2.38 (0.84)
  N
  0 1
  1 20
  2 30
  3 59
  4 3
     
  Significant markers N = 16
  pos. correlated 9
  neg. correlated 7
List of top 10 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S8.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-125B-1 0.4123 5.674e-06 0.00296
HSA-MIR-206 -0.4531 3.109e-05 0.0162
HSA-MIR-190 -0.378 3.665e-05 0.019
HSA-MIR-199A-1 0.3695 5.621e-05 0.0291
HSA-MIR-378 -0.3657 6.801e-05 0.0352
HSA-MIR-199A-2 0.3547 0.000116 0.0598
HSA-MIR-708 0.34 0.0002294 0.118
HSA-MIR-100 0.3361 0.0002734 0.141
HSA-MIR-556 -0.3893 0.0003275 0.168
HSA-MIR-215 -0.3299 0.0003596 0.184
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 Mean (SD) 0.62 (0.78)
  N
  0 58
  1 41
  2 8
  3 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 89
  M1 1
  M1A 2
  MX 17
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

Table S11.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 18
  MALE 106
     
  Significant markers N = 0
Clinical variable #8: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 37.24 (21)
  Significant markers N = 0
Clinical variable #9: 'RACE'

57 miRs related to 'RACE'.

Table S13.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 35
  BLACK OR AFRICAN AMERICAN 1
  WHITE 83
     
  Significant markers N = 57
List of top 10 miRs differentially expressed by 'RACE'

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

ANOVA_P Q
HSA-MIR-944 1.08e-09 5.63e-07
HSA-MIR-708 2.25e-09 1.17e-06
HSA-MIR-194-1 4.325e-09 2.24e-06
HSA-MIR-149 6.045e-09 3.13e-06
HSA-MIR-205 6.877e-09 3.56e-06
HSA-MIR-34B 8.147e-09 4.2e-06
HSA-MIR-193B 1.406e-08 7.24e-06
HSA-MIR-194-2 2.13e-08 1.09e-05
HSA-MIR-192 5.74e-08 2.94e-05
HSA-MIR-365-2 5.819e-08 2.98e-05
Methods & Data
Input

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

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

  • Number of patients = 124

  • Number of miRs = 521

  • Number of clinical features = 9

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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle