Correlation between miRseq expression and clinical features
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C12B8WNF
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/C12B8WNF
Overview
Introduction

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

Summary

Testing the association between 544 miRs and 9 clinical features across 158 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one miRs.

  • 1 miR correlated to 'Time to Death'.

    • HSA-MIR-549

  • 2 miRs correlated to 'AGE'.

    • HSA-MIR-424 ,  HSA-MIR-1-2

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

    • HSA-MIR-202 ,  HSA-MIR-509-3 ,  HSA-MIR-509-1 ,  HSA-MIR-514-1 ,  HSA-MIR-509-2 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-944 ,  HSA-MIR-205 ,  HSA-MIR-194-2 ,  HSA-MIR-192 ,  HSA-MIR-194-1 ,  ...

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

    • HSA-MIR-143 ,  HSA-MIR-660 ,  HSA-LET-7F-2 ,  HSA-MIR-19A ,  HSA-MIR-20A ,  ...

  • No miRs correlated to 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'NUMBERPACKYEARSSMOKED', 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=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test N=2 older N=1 younger N=1
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE t test   N=0        
PATHOLOGY M STAGE ANOVA test N=11        
HISTOLOGICAL TYPE ANOVA test N=30        
RADIATIONS RADIATION REGIMENINDICATION t test N=23 yes N=16 no N=7
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

One miR related to 'Time to Death'.

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

Time to Death Duration (Months) 0-177 (median=12.7)
  censored N = 120
  death N = 31
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one miR significantly associated with 'Time to Death' by Cox regression test

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

HazardRatio Wald_P Q C_index
HSA-MIR-549 2.3 6.472e-05 0.035 0.783

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-549 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.47e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 miRs related to 'AGE'.

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

AGE Mean (SD) 47.64 (13)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 2 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-424 -0.326 3.097e-05 0.0168
HSA-MIR-1-2 0.3141 6.537e-05 0.0355

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-424 to 'AGE'. P value = 3.1e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PATHOLOGY.T.STAGE'

No miR related to 'PATHOLOGY.T.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.34 (0.6)
  N
  1 86
  2 31
  3 2
  4 2
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.N.STAGE'

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 78
  class1 39
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.M.STAGE'

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

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 68
  M1 3
  MX 54
     
  Significant markers N = 11
List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S8.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
HSA-MIR-202 1.014e-07 5.51e-05
HSA-MIR-509-3 2.558e-07 0.000139
HSA-MIR-509-1 3.608e-07 0.000196
HSA-MIR-514-1 6.149e-07 0.000333
HSA-MIR-509-2 9.271e-07 0.000501
HSA-MIR-514-2 1.091e-06 0.000588
HSA-MIR-514-3 1.838e-06 0.000989
HSA-MIR-449A 1.306e-05 0.00701
HSA-MIR-345 3.585e-05 0.0192
HSA-MIR-190B 3.603e-05 0.0193

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-202 to 'PATHOLOGY.M.STAGE'. P value = 1.01e-07 with ANOVA analysis.

Clinical variable #6: 'HISTOLOGICAL.TYPE'

30 miRs related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ADENOSQUAMOUS 2
  CERVICAL SQUAMOUS CELL CARCINOMA 132
  ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE 4
  ENDOCERVICAL TYPE OF ADENOCARCINOMA 16
  ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX 1
  MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE 3
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S10.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-944 2.701e-34 1.47e-31
HSA-MIR-205 2.586e-29 1.4e-26
HSA-MIR-194-2 1.016e-19 5.51e-17
HSA-MIR-192 1.15e-19 6.22e-17
HSA-MIR-194-1 1.996e-18 1.08e-15
HSA-MIR-375 3.56e-15 1.92e-12
HSA-MIR-203 7.83e-13 4.21e-10
HSA-MIR-215 1.227e-10 6.59e-08
HSA-MIR-10A 5.567e-10 2.98e-07
HSA-MIR-1293 1.102e-08 5.9e-06

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-944 to 'HISTOLOGICAL.TYPE'. P value = 2.7e-34 with ANOVA analysis.

Clinical variable #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S11.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 30
  YES 128
     
  Significant markers N = 23
  Higher in YES 16
  Higher in NO 7
List of top 10 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S12.  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-143 -7.04 1.861e-09 1.01e-06 0.8151
HSA-MIR-660 6.77 3.066e-08 1.66e-05 0.8365
HSA-LET-7F-2 -6.27 4.558e-08 2.46e-05 0.788
HSA-MIR-19A 6.16 1.458e-07 7.86e-05 0.7995
HSA-MIR-20A 6 3.373e-07 0.000181 0.8083
HSA-MIR-338 5.65 6.463e-07 0.000347 0.7911
HSA-MIR-93 5.73 6.933e-07 0.000372 0.788
HSA-MIR-15A 5.65 8.587e-07 0.000459 0.807
HSA-MIR-19B-2 5.5 1.223e-06 0.000653 0.7831
HSA-MIR-374A -5.31 2.505e-06 0.00134 0.7703

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-143 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.86e-09 with T-test analysis.

Clinical variable #8: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 18.18 (12)
  Significant markers N = 0
Clinical variable #9: 'NUMBER.OF.LYMPH.NODES'

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

Table S14.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.24 (2.8)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 158

  • Number of miRs = 544

  • 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

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.

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] 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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