Correlation between miRseq expression and clinical features
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C19S1PPD
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/C19S1PPD
Overview
Introduction

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

Summary

Testing the association between 548 miRs and 12 clinical features across 408 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one miRs.

  • 8 miRs correlated to 'Time to Death'.

    • HSA-MIR-377 ,  HSA-MIR-493 ,  HSA-MIR-654 ,  HSA-MIR-337 ,  HSA-MIR-758 ,  ...

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

    • HSA-LET-7B ,  HSA-LET-7A-2 ,  HSA-LET-7A-3 ,  HSA-LET-7A-1 ,  HSA-MIR-143 ,  ...

  • 3 miRs correlated to 'GENDER'.

    • HSA-MIR-187 ,  HSA-MIR-130B ,  HSA-MIR-15B

  • 3 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-552 ,  HSA-MIR-9-1 ,  HSA-MIR-9-2

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

    • HSA-MIR-660 ,  HSA-MIR-362 ,  HSA-MIR-152 ,  HSA-MIR-340 ,  HSA-MIR-19A ,  ...

  • 9 miRs correlated to 'NUMBERPACKYEARSSMOKED'.

    • HSA-MIR-152 ,  HSA-MIR-1180 ,  HSA-MIR-151 ,  HSA-MIR-940 ,  HSA-MIR-744 ,  ...

  • 5 miRs correlated to 'NUMBER.OF.LYMPH.NODES'.

    • HSA-MIR-421 ,  HSA-MIR-411 ,  HSA-MIR-758 ,  HSA-MIR-1293 ,  HSA-MIR-379

  • No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', and 'YEAROFTOBACCOSMOKINGONSET'.

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=8 shorter survival N=8 longer survival N=0
AGE Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE t test N=11 mx N=5 m0 N=6
GENDER t test N=3 male N=2 female N=1
HISTOLOGICAL TYPE ANOVA test N=3        
RADIATIONS RADIATION REGIMENINDICATION t test N=9 yes N=5 no N=4
NUMBERPACKYEARSSMOKED Spearman correlation test N=9 higher numberpackyearssmoked N=9 lower numberpackyearssmoked N=0
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=5 higher number.of.lymph.nodes N=4 lower number.of.lymph.nodes N=1
Clinical variable #1: 'Time to Death'

8 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-211 (median=16.2)
  censored N = 249
  death N = 154
     
  Significant markers N = 8
  associated with shorter survival 8
  associated with longer survival 0
List of 8 miRs significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 8 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-377 1.33 2.288e-05 0.013 0.599
HSA-MIR-493 1.37 2.329e-05 0.013 0.603
HSA-MIR-654 1.31 2.802e-05 0.015 0.599
HSA-MIR-337 1.31 3.072e-05 0.017 0.605
HSA-MIR-758 1.33 3.968e-05 0.022 0.596
HSA-MIR-127 1.4 5.636e-05 0.031 0.6
HSA-MIR-381 1.31 8.231e-05 0.045 0.598
HSA-MIR-382 1.38 9.239e-05 0.05 0.592

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-377 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.29e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No miR related to 'AGE'.

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

AGE Mean (SD) 60.86 (12)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 24
  STAGE II 62
  STAGE III 65
  STAGE IVA 194
  STAGE IVB 8
     
  Significant markers N = 0
Clinical variable #4: '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) 2.85 (1)
  N
  1 39
  2 105
  3 84
  4 130
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Mean (SD) 1.03 (0.95)
  N
  0 133
  1 52
  2 129
  3 7
     
  Significant markers N = 0
Clinical variable #6: '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 103
  MX 43
     
  Significant markers N = 11
  Higher in MX 5
  Higher in M0 6
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'

T(pos if higher in 'MX') ttestP Q AUC
HSA-LET-7B -5.92 3.928e-08 2.15e-05 0.7494
HSA-LET-7A-2 -5.5 1.802e-07 9.86e-05 0.7105
HSA-LET-7A-3 -5.47 2.013e-07 0.00011 0.7103
HSA-LET-7A-1 -5.47 2.088e-07 0.000114 0.709
HSA-MIR-143 -5.17 1.149e-06 0.000625 0.7225
HSA-MIR-450A-2 5.18 1.685e-06 0.000915 0.7456
HSA-MIR-93 4.56 1.657e-05 0.00898 0.7279
HSA-MIR-20A 4.47 2.311e-05 0.0125 0.7157
HSA-MIR-196A-2 4.39 3.863e-05 0.0209 0.7343
HSA-MIR-450A-1 4.32 5.153e-05 0.0278 0.7193

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-LET-7B to 'PATHOLOGY.M.STAGE'. P value = 3.93e-08 with T-test analysis.

Clinical variable #7: 'GENDER'

3 miRs related to 'GENDER'.

Table S9.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 118
  MALE 290
     
  Significant markers N = 3
  Higher in MALE 2
  Higher in FEMALE 1
List of 3 miRs differentially expressed by 'GENDER'

Table S10.  Get Full Table List of 3 miRs differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-187 -4.25 2.951e-05 0.0162 0.6211
HSA-MIR-130B 4.17 4.031e-05 0.0221 0.6114
HSA-MIR-15B 4.1 5.758e-05 0.0314 0.6243

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-187 to 'GENDER'. P value = 2.95e-05 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

3 miRs related to 'HISTOLOGICAL.TYPE'.

Table S11.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  HEAD AND NECK SQUAMOUS CELL CARCINOMA 401
  HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT 1
  HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE 6
     
  Significant markers N = 3
List of 3 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S12.  Get Full Table List of 3 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-552 8.216e-13 4.49e-10
HSA-MIR-9-1 1.957e-05 0.0107
HSA-MIR-9-2 2.063e-05 0.0112

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-552 to 'HISTOLOGICAL.TYPE'. P value = 8.22e-13 with ANOVA analysis.

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

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

Table S13.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 80
  YES 328
     
  Significant markers N = 9
  Higher in YES 5
  Higher in NO 4
List of 9 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S14.  Get Full Table List of 9 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-660 6.72 4.897e-10 2.68e-07 0.7231
HSA-MIR-362 5.23 5.443e-07 0.000298 0.6645
HSA-MIR-152 -4.42 1.599e-05 0.00873 0.6053
HSA-MIR-340 -4.36 2.714e-05 0.0148 0.652
HSA-MIR-19A 4.16 5.429e-05 0.0295 0.6203
HSA-MIR-2355 4.14 6.433e-05 0.0349 0.6527
HSA-MIR-374A -4.11 7.434e-05 0.0403 0.6327
HSA-MIR-628 -4.1 8.574e-05 0.0464 0.6423
HSA-MIR-1307 4.06 8.932e-05 0.0482 0.6476

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-660 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.9e-10 with T-test analysis.

Clinical variable #10: 'NUMBERPACKYEARSSMOKED'

9 miRs related to 'NUMBERPACKYEARSSMOKED'.

Table S15.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 46.94 (38)
  Significant markers N = 9
  pos. correlated 9
  neg. correlated 0
List of 9 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S16.  Get Full Table List of 9 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-152 0.3227 5.672e-07 0.000311
HSA-MIR-1180 0.2958 5.026e-06 0.00275
HSA-MIR-151 0.2796 1.682e-05 0.00918
HSA-MIR-940 0.2697 3.527e-05 0.0192
HSA-MIR-744 0.2685 3.683e-05 0.02
HSA-MIR-1266 0.273 4.063e-05 0.0221
HSA-MIR-1224 0.3634 4.197e-05 0.0227
HSA-MIR-1249 0.266 4.389e-05 0.0237
HSA-MIR-3677 0.2562 8.503e-05 0.0459

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-152 to 'NUMBERPACKYEARSSMOKED'. P value = 5.67e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S17.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1966.46 (13)
  Significant markers N = 0
Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

5 miRs related to 'NUMBER.OF.LYMPH.NODES'.

Table S18.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.33 (4.7)
  Significant markers N = 5
  pos. correlated 4
  neg. correlated 1
List of 5 miRs significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S19.  Get Full Table List of 5 miRs significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-421 0.2562 4.893e-06 0.00268
HSA-MIR-411 0.2365 2.429e-05 0.0133
HSA-MIR-758 0.2307 3.899e-05 0.0213
HSA-MIR-1293 -0.2269 6.187e-05 0.0337
HSA-MIR-379 0.2244 6.355e-05 0.0346

Figure S7.  Get High-res Image As an example, this figure shows the association of HSA-MIR-421 to 'NUMBER.OF.LYMPH.NODES'. P value = 4.89e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 408

  • Number of miRs = 548

  • Number of clinical features = 12

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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