Correlation between miRseq expression and clinical features
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1G73C3V
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/C1G73C3V
Overview
Introduction

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

Summary

Testing the association between 547 miRs and 12 clinical features across 377 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one miRs.

  • 22 miRs correlated to 'Time to Death'.

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

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

    • HSA-MIR-421

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

    • HSA-LET-7B ,  HSA-LET-7A-2 ,  HSA-LET-7A-3 ,  HSA-LET-7A-1 ,  HSA-MIR-196A-2 ,  ...

  • 1 miR correlated to 'GENDER'.

    • HSA-MIR-15B

  • 3 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-552 ,  HSA-MIR-944 ,  HSA-MIR-664

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

    • HSA-MIR-660 ,  HSA-MIR-362

  • 8 miRs correlated to 'NUMBERPACKYEARSSMOKED'.

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

  • 1 miR correlated to 'NUMBER.OF.LYMPH.NODES'.

    • HSA-MIR-421

  • No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.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=22 shorter survival N=22 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=1 higher stage N=1 lower stage N=0
PATHOLOGY M STAGE t test N=12 mx N=6 m0 N=6
GENDER t test N=1 male N=1 female N=0
HISTOLOGICAL TYPE ANOVA test N=3        
RADIATIONS RADIATION REGIMENINDICATION t test N=2 yes N=2 no N=0
NUMBERPACKYEARSSMOKED Spearman correlation test N=8 higher numberpackyearssmoked N=8 lower numberpackyearssmoked N=0
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=1 higher number.of.lymph.nodes N=1 lower number.of.lymph.nodes N=0
Clinical variable #1: 'Time to Death'

22 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-210.9 (median=15.9)
  censored N = 232
  death N = 140
     
  Significant markers N = 22
  associated with shorter survival 22
  associated with longer survival 0
List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

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-758 1.44 1.323e-06 0.00072 0.623
HSA-MIR-377 1.4 1.857e-06 0.001 0.626
HSA-MIR-654 1.38 2.402e-06 0.0013 0.623
HSA-MIR-493 1.45 2.414e-06 0.0013 0.626
HSA-MIR-337 1.37 3.708e-06 0.002 0.629
HSA-MIR-382 1.49 5.095e-06 0.0028 0.621
HSA-MIR-127 1.48 6.246e-06 0.0034 0.623
HSA-MIR-379 1.42 9.893e-06 0.0053 0.627
HSA-MIR-487B 1.42 1.361e-05 0.0073 0.623
HSA-MIR-154 1.39 1.528e-05 0.0082 0.616

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-758 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.32e-06 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) 60.78 (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 58
  STAGE III 58
  STAGE IVA 179
  STAGE IVB 6
     
  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 37
  2 97
  3 76
  4 120
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Mean (SD) 1.02 (0.95)
  N
  0 124
  1 45
  2 121
  3 5
     
  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 S7.  Get Full Table List of one miR significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-421 0.2281 7.937e-05 0.0434

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-421 to 'PATHOLOGY.N.STAGE'. P value = 7.94e-05 with Spearman correlation analysis.

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

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

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

PATHOLOGY.M.STAGE Labels N
  M0 89
  MX 28
     
  Significant markers N = 12
  Higher in MX 6
  Higher in M0 6
List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S9.  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 -6.07 8.642e-08 4.73e-05 0.8022
HSA-LET-7A-2 -5.5 3.212e-07 0.000175 0.748
HSA-LET-7A-3 -5.49 3.32e-07 0.000181 0.7496
HSA-LET-7A-1 -5.47 3.609e-07 0.000196 0.7472
HSA-MIR-196A-2 5.47 1.715e-06 0.000931 0.8006
HSA-MIR-93 4.92 9.755e-06 0.00529 0.7801
HSA-MIR-181B-2 4.81 1.477e-05 0.00799 0.7669
HSA-MIR-143 -4.54 3.142e-05 0.017 0.7319
HSA-MIR-151 -4.46 4.175e-05 0.0225 0.7348
HSA-MIR-450A-2 4.44 5.68e-05 0.0306 0.752

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-LET-7B to 'PATHOLOGY.M.STAGE'. P value = 8.64e-08 with T-test analysis.

Clinical variable #7: 'GENDER'

One miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 109
  MALE 268
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one miR differentially expressed by 'GENDER'

Table S11.  Get Full Table List of one miR differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-15B 4.08 6.321e-05 0.0346 0.6309

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-15B to 'GENDER'. P value = 6.32e-05 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

3 miRs related to 'HISTOLOGICAL.TYPE'.

Table S12.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  HEAD AND NECK SQUAMOUS CELL CARCINOMA 372
  HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT 1
  HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE 4
     
  Significant markers N = 3
List of 3 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S13.  Get Full Table List of 3 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-552 4.528e-12 2.45e-09
HSA-MIR-944 2.747e-05 0.0149
HSA-MIR-664 5.129e-05 0.0277

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-552 to 'HISTOLOGICAL.TYPE'. P value = 4.53e-12 with ANOVA analysis.

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

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

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 75
  YES 302
     
  Significant markers N = 2
  Higher in YES 2
  Higher in NO 0
List of 2 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of 2 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-660 5.96 2.441e-08 1.34e-05 0.7095
HSA-MIR-362 4.34 2.757e-05 0.0151 0.6464

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-660 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.44e-08 with T-test analysis.

Clinical variable #10: 'NUMBERPACKYEARSSMOKED'

8 miRs related to 'NUMBERPACKYEARSSMOKED'.

Table S16.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 47.38 (39)
  Significant markers N = 8
  pos. correlated 8
  neg. correlated 0
List of 8 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S17.  Get Full Table List of 8 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-152 0.3514 1.386e-07 7.58e-05
HSA-MIR-1180 0.3083 4.547e-06 0.00248
HSA-MIR-151 0.2991 8.956e-06 0.00488
HSA-MIR-744 0.2891 1.812e-05 0.00986
HSA-MIR-940 0.2889 1.926e-05 0.0105
HSA-MIR-1266 0.2789 5.145e-05 0.0279
HSA-MIR-106B 0.2684 7.293e-05 0.0395
HSA-MIR-1224 0.3618 8.223e-05 0.0444

Figure S7.  Get High-res Image As an example, this figure shows the association of HSA-MIR-152 to 'NUMBERPACKYEARSSMOKED'. P value = 1.39e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S18.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1966.34 (13)
  Significant markers N = 0
Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

One miR related to 'NUMBER.OF.LYMPH.NODES'.

Table S19.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.49 (4.9)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S20.  Get Full Table List of one miR significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-421 0.2426 3.71e-05 0.0203

Figure S8.  Get High-res Image As an example, this figure shows the association of HSA-MIR-421 to 'NUMBER.OF.LYMPH.NODES'. P value = 3.71e-05 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 = 377

  • Number of miRs = 547

  • 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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