Thyroid Adenocarcinoma: Correlation between miRseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 533 genes and 5 clinical features across 150 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • HSA-MIR-1229

  • 76 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-21 ,  HSA-MIR-146B ,  HSA-MIR-3926-1 ,  HSA-MIR-7-2 ,  HSA-MIR-31 ,  ...

  • 8 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-888 ,  HSA-MIR-3130-1 ,  HSA-MIR-1269 ,  HSA-MIR-2276 ,  HSA-MIR-374A ,  ...

  • 3 genes correlated to 'NEOADJUVANT.THERAPY'.

    • HSA-MIR-9-1 ,  HSA-MIR-424 ,  HSA-MIR-129-1

  • No genes correlated to 'GENDER'

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 genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
AGE Spearman correlation test N=1 older N=1 younger N=0
GENDER t test   N=0        
HISTOLOGICAL TYPE ANOVA test N=76        
RADIATIONS RADIATION REGIMENINDICATION t test N=8 yes N=6 no N=2
NEOADJUVANT THERAPY t test N=3 yes N=2 no N=1
Clinical variable #1: 'AGE'

One gene related to 'AGE'.

Table S1.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 46.62 (16)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-1229 0.3431 6.026e-05 0.0321

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1229 to 'AGE'. P value = 6.03e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 108
  MALE 42
     
  Significant markers N = 0
Clinical variable #3: 'HISTOLOGICAL.TYPE'

76 genes related to 'HISTOLOGICAL.TYPE'.

Table S4.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  OTHER 6
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 78
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 50
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 16
     
  Significant markers N = 76
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-21 1.362e-17 7.26e-15
HSA-MIR-146B 2.206e-13 1.17e-10
HSA-MIR-3926-1 4.126e-12 2.19e-09
HSA-MIR-7-2 6.431e-11 3.41e-08
HSA-MIR-31 1.927e-10 1.02e-07
HSA-MIR-652 9.885e-10 5.22e-07
HSA-MIR-345 1.427e-09 7.52e-07
HSA-MIR-22 2.249e-09 1.18e-06
HSA-MIR-30C-2 3.259e-09 1.71e-06
HSA-MIR-511-1 3.483e-09 1.83e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-21 to 'HISTOLOGICAL.TYPE'. P value = 1.36e-17 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

8 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 14
  YES 136
     
  Significant markers N = 8
  Higher in YES 6
  Higher in NO 2
List of 8 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of 8 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-888 6.02 4.502e-07 0.000237 0.8571
HSA-MIR-3130-1 -6.19 8.083e-07 0.000425 0.8102
HSA-MIR-1269 5.6 1.515e-06 0.000796 0.845
HSA-MIR-2276 5.93 3.052e-06 0.0016 0.7868
HSA-MIR-374A -5.5 1.842e-05 0.00964 0.8025
HSA-MIR-324 5.11 2.586e-05 0.0135 0.7563
HSA-MIR-1976 5.08 4.102e-05 0.0214 0.7883
HSA-MIR-20B 4.75 9.097e-05 0.0473 0.7646

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-888 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.5e-07 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

3 genes related to 'NEOADJUVANT.THERAPY'.

Table S8.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 3
  YES 147
     
  Significant markers N = 3
  Higher in YES 2
  Higher in NO 1
List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S9.  Get Full Table List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-9-1 8.66 2.342e-10 8.88e-08 0.7937
HSA-MIR-424 -8.37 6.389e-08 2.42e-05 0.8299
HSA-MIR-129-1 12.61 1.064e-07 4.01e-05 0.9

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-9-1 to 'NEOADJUVANT.THERAPY'. P value = 2.34e-10 with T-test analysis.

Methods & Data
Input

  • Expresson data file = THCA.miRseq_RPKM_log2.txt

  • Clinical data file = THCA.clin.merged.picked.txt

  • Number of patients = 150

  • Number of genes = 533

  • Number of clinical features = 5

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] 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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