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

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

Summary

Testing the association between 175 genes and 5 clinical features across 155 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

- 18 genes correlated to 'AGE'.

- SNAI2|SNAIL-M-C , AKT1 AKT2 AKT3|AKT_PS473-R-V , EEF2|EEF2-R-V , EGFR|EGFR_PY1068-R-V , PIK3R1|PI3K-P85-R-V , ...

- 46 genes correlated to 'HISTOLOGICAL.TYPE'.

- ANXA1|ANNEXIN_I-R-V , FN1|FIBRONECTIN-R-C , CHEK2|CHK2-M-C , IRS1|IRS1-R-V , C12ORF5|TIGAR-R-V , ...

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

- ERBB2|HER2_PY1248-R-V , ARID1A|ARID1A-M-V , WWTR1|TAZ_PS89-R-C , CDKN1B|P27_PT157-R-C , MET|C-MET-M-C , ...

- 2 genes correlated to 'NEOADJUVANT.THERAPY'.

- NF2|NF2-R-C , EIF4E|EIF4E-R-V

- 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=18 | older | N=8 | younger | N=10
GENDER | t test | N=0 |  |  |  |
HISTOLOGICAL TYPE | ANOVA test | N=46 |  |  |  |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=8 | yes | N=6 | no | N=2
NEOADJUVANT THERAPY | t test | N=2 | yes | N=0 | no | N=2

Clinical variable #1: 'AGE'

18 genes related to 'AGE'.

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

AGE | Mean (SD) | 46.58 (16)
| Significant markers | N = 18
| pos. correlated | 8
| neg. correlated | 10

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S2. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
SNAI2|SNAIL-M-C | 0.3726 | 1.801e-06 | 0.000315
AKT1 AKT2 AKT3|AKT_PS473-R-V | 0.3708 | 2.044e-06 | 0.000356
EEF2|EEF2-R-V | -0.3662 | 2.777e-06 | 0.00048
EGFR|EGFR_PY1068-R-V | 0.3483 | 8.944e-06 | 0.00154
PIK3R1|PI3K-P85-R-V | -0.3411 | 1.403e-05 | 0.0024
ATM|ATM-R-C | -0.3361 | 1.91e-05 | 0.00325
MAPK8|JNK_PT183_PT185-R-V | 0.3346 | 2.089e-05 | 0.00353
YAP1|YAP_PS127-R-C | -0.3291 | 2.894e-05 | 0.00486
AKT1 AKT2 AKT3|AKT_PT308-R-V | 0.3283 | 3.034e-05 | 0.00507
BID|BID-R-C | -0.3283 | 3.043e-05 | 0.00507

Figure S1. Get High-res Image As an example, this figure shows the association of SNAI2|SNAIL-M-C to 'AGE'. P value = 1.8e-06 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 | 106
| MALE | 49
| Significant markers | N = 0

Clinical variable #3: 'HISTOLOGICAL.TYPE'

46 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE | Labels | N
| OTHER | 7
| THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | 83
| THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | 52
| THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) | 13
| Significant markers | N = 46

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
--- | --- | ---
ANXA1|ANNEXIN_I-R-V | 7.948e-19 | 1.39e-16
FN1|FIBRONECTIN-R-C | 4.058e-13 | 7.06e-11
CHEK2|CHK2-M-C | 9.245e-11 | 1.6e-08
IRS1|IRS1-R-V | 1.125e-10 | 1.94e-08
C12ORF5|TIGAR-R-V | 1.268e-10 | 2.17e-08
PIK3R1|PI3K-P85-R-V | 5.673e-10 | 9.64e-08
RAF1|C-RAF_PS338-R-C | 1.018e-09 | 1.72e-07
CDKN1B|P27_PT157-R-C | 1.112e-09 | 1.87e-07
STK11|LKB1-M-C | 8.732e-09 | 1.46e-06
MET|C-MET-M-C | 8.905e-09 | 1.48e-06

Figure S2. Get High-res Image As an example, this figure shows the association of ANXA1|ANNEXIN_I-R-V to 'HISTOLOGICAL.TYPE'. P value = 7.95e-19 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 | 13
| YES | 142
| 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
--- | --- | --- | --- | ---
ERBB2|HER2_PY1248-R-V | 4.72 | 4.656e-05 | 0.00815 | 0.7161
ARID1A|ARID1A-M-V | 4.48 | 0.0001079 | 0.0188 | 0.7048
WWTR1|TAZ_PS89-R-C | 4.93 | 0.0001088 | 0.0188 | 0.8028
CDKN1B|P27_PT157-R-C | 4.66 | 0.000151 | 0.026 | 0.7557
MET|C-MET-M-C | 4.35 | 0.0001511 | 0.026 | 0.6853
IRS1|IRS1-R-V | 4.5 | 0.0001591 | 0.027 | 0.7183
DVL3|DVL3-R-V | -4.63 | 0.0001852 | 0.0313 | 0.7841
BCL2L1|BCL-XL-R-V | -4.78 | 0.0002339 | 0.0393 | 0.825

Figure S3. Get High-res Image As an example, this figure shows the association of ERBB2|HER2_PY1248-R-V to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.66e-05 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

2 genes related to 'NEOADJUVANT.THERAPY'.

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

NEOADJUVANT.THERAPY | Labels | N
| NO | 3
| YES | 152
| Significant markers | N = 2
| Higher in YES | 0
| Higher in NO | 2

List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S9. Get Full Table List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
NF2|NF2-R-C | -7.23 | 1.596e-05 | 0.00279 | 0.7939
EIF4E|EIF4E-R-V | -9.72 | 2.247e-05 | 0.00391 | 0.8816

Figure S4. Get High-res Image As an example, this figure shows the association of NF2|NF2-R-C to 'NEOADJUVANT.THERAPY'. P value = 1.6e-05 with T-test analysis.

Methods & Data
Input

- Expresson data file = THCA.rppa.txt

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

- Number of patients = 155

- Number of genes = 175

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle