Correlation between RPPA expression and clinical features
Thyroid Adenocarcinoma (RiskCategory_Low)
01 July 2013  |  awg_thca__2013_07_01
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1R49NZ3
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 11 clinical features across 78 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 2 genes correlated to 'AGE'.

    • XIAP|XIAP-R-C ,  EEF2|EEF2-R-V

  • 3 genes correlated to 'HISTOLOGICAL.TYPE'.

    • ANXA1|ANNEXIN_I-R-V ,  XIAP|XIAP-R-C ,  PIK3R1|PI3K-P85-R-V

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

    • ANXA1|ANNEXIN_I-R-V ,  CCNE2|CYCLIN_E2-R-C ,  RAD51|RAD51-M-C

  • 2 genes correlated to 'RADIATIONEXPOSURE'.

    • YAP1|YAP-R-V ,  SETD2|SETD2-R-C

  • 1 gene correlated to 'DISTANT.METASTASIS'.

    • RAD50|RAD50-M-C

  • 1 gene correlated to 'LYMPH.NODE.METASTASIS'.

    • STAT3|STAT3_PY705-R-V

  • No genes correlated to 'GENDER', 'COMPLETENESS.OF.RESECTION', 'NEOPLASM.DISEASESTAGE', 'MULTIFOCALITY', and 'TUMOR.SIZE'.

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=2 older N=0 younger N=2
GENDER t test   N=0        
HISTOLOGICAL TYPE t test N=3 thyroid papillary carcinoma - follicular (>= 99% follicular patterned) N=0 thyroid papillary carcinoma - classical/usual N=3
RADIATIONS RADIATION REGIMENINDICATION t test N=3 yes N=2 no N=1
RADIATIONEXPOSURE t test N=2 yes N=0 no N=2
DISTANT METASTASIS t test N=1 mx N=1 m0 N=0
LYMPH NODE METASTASIS t test N=1 nx N=0 n0 N=1
COMPLETENESS OF RESECTION ANOVA test   N=0        
NEOPLASM DISEASESTAGE t test   N=0        
MULTIFOCALITY t test   N=0        
TUMOR SIZE Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 47.85 (16)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
XIAP|XIAP-R-C -0.4051 0.0002339 0.0409
EEF2|EEF2-R-V -0.4037 0.0002471 0.043

Figure S1.  Get High-res Image As an example, this figure shows the association of XIAP|XIAP-R-C to 'AGE'. P value = 0.000234 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 58
  MALE 20
     
  Significant markers N = 0
Clinical variable #3: 'HISTOLOGICAL.TYPE'

3 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 49
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 29
     
  Significant markers N = 3
  Higher in THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 0
  Higher in THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 3
List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5.  Get Full Table List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

T(pos if higher in 'THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)') ttestP Q AUC
ANXA1|ANNEXIN_I-R-V -4.98 5.004e-06 0.000876 0.7776
XIAP|XIAP-R-C -4.54 2.514e-05 0.00437 0.7804
PIK3R1|PI3K-P85-R-V -3.84 0.0002498 0.0432 0.7333

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 = 5e-06 with T-test analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 75
     
  Significant markers N = 3
  Higher in YES 2
  Higher in NO 1
List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
ANXA1|ANNEXIN_I-R-V -8.44 1.292e-07 2.26e-05 0.8711
CCNE2|CYCLIN_E2-R-C 4.77 0.0001117 0.0194 0.7689
RAD51|RAD51-M-C 4.17 0.0001682 0.0291 0.7156

Figure S3.  Get High-res Image As an example, this figure shows the association of ANXA1|ANNEXIN_I-R-V to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.29e-07 with T-test analysis.

Clinical variable #5: 'RADIATIONEXPOSURE'

2 genes related to 'RADIATIONEXPOSURE'.

Table S8.  Basic characteristics of clinical feature: 'RADIATIONEXPOSURE'

RADIATIONEXPOSURE Labels N
  NO 64
  YES 3
     
  Significant markers N = 2
  Higher in YES 0
  Higher in NO 2
List of 2 genes differentially expressed by 'RADIATIONEXPOSURE'

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

T(pos if higher in 'YES') ttestP Q AUC
YAP1|YAP-R-V -4.65 1.923e-05 0.00337 0.7188
SETD2|SETD2-R-C -4.59 4.094e-05 0.00712 0.7604

Figure S4.  Get High-res Image As an example, this figure shows the association of YAP1|YAP-R-V to 'RADIATIONEXPOSURE'. P value = 1.92e-05 with T-test analysis.

Clinical variable #6: 'DISTANT.METASTASIS'

One gene related to 'DISTANT.METASTASIS'.

Table S10.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 37
  MX 40
     
  Significant markers N = 1
  Higher in MX 1
  Higher in M0 0
List of one gene differentially expressed by 'DISTANT.METASTASIS'

Table S11.  Get Full Table List of one gene differentially expressed by 'DISTANT.METASTASIS'

T(pos if higher in 'MX') ttestP Q AUC
RAD50|RAD50-M-C 4.41 3.415e-05 0.00598 0.773

Figure S5.  Get High-res Image As an example, this figure shows the association of RAD50|RAD50-M-C to 'DISTANT.METASTASIS'. P value = 3.41e-05 with T-test analysis.

Clinical variable #7: 'LYMPH.NODE.METASTASIS'

One gene related to 'LYMPH.NODE.METASTASIS'.

Table S12.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 58
  NX 20
     
  Significant markers N = 1
  Higher in NX 0
  Higher in N0 1
List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S13.  Get Full Table List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

T(pos if higher in 'NX') ttestP Q AUC
STAT3|STAT3_PY705-R-V -3.92 0.0002504 0.0438 0.7845

Figure S6.  Get High-res Image As an example, this figure shows the association of STAT3|STAT3_PY705-R-V to 'LYMPH.NODE.METASTASIS'. P value = 0.00025 with T-test analysis.

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S14.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 69
  R1 1
  RX 3
     
  Significant markers N = 0
Clinical variable #9: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S15.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 52
  STAGE II 25
     
  Significant markers N = 0
Clinical variable #10: 'MULTIFOCALITY'

No gene related to 'MULTIFOCALITY'.

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

MULTIFOCALITY Labels N
  MULTIFOCAL 38
  UNIFOCAL 36
     
  Significant markers N = 0
Clinical variable #11: 'TUMOR.SIZE'

No gene related to 'TUMOR.SIZE'.

Table S17.  Basic characteristics of clinical feature: 'TUMOR.SIZE'

TUMOR.SIZE Mean (SD) 2.65 (1.2)
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = THCA-RiskCategory_Low.rppa.txt

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

  • Number of patients = 78

  • Number of genes = 175

  • Number of clinical features = 11

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)