Correlation between RPPA expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1C827BC
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/C1C827BC
Overview
Introduction

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

Summary

Testing the association between 174 genes and 12 clinical features across 195 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 2 genes correlated to 'AGE'.

    • PCNA|PCNA-M-V ,  RAD51|RAD51-M-C

  • 4 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CCNE2|CYCLIN_E2-R-C ,  KIT|C-KIT-R-V ,  ARID1A|ARID1A-M-V ,  NCOA3|AIB1-M-V

  • 1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • CCNE1|CYCLIN_E1-M-V

  • 5 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • RPS6|S6-R-C ,  STAT3|STAT3_PY705-R-V ,  XRCC1|XRCC1-R-C ,  FRAP1|MTOR-R-V ,  MAPK1 MAPK3|MAPK_PT202_Y204-R-V

  • 2 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • RPS6|S6-R-C ,  FRAP1|MTOR-R-V

  • 1 gene correlated to 'NEOPLASM.DISEASESTAGE'.

    • RPS6|S6_PS235_S236-R-V

  • No genes correlated to 'Time to Death', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', and 'DISTANT.METASTASIS'.

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
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=2 older N=0 younger N=2
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=4        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=1 higher yearoftobaccosmokingonset N=1 lower yearoftobaccosmokingonset N=0
DISTANT METASTASIS t test   N=0        
LYMPH NODE METASTASIS ANOVA test N=5        
COMPLETENESS OF RESECTION ANOVA test N=2        
NEOPLASM DISEASESTAGE ANOVA test N=1        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0-173.8 (median=16.2)
  censored N = 109
  death N = 73
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 67.41 (9.5)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
PCNA|PCNA-M-V -0.3209 7.564e-06 0.00132
RAD51|RAD51-M-C -0.2743 0.0001453 0.0251

Figure S1.  Get High-res Image As an example, this figure shows the association of PCNA|PCNA-M-V to 'AGE'. P value = 7.56e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 49
  MALE 146
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 30.29 (39)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

4 genes related to 'HISTOLOGICAL.TYPE'.

Table S6.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 3
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 2
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 190
     
  Significant markers N = 4
List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CCNE2|CYCLIN_E2-R-C 5.389e-06 0.000938
KIT|C-KIT-R-V 2.721e-05 0.00471
ARID1A|ARID1A-M-V 4.563e-05 0.00785
NCOA3|AIB1-M-V 6.086e-05 0.0104

Figure S2.  Get High-res Image As an example, this figure shows the association of CCNE2|CYCLIN_E2-R-C to 'HISTOLOGICAL.TYPE'. P value = 5.39e-06 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S8.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 9
  YES 186
     
  Significant markers N = 0
Clinical variable #7: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 51.69 (32)
  Significant markers N = 0
Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

One gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1958.47 (12)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

Table S11.  Get Full Table List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

SpearmanCorr corrP Q
CCNE1|CYCLIN_E1-M-V 0.3444 5.623e-05 0.00978

Figure S3.  Get High-res Image As an example, this figure shows the association of CCNE1|CYCLIN_E1-M-V to 'YEAROFTOBACCOSMOKINGONSET'. P value = 5.62e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S12.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 176
  MX 16
     
  Significant markers N = 0
Clinical variable #10: 'LYMPH.NODE.METASTASIS'

5 genes related to 'LYMPH.NODE.METASTASIS'.

Table S13.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 124
  N1 50
  N2 16
  N3 4
  NX 1
     
  Significant markers N = 5
List of 5 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S14.  Get Full Table List of 5 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
RPS6|S6-R-C 2.776e-05 0.00483
STAT3|STAT3_PY705-R-V 5.417e-05 0.00937
XRCC1|XRCC1-R-C 0.0001922 0.0331
FRAP1|MTOR-R-V 0.0002084 0.0356
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 0.0002808 0.0477

Figure S4.  Get High-res Image As an example, this figure shows the association of RPS6|S6-R-C to 'LYMPH.NODE.METASTASIS'. P value = 2.78e-05 with ANOVA analysis.

Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

2 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S15.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 145
  R1 4
  R2 4
  RX 9
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S16.  Get Full Table List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
RPS6|S6-R-C 3.051e-05 0.00531
FRAP1|MTOR-R-V 0.0001805 0.0312

Figure S5.  Get High-res Image As an example, this figure shows the association of RPS6|S6-R-C to 'COMPLETENESS.OF.RESECTION'. P value = 3.05e-05 with ANOVA analysis.

Clinical variable #12: 'NEOPLASM.DISEASESTAGE'

One gene related to 'NEOPLASM.DISEASESTAGE'.

Table S17.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 1
  STAGE IA 27
  STAGE IB 70
  STAGE II 1
  STAGE IIA 21
  STAGE IIB 35
  STAGE IIIA 23
  STAGE IIIB 14
     
  Significant markers N = 1
List of one gene differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S18.  Get Full Table List of one gene differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
RPS6|S6_PS235_S236-R-V 1.466e-05 0.00255

Figure S6.  Get High-res Image As an example, this figure shows the association of RPS6|S6_PS235_S236-R-V to 'NEOPLASM.DISEASESTAGE'. P value = 1.47e-05 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUSC-TP.rppa.txt

  • Clinical data file = LUSC-TP.clin.merged.picked.txt

  • Number of patients = 195

  • Number of genes = 174

  • 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

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] 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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle