Correlation between RPPA expression and clinical features
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 14 clinical features across 194 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • MSH6|MSH6-R-C

  • 2 genes correlated to 'AGE'.

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

  • 5 genes correlated to 'HISTOLOGICAL.TYPE'.

    • EGFR|EGFR_PY1173-R-C ,  CCNE2|CYCLIN_E2-R-C ,  NCOA3|AIB1-M-V ,  KIT|C-KIT-R-V ,  ARID1A|ARID1A-M-V

  • 2 genes correlated to 'PATHOLOGY.T'.

    • BID|BID-R-C ,  DVL3|DVL3-R-V

  • 2 genes correlated to 'PATHOLOGY.N'.

    • MAPK1 MAPK3|MAPK_PT202_Y204-R-V ,  STAT3|STAT3_PY705-R-V

  • 3 genes correlated to 'TUMOR.STAGE'.

    • MAPK1 MAPK3|MAPK_PT202_Y204-R-V ,  STAT3|STAT3_PY705-R-V ,  CCND1|CYCLIN_D1-R-V

  • 1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • CCNE1|CYCLIN_E1-M-V

  • No genes correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGICSPREAD(M)', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'TOBACCOSMOKINGHISTORYINDICATOR'.

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=1 shorter survival N=0 longer survival N=1
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=5        
PATHOLOGY T Spearman correlation test N=2 higher pT N=2 lower pT N=0
PATHOLOGY N Spearman correlation test N=2 higher pN N=0 lower pN N=2
PATHOLOGICSPREAD(M) t test   N=0        
TUMOR STAGE Spearman correlation test N=3 higher stage N=1 lower stage N=2
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
STOPPEDSMOKINGYEAR Spearman correlation test   N=0        
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=1 higher yearoftobaccosmokingonset N=1 lower yearoftobaccosmokingonset N=0
Clinical variable #1: 'Time to Death'

One 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=15.8)
  censored N = 109
  death N = 72
     
  Significant markers N = 1
  associated with shorter survival 0
  associated with longer survival 1
List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
MSH6|MSH6-R-C 0.5 0.0002128 0.037 0.34

Figure S1.  Get High-res Image As an example, this figure shows the association of MSH6|MSH6-R-C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.000213 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 67.45 (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 S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
PCNA|PCNA-M-V -0.3221 7.331e-06 0.00128
RAD51|RAD51-M-C -0.2791 0.0001144 0.0198

Figure S2.  Get High-res Image As an example, this figure shows the association of PCNA|PCNA-M-V to 'AGE'. P value = 7.33e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 49
  MALE 145
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S6.  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'

5 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 3
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 189
     
  Significant markers N = 5
List of 5 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 5 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
EGFR|EGFR_PY1173-R-C 1.552e-06 0.00027
CCNE2|CYCLIN_E2-R-C 2.425e-05 0.0042
NCOA3|AIB1-M-V 6.804e-05 0.0117
KIT|C-KIT-R-V 0.0001128 0.0193
ARID1A|ARID1A-M-V 0.0001154 0.0196

Figure S3.  Get High-res Image As an example, this figure shows the association of EGFR|EGFR_PY1173-R-C to 'HISTOLOGICAL.TYPE'. P value = 1.55e-06 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

2 genes related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.99 (0.75)
  N
  T1 44
  T2 119
  T3 20
  T4 11
     
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S10.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
BID|BID-R-C 0.2859 5.332e-05 0.00928
DVL3|DVL3-R-V 0.2601 0.0002495 0.0432

Figure S4.  Get High-res Image As an example, this figure shows the association of BID|BID-R-C to 'PATHOLOGY.T'. P value = 5.33e-05 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.48 (0.73)
  N
  N0 124
  N1 50
  N2 15
  N3 4
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S12.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
MAPK1 MAPK3|MAPK_PT202_Y204-R-V -0.3066 1.444e-05 0.00251
STAT3|STAT3_PY705-R-V -0.2705 0.0001416 0.0245

Figure S5.  Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'PATHOLOGY.N'. P value = 1.44e-05 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

No gene related to 'PATHOLOGICSPREAD(M)'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 175
  MX 16
     
  Significant markers N = 0
Clinical variable #9: 'TUMOR.STAGE'

3 genes related to 'TUMOR.STAGE'.

Table S14.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.68 (0.77)
  N
  Stage 1 98
  Stage 2 57
  Stage 3 36
     
  Significant markers N = 3
  pos. correlated 1
  neg. correlated 2
List of 3 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S15.  Get Full Table List of 3 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MAPK1 MAPK3|MAPK_PT202_Y204-R-V -0.2807 8.402e-05 0.0146
STAT3|STAT3_PY705-R-V -0.2737 0.0001274 0.022
CCND1|CYCLIN_D1-R-V 0.271 0.0001495 0.0257

Figure S6.  Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'TUMOR.STAGE'. P value = 8.4e-05 with Spearman correlation analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S16.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 8
  YES 186
     
  Significant markers N = 0
Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S17.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 51.71 (33)
  Significant markers N = 0
Clinical variable #12: 'STOPPEDSMOKINGYEAR'

No gene related to 'STOPPEDSMOKINGYEAR'.

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

STOPPEDSMOKINGYEAR Mean (SD) 1996.38 (12)
  Significant markers N = 0
Clinical variable #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

No gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S19.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 91
  CURRENT REFORMED SMOKER FOR > 15 YEARS 48
  CURRENT SMOKER 39
  LIFELONG NON-SMOKER 10
     
  Significant markers N = 0
Clinical variable #14: 'YEAROFTOBACCOSMOKINGONSET'

One gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S20.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1958.43 (12)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

Table S21.  Get Full Table List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

SpearmanCorr corrP Q
CCNE1|CYCLIN_E1-M-V 0.3419 7.311e-05 0.0127

Figure S7.  Get High-res Image As an example, this figure shows the association of CCNE1|CYCLIN_E1-M-V to 'YEAROFTOBACCOSMOKINGONSET'. P value = 7.31e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 194

  • Number of genes = 174

  • Number of clinical features = 14

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.

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)
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