Correlation between mRNA expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 18632 genes and 6 clinical features across 564 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • ZFHX4

  • 178 genes correlated to 'AGE'.

    • STS ,  GREB1 ,  GNPNAT1 ,  DEPDC6 ,  EIF4E3 ,  ...

  • 3 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • SPINK8 ,  PTBP1 ,  EBI3

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • WDR60

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

    • RAPGEF1 ,  SNX9 ,  EIF4G2 ,  PRRX1 ,  ZNF507 ,  ...

  • No genes correlated to 'NEOADJUVANT.THERAPY'

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=1 longer survival N=0
AGE Spearman correlation test N=178 older N=72 younger N=106
PRIMARY SITE OF DISEASE ANOVA test N=3        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
RADIATIONS RADIATION REGIMENINDICATION t test N=25 yes N=18 no N=7
NEOADJUVANT THERAPY t test   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) 0.3-180.2 (median=28.3)
  censored N = 267
  death N = 292
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
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
ZFHX4 1.64 1.27e-06 0.024 0.574

Figure S1.  Get High-res Image As an example, this figure shows the association of ZFHX4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.27e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

178 genes related to 'AGE'.

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

AGE Mean (SD) 59.69 (12)
  Significant markers N = 178
  pos. correlated 72
  neg. correlated 106
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
STS -0.3021 3.889e-13 7.25e-09
GREB1 -0.2914 2.762e-12 5.15e-08
GNPNAT1 -0.2909 3.005e-12 5.6e-08
DEPDC6 -0.2908 3.077e-12 5.73e-08
EIF4E3 -0.284 1.02e-11 1.9e-07
SLCO1A2 0.2838 1.06e-11 1.97e-07
GEMIN8 -0.2729 6.715e-11 1.25e-06
NPAL2 -0.2697 1.136e-10 2.12e-06
CCDC91 0.269 1.268e-10 2.36e-06
PRPS2 -0.2678 1.554e-10 2.89e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of STS to 'AGE'. P value = 3.89e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

3 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  OMENTUM 2
  OVARY 560
  PERITONEUM (OVARY) 2
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6.  Get Full Table List of 3 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
SPINK8 8.956e-15 1.67e-10
PTBP1 1.585e-08 0.000295
EBI3 4.421e-07 0.00824

Figure S3.  Get High-res Image As an example, this figure shows the association of SPINK8 to 'PRIMARY.SITE.OF.DISEASE'. P value = 8.96e-15 with ANOVA analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 75.64 (13)
  Score N
  40 2
  60 20
  80 49
  100 7
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
WDR60 0.5131 1.561e-06 0.0291

Figure S4.  Get High-res Image As an example, this figure shows the association of WDR60 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.56e-06 with Spearman correlation analysis.

Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S9.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 561
     
  Significant markers N = 25
  Higher in YES 18
  Higher in NO 7
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
RAPGEF1 21.48 9.569e-49 1.78e-44 0.8378
SNX9 -15.62 4.157e-33 7.74e-29 0.7623
EIF4G2 14.29 1.684e-23 3.14e-19 0.7629
PRRX1 13.58 9.302e-20 1.73e-15 0.6542
ZNF507 -23.25 5.634e-15 1.05e-10 0.9002
ACTL7B -11.43 8.136e-15 1.52e-10 0.7094
CNKSR2 -8.76 1.192e-14 2.22e-10 0.7249
AKAP9 16.8 2.418e-14 4.5e-10 0.7903
CRIP2 9.78 3.517e-14 6.55e-10 0.694
NF2 17.14 3.701e-13 6.89e-09 0.8491

Figure S5.  Get High-res Image As an example, this figure shows the association of RAPGEF1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.57e-49 with T-test analysis.

Clinical variable #6: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S11.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 456
  YES 108
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = OV.medianexp.txt

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

  • Number of patients = 564

  • Number of genes = 18632

  • Number of clinical features = 6

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

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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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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