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 17814 genes and 8 clinical features across 32 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

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

    • RTN4IP1

  • 16 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • HMGB4 ,  FGF21 ,  KCNK10 ,  SPAG11B ,  EDC3 ,  ...

  • No genes correlated to 'Time to Death', 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.

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=0        
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
LYMPH NODE METASTASIS ANOVA test N=1        
NEOPLASM DISEASESTAGE ANOVA test N=16        
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) 0.5-56.8 (median=14)
  censored N = 27
  death N = 4
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 65.7 (11)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 18
  MALE 14
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S4.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 56.67 (49)
  Score N
  0 1
  80 1
  90 1
     
  Significant markers N = 0
Clinical variable #5: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 41.15 (15)
  Significant markers N = 0
Clinical variable #6: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S6.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1968.53 (11)
  Significant markers N = 0
Clinical variable #7: 'LYMPH.NODE.METASTASIS'

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

Table S7.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 23
  N1 4
  N2 4
  NX 1
     
  Significant markers N = 1
List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S8.  Get Full Table List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
RTN4IP1 1.542e-06 0.0275

Figure S1.  Get High-res Image As an example, this figure shows the association of RTN4IP1 to 'LYMPH.NODE.METASTASIS'. P value = 1.54e-06 with ANOVA analysis.

Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

16 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S9.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE IA 12
  STAGE IB 11
  STAGE IIA 1
  STAGE IIB 3
  STAGE IIIA 3
  STAGE IV 2
     
  Significant markers N = 16
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HMGB4 1.497e-09 2.67e-05
FGF21 2.968e-09 5.29e-05
KCNK10 4.947e-09 8.81e-05
SPAG11B 6.282e-09 0.000112
EDC3 1.483e-08 0.000264
SERPINB11 2.222e-08 0.000396
NTS 5.821e-08 0.00104
NLRP11 9.497e-08 0.00169
PRB1 2.339e-07 0.00416
NLRP5 6.754e-07 0.012

Figure S2.  Get High-res Image As an example, this figure shows the association of HMGB4 to 'NEOPLASM.DISEASESTAGE'. P value = 1.5e-09 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUAD-TP.medianexp.txt

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

  • Number of patients = 32

  • Number of genes = 17814

  • Number of clinical features = 8

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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