Correlation between mRNAseq 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 18210 genes and 5 clinical features across 152 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • LRRC61|65999

  • 26 genes correlated to 'GENDER'.

    • XIST|7503 ,  RPS4Y1|6192 ,  KDM5D|8284 ,  DDX3Y|8653 ,  USP9Y|8287 ,  ...

  • No genes correlated to 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=0        
GENDER t test N=26 male N=15 female N=11
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION 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) 0.2-54 (median=9.1)
  censored N = 39
  death N = 113
     
  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
LRRC61|65999 1.33 1.874e-07 0.0034 0.653

Figure S1.  Get High-res Image As an example, this figure shows the association of LRRC61|65999 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.87e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 59.74 (14)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

26 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 53
  MALE 99
     
  Significant markers N = 26
  Higher in MALE 15
  Higher in FEMALE 11
List of top 10 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
XIST|7503 -39.42 1.453e-57 2.64e-53 1
RPS4Y1|6192 48.88 5.191e-54 9.45e-50 1
KDM5D|8284 47.52 7.562e-51 1.38e-46 1
DDX3Y|8653 51.73 7.177e-50 1.31e-45 1
USP9Y|8287 51.52 6.071e-48 1.1e-43 1
CYORF15A|246126 42.41 3.272e-44 5.95e-40 1
TSIX|9383 -22.74 4.953e-42 9.01e-38 0.9977
EIF1AY|9086 41.86 1.563e-41 2.84e-37 1
ZFY|7544 50.2 3.146e-41 5.72e-37 1
PRKY|5616 25.86 3.571e-35 6.5e-31 0.9989

Figure S2.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.45e-57 with T-test analysis.

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) 75.75 (14)
  Significant markers N = 0
Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 102
  YES 50
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = GBM-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 152

  • Number of genes = 18210

  • Number of clinical features = 5

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

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] 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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