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 6 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 13 genes correlated to 'GENDER'.

    • DDX3Y ,  RPS4Y1 ,  CYORF15A ,  EIF1AY ,  RPS4Y2 ,  ...

  • 64 genes correlated to 'DISTANT.METASTASIS'.

    • IGFBPL1 ,  CYORF15B ,  DDX3Y ,  EIF1AY ,  OAS1 ,  ...

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

    • C10ORF97 ,  LCE2C ,  SLC35A5 ,  SRP9 ,  VPS24 ,  ...

  • 66 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • TCF23 ,  PAX4 ,  OPRS1 ,  SNRP70 ,  JARID1C ,  ...

  • No genes correlated to 'Time to Death', and 'AGE'.

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=13 male N=13 female N=0
DISTANT METASTASIS t test N=64 m1 N=23 m0 N=41
LYMPH NODE METASTASIS ANOVA test N=41        
NEOPLASM DISEASESTAGE ANOVA test N=66        
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) 0.5-101.1 (median=32.6)
  censored N = 58
  death N = 13
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 60.55 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

13 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 29
  MALE 43
     
  Significant markers N = 13
  Higher in MALE 13
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S4.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
DDX3Y 13.71 6.875e-21 1.22e-16 0.9575
RPS4Y1 14.08 1.088e-20 1.94e-16 0.9599
CYORF15A 13.48 2.222e-20 3.96e-16 0.9655
EIF1AY 13.4 2.577e-20 4.59e-16 0.9623
RPS4Y2 13.25 3.662e-20 6.52e-16 0.9527
JARID1D 12.43 5.763e-19 1.03e-14 0.9583
ZFY 11.55 9.008e-18 1.6e-13 0.9615
CYORF15B 11 6.54e-17 1.16e-12 0.9415
UTY 10.7 2.396e-16 4.27e-12 0.9479
USP9Y 9.59 2.26e-14 4.02e-10 0.9318

Figure S1.  Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 6.87e-21 with T-test analysis.

Clinical variable #4: 'DISTANT.METASTASIS'

64 genes related to 'DISTANT.METASTASIS'.

Table S5.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 67
  M1 5
     
  Significant markers N = 64
  Higher in M1 23
  Higher in M0 41
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

T(pos if higher in 'M1') ttestP Q AUC
IGFBPL1 -10.69 2.075e-15 3.7e-11 0.8955
CYORF15B -10.34 1.448e-14 2.58e-10 0.9224
DDX3Y -9.7 1.45e-14 2.58e-10 0.809
EIF1AY -10.87 2.906e-14 5.18e-10 0.9493
OAS1 9.58 2.205e-12 3.93e-08 0.9433
RESP18 9.36 1.212e-11 2.16e-07 0.9463
PELI2 -8.79 1.784e-11 3.18e-07 0.8657
UTY -10.33 3.74e-11 6.66e-07 0.9821
JARID1D -10.14 4.94e-11 8.8e-07 0.9045
HOXA7 -9.6 6.012e-11 1.07e-06 0.8776

Figure S2.  Get High-res Image As an example, this figure shows the association of IGFBPL1 to 'DISTANT.METASTASIS'. P value = 2.07e-15 with T-test analysis.

Clinical variable #5: 'LYMPH.NODE.METASTASIS'

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

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

LYMPH.NODE.METASTASIS Labels N
  N0 35
  N1 3
  NX 34
     
  Significant markers N = 41
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
C10ORF97 3.728e-09 6.64e-05
LCE2C 6.296e-09 0.000112
SLC35A5 4.802e-08 0.000855
SRP9 5.772e-08 0.00103
VPS24 6.125e-08 0.00109
ST13 1.153e-07 0.00205
MAPK15 1.318e-07 0.00235
OR2F2 2.026e-07 0.00361
TMEM38B 2.54e-07 0.00452
DEFB106B 2.806e-07 0.005

Figure S3.  Get High-res Image As an example, this figure shows the association of C10ORF97 to 'LYMPH.NODE.METASTASIS'. P value = 3.73e-09 with ANOVA analysis.

Clinical variable #6: 'NEOPLASM.DISEASESTAGE'

66 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 40
  STAGE II 13
  STAGE III 14
  STAGE IV 5
     
  Significant markers N = 66
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
TCF23 6.108e-12 1.09e-07
PAX4 6.492e-11 1.16e-06
OPRS1 9.388e-10 1.67e-05
SNRP70 2.371e-09 4.22e-05
JARID1C 1.003e-08 0.000179
MSTO1 1.063e-08 0.000189
SBNO2 1.133e-08 0.000202
ZNF646 2.465e-08 0.000439
GPR152 3.295e-08 0.000587
POM121L1 3.476e-08 0.000619

Figure S4.  Get High-res Image As an example, this figure shows the association of TCF23 to 'NEOPLASM.DISEASESTAGE'. P value = 6.11e-12 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 72

  • Number of genes = 17814

  • 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

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)
Made with Nozzle