Correlation between mRNA expression and clinical features
Rectum Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1WH2N2T
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WH2N2T
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 9 clinical features across 69 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 11 genes correlated to 'GENDER'.

    • DDX3Y ,  RPS4Y1 ,  RPS4Y2 ,  EIF1AY ,  JARID1D ,  ...

  • 10 genes correlated to 'HISTOLOGICAL.TYPE'.

    • TLE6 ,  FBXO2 ,  PLCB2 ,  AGR3 ,  RAB27B ,  ...

  • 16 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • CASQ2 ,  LDB3 ,  NNAT ,  PSD ,  STMN4 ,  ...

  • 2 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • ACMSD ,  LHFPL3

  • No genes correlated to 'Time to Death', 'AGE', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.

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=11 male N=11 female N=0
HISTOLOGICAL TYPE t test N=10 rectal mucinous adenocarcinoma N=8 rectal adenocarcinoma N=2
DISTANT METASTASIS t test   N=0        
LYMPH NODE METASTASIS ANOVA test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=16        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test N=2        
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) 0.9-52 (median=4)
  censored N = 40
  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) 66.62 (11)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

11 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 31
  MALE 38
     
  Significant markers N = 11
  Higher in MALE 11
  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 12.63 2.827e-19 5.04e-15 0.9635
RPS4Y1 12 2.772e-17 4.94e-13 0.9228
RPS4Y2 11.53 1.686e-16 3e-12 0.9635
EIF1AY 11 1.948e-16 3.47e-12 0.9576
JARID1D 10.56 7.559e-16 1.35e-11 0.9482
CYORF15A 10.06 1.466e-14 2.61e-10 0.9465
UTY 8.06 3.114e-11 5.55e-07 0.9134
CYORF15B 7.86 5.263e-11 9.37e-07 0.9049
ZFY 7.56 1.75e-10 3.12e-06 0.893
TTTY14 5.79 2.479e-07 0.00441 0.9011

Figure S1.  Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 2.83e-19 with T-test analysis.

Clinical variable #4: 'HISTOLOGICAL.TYPE'

10 genes related to 'HISTOLOGICAL.TYPE'.

Table S5.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  RECTAL ADENOCARCINOMA 58
  RECTAL MUCINOUS ADENOCARCINOMA 7
     
  Significant markers N = 10
  Higher in RECTAL MUCINOUS ADENOCARCINOMA 8
  Higher in RECTAL ADENOCARCINOMA 2
List of 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S6.  Get Full Table List of 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

T(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') ttestP Q AUC
TLE6 13.32 1.328e-18 2.37e-14 0.9828
FBXO2 -7.35 8.488e-10 1.51e-05 0.8522
PLCB2 7.42 4.681e-08 0.000834 0.9163
AGR3 6.7 2.069e-07 0.00369 0.8695
RAB27B 6.58 2.286e-07 0.00407 0.8695
BACE2 6.35 2.907e-07 0.00518 0.8473
OXCT1 5.87 7.735e-07 0.0138 0.9015
CARD6 6.01 9.658e-07 0.0172 0.8399
USP42 -5.76 1.952e-06 0.0348 0.8399
TTLL7 7.32 1.956e-06 0.0348 0.9458

Figure S2.  Get High-res Image As an example, this figure shows the association of TLE6 to 'HISTOLOGICAL.TYPE'. P value = 1.33e-18 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S7.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 57
  M1 12
     
  Significant markers N = 0
Clinical variable #6: 'LYMPH.NODE.METASTASIS'

No gene related to 'LYMPH.NODE.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 42
  N1 14
  N1A 1
  N2 12
     
  Significant markers N = 0
Clinical variable #7: 'COMPLETENESS.OF.RESECTION'

16 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S9.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 57
  R1 1
  R2 10
     
  Significant markers N = 16
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
CASQ2 8.109e-11 1.44e-06
LDB3 3.545e-10 6.31e-06
NNAT 7.166e-10 1.28e-05
PSD 1.319e-08 0.000235
STMN4 2.034e-08 0.000362
DMN 7.983e-08 0.00142
GPM6A 5.838e-07 0.0104
MYH3 6.403e-07 0.0114
SYT4 7.534e-07 0.0134
KIAA1881 1.054e-06 0.0188

Figure S3.  Get High-res Image As an example, this figure shows the association of CASQ2 to 'COMPLETENESS.OF.RESECTION'. P value = 8.11e-11 with ANOVA analysis.

Clinical variable #8: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S11.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.19 (5.1)
  Significant markers N = 0
Clinical variable #9: 'NEOPLASM.DISEASESTAGE'

2 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S12.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 18
  STAGE II 3
  STAGE IIA 20
  STAGE III 2
  STAGE IIIB 10
  STAGE IIIC 4
  STAGE IV 11
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S13.  Get Full Table List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
ACMSD 7.275e-07 0.013
LHFPL3 1.601e-06 0.0285

Figure S4.  Get High-res Image As an example, this figure shows the association of ACMSD to 'NEOPLASM.DISEASESTAGE'. P value = 7.28e-07 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 69

  • Number of genes = 17814

  • Number of clinical features = 9

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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