Correlation between mRNAseq expression and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C15X27JR
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15X27JR
Overview
Introduction

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

Summary

Testing the association between 17786 genes and 8 clinical features across 147 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 2 genes correlated to 'AGE'.

    • PCM1|5108 ,  FUT4|2526

  • 2 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • KCNT1|57582 ,  YSK4|80122

  • 42 genes correlated to 'PATHOLOGY.N.STAGE'.

    • AGPAT2|10555 ,  PI4KAP2|375133 ,  ATP5F1|515 ,  LRRC1|55227 ,  STAB2|55576 ,  ...

  • 1 gene correlated to 'PATHOLOGY.M.STAGE'.

    • KCNT1|57582

  • 53 genes correlated to 'GENDER'.

    • XIST|7503 ,  RPS4Y1|6192 ,  ZFY|7544 ,  TSIX|9383 ,  DDX3Y|8653 ,  ...

  • 1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

    • INO80B|83444

  • No genes correlated to 'Time to Death', and 'PATHOLOGY.T.STAGE'.

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=2 older N=0 younger N=2
NEOPLASM DISEASESTAGE ANOVA test N=2        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE t test N=42 class1 N=31 class0 N=11
PATHOLOGY M STAGE ANOVA test N=1        
GENDER t test N=53 male N=34 female N=19
COMPLETENESS OF RESECTION ANOVA test N=1        
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) 0-113 (median=14.1)
  censored N = 83
  death N = 62
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 61.72 (14)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
PCM1|5108 -0.3919 1.087e-06 0.0193
FUT4|2526 -0.382 2.12e-06 0.0377

Figure S1.  Get High-res Image As an example, this figure shows the association of PCM1|5108 to 'AGE'. P value = 1.09e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

2 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 57
  STAGE II 34
  STAGE III 2
  STAGE IIIA 31
  STAGE IIIB 3
  STAGE IIIC 6
  STAGE IV 1
  STAGE IVA 1
  STAGE IVB 2
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
KCNT1|57582 1.477e-07 0.00263
YSK4|80122 1.138e-06 0.0202

Figure S2.  Get High-res Image As an example, this figure shows the association of KCNT1|57582 to 'NEOPLASM.DISEASESTAGE'. P value = 1.48e-07 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

No gene related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.99 (0.97)
  N
  1 60
  2 38
  3 40
  4 9
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

42 genes related to 'PATHOLOGY.N.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 95
  class1 3
     
  Significant markers N = 42
  Higher in class1 31
  Higher in class0 11
List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

T(pos if higher in 'class1') ttestP Q AUC
AGPAT2|10555 7.95 3.956e-12 6.3e-08 0.8105
PI4KAP2|375133 10.5 1.286e-10 2.05e-06 0.9228
ATP5F1|515 7.16 4.1e-10 6.53e-06 0.786
LRRC1|55227 7.78 5.939e-10 9.46e-06 0.8246
STAB2|55576 -7.48 8.585e-10 1.37e-05 0.7851
ARMCX3|51566 8.03 9.992e-10 1.59e-05 0.7965
WARS2|10352 10.14 1.413e-09 2.25e-05 0.9228
SFRS6|6431 7.66 2.989e-09 4.76e-05 0.793
BAT4|7918 -6.53 3.107e-09 4.95e-05 0.7158
CYTH4|27128 6.44 4.803e-09 7.64e-05 0.7649

Figure S3.  Get High-res Image As an example, this figure shows the association of AGPAT2|10555 to 'PATHOLOGY.N.STAGE'. P value = 3.96e-12 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

One gene related to 'PATHOLOGY.M.STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 113
  M1 3
  MX 31
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

Table S10.  Get Full Table List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
KCNT1|57582 1.026e-09 1.83e-05

Figure S4.  Get High-res Image As an example, this figure shows the association of KCNT1|57582 to 'PATHOLOGY.M.STAGE'. P value = 1.03e-09 with ANOVA analysis.

Clinical variable #7: 'GENDER'

53 genes related to 'GENDER'.

Table S11.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 56
  MALE 91
     
  Significant markers N = 53
  Higher in MALE 34
  Higher in FEMALE 19
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
XIST|7503 -16.59 6.457e-32 1.15e-27 0.9714
RPS4Y1|6192 27.17 1.77e-29 3.15e-25 0.9974
ZFY|7544 24.45 9.349e-26 1.66e-21 0.9961
TSIX|9383 -15.13 1.134e-24 2.02e-20 0.9759
DDX3Y|8653 25.72 1.194e-21 2.12e-17 0.9986
PRKY|5616 18.9 1.285e-20 2.28e-16 0.9943
KDM5D|8284 20.92 1.697e-16 3.02e-12 0.9969
NLGN4Y|22829 13.37 9.035e-16 1.61e-11 0.9718
KDM5C|8242 -8.26 1.981e-13 3.52e-09 0.8373
NCRNA00183|554203 -6.61 1.282e-09 2.28e-05 0.7906

Figure S5.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 6.46e-32 with T-test analysis.

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S13.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 120
  R1 11
  R2 1
  RX 10
     
  Significant markers N = 1
List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S14.  Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
INO80B|83444 2.792e-06 0.0497

Figure S6.  Get High-res Image As an example, this figure shows the association of INO80B|83444 to 'COMPLETENESS.OF.RESECTION'. P value = 2.79e-06 with ANOVA analysis.

Methods & Data
Input

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

  • Clinical data file = LIHC-TP.merged_data.txt

  • Number of patients = 147

  • Number of genes = 17786

  • 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

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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