Correlation between mRNAseq expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1B856BS
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1B856BS
Overview
Introduction

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

Summary

Testing the association between 18536 genes and 14 clinical features across 258 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • PRSS12|8492

  • 29 genes correlated to 'GENDER'.

    • XIST|7503 ,  PRKY|5616 ,  ZFY|7544 ,  RPS4Y1|6192 ,  TSIX|9383 ,  ...

  • 32 genes correlated to 'HISTOLOGICAL.TYPE'.

    • SNIP1|79753 ,  SF3A3|10946 ,  PCMTD2|55251 ,  UTP11L|51118 ,  XIAP|331 ,  ...

  • 1 gene correlated to 'PATHOLOGY.T'.

    • GLCCI1|113263

  • 9 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • DEDD|9191 ,  TMCO1|54499 ,  PEX19|5824 ,  SDHC|6391 ,  B4GALT3|8703 ,  ...

  • 13 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • GUCY2E|390226 ,  PIRT|644139 ,  C5ORF47|133491 ,  FLJ36000|284124 ,  FAM170B|170370 ,  ...

  • 1 gene correlated to 'NUMBERPACKYEARSSMOKED'.

    • CCL3L3|414062

  • 4 genes correlated to 'STOPPEDSMOKINGYEAR'.

    • C20ORF103|24141 ,  BACH1|571 ,  TSEN54|283989 ,  AKR7L|246181

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.N', 'TUMOR.STAGE', 'TOBACCOSMOKINGHISTORYINDICATOR', 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=1 older N=1 younger N=0
GENDER t test N=29 male N=13 female N=16
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=32        
PATHOLOGY T Spearman correlation test N=1 higher pT N=0 lower pT N=1
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=9        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=13 yes N=12 no N=1
NUMBERPACKYEARSSMOKED Spearman correlation test N=1 higher numberpackyearssmoked N=0 lower numberpackyearssmoked N=1
STOPPEDSMOKINGYEAR Spearman correlation test N=4 higher stoppedsmokingyear N=2 lower stoppedsmokingyear N=2
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
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) 0-173.8 (median=17)
  censored N = 141
  death N = 100
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 67.61 (8.5)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
PRSS12|8492 0.3573 6.561e-09 0.000122

Figure S1.  Get High-res Image As an example, this figure shows the association of PRSS12|8492 to 'AGE'. P value = 6.56e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

29 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 67
  MALE 191
     
  Significant markers N = 29
  Higher in MALE 13
  Higher in FEMALE 16
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 -32.79 7.843e-83 1.45e-78 0.9723
PRKY|5616 23.52 5.771e-47 1.07e-42 0.9889
ZFY|7544 32.78 1.072e-46 1.99e-42 0.9998
RPS4Y1|6192 30.97 3.749e-39 6.95e-35 1
TSIX|9383 -13.77 1.109e-27 2.06e-23 0.944
DDX3Y|8653 25.94 4e-27 7.41e-23 1
NLGN4Y|22829 19.32 8.597e-26 1.59e-21 0.9907
KDM5D|8284 24.34 5.906e-20 1.09e-15 1
EIF1AY|9086 24 7.216e-19 1.34e-14 1
CYORF15A|246126 20.92 7.308e-18 1.35e-13 0.999

Figure S2.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 7.84e-83 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) 25.29 (38)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

32 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 6
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 249
     
  Significant markers N = 32
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
SNIP1|79753 2.308e-21 4.28e-17
SF3A3|10946 9.908e-21 1.84e-16
PCMTD2|55251 3.712e-19 6.88e-15
UTP11L|51118 1.473e-17 2.73e-13
XIAP|331 1.627e-17 3.01e-13
INPP5B|3633 6.398e-15 1.19e-10
GNL2|29889 1.952e-14 3.62e-10
C1ORF109|54955 6.957e-14 1.29e-09
MTMR10|54893 8.311e-14 1.54e-09
ATG7|10533 7.631e-13 1.41e-08

Figure S3.  Get High-res Image As an example, this figure shows the association of SNIP1|79753 to 'HISTOLOGICAL.TYPE'. P value = 2.31e-21 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.99 (0.73)
  N
  T1 56
  T2 163
  T3 25
  T4 14
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S10.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
GLCCI1|113263 -0.2894 2.268e-06 0.042

Figure S4.  Get High-res Image As an example, this figure shows the association of GLCCI1|113263 to 'PATHOLOGY.T'. P value = 2.27e-06 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.48 (0.72)
  N
  N0 164
  N1 68
  N2 20
  N3 5
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

9 genes related to 'PATHOLOGICSPREAD(M)'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 230
  M1 4
  MX 18
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S13.  Get Full Table List of 9 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
DEDD|9191 7.78e-09 0.000144
TMCO1|54499 1.396e-07 0.00259
PEX19|5824 1.982e-07 0.00367
SDHC|6391 4.812e-07 0.00892
B4GALT3|8703 5.77e-07 0.0107
TAC3|6866 7.584e-07 0.0141
POU2F1|5451 1.045e-06 0.0194
TOMM40L|84134 1.812e-06 0.0336
MED30|90390 2.111e-06 0.0391

Figure S5.  Get High-res Image As an example, this figure shows the association of DEDD|9191 to 'PATHOLOGICSPREAD(M)'. P value = 7.78e-09 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S14.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.69 (0.83)
  N
  Stage 1 135
  Stage 2 66
  Stage 3 49
  Stage 4 4
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

13 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S15.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 8
  YES 250
     
  Significant markers N = 13
  Higher in YES 12
  Higher in NO 1
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S16.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
GUCY2E|390226 13.71 4.545e-25 8.36e-21 0.8942
PIRT|644139 8.99 9.73e-15 1.79e-10 0.8742
C5ORF47|133491 9.18 1.092e-11 2.01e-07 0.8028
FLJ36000|284124 10.27 2.913e-10 5.36e-06 0.8735
FAM170B|170370 6.65 4.333e-09 7.97e-05 0.7867
LOC283392|283392 7.74 6.104e-09 0.000112 0.7913
C7ORF34|135927 6.74 3.248e-08 0.000597 0.7866
RNF186|54546 6.06 4.778e-08 0.000879 0.7922
GLIPR1L1|256710 7.08 5.637e-08 0.00104 0.78
MYT1L|23040 5.75 1.277e-07 0.00235 0.7889

Figure S6.  Get High-res Image As an example, this figure shows the association of GUCY2E|390226 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.55e-25 with T-test analysis.

Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

One gene related to 'NUMBERPACKYEARSSMOKED'.

Table S17.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 53.59 (34)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S18.  Get Full Table List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
CCL3L3|414062 -0.3759 1.434e-06 0.0266

Figure S7.  Get High-res Image As an example, this figure shows the association of CCL3L3|414062 to 'NUMBERPACKYEARSSMOKED'. P value = 1.43e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'STOPPEDSMOKINGYEAR'

4 genes related to 'STOPPEDSMOKINGYEAR'.

Table S19.  Basic characteristics of clinical feature: 'STOPPEDSMOKINGYEAR'

STOPPEDSMOKINGYEAR Mean (SD) 1997.17 (12)
  Significant markers N = 4
  pos. correlated 2
  neg. correlated 2
List of 4 genes significantly correlated to 'STOPPEDSMOKINGYEAR' by Spearman correlation test

Table S20.  Get Full Table List of 4 genes significantly correlated to 'STOPPEDSMOKINGYEAR' by Spearman correlation test

SpearmanCorr corrP Q
C20ORF103|24141 -0.3539 1.098e-06 0.0203
BACH1|571 -0.3525 1.218e-06 0.0226
TSEN54|283989 0.3448 2.136e-06 0.0396
AKR7L|246181 0.3444 2.196e-06 0.0407

Figure S8.  Get High-res Image As an example, this figure shows the association of C20ORF103|24141 to 'STOPPEDSMOKINGYEAR'. P value = 1.1e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

No gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S21.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 133
  CURRENT REFORMED SMOKER FOR > 15 YEARS 62
  CURRENT SMOKER 45
  LIFELONG NON-SMOKER 12
     
  Significant markers N = 0
Clinical variable #14: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S22.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1957.71 (11)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 258

  • Number of genes = 18536

  • Number of clinical features = 14

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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