Correlation between mRNA expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C18C9V94
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18C9V94
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 17814 genes and 14 clinical features across 154 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • GIPC3 ,  ALS2CR4 ,  PDZD11 ,  MRPL52 ,  OSGEP ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • NUPL2 ,  DUSP11 ,  RSPO2 ,  GDF10 ,  BLZF1 ,  ...

  • 15 genes correlated to 'GENDER'.

    • CYORF15A ,  JARID1D ,  CYORF15B ,  C1QL2 ,  EIF1 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • NFRKB ,  HEXA ,  WASF2 ,  SFMBT2 ,  ELA2A ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 'COMPLETENESS_OF_RESECTION', 'RACE', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=5 younger N=25
NEOPLASM_DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=17 lower stage N=13
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=15 male N=15 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=12 lower score N=18
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
COMPLETENESS_OF_RESECTION Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.4-174.1 (median=23)
  censored N = 83
  death N = 70
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 66.53 (8.6)
  Significant markers N = 30
  pos. correlated 5
  neg. correlated 25
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
GIPC3 -0.3508 9.371e-06 0.0971
ALS2CR4 0.342 1.62e-05 0.0971
PDZD11 -0.3377 2.096e-05 0.0971
MRPL52 -0.337 2.18e-05 0.0971
OSGEP -0.3326 2.83e-05 0.101
KCNK7 -0.3243 4.577e-05 0.119
MRPL2 -0.3234 4.826e-05 0.119
FBXO33 -0.3216 5.358e-05 0.119
RDH12 -0.3035 0.0001442 0.173
IQWD1 0.3026 0.0001514 0.173
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

No gene related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE IA 20
  STAGE IB 61
  STAGE IIA 7
  STAGE IIB 27
  STAGE IIIA 19
  STAGE IIIB 15
  STAGE IV 4
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.04 (0.77)
  N
  T1 30
  T2 100
  T3 12
  T4 12
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S6.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
NUPL2 0.3465 1.069e-05 0.122
DUSP11 0.3425 1.371e-05 0.122
RSPO2 -0.3277 3.342e-05 0.156
GDF10 -0.3269 3.511e-05 0.156
BLZF1 0.3205 5.072e-05 0.157
DMC1 0.3197 5.316e-05 0.157
C1ORF124 0.3124 7.988e-05 0.157
CHAC2 0.3109 8.676e-05 0.157
SLFNL1 -0.308 0.0001018 0.157
RPP30 0.308 0.0001022 0.157
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Mean (SD) 0.53 (0.79)
  N
  N0 96
  N1 40
  N2 13
  N3 5
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 146
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

15 genes related to 'GENDER'.

Table S9.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 44
  MALE 110
     
  Significant markers N = 15
  Higher in MALE 15
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 15 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CYORF15A 4796 2.084e-21 7.43e-18 0.9909
JARID1D 4755 9.933e-21 2.95e-17 0.9824
CYORF15B 4582 5.388e-18 1.07e-14 0.9467
C1QL2 1374 2.896e-05 0.0322 0.7161
EIF1 3462.5 3.08e-05 0.0323 0.7154
CA4 1397.5 4.361e-05 0.0375 0.7113
CABLES1 1399.5 4.514e-05 0.0375 0.7108
INSM1 1400 4.553e-05 0.0375 0.7107
NKX2-1 1401 4.632e-05 0.0375 0.7105
FOXA2 1414 5.784e-05 0.0429 0.7079
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 26.3 (39)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S12.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
NFRKB -0.7617 3.928e-06 0.0407
HEXA 0.7584 4.57e-06 0.0407
WASF2 -0.7384 1.099e-05 0.0597
SFMBT2 0.7263 1.798e-05 0.0597
ELA2A 0.726 1.819e-05 0.0597
CNKSR2 0.7234 2.011e-05 0.0597
RNF34 -0.7041 4.16e-05 0.0934
ACTA1 0.699 4.994e-05 0.0934
ZNF688 0.695 5.749e-05 0.0934
SMPD3 0.6906 6.688e-05 0.0934
Clinical variable #9: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

Table S13.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 5
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 148
     
  Significant markers N = 0
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S14.  Basic characteristics of clinical feature: 'NUMBER_PACK_YEARS_SMOKED'

NUMBER_PACK_YEARS_SMOKED Mean (SD) 54.83 (36)
  Significant markers N = 0
Clinical variable #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

Table S15.  Basic characteristics of clinical feature: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1958.02 (11)
  Significant markers N = 0
Clinical variable #12: 'COMPLETENESS_OF_RESECTION'

No gene related to 'COMPLETENESS_OF_RESECTION'.

Table S16.  Basic characteristics of clinical feature: 'COMPLETENESS_OF_RESECTION'

COMPLETENESS_OF_RESECTION Labels N
  R0 139
  R1 3
  R2 2
  RX 5
     
  Significant markers N = 0
Clinical variable #13: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 3
  BLACK OR AFRICAN AMERICAN 7
  WHITE 91
     
  Significant markers N = 0
Clinical variable #14: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S18.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 4
  NOT HISPANIC OR LATINO 88
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 154

  • Number of genes = 17814

  • Number of clinical features = 14

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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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