Lung Squamous Cell Carcinoma: Correlation between mRNA expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
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 154 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 13 genes correlated to 'GENDER'.

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

  • 16 genes correlated to 'HISTOLOGICAL.TYPE'.

    • FAM5B ,  NUT ,  A2BP1 ,  SPINK7 ,  CAPZA3 ,  ...

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

    • NSD1 ,  C7ORF30 ,  RGPD5 ,  GPR97 ,  CD274 ,  ...

  • 2 genes correlated to 'NEOADJUVANT.THERAPY'.

    • MAGOH ,  HEATR5B

  • No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', and 'PATHOLOGY.N'.

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
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=16        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) t test N=16 m1 N=7 m0 N=9
NEOADJUVANT THERAPY t test N=2 yes N=1 no 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) 0.4-173.8 (median=18.2)
  censored N = 86
  death N = 62
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 66.53 (8.6)
  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 44
  MALE 110
     
  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
RPS4Y1 41.28 6.117e-81 1.09e-76 1
RPS4Y2 33.34 1.553e-70 2.77e-66 1
DDX3Y 26.87 9.588e-55 1.71e-50 0.9998
EIF1AY 24.61 3.14e-50 5.59e-46 0.9967
CYORF15A 18.75 1.076e-35 1.92e-31 0.9897
UTY 15.97 1.406e-32 2.5e-28 0.964
JARID1D 16.81 7.289e-30 1.3e-25 0.981
ZFY 13.73 9.25e-27 1.65e-22 0.9426
TTTY14 12.34 1.436e-24 2.56e-20 0.9531
CYORF15B 13.3 7.232e-23 1.29e-18 0.9442

Figure S1.  Get High-res Image As an example, this figure shows the association of RPS4Y1 to 'GENDER'. P value = 6.12e-81 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 24.23 (38)
  Score N
  0 18
  50 2
  70 1
  90 4
  100 1
     
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

16 genes related to 'HISTOLOGICAL.TYPE'.

Table S6.  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 = 16
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
FAM5B 9.102e-11 1.62e-06
NUT 9.666e-11 1.72e-06
A2BP1 6.66e-09 0.000119
SPINK7 3.515e-08 0.000626
CAPZA3 8.641e-08 0.00154
PDS5B 1.278e-07 0.00228
GABRA4 1.528e-07 0.00272
UGT2B10 1.636e-07 0.00291
CCDC100 1.945e-07 0.00346
MGC21881 2.886e-07 0.00514

Figure S2.  Get High-res Image As an example, this figure shows the association of FAM5B to 'HISTOLOGICAL.TYPE'. P value = 9.1e-11 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.04 (0.77)
  N
  T1 30
  T2 100
  T3 12
  T4 12
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.53 (0.79)
  N
  N0 96
  N1 40
  N2 13
  N3 5
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

16 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 146
  M1 4
     
  Significant markers N = 16
  Higher in M1 7
  Higher in M0 9
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

T(pos if higher in 'M1') ttestP Q AUC
NSD1 -10.68 2.372e-19 4.23e-15 0.8134
C7ORF30 8.4 4.367e-14 7.78e-10 0.7911
RGPD5 -11.89 2.11e-13 3.76e-09 0.8887
GPR97 8.41 1.576e-10 2.81e-06 0.8185
CD274 -9.15 2.145e-09 3.82e-05 0.8168
C3ORF26 8.79 1.87e-08 0.000333 0.8288
KRT24 -5.93 5.731e-08 0.00102 0.5873
DEFB103A -8.41 2.299e-07 0.00409 0.7877
POPDC2 5.46 2.591e-07 0.00461 0.7038
PON3 6.62 3.57e-07 0.00636 0.7774

Figure S3.  Get High-res Image As an example, this figure shows the association of NSD1 to 'PATHOLOGICSPREAD(M)'. P value = 2.37e-19 with T-test analysis.

Clinical variable #9: 'NEOADJUVANT.THERAPY'

2 genes related to 'NEOADJUVANT.THERAPY'.

Table S12.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 13
  YES 141
     
  Significant markers N = 2
  Higher in YES 1
  Higher in NO 1
List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S13.  Get Full Table List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
MAGOH 5.84 6.435e-07 0.0115 0.7141
HEATR5B -5.59 1.136e-06 0.0202 0.773

Figure S4.  Get High-res Image As an example, this figure shows the association of MAGOH to 'NEOADJUVANT.THERAPY'. P value = 6.43e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LUSC.medianexp.txt

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

  • Number of patients = 154

  • 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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