Lung Squamous Cell Carcinoma: Correlation between mRNAseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 18545 genes and 11 clinical features across 220 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • FAM65A|79567

  • 1 gene correlated to 'AGE'.

    • PRSS12|8492

  • 32 genes correlated to 'GENDER'.

    • XIST|7503 ,  PRKY|5616 ,  ZFY|7544 ,  RPS4Y1|6192 ,  DDX3Y|8653 ,  ...

  • 15 genes correlated to 'HISTOLOGICAL.TYPE'.

    • PCMTD2|55251 ,  MTMR10|54893 ,  ATG7|10533 ,  ALG10B|144245 ,  NUP93|9688 ,  ...

  • 2 genes correlated to 'PATHOLOGY.T'.

    • DMRTC1B|728656 ,  CASQ1|844

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

    • DEDD|9191 ,  PEX19|5824 ,  TMCO1|54499 ,  TAC3|6866 ,  SDHC|6391 ,  ...

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

    • C17ORF54|283982 ,  SLC10A2|6555 ,  PIRT|644139 ,  SAGE1|55511 ,  IL1F7|27178 ,  ...

  • 1 gene correlated to 'NEOADJUVANT.THERAPY'.

    • C7ORF16|10842

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.N', and 'TUMOR.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=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test N=1 older N=1 younger N=0
GENDER t test N=32 male N=13 female N=19
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=15        
PATHOLOGY T Spearman correlation test N=2 higher pT N=0 lower pT N=2
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=7        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=20 yes N=15 no N=5
NEOADJUVANT THERAPY t test N=1 yes N=1 no N=0
Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.1-173.8 (median=18.5)
  censored N = 119
  death N = 85
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
FAM65A|79567 2.7 2.507e-06 0.047 0.636

Figure S1.  Get High-res Image As an example, this figure shows the association of FAM65A|79567 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.51e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 67.47 (8.4)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
PRSS12|8492 0.3647 4.877e-08 0.000904

Figure S2.  Get High-res Image As an example, this figure shows the association of PRSS12|8492 to 'AGE'. P value = 4.88e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

32 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 59
  MALE 161
     
  Significant markers N = 32
  Higher in MALE 13
  Higher in FEMALE 19
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
XIST|7503 -34.1 4.098e-78 7.6e-74 0.9814
PRKY|5616 22.32 3.397e-42 6.3e-38 0.9887
ZFY|7544 29.59 4.78e-40 8.86e-36 0.9997
RPS4Y1|6192 29.73 4.497e-37 8.34e-33 1
DDX3Y|8653 25.24 1.213e-26 2.25e-22 1
NLGN4Y|22829 19.83 4.157e-25 7.7e-21 0.9974
TSIX|9383 -13.12 1.091e-24 2.02e-20 0.9438
KDM5D|8284 24.05 2.701e-20 5.01e-16 1
CYORF15A|246126 20.67 1.952e-18 3.62e-14 0.9988
EIF1AY|9086 22.83 5.623e-18 1.04e-13 1

Figure S3.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 4.1e-78 with T-test analysis.

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

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

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 17.73 (34)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

15 genes related to 'HISTOLOGICAL.TYPE'.

Table S8.  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 SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 212
     
  Significant markers N = 15
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
PCMTD2|55251 3.122e-18 5.79e-14
MTMR10|54893 9.388e-15 1.74e-10
ATG7|10533 3.062e-13 5.67e-09
ALG10B|144245 1.441e-11 2.67e-07
NUP93|9688 7.304e-11 1.35e-06
C16ORF63|123811 1.29e-09 2.39e-05
PPAN-P2RY11|692312 1.928e-09 3.57e-05
PHIP|55023 2.507e-08 0.000464
VGLL4|9686 7.302e-08 0.00135
PDS5B|23047 1.447e-07 0.00268

Figure S4.  Get High-res Image As an example, this figure shows the association of PCMTD2|55251 to 'HISTOLOGICAL.TYPE'. P value = 3.12e-18 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

2 genes related to 'PATHOLOGY.T'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.99 (0.76)
  N
  T1 51
  T2 133
  T3 23
  T4 13
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S11.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
DMRTC1B|728656 -0.3448 6.132e-07 0.0114
CASQ1|844 -0.3266 1.313e-06 0.0244

Figure S5.  Get High-res Image As an example, this figure shows the association of DMRTC1B|728656 to 'PATHOLOGY.T'. P value = 6.13e-07 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.49 (0.75)
  N
  N0 142
  N1 54
  N2 19
  N3 5
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

7 genes related to 'PATHOLOGICSPREAD(M)'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 203
  M1 4
  MX 9
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S14.  Get Full Table List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
DEDD|9191 1.624e-08 0.000301
PEX19|5824 4.279e-08 0.000793
TMCO1|54499 1.583e-07 0.00294
TAC3|6866 2.407e-07 0.00446
SDHC|6391 4.994e-07 0.00926
B4GALT3|8703 1.807e-06 0.0335
TADA1|117143 1.879e-06 0.0348

Figure S6.  Get High-res Image As an example, this figure shows the association of DEDD|9191 to 'PATHOLOGICSPREAD(M)'. P value = 1.62e-08 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S15.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.72 (0.86)
  N
  Stage 1 114
  Stage 2 54
  Stage 3 46
  Stage 4 4
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S16.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 6
  YES 214
     
  Significant markers N = 20
  Higher in YES 15
  Higher in NO 5
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
C17ORF54|283982 10.91 5.064e-19 9.24e-15 0.8444
SLC10A2|6555 9.66 8.143e-16 1.49e-11 0.8542
PIRT|644139 7.95 5.566e-12 1.02e-07 0.8636
SAGE1|55511 11.17 9.356e-11 1.71e-06 0.8778
IL1F7|27178 8.17 1.128e-10 2.06e-06 0.8968
PASD1|139135 7.38 1.448e-10 2.64e-06 0.8805
FLJ36000|284124 9.64 2.214e-10 4.04e-06 0.8852
SLC39A12|221074 8.23 1.568e-08 0.000286 0.8671
FBXL21|26223 -6.92 1.973e-08 0.00036 0.7073
AHSP|51327 6.4 1.981e-08 0.000361 0.7403

Figure S7.  Get High-res Image As an example, this figure shows the association of C17ORF54|283982 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 5.06e-19 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S18.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 21
  YES 199
     
  Significant markers N = 1
  Higher in YES 1
  Higher in NO 0
List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S19.  Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
C7ORF16|10842 6.4 1.846e-08 0.000342 0.8125

Figure S8.  Get High-res Image As an example, this figure shows the association of C7ORF16|10842 to 'NEOADJUVANT.THERAPY'. P value = 1.85e-08 with T-test analysis.

Methods & Data
Input

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

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

  • Number of patients = 220

  • Number of genes = 18545

  • Number of clinical features = 11

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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