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

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17600 genes and 11 clinical features across 92 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

- 1 gene correlated to 'GENDER'.

- DDX43

- 64 genes correlated to 'HISTOLOGICAL.TYPE'.

- BSDC1 , CHMP4A , C16ORF63 , UQCRB , C13ORF34 , ...

- 23 genes correlated to 'PATHOLOGICSPREAD(M)'.

- ACTR6 , RASSF8 , RNF8 , ALOXE3 , CR2 , ...

- 70 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- RPS2 , ERBB2 , TMPO , NPBWR1 , HOXC11 , ...

- 11 genes correlated to 'NEOADJUVANT.THERAPY'.

- C16ORF73 , IRF8 , BRE , LOC100302650 , ZBTB38 , ...

- No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', '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=0 |  |  |  |
AGE | Spearman correlation test | N=0 |  |  |  |
GENDER | t test | N=1 | male | N=0 | female | N=1
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | ANOVA test | N=64 |  |  |  |
PATHOLOGY T | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY N | Spearman correlation test | N=0 |  |  |  |
PATHOLOGICSPREAD(M) | ANOVA test | N=23 |  |  |  |
TUMOR STAGE | Spearman correlation test | N=0 |  |  |  |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=70 | yes | N=63 | no | N=7
NEOADJUVANT THERAPY | t test | N=11 | yes | N=6 | no | N=5

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) | 0.1-173.8 (median=23.1)
| censored | N = 42
| death | N = 38
| Significant markers | N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE | Mean (SD) | 69.5 (8.5)
| Significant markers | N = 0

Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 26
| MALE | 66
| Significant markers | N = 1
| Higher in MALE | 0
| Higher in FEMALE | 1

List of one gene differentially expressed by 'GENDER'

Table S4. Get Full Table List of one gene differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
DDX43 | -5.79 | 1.617e-07 | 0.00285 | 0.8316

Figure S1. Get High-res Image As an example, this figure shows the association of DDX43 to 'GENDER'. P value = 1.62e-07 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) | 10.91 (27)
| Score | N
| 0 | 18
| 20 | 1
| 40 | 1
| 90 | 2
| Significant markers | N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

64 genes related to 'HISTOLOGICAL.TYPE'.

Table S6. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE | Labels | N
| LUNG BASALOID SQUAMOUS CELL CARCINOMA | 3
| LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | 1
| LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 88
| Significant markers | N = 64

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
--- | --- | ---
BSDC1 | 1.724e-53 | 3.03e-49
CHMP4A | 4.429e-45 | 7.79e-41
C16ORF63 | 2.387e-23 | 4.2e-19
UQCRB | 7.76e-23 | 1.37e-18
C13ORF34 | 2.149e-21 | 3.78e-17
C13ORF37 | 2.149e-21 | 3.78e-17
NME1 | 2.476e-18 | 4.36e-14
PINX1 | 1.15e-17 | 2.02e-13
CRLS1 | 6.637e-15 | 1.17e-10
HYAL4 | 8.894e-15 | 1.56e-10

Figure S2. Get High-res Image As an example, this figure shows the association of BSDC1 to 'HISTOLOGICAL.TYPE'. P value = 1.72e-53 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) | 1.89 (0.75)
|  | N
| T1 | 28
| T2 | 49
| T3 | 12
| T4 | 3
| 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) | 0.43 (0.67)
|  | N
| N0 | 61
| N1 | 22
| N2 | 9
| Significant markers | N = 0

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

23 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) | Labels | N
| M0 | 82
| M1 | 1
| MX | 9
| Significant markers | N = 23

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)'

| ANOVA_P | Q
--- | --- | ---
ACTR6 | 6.376e-57 | 1.12e-52
RASSF8 | 1.742e-38 | 3.07e-34
RNF8 | 9.601e-36 | 1.69e-31
ALOXE3 | 1.584e-18 | 2.79e-14
CR2 | 5.131e-18 | 9.03e-14
DEPDC1 | 3.806e-15 | 6.7e-11
IVD | 5.919e-15 | 1.04e-10
ZNF30 | 9.524e-13 | 1.68e-08
RGS17 | 3.037e-11 | 5.34e-07
PGBD1 | 2.109e-10 | 3.71e-06

Figure S3. Get High-res Image As an example, this figure shows the association of ACTR6 to 'PATHOLOGICSPREAD(M)'. P value = 6.38e-57 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S12. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 1.7 (0.81)
|  | N
| Stage 1 | 46
| Stage 2 | 27
| Stage 3 | 17
| Stage 4 | 1
| Significant markers | N = 0

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S13. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 5
| YES | 87
| Significant markers | N = 70
| Higher in YES | 63
| Higher in NO | 7

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S14. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
RPS2 | 10.25 | 1.948e-12 | 3.43e-08 | 0.9341
ERBB2 | 8.48 | 5.029e-12 | 8.85e-08 | 0.9425
TMPO | 7.44 | 2.931e-10 | 5.16e-06 | 0.9609
NPBWR1 | 7.09 | 3.079e-10 | 5.42e-06 | 0.8552
HOXC11 | 8.81 | 3.251e-10 | 5.72e-06 | 0.9034
RYR1 | 7.21 | 3.432e-10 | 6.04e-06 | 0.8575
RNF135 | 6.74 | 1.447e-09 | 2.55e-05 | 0.869
SLFN11 | 6.6 | 3.191e-09 | 5.61e-05 | 0.6759
TRPS1 | 8.45 | 3.994e-09 | 7.03e-05 | 0.9057
HPGDS | -7.2 | 4.216e-09 | 7.42e-05 | 0.823

Figure S4. Get High-res Image As an example, this figure shows the association of RPS2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.95e-12 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

11 genes related to 'NEOADJUVANT.THERAPY'.

Table S15. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 11
| YES | 81
| Significant markers | N = 11
| Higher in YES | 6
| Higher in NO | 5

List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S16. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
C16ORF73 | -6.82 | 6.658e-09 | 0.000117 | 0.8284
IRF8 | -7.38 | 2.094e-08 | 0.000369 | 0.8305
BRE | 6.08 | 1.376e-07 | 0.00242 | 0.8541
LOC100302650 | 6.08 | 1.376e-07 | 0.00242 | 0.8541
ZBTB38 | -5.71 | 1.526e-07 | 0.00268 | 0.7183
ATAD2 | 5.64 | 1.937e-07 | 0.00341 | 0.7531
HSF5 | -5.73 | 3.584e-07 | 0.00631 | 0.8058
FAM124A | 5.27 | 1.034e-06 | 0.0182 | 0.7744
FAM122A | 5.32 | 1.416e-06 | 0.0249 | 0.7542
CCDC9 | 5.18 | 1.694e-06 | 0.0298 | 0.7727

Figure S5. Get High-res Image As an example, this figure shows the association of C16ORF73 to 'NEOADJUVANT.THERAPY'. P value = 6.66e-09 with T-test analysis.

Methods & Data
Input

- Expresson data file = LUSC.meth.for_correlation.filtered_data.txt

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

- Number of patients = 92

- Number of genes = 17600

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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