This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 19761 genes and 10 clinical features across 66 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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72 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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C20ORF46 , WTIP , FOXC1 , TASP1 , TSPYL5 , ...
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123 genes correlated to 'PATHOLOGY.M.STAGE'.
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GRID1 , DOK5 , QRFPR , NPY , SHE , ...
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3 genes correlated to 'NUMBERPACKYEARSSMOKED'.
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GRHL1 , LOC100133669 , LY6E
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No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'YEAROFTOBACCOSMOKINGONSET'.
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 | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=72 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | ANOVA test | N=123 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=3 | higher numberpackyearssmoked | N=3 | lower numberpackyearssmoked | N=0 |
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.6-152 (median=64) |
censored | N = 57 | |
death | N = 8 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 51.52 (14) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 21 | |
STAGE II | 25 | |
STAGE III | 14 | |
STAGE IV | 6 | |
Significant markers | N = 72 |
Table S4. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
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C20ORF46 | 9.532e-12 | 1.88e-07 |
WTIP | 8.711e-11 | 1.72e-06 |
FOXC1 | 1.253e-10 | 2.48e-06 |
TASP1 | 1.831e-10 | 3.62e-06 |
TSPYL5 | 1.895e-10 | 3.74e-06 |
FAM135B | 1.64e-09 | 3.24e-05 |
ADCY1 | 2.902e-09 | 5.73e-05 |
C8ORF47 | 3.779e-09 | 7.46e-05 |
AQP5 | 7.714e-09 | 0.000152 |
CPNE9 | 1.201e-08 | 0.000237 |
Figure S1. Get High-res Image As an example, this figure shows the association of C20ORF46 to 'NEOPLASM.DISEASESTAGE'. P value = 9.53e-12 with ANOVA analysis.
![](V3ex.png)
Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'
PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.85) |
N | ||
1 | 21 | |
2 | 25 | |
3 | 18 | |
4 | 2 | |
Significant markers | N = 0 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'
PATHOLOGY.N.STAGE | Mean (SD) | 0.16 (0.47) |
N | ||
0 | 40 | |
1 | 3 | |
2 | 2 | |
Significant markers | N = 0 |
Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'
PATHOLOGY.M.STAGE | Labels | N |
M0 | 34 | |
M1 | 2 | |
MX | 9 | |
Significant markers | N = 123 |
Table S8. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'
ANOVA_P | Q | |
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GRID1 | 2.836e-31 | 5.6e-27 |
DOK5 | 1.608e-28 | 3.18e-24 |
QRFPR | 2.36e-22 | 4.66e-18 |
NPY | 1.474e-21 | 2.91e-17 |
SHE | 2.153e-18 | 4.25e-14 |
TDRD10 | 2.153e-18 | 4.25e-14 |
CPNE9 | 4.402e-18 | 8.7e-14 |
FSTL3 | 3.435e-15 | 6.78e-11 |
KCTD15 | 9.744e-15 | 1.92e-10 |
ZNF880 | 8.056e-14 | 1.59e-09 |
Figure S2. Get High-res Image As an example, this figure shows the association of GRID1 to 'PATHOLOGY.M.STAGE'. P value = 2.84e-31 with ANOVA analysis.
![](V6ex.png)
Table S9. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 27 | |
MALE | 39 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S10. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 89.09 (9.4) |
Score | N | |
70 | 1 | |
80 | 2 | |
90 | 5 | |
100 | 3 | |
Significant markers | N = 0 |
Table S11. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'
NUMBERPACKYEARSSMOKED | Mean (SD) | 25.09 (22) |
Significant markers | N = 3 | |
pos. correlated | 3 | |
neg. correlated | 0 |
Table S12. Get Full Table List of 3 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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GRHL1 | 0.9704 | 7.42e-07 | 0.0147 |
LOC100133669 | 0.9658 | 1.403e-06 | 0.0277 |
LY6E | 0.9658 | 1.403e-06 | 0.0277 |
Figure S3. Get High-res Image As an example, this figure shows the association of GRHL1 to 'NUMBERPACKYEARSSMOKED'. P value = 7.42e-07 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V9ex.png)
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Expresson data file = KICH-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = KICH-TP.merged_data.txt
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Number of patients = 66
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Number of genes = 19761
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Number of clinical features = 10
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
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
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
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
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.
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.