(primary solid tumor cohort)
This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17814 genes and 8 clinical features across 69 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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11 genes correlated to 'GENDER'.
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DDX3Y , RPS4Y1 , RPS4Y2 , EIF1AY , JARID1D , ...
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12 genes correlated to 'HISTOLOGICAL.TYPE'.
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TLE6 , FBXO2 , USP42 , AGR3 , CARD6 , ...
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No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
Complete statistical result table is provided in Supplement Table 1
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=11 | male | N=11 | female | N=0 |
HISTOLOGICAL TYPE | t test | N=12 | rectal mucinous adenocarcinoma | N=6 | rectal adenocarcinoma | N=6 |
PATHOLOGY T | Spearman correlation test | N=0 | ||||
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | t test | N=0 | ||||
TUMOR STAGE | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 0.9-52 (median=6) |
censored | N = 35 | |
death | N = 4 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 66.62 (11) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 31 | |
MALE | 38 | |
Significant markers | N = 11 | |
Higher in MALE | 11 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
DDX3Y | 12.58 | 3.494e-19 | 6.22e-15 | 0.9652 |
RPS4Y1 | 11.98 | 2.93e-17 | 5.22e-13 | 0.9244 |
RPS4Y2 | 11.51 | 1.822e-16 | 3.24e-12 | 0.9669 |
EIF1AY | 10.97 | 2.286e-16 | 4.07e-12 | 0.9593 |
JARID1D | 10.54 | 8.569e-16 | 1.53e-11 | 0.9491 |
CYORF15A | 10.03 | 1.685e-14 | 3e-10 | 0.9491 |
UTY | 7.97 | 4.7e-11 | 8.37e-07 | 0.9177 |
CYORF15B | 7.8 | 7.005e-11 | 1.25e-06 | 0.9032 |
ZFY | 7.67 | 1.14e-10 | 2.03e-06 | 0.8964 |
TTTY14 | 5.84 | 1.885e-07 | 0.00336 | 0.8973 |
HISTOLOGICAL.TYPE | Labels | N |
RECTAL ADENOCARCINOMA | 58 | |
RECTAL MUCINOUS ADENOCARCINOMA | 7 | |
Significant markers | N = 12 | |
Higher in RECTAL MUCINOUS ADENOCARCINOMA | 6 | |
Higher in RECTAL ADENOCARCINOMA | 6 |
T(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') | ttestP | Q | AUC | |
---|---|---|---|---|
TLE6 | 11.58 | 2.77e-13 | 4.93e-09 | 0.9828 |
FBXO2 | -7.64 | 2.189e-10 | 3.9e-06 | 0.867 |
USP42 | -6.71 | 2.684e-08 | 0.000478 | 0.867 |
AGR3 | 6.79 | 8.791e-08 | 0.00157 | 0.8768 |
CARD6 | 6.15 | 1.932e-07 | 0.00344 | 0.8448 |
LOC643641 | -6.6 | 4.407e-07 | 0.00785 | 0.8547 |
MECR | -5.75 | 4.437e-07 | 0.0079 | 0.7734 |
RAB27B | 6.36 | 4.44e-07 | 0.00791 | 0.8645 |
TTLL7 | 7.44 | 7.9e-07 | 0.0141 | 0.936 |
PLCB2 | 6.6 | 9.349e-07 | 0.0166 | 0.8966 |
PATHOLOGY.T | Mean (SD) | 2.7 (0.69) |
N | ||
T1 | 5 | |
T2 | 15 | |
T3 | 45 | |
T4 | 4 | |
Significant markers | N = 0 |
PATHOLOGY.N | Mean (SD) | 0.57 (0.78) |
N | ||
N0 | 42 | |
N1 | 15 | |
N2 | 12 | |
Significant markers | N = 0 |
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 57 | |
M1 | 12 | |
Significant markers | N = 0 |
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Expresson data file = READ-TP.medianexp.txt
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Clinical data file = READ-TP.clin.merged.picked.txt
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Number of patients = 69
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Number of genes = 17814
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Number of clinical features = 8
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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.