This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.
Testing the association between 23891 genes and 9 clinical features across 66 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.
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51 genes correlated to 'AGE'.
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KPNA7|402569_CALCULATED , RIPK3|11035_CALCULATED , TRIM31|11074_CALCULATED , DQ572107|?_CALCULATED , MIR4497|100616454_CALCULATED , ...
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41 genes correlated to 'PATHOLOGY.T.STAGE'.
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CLCN5|1184_CALCULATED , MOCOS|55034_CALCULATED , SEC16B|89866_CALCULATED , ARHGEF7|8874_CALCULATED , YBX2|51087_CALCULATED , ...
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309 genes correlated to 'RACE'.
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TMC5|79838_CALCULATED , TSPAN8|7103_CALCULATED , ZG16B|124220_CALCULATED , CDC42EP5|148170_CALCULATED , CDH17|1015_CALCULATED , ...
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No genes correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', and 'NUMBERPACKYEARSSMOKED'.
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=51 | older | N=21 | younger | N=30 |
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=41 | higher stage | N=0 | lower stage | N=41 |
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
RACE | Wilcoxon test | N=309 | white | N=309 | asian | N=0 |
Time to Death | Duration (Months) | 0-33.3 (median=3.2) |
censored | N = 44 | |
death | N = 20 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 62.11 (13) |
Significant markers | N = 51 | |
pos. correlated | 21 | |
neg. correlated | 30 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
KPNA7|402569_CALCULATED | 0.6208 | 2.685e-08 | 0.000641 |
RIPK3|11035_CALCULATED | 0.6025 | 8.676e-08 | 0.00207 |
TRIM31|11074_CALCULATED | 0.5784 | 3.657e-07 | 0.00874 |
DQ572107|?_CALCULATED | 0.5644 | 7.966e-07 | 0.019 |
MIR4497|100616454_CALCULATED | -0.5952 | 1.044e-06 | 0.0249 |
CELSR2|1952_CALCULATED | -0.5543 | 1.372e-06 | 0.0328 |
TMC5|79838_CALCULATED | 0.5528 | 1.488e-06 | 0.0355 |
ACY3|91703_CALCULATED | 0.5516 | 1.583e-06 | 0.0378 |
PGC|5225_CALCULATED | 0.5818 | 1.671e-06 | 0.0399 |
ARHGEF19|128272_CALCULATED | -0.547 | 2.015e-06 | 0.0481 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 4 | |
STAGE IA | 3 | |
STAGE IB | 2 | |
STAGE II | 1 | |
STAGE IIA | 21 | |
STAGE IIB | 10 | |
STAGE III | 6 | |
STAGE IIIA | 6 | |
STAGE IIIB | 5 | |
STAGE IIIC | 1 | |
STAGE IV | 1 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.48 (0.79) |
N | ||
1 | 9 | |
2 | 16 | |
3 | 34 | |
4 | 2 | |
Significant markers | N = 41 | |
pos. correlated | 0 | |
neg. correlated | 41 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
CLCN5|1184_CALCULATED | -0.5934 | 4.651e-07 | 0.0111 |
MOCOS|55034_CALCULATED | -0.5882 | 6.187e-07 | 0.0148 |
SEC16B|89866_CALCULATED | -0.5881 | 6.249e-07 | 0.0149 |
ARHGEF7|8874_CALCULATED | -0.5783 | 1.053e-06 | 0.0252 |
YBX2|51087_CALCULATED | -0.5688 | 1.729e-06 | 0.0413 |
MED14|9282_CALCULATED | -0.5683 | 1.776e-06 | 0.0424 |
ANO9|338440_CALCULATED | -0.5681 | 1.795e-06 | 0.0429 |
LOC731275|731275_CALCULATED | -0.5624 | 2.389e-06 | 0.0571 |
XYLB|9942_CALCULATED | -0.5596 | 2.748e-06 | 0.0656 |
ATP11A|23250_CALCULATED | -0.5585 | 2.913e-06 | 0.0696 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.57 (0.76) |
N | ||
0 | 35 | |
1 | 18 | |
2 | 7 | |
3 | 1 | |
Significant markers | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 49 | |
M1A | 1 | |
MX | 7 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 10 | |
MALE | 56 | |
Significant markers | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 36.85 (21) |
Significant markers | N = 0 |
RACE | Labels | N |
ASIAN | 27 | |
WHITE | 36 | |
Significant markers | N = 309 | |
Higher in WHITE | 309 | |
Higher in ASIAN | 0 |
W(pos if higher in 'WHITE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
TMC5|79838_CALCULATED | 911 | 3.726e-09 | 8.9e-05 | 0.9372 |
TSPAN8|7103_CALCULATED | 873 | 7.901e-09 | 0.000189 | 0.9327 |
ZG16B|124220_CALCULATED | 882.5 | 3.784e-08 | 0.000903 | 0.9079 |
CDC42EP5|148170_CALCULATED | 877 | 5.841e-08 | 0.00139 | 0.9023 |
CDH17|1015_CALCULATED | 872.5 | 8.245e-08 | 0.00197 | 0.8976 |
RIPK3|11035_CALCULATED | 872 | 8.595e-08 | 0.00205 | 0.8971 |
FAM83E|54854_CALCULATED | 871 | 9.277e-08 | 0.00221 | 0.8961 |
C11ORF9|745_CALCULATED | 870 | 1.002e-07 | 0.00239 | 0.8951 |
VILL|50853_CALCULATED | 869.5 | 1.04e-07 | 0.00248 | 0.8945 |
HABP2|3026_CALCULATED | 820 | 1.051e-07 | 0.00251 | 0.9011 |
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Expresson data file = ESCA-TP.mRNAseq_RPKM_log2.txt
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Clinical data file = ESCA-TP.merged_data.txt
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Number of patients = 66
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Number of genes = 23891
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Number of clinical features = 9
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.