This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17848 genes and 7 clinical features across 60 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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8 genes correlated to 'GENDER'.
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XIST|7503 , TSIX|9383 , ZFY|7544 , KDM5C|8242 , EIF1AX|1964 , ...
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6 genes correlated to 'LYMPH.NODE.METASTASIS'.
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DTX3L|151636 , CCDC111|201973 , CASP3|836 , MID2|11043 , ACY3|91703 , ...
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1 gene correlated to 'COMPLETENESS.OF.RESECTION'.
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SLC16A11|162515
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5 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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CCDC111|201973 , SGEF|26084 , CASP3|836 , DTX3L|151636 , ACY3|91703
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No genes correlated to 'Time to Death', 'AGE', and 'DISTANT.METASTASIS'.
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=8 | male | N=4 | female | N=4 |
DISTANT METASTASIS | ANOVA test | N=0 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=6 | ||||
COMPLETENESS OF RESECTION | ANOVA test | N=1 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=5 |
Time to Death | Duration (Months) | 0.1-83.6 (median=14.3) |
censored | N = 32 | |
death | N = 23 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.66 (15) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 23 | |
MALE | 37 | |
Significant markers | N = 8 | |
Higher in MALE | 4 | |
Higher in FEMALE | 4 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
XIST|7503 | -14.22 | 1.96e-15 | 3.5e-11 | 0.9861 |
TSIX|9383 | -9.48 | 5.074e-10 | 9.05e-06 | 0.9814 |
ZFY|7544 | 12.05 | 2.187e-08 | 0.00039 | 0.9919 |
KDM5C|8242 | -6.19 | 6.706e-08 | 0.0012 | 0.8719 |
EIF1AX|1964 | -6.31 | 7.425e-08 | 0.00132 | 0.8754 |
NLGN4Y|22829 | 9.56 | 1.155e-07 | 0.00206 | 0.9811 |
PRKY|5616 | 9.24 | 6.111e-07 | 0.0109 | 0.9842 |
TERF1|7013 | 5.46 | 1.228e-06 | 0.0219 | 0.8496 |
DISTANT.METASTASIS | Labels | N |
M0 | 39 | |
M1 | 1 | |
MX | 20 | |
Significant markers | N = 0 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 40 | |
N1 | 1 | |
NX | 18 | |
Significant markers | N = 6 |
ANOVA_P | Q | |
---|---|---|
DTX3L|151636 | 7.274e-10 | 1.3e-05 |
CCDC111|201973 | 8.218e-10 | 1.47e-05 |
CASP3|836 | 4.664e-09 | 8.32e-05 |
MID2|11043 | 4.705e-07 | 0.0084 |
ACY3|91703 | 1.268e-06 | 0.0226 |
PARP14|54625 | 1.724e-06 | 0.0308 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 43 | |
R1 | 7 | |
R2 | 1 | |
RX | 6 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
SLC16A11|162515 | 5.168e-08 | 0.000922 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 23 | |
STAGE II | 11 | |
STAGE IIIA | 13 | |
STAGE IIIB | 3 | |
STAGE IIIC | 1 | |
STAGE IVB | 1 | |
Significant markers | N = 5 |
ANOVA_P | Q | |
---|---|---|
CCDC111|201973 | 1.841e-08 | 0.000329 |
SGEF|26084 | 2.555e-08 | 0.000456 |
CASP3|836 | 7.189e-08 | 0.00128 |
DTX3L|151636 | 7.863e-08 | 0.0014 |
ACY3|91703 | 1.423e-07 | 0.00254 |
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Expresson data file = LIHC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = LIHC-TP.clin.merged.picked.txt
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Number of patients = 60
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Number of genes = 17848
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Number of clinical features = 7
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 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 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.