This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17758 genes and 8 clinical features across 115 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.
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1 gene correlated to 'AGE'.
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PMS2L2|5380
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3 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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THNSL2|55258 , KCNT1|57582 , LOC399815|399815
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5 genes correlated to 'PATHOLOGY.T.STAGE'.
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TFAP2A|7020 , RNF123|63891 , POLA2|23649 , CARKD|55739 , RBPJ|3516
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32 genes correlated to 'PATHOLOGY.N.STAGE'.
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PI4KAP2|375133 , WARS2|10352 , AGPAT2|10555 , PRSS35|167681 , SFRS6|6431 , ...
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1 gene correlated to 'PATHOLOGY.M.STAGE'.
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KCNT1|57582
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32 genes correlated to 'GENDER'.
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XIST|7503 , ZFY|7544 , RPS4Y1|6192 , TSIX|9383 , PRKY|5616 , ...
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1 gene correlated to 'COMPLETENESS.OF.RESECTION'.
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PGA5|5222
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No genes correlated to 'Time to Death'
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=1 | older | N=1 | younger | N=0 |
NEOPLASM DISEASESTAGE | ANOVA test | N=3 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=5 | higher stage | N=3 | lower stage | N=2 |
PATHOLOGY N STAGE | t test | N=32 | class1 | N=23 | class0 | N=9 |
PATHOLOGY M STAGE | ANOVA test | N=1 | ||||
GENDER | t test | N=32 | male | N=18 | female | N=14 |
COMPLETENESS OF RESECTION | ANOVA test | N=1 |
Time to Death | Duration (Months) | 0-113 (median=14.9) |
censored | N = 61 | |
death | N = 51 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.51 (14) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
PMS2L2|5380 | 0.4249 | 2.715e-06 | 0.0482 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 43 | |
STAGE II | 25 | |
STAGE III | 2 | |
STAGE IIIA | 25 | |
STAGE IIIB | 2 | |
STAGE IIIC | 5 | |
STAGE IV | 1 | |
STAGE IVA | 1 | |
STAGE IVB | 1 | |
Significant markers | N = 3 |
ANOVA_P | Q | |
---|---|---|
THNSL2|55258 | 1.577e-08 | 0.00028 |
KCNT1|57582 | 1.764e-08 | 0.000313 |
LOC399815|399815 | 1.988e-06 | 0.0353 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.01 (0.97) |
N | ||
1 | 46 | |
2 | 28 | |
3 | 33 | |
4 | 7 | |
Significant markers | N = 5 | |
pos. correlated | 3 | |
neg. correlated | 2 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
TFAP2A|7020 | 0.4561 | 5.504e-07 | 0.00977 |
RNF123|63891 | -0.4383 | 1.073e-06 | 0.019 |
POLA2|23649 | 0.4353 | 1.299e-06 | 0.0231 |
CARKD|55739 | -0.4244 | 2.532e-06 | 0.045 |
RBPJ|3516 | 0.4232 | 2.714e-06 | 0.0482 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 73 | |
class1 | 3 | |
Significant markers | N = 32 | |
Higher in class1 | 23 | |
Higher in class0 | 9 |
T(pos if higher in 'class1') | ttestP | Q | AUC | |
---|---|---|---|---|
PI4KAP2|375133 | 8.83 | 1.598e-10 | 2.54e-06 | 0.8995 |
WARS2|10352 | 8.95 | 3.243e-10 | 5.16e-06 | 0.9132 |
AGPAT2|10555 | 7.1 | 6.399e-10 | 1.02e-05 | 0.8082 |
PRSS35|167681 | -7.33 | 3.03e-09 | 4.83e-05 | 0.8545 |
SFRS6|6431 | 7.04 | 7.369e-09 | 0.000117 | 0.7991 |
ATP5F1|515 | 6.42 | 1.409e-08 | 0.000224 | 0.7854 |
WNT5A|7474 | 6.76 | 1.501e-08 | 0.000239 | 0.7991 |
STAB2|55576 | -6.63 | 2.319e-08 | 0.000369 | 0.7814 |
ARMCX3|51566 | 6.68 | 2.827e-08 | 0.00045 | 0.7717 |
CYTH4|27128 | 6.04 | 5.752e-08 | 0.000915 | 0.8037 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 88 | |
M1 | 2 | |
MX | 25 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
KCNT1|57582 | 7.998e-11 | 1.42e-06 |
GENDER | Labels | N |
FEMALE | 44 | |
MALE | 71 | |
Significant markers | N = 32 | |
Higher in MALE | 18 | |
Higher in FEMALE | 14 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
XIST|7503 | -13.76 | 2.615e-23 | 4.64e-19 | 0.9662 |
ZFY|7544 | 21.02 | 7e-20 | 1.24e-15 | 0.9947 |
RPS4Y1|6192 | 21.92 | 2.007e-19 | 3.56e-15 | 0.996 |
TSIX|9383 | -12.15 | 6.692e-17 | 1.19e-12 | 0.9671 |
PRKY|5616 | 15.75 | 1.706e-15 | 3.03e-11 | 0.9927 |
DDX3Y|8653 | 18.92 | 1.447e-13 | 2.57e-09 | 0.9982 |
NLGN4Y|22829 | 13.8 | 3.923e-13 | 6.96e-09 | 0.985 |
KDM5D|8284 | 17.08 | 3.441e-12 | 6.11e-08 | 0.996 |
KDM5C|8242 | -6.74 | 1.099e-09 | 1.95e-05 | 0.8182 |
BMP8B|656 | -6.37 | 9.276e-09 | 0.000165 | 0.8075 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 91 | |
R1 | 10 | |
R2 | 1 | |
RX | 8 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
PGA5|5222 | 7.76e-07 | 0.0138 |
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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 = 115
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Number of genes = 17758
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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 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.