This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18332 genes and 5 clinical features across 85 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
-
1 gene correlated to 'AGE'.
-
CAMK2B|816
-
109 genes correlated to 'HISTOLOGICAL.TYPE'.
-
HIF3A|64344 , MX2|4600 , PRKCI|5584 , NPR1|4881 , ZNF334|55713 , ...
-
8 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
-
P2RY4|5030 , FRMPD1|22844 , CYP4Z2P|163720 , ZIK1|284307 , KRT5|3852 , ...
-
1 gene correlated to 'COMPLETENESS.OF.RESECTION'.
-
IGFBP1|3484
-
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 |
HISTOLOGICAL TYPE | ANOVA test | N=109 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=8 | yes | N=2 | no | N=6 |
COMPLETENESS OF RESECTION | ANOVA test | N=1 |
Time to Death | Duration (Months) | 0-102 (median=5.3) |
censored | N = 71 | |
death | N = 12 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 65.67 (12) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
CAMK2B|816 | 0.5022 | 1.13e-06 | 0.0207 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 46 | |
MIXED SEROUS AND ENDOMETRIOID | 5 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 34 | |
Significant markers | N = 109 |
ANOVA_P | Q | |
---|---|---|
HIF3A|64344 | 3.528e-14 | 6.47e-10 |
MX2|4600 | 4.115e-13 | 7.54e-09 |
PRKCI|5584 | 8.125e-11 | 1.49e-06 |
NPR1|4881 | 1.819e-10 | 3.33e-06 |
ZNF334|55713 | 2.237e-10 | 4.1e-06 |
L1CAM|3897 | 5.575e-10 | 1.02e-05 |
TRO|7216 | 1.557e-09 | 2.85e-05 |
SLC8A1|6546 | 1.93e-09 | 3.54e-05 |
FIGNL2|401720 | 3.414e-09 | 6.26e-05 |
NR1I2|8856 | 4.015e-09 | 7.36e-05 |
8 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 6 | |
YES | 79 | |
Significant markers | N = 8 | |
Higher in YES | 2 | |
Higher in NO | 6 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
P2RY4|5030 | -9.26 | 8.808e-11 | 1.56e-06 | 0.8889 |
FRMPD1|22844 | -7.8 | 2.649e-10 | 4.68e-06 | 0.8284 |
CYP4Z2P|163720 | -7.82 | 2.54e-09 | 4.49e-05 | 0.892 |
ZIK1|284307 | 6.46 | 4.13e-07 | 0.00729 | 0.8485 |
KRT5|3852 | -6.58 | 5.174e-07 | 0.00913 | 0.8355 |
OSTBETA|123264 | 7.29 | 1.44e-06 | 0.0254 | 0.8596 |
GJB6|10804 | -5.24 | 1.504e-06 | 0.0265 | 0.7649 |
LTF|4057 | -6.83 | 1.568e-06 | 0.0277 | 0.8418 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 52 | |
R1 | 2 | |
R2 | 4 | |
RX | 4 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
IGFBP1|3484 | 7.29e-07 | 0.0134 |
-
Expresson data file = UCEC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
-
Clinical data file = UCEC-TP.clin.merged.picked.txt
-
Number of patients = 85
-
Number of genes = 18332
-
Number of clinical features = 5
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.