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 526 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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RPS26 , PPP1R14D
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409 genes correlated to 'AGE'.
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ESR1 , CNTNAP3 , MAGED4B , KRT17 , FOXD2 , ...
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6 genes correlated to 'GENDER'.
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PI3 , TMEM16C , CACNG1 , RP13-36C9.6 , MAPK4 , ...
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1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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OR13C4
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2 genes correlated to 'LYMPH.NODE.METASTASIS'.
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GUCA2A , ASZ1
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1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.
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CSDE1
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5 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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PGC , BMPER , LRP4 , GPER , PRSS2
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No genes correlated to 'DISTANT.METASTASIS'
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=2 | shorter survival | N=2 | longer survival | N=0 |
AGE | Spearman correlation test | N=409 | older | N=195 | younger | N=214 |
GENDER | t test | N=6 | male | N=1 | female | N=5 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1 | yes | N=1 | no | N=0 |
DISTANT METASTASIS | ANOVA test | N=0 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=2 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=1 | higher number.of.lymph.nodes | N=0 | lower number.of.lymph.nodes | N=1 |
NEOPLASM DISEASESTAGE | ANOVA test | N=5 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-223.4 (median=24.2) |
censored | N = 429 | |
death | N = 65 | |
Significant markers | N = 2 | |
associated with shorter survival | 2 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
RPS26 | 2.7 | 9.674e-08 | 0.0017 | 0.682 |
PPP1R14D | 2.3 | 2.933e-07 | 0.0052 | 0.592 |
Figure S1. Get High-res Image As an example, this figure shows the association of RPS26 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.67e-08 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 57.92 (13) |
Significant markers | N = 409 | |
pos. correlated | 195 | |
neg. correlated | 214 |
Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
---|---|---|---|
ESR1 | 0.4216 | 4.416e-24 | 7.87e-20 |
CNTNAP3 | -0.299 | 2.509e-12 | 4.47e-08 |
MAGED4B | -0.2963 | 4.052e-12 | 7.22e-08 |
KRT17 | -0.2955 | 4.657e-12 | 8.29e-08 |
FOXD2 | 0.2906 | 1.082e-11 | 1.93e-07 |
KLK6 | -0.2902 | 1.15e-11 | 2.05e-07 |
NUDT16 | 0.289 | 1.415e-11 | 2.52e-07 |
PPP1R14C | -0.2866 | 2.11e-11 | 3.76e-07 |
SYT8 | -0.2848 | 2.851e-11 | 5.08e-07 |
MGC102966 | -0.2847 | 2.899e-11 | 5.16e-07 |
Figure S2. Get High-res Image As an example, this figure shows the association of ESR1 to 'AGE'. P value = 4.42e-24 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V2ex.png)
Table S5. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 520 | |
MALE | 6 | |
Significant markers | N = 6 | |
Higher in MALE | 1 | |
Higher in FEMALE | 5 |
Table S6. Get Full Table List of 6 genes differentially expressed by 'GENDER'
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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PI3 | -9.21 | 6.424e-11 | 1.14e-06 | 0.708 |
TMEM16C | -14.25 | 1.234e-10 | 2.2e-06 | 0.9256 |
CACNG1 | 18.35 | 1.806e-08 | 0.000322 | 0.9609 |
RP13-36C9.6 | -6.21 | 9.225e-07 | 0.0164 | 0.6635 |
MAPK4 | -9.12 | 1.37e-06 | 0.0244 | 0.8016 |
PLA2G3 | -10.61 | 1.719e-06 | 0.0306 | 0.8301 |
Figure S3. Get High-res Image As an example, this figure shows the association of PI3 to 'GENDER'. P value = 6.42e-11 with T-test analysis.
![](V3ex.png)
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 148 | |
YES | 378 | |
Significant markers | N = 1 | |
Higher in YES | 1 | |
Higher in NO | 0 |
Table S8. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
OR13C4 | 5.02 | 9.688e-07 | 0.0173 | 0.6263 |
Figure S4. Get High-res Image As an example, this figure shows the association of OR13C4 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.69e-07 with T-test analysis.
![](V4ex.png)
Table S9. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'
DISTANT.METASTASIS | Labels | N |
CM0 (I+) | 2 | |
M0 | 369 | |
M1 | 8 | |
MX | 10 | |
Significant markers | N = 0 |
Table S10. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 118 | |
N0 (I+) | 7 | |
N0 (I-) | 59 | |
N1 | 41 | |
N1A | 62 | |
N1B | 14 | |
N1C | 2 | |
N1MI | 11 | |
N2 | 24 | |
N2A | 24 | |
N3 | 5 | |
N3A | 16 | |
NX | 6 | |
Significant markers | N = 2 |
Table S11. Get Full Table List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'
ANOVA_P | Q | |
---|---|---|
GUCA2A | 6.031e-12 | 1.07e-07 |
ASZ1 | 3.589e-08 | 0.000639 |
Figure S5. Get High-res Image As an example, this figure shows the association of GUCA2A to 'LYMPH.NODE.METASTASIS'. P value = 6.03e-12 with ANOVA analysis.
![](V6ex.png)
Table S12. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'
NUMBER.OF.LYMPH.NODES | Mean (SD) | 1.83 (3.5) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
Table S13. Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test
SpearmanCorr | corrP | Q | |
---|---|---|---|
CSDE1 | -0.2433 | 6.958e-07 | 0.0124 |
Figure S6. Get High-res Image As an example, this figure shows the association of CSDE1 to 'NUMBER.OF.LYMPH.NODES'. P value = 6.96e-07 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V7ex.png)
Table S14. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 41 | |
STAGE IA | 22 | |
STAGE IB | 2 | |
STAGE IIA | 136 | |
STAGE IIB | 83 | |
STAGE IIIA | 60 | |
STAGE IIIB | 12 | |
STAGE IIIC | 14 | |
STAGE IV | 8 | |
STAGE X | 11 | |
Significant markers | N = 5 |
Table S15. Get Full Table List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
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PGC | 4.234e-14 | 7.54e-10 |
BMPER | 2.903e-09 | 5.17e-05 |
LRP4 | 2.449e-08 | 0.000436 |
GPER | 1.558e-07 | 0.00277 |
PRSS2 | 5.544e-07 | 0.00987 |
Figure S7. Get High-res Image As an example, this figure shows the association of PGC to 'NEOPLASM.DISEASESTAGE'. P value = 4.23e-14 with ANOVA analysis.
![](V8ex.png)
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Expresson data file = BRCA-TP.medianexp.txt
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Clinical data file = BRCA-TP.clin.merged.picked.txt
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Number of patients = 526
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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 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.