Ovarian Serous Cystadenocarcinoma: Correlation between mRNA expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 18632 genes and 6 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

- 1 gene correlated to 'Time to Death'.

- ZFHX4

- 183 genes correlated to 'AGE'.

- STS , GNPNAT1 , GREB1 , DEPDC6 , SLCO1A2 , ...

- 4 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

- SPINK8 , PTBP1 , EBI3 , LOC390110

- 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

- WDR60

- 28 genes correlated to 'TUMOR.STAGE'.

- BACE1 , PTRF , FREQ , PRRG1 , GAS1 , ...

- 25 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- RAPGEF1 , SNX9 , EIF4G2 , PRRX1 , ZNF507 , ...

Results
Overview of the results

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=1 | shorter survival | N=1 | longer survival | N=0
AGE | Spearman correlation test | N=183 | older | N=73 | younger | N=110
PRIMARY SITE OF DISEASE | ANOVA test | N=4 |  |  |  |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0
TUMOR STAGE | Spearman correlation test | N=28 | higher stage | N=25 | lower stage | N=3
RADIATIONS RADIATION REGIMENINDICATION | t test | N=25 | yes | N=18 | no | N=7

Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'

Time to Death | Duration (Months) | 0.3-180.2 (median=28.3)
| censored | N = 267
| death | N = 290
| Significant markers | N = 1
| associated with shorter survival | 1
| associated with longer survival | 0

List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
ZFHX4 | 1.63 | 1.961e-06 | 0.037 | 0.574

Figure S1. Get High-res Image As an example, this figure shows the association of ZFHX4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.96e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

183 genes related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 59.72 (12)
| Significant markers | N = 183
| pos. correlated | 73
| neg. correlated | 110

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
STS | -0.3018 | 4.535e-13 | 8.45e-09
GNPNAT1 | -0.2996 | 6.839e-13 | 1.27e-08
GREB1 | -0.2984 | 8.545e-13 | 1.59e-08
DEPDC6 | -0.2979 | 9.442e-13 | 1.76e-08
SLCO1A2 | 0.2878 | 5.726e-12 | 1.07e-07
EIF4E3 | -0.2849 | 9.423e-12 | 1.76e-07
NPAL2 | -0.2736 | 6.483e-11 | 1.21e-06
C9ORF103 | -0.2731 | 6.964e-11 | 1.3e-06
GEMIN8 | -0.27 | 1.16e-10 | 2.16e-06
ODZ1 | 0.2688 | 1.416e-10 | 2.64e-06

Figure S2. Get High-res Image As an example, this figure shows the association of STS to 'AGE'. P value = 4.54e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

4 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE | Labels | N
| OMENTUM | 2
| OVARY | 558
| PERITONEUM (OVARY) | 2
| Significant markers | N = 4

List of 4 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6. Get Full Table List of 4 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

| ANOVA_P | Q
--- | --- | ---
SPINK8 | 9.933e-16 | 1.85e-11
PTBP1 | 1.007e-08 | 0.000188
EBI3 | 4.195e-07 | 0.00781
LOC390110 | 2.486e-06 | 0.0463

Figure S3. Get High-res Image As an example, this figure shows the association of SPINK8 to 'PRIMARY.SITE.OF.DISEASE'. P value = 9.93e-16 with ANOVA analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.64 (13)
| Score | N
| 40 | 2
| 60 | 20
| 80 | 49
| 100 | 7
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8. Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
WDR60 | 0.5131 | 1.561e-06 | 0.0291

Figure S4. Get High-res Image As an example, this figure shows the association of WDR60 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.56e-06 with Spearman correlation analysis.

Clinical variable #5: 'TUMOR.STAGE'

28 genes related to 'TUMOR.STAGE'.

Table S9. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 3.05 (0.56)
|  | N
| Stage 1 | 16
| Stage 2 | 26
| Stage 3 | 431
| Stage 4 | 85
| Significant markers | N = 28
| pos. correlated | 25
| neg. correlated | 3

List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S10. Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
BACE1 | 0.28 | 1.654e-11 | 3.08e-07
PTRF | 0.2454 | 4.269e-09 | 7.95e-05
FREQ | 0.2307 | 3.545e-08 | 0.00066
PRRG1 | 0.2299 | 3.972e-08 | 0.00074
GAS1 | 0.2222 | 1.131e-07 | 0.00211
PDK1 | -0.2199 | 1.541e-07 | 0.00287
PLCD3 | 0.2178 | 2.043e-07 | 0.00381
CELSR2 | 0.2135 | 3.564e-07 | 0.00664
NBL1 | 0.2112 | 4.791e-07 | 0.00892
AKAP12 | 0.2091 | 6.215e-07 | 0.0116

Figure S5. Get High-res Image As an example, this figure shows the association of BACE1 to 'TUMOR.STAGE'. P value = 1.65e-11 with Spearman correlation analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

25 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S11. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 3
| YES | 559
| Significant markers | N = 25
| Higher in YES | 18
| Higher in NO | 7

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S12. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
RAPGEF1 | 21.41 | 1.075e-48 | 2e-44 | 0.8372
SNX9 | -15.64 | 3.191e-33 | 5.94e-29 | 0.7633
EIF4G2 | 14.34 | 1.53e-23 | 2.85e-19 | 0.7639
PRRX1 | 13.55 | 1.099e-19 | 2.05e-15 | 0.653
ZNF507 | -23.17 | 5.921e-15 | 1.1e-10 | 0.8998
ACTL7B | -11.46 | 7.486e-15 | 1.39e-10 | 0.7102
CNKSR2 | -8.77 | 1.11e-14 | 2.07e-10 | 0.7257
AKAP9 | 16.71 | 2.413e-14 | 4.49e-10 | 0.7895
CRIP2 | 9.87 | 2.984e-14 | 5.56e-10 | 0.6947
CALR | 7.62 | 1.079e-13 | 2.01e-09 | 0.5963

Figure S6. Get High-res Image As an example, this figure shows the association of RAPGEF1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.08e-48 with T-test analysis.

Methods & Data
Input

- Expresson data file = OV-TP.medianexp.txt

- Clinical data file = OV-TP.clin.merged.picked.txt

- Number of patients = 562

- Number of genes = 18632

- Number of clinical features = 6

Survival analysis

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

Correlation analysis

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

ANOVA analysis

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

Student's t-test analysis

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

Q value calculation

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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