Prostate Adenocarcinoma: Correlation between mRNAseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 18155 genes and 3 clinical features across 53 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

- 1 gene correlated to 'AGE'.

- OSBPL9|114883

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

- SCN11A|11280 , TSPAN19|144448 , ZNF596|169270 , SFRS2|6427 , VN1R1|57191 , ...

- 77 genes correlated to 'NEOADJUVANT.THERAPY'.

- KIAA1644|85352 , SPOCK3|50859 , PDPN|10630 , HSPB8|26353 , DCBLD2|131566 , ...

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 |
--- | --- | --- | --- | --- | --- | ---
AGE | Spearman correlation test | N=1 | older | N=0 | younger | N=1
RADIATIONS RADIATION REGIMENINDICATION | t test | N=10 | yes | N=3 | no | N=7
NEOADJUVANT THERAPY | t test | N=77 | yes | N=59 | no | N=18

Clinical variable #1: 'AGE'

One gene related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 61.3 (7.2)
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
OSBPL9|114883 | -0.6211 | 6.951e-07 | 0.0126

Figure S1. Get High-res Image As an example, this figure shows the association of OSBPL9|114883 to 'AGE'. P value = 6.95e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S3. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 4
| YES | 49
| Significant markers | N = 10
| Higher in YES | 3
| Higher in NO | 7

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

Table S4. Get Full Table List of 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
SCN11A|11280 | -8.92 | 7.261e-08 | 0.00125 | 0.9583
TSPAN19|144448 | 7.97 | 1.119e-07 | 0.00192 | 0.907
ZNF596|169270 | -7.67 | 2.379e-07 | 0.00408 | 0.9235
SFRS2|6427 | -6.38 | 3.135e-07 | 0.00538 | 0.8367
VN1R1|57191 | -6.13 | 3.556e-07 | 0.0061 | 0.8724
UBTF|7343 | -6.6 | 4.129e-07 | 0.00709 | 0.8724
FGFR3|2261 | -5.46 | 1.407e-06 | 0.0241 | 0.7806
C15ORF57|90416 | 5.56 | 1.485e-06 | 0.0255 | 0.7908
ELP3|55140 | -5.36 | 2.3e-06 | 0.0395 | 0.7347
ABHD11|83451 | 5.76 | 2.676e-06 | 0.0459 | 0.852

Figure S2. Get High-res Image As an example, this figure shows the association of SCN11A|11280 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.26e-08 with T-test analysis.

Clinical variable #3: 'NEOADJUVANT.THERAPY'

77 genes related to 'NEOADJUVANT.THERAPY'.

Table S5. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 3
| YES | 50
| Significant markers | N = 77
| Higher in YES | 59
| Higher in NO | 18

List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
KIAA1644|85352 | 15.32 | 2.098e-17 | 3.38e-13 | 1
SPOCK3|50859 | 14.03 | 2.622e-17 | 4.22e-13 | 0.9867
PDPN|10630 | 13.08 | 1.503e-16 | 2.42e-12 | 0.9467
HSPB8|26353 | 16.25 | 1.626e-16 | 2.62e-12 | 1
DCBLD2|131566 | 11.25 | 2.2e-15 | 3.54e-11 | 0.94
PTPN9|5780 | 11.21 | 3.201e-15 | 5.15e-11 | 0.9467
TGFB1I1|7041 | 13.78 | 1.156e-13 | 1.86e-09 | 0.98
FGFR1|2260 | 9.95 | 2.136e-13 | 3.44e-09 | 0.92
ACSL6|23305 | -9.85 | 2.709e-13 | 4.36e-09 | 0.9867
FAM13C|220965 | 11.14 | 7.037e-13 | 1.13e-08 | 0.96

Figure S3. Get High-res Image As an example, this figure shows the association of KIAA1644|85352 to 'NEOADJUVANT.THERAPY'. P value = 2.1e-17 with T-test analysis.

Methods & Data
Input

- Expresson data file = PRAD.uncv2.mRNAseq_RSEM_normalized_log2.txt

- Clinical data file = PRAD.clin.merged.picked.txt

- Number of patients = 53

- Number of genes = 18155

- Number of clinical features = 3

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

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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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