Skin Cutaneous Melanoma: Correlation between mRNAseq expression and clinical features
(BRAF_Hotspot_Mutants cohort)
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 18144 genes and 7 clinical features across 67 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

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

- KRTAP1-5|83895 , AGXT|189

- 13 genes correlated to 'GENDER'.

- ZFY|7544 , USP9Y|8287 , CYORF15B|84663 , PRKY|5616 , RPS4Y1|6192 , ...

- 9 genes correlated to 'DISTANT.METASTASIS'.

- PAX3|5077 , LOC728640|728640 , OCRL|4952 , ZNF546|339327 , USP9X|8239 , ...

- 1 gene correlated to 'LYMPH.NODE.METASTASIS'.

- CABP7|164633

- No genes correlated to 'Time to Death', 'AGE', and 'NEOPLASM.DISEASESTAGE'.

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=0 |  |  |  |
AGE | Spearman correlation test | N=0 |  |  |  |
PRIMARY SITE OF DISEASE | ANOVA test | N=2 |  |  |  |
GENDER | t test | N=13 | male | N=11 | female | N=2
DISTANT METASTASIS | ANOVA test | N=9 |  |  |  |
LYMPH NODE METASTASIS | ANOVA test | N=1 |  |  |  |
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death | Duration (Months) | 3.7-357.4 (median=56)
| censored | N = 32
| death | N = 34
| Significant markers | N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

Table S2. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 50.27 (16)
| Significant markers | N = 0

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

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

Table S3. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE | Labels | N
| DISTANT METASTASIS | 6
| PRIMARY TUMOR | 1
| REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 12
| REGIONAL LYMPH NODE | 48
| Significant markers | N = 2

List of 2 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S4. Get Full Table List of 2 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

| ANOVA_P | Q
--- | --- | ---
KRTAP1-5|83895 | 5.656e-09 | 0.000103
AGXT|189 | 2.412e-06 | 0.0438

Figure S1. Get High-res Image As an example, this figure shows the association of KRTAP1-5|83895 to 'PRIMARY.SITE.OF.DISEASE'. P value = 5.66e-09 with ANOVA analysis.

Clinical variable #4: 'GENDER'

13 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 22
| MALE | 45
| Significant markers | N = 13
| Higher in MALE | 11
| Higher in FEMALE | 2

List of top 10 genes differentially expressed by 'GENDER'

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

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
ZFY|7544 | 20.79 | 4.412e-29 | 8e-25 | 1
USP9Y|8287 | 23.34 | 6.963e-24 | 1.26e-19 | 1
CYORF15B|84663 | 19.54 | 1.148e-23 | 2.08e-19 | 1
PRKY|5616 | 17.57 | 1.162e-21 | 2.11e-17 | 1
RPS4Y1|6192 | 20.34 | 5.237e-20 | 9.49e-16 | 1
XIST|7503 | -12.37 | 1.466e-17 | 2.66e-13 | 0.9607
KDM5D|8284 | 18.26 | 2.864e-11 | 5.19e-07 | 1
DDX3Y|8653 | 18.12 | 1.647e-10 | 2.99e-06 | 1
TTTY15|64595 | 18.13 | 2.752e-10 | 4.99e-06 | 1
TSIX|9383 | -8.33 | 3.626e-10 | 6.57e-06 | 0.9562

Figure S2. Get High-res Image As an example, this figure shows the association of ZFY|7544 to 'GENDER'. P value = 4.41e-29 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

9 genes related to 'DISTANT.METASTASIS'.

Table S7. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS | Labels | N
| M0 | 56
| M1 | 1
| M1A | 1
| M1B | 1
| M1C | 2
| Significant markers | N = 9

List of 9 genes differentially expressed by 'DISTANT.METASTASIS'

Table S8. Get Full Table List of 9 genes differentially expressed by 'DISTANT.METASTASIS'

| ANOVA_P | Q
--- | --- | ---
PAX3|5077 | 2.127e-10 | 3.85e-06
LOC728640|728640 | 1.159e-08 | 0.00021
OCRL|4952 | 5.946e-08 | 0.00107
ZNF546|339327 | 1.109e-07 | 0.002
USP9X|8239 | 4.177e-07 | 0.00755
ZNHIT2|741 | 5.187e-07 | 0.00937
ZNF658|26149 | 5.291e-07 | 0.00956
NUDT12|83594 | 9.66e-07 | 0.0175
MAP3K13|9175 | 1.473e-06 | 0.0266

Figure S3. Get High-res Image As an example, this figure shows the association of PAX3|5077 to 'DISTANT.METASTASIS'. P value = 2.13e-10 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

One gene related to 'LYMPH.NODE.METASTASIS'.

Table S9. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS | Labels | N
| N0 | 33
| N1 | 1
| N1A | 2
| N1B | 7
| N2A | 1
| N2B | 5
| N2C | 3
| N3 | 7
| NX | 2
| Significant markers | N = 1

List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S10. Get Full Table List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

| ANOVA_P | Q
--- | --- | ---
CABP7|164633 | 1.634e-08 | 0.000296

Figure S4. Get High-res Image As an example, this figure shows the association of CABP7|164633 to 'LYMPH.NODE.METASTASIS'. P value = 1.63e-08 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S11. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| I OR II NOS | 2
| STAGE I | 10
| STAGE IA | 2
| STAGE IB | 5
| STAGE II | 8
| STAGE IIA | 3
| STAGE IIC | 2
| STAGE III | 5
| STAGE IIIA | 2
| STAGE IIIB | 7
| STAGE IIIC | 9
| STAGE IV | 3
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = SKCM-BRAF_Hotspot_Mutants.uncv2.mRNAseq_RSEM_normalized_log2.txt

- Clinical data file = SKCM-BRAF_Hotspot_Mutants.clin.merged.picked.txt

- Number of patients = 67

- Number of genes = 18144

- Number of clinical features = 7

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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