Skin Cutaneous Melanoma: Correlation between mRNAseq expression and clinical features
(metastatic tumor cohort)
Maintained by Juok Cho (Broad Institute)

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


Testing the association between 18092 genes and 3 clinical features across 141 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • 4 genes correlated to 'Time to Death'.

    • DNAJB9|4189 ,  ZNF25|219749 ,  PRKAR2B|5577 ,  EID1|23741

  • 1 gene correlated to 'AGE'.

    • ACOX2|8309

  • 17 genes correlated to 'GENDER'.

    • ZFY|7544 ,  XIST|7503 ,  PRKY|5616 ,  RPS4Y1|6192 ,  CYORF15B|84663 ,  ...

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=4 shorter survival N=0 longer survival N=4
AGE Spearman correlation test N=1 older N=0 younger N=1
GENDER t test N=17 male N=12 female N=5
Clinical variable #1: 'Time to Death'

4 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.2-346 (median=47.5)
  censored N = 70
  death N = 68
  Significant markers N = 4
  associated with shorter survival 0
  associated with longer survival 4
List of 4 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 4 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
DNAJB9|4189 0.51 8.695e-07 0.016 0.329
ZNF25|219749 0.61 1.176e-06 0.021 0.344
PRKAR2B|5577 0.69 1.571e-06 0.028 0.336
EID1|23741 0.5 2.398e-06 0.043 0.336

Figure S1.  Get High-res Image As an example, this figure shows the association of DNAJB9|4189 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 8.69e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 56.06 (16)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ACOX2|8309 -0.4468 3.514e-08 0.000636

Figure S2.  Get High-res Image As an example, this figure shows the association of ACOX2|8309 to 'AGE'. P value = 3.51e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

17 genes related to 'GENDER'.

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

  MALE 91
  Significant markers N = 17
  Higher in MALE 12
  Higher in FEMALE 5
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 29.49 3.831e-57 6.93e-53 0.998
XIST|7503 -20.36 9.187e-41 1.66e-36 0.9781
PRKY|5616 23.89 1.447e-40 2.62e-36 0.9987
RPS4Y1|6192 27 1.446e-36 2.61e-32 1
CYORF15B|84663 30.53 4.471e-34 8.08e-30 1
DDX3Y|8653 28.33 1.248e-30 2.26e-26 1
KDM5D|8284 27.15 5.568e-25 1.01e-20 1
TSIX|9383 -13.94 4.454e-24 8.05e-20 0.9754
EIF1AY|9086 25.39 1.164e-19 2.1e-15 1
TTTY15|64595 21.69 3.336e-19 6.03e-15 0.9976

Figure S3.  Get High-res Image As an example, this figure shows the association of ZFY|7544 to 'GENDER'. P value = 3.83e-57 with T-test analysis.

Methods & Data
  • Expresson data file = SKCM-TM.uncv2.mRNAseq_RSEM_normalized_log2.txt

  • Clinical data file = SKCM-TP.clin.merged.picked.txt

  • Number of patients = 141

  • Number of genes = 18092

  • Number of clinical features = 3

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

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.

[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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] 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)