Correlation between mRNAseq expression and clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1F18Z4V
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1F18Z4V
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features. The input file "LAML-TB.uncv2.mRNAseq_RSEM_normalized_log2.txt" is generated in the pipeline mRNAseq_Preprocess in the stddata run.

Summary

Testing the association between 17276 genes and 4 clinical features across 173 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

- 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- CLINT1|9685 , CBR1|873 , FLJ22536|401237 , RPS24|6229 , LNP1|348801 , ...

- 30 genes correlated to 'YEARS_TO_BIRTH'.

- GBP2|2634 , PI4K2A|55361 , FBXO32|114907 , C7ORF58|79974 , SLC22A16|85413 , ...

- 7 genes correlated to 'GENDER'.

- NCRNA00183|554203 , HDHD1A|8226 , CD24|100133941 , KAL1|3730 , SCARNA9L|100158262 , ...

- No genes correlated to 'RACE'

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 P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant genes | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 |  | N=NA |  | N=NA
YEARS_TO_BIRTH | Spearman correlation test | N=30 | older | N=25 | younger | N=5
GENDER | Wilcoxon test | N=7 | male | N=7 | female | N=0
RACE | Kruskal-Wallis test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 0-94.1 (median=11)
| censored | N = 59
| death | N = 113
| Significant markers | N = 30
| associated with shorter survival | NA
| associated with longer survival | NA

List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

| logrank_P | Q | C_index
--- | --- | --- | ---
CLINT1|9685 | 2.14e-08 | 0.00037 | 0.378
CBR1|873 | 5.45e-07 | 0.0031 | 0.659
FLJ22536|401237 | 5.46e-07 | 0.0031 | 0.392
RPS24|6229 | 7.89e-07 | 0.0034 | 0.416
LNP1|348801 | 1.16e-06 | 0.0035 | 0.372
KLHL12|59349 | 1.42e-06 | 0.0035 | 0.572
ADSS|159 | 1.69e-06 | 0.0035 | 0.357
PTP4A3|11156 | 1.8e-06 | 0.0035 | 0.634
ISG20|3669 | 1.81e-06 | 0.0035 | 0.565
ECE1|1889 | 2.33e-06 | 0.004 | 0.581

Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH | Mean (SD) | 55.27 (16)
| Significant markers | N = 30
| pos. correlated | 25
| neg. correlated | 5

List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

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

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
GBP2|2634 | 0.4101 | 2.1e-08 | 0.000257
PI4K2A|55361 | 0.406 | 2.98e-08 | 0.000257
FBXO32|114907 | 0.368 | 6.309e-07 | 0.00275
C7ORF58|79974 | 0.372 | 6.373e-07 | 0.00275
SLC22A16|85413 | -0.3624 | 9.597e-07 | 0.00305
HK2|3099 | -0.3611 | 1.059e-06 | 0.00305
PPARD|5467 | 0.3581 | 1.314e-06 | 0.00324
TMEM117|84216 | 0.3708 | 1.616e-06 | 0.00329
STK16|8576 | 0.3544 | 1.716e-06 | 0.00329
KLRF1|51348 | 0.3514 | 2.129e-06 | 0.00368

Clinical variable #3: 'GENDER'

7 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 80
| MALE | 93
| Significant markers | N = 7
| Higher in MALE | 7
| Higher in FEMALE | 0

List of 7 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of 7 genes differentially expressed by 'GENDER'. 23 significant gene(s) located in sex chromosomes is(are) filtered out.

| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
NCRNA00183|554203 | 1159 | 6.406e-15 | 1.23e-11 | 0.8442
HDHD1A|8226 | 1995 | 1.518e-07 | 0.000164 | 0.7319
CD24|100133941 | 5374 | 4.798e-07 | 0.00046 | 0.7223
KAL1|3730 | 2223 | 7.319e-06 | 0.00602 | 0.7655
SCARNA9L|100158262 | 2321 | 2.064e-05 | 0.0155 | 0.688
BAG4|9530 | 5025 | 7.137e-05 | 0.0457 | 0.6754
FRG1B|284802 | 5000 | 9.797e-05 | 0.0604 | 0.672

Clinical variable #4: 'RACE'

No gene related to 'RACE'.

Table S7. Basic characteristics of clinical feature: 'RACE'

RACE | Labels | N
| ASIAN | 2
| BLACK OR AFRICAN AMERICAN | 13
| WHITE | 156
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = LAML-TB.uncv2.mRNAseq_RSEM_normalized_log2.txt

- Clinical data file = LAML-TB.merged_data.txt

- Number of patients = 173

- Number of genes = 17276

- Number of clinical features = 4

Selected clinical features

- Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

- There are also useful links about clinical features.

- CDE Browser

- Data dictionary for clinical data

- NCI wiki - Biospecimen and Clinical XSD Files Specification

- Survival time data

- Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

- if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

- if 'vital_status'==0(alive),

- if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

- if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

- if 'vital_status'==NA,excludes this case in survival analysis and report the case.

- cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

- This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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)
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