Correlation between miRseq expression and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1N014ZJ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N014ZJ
Overview
Introduction

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

Summary

Testing the association between 545 miRs and 8 clinical features across 123 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one miRs.

- 3 miRs correlated to 'PATHOLOGY.N.STAGE'.

- HSA-MIR-29B-1 , HSA-MIR-200C , HSA-MIR-29B-2

- No miRs correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', and 'COMPLETENESS.OF.RESECTION'.

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 miRs that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature | Statistical test | Significant miRs | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
Time to Death | Cox regression test | N=0 |  |  |  |
AGE | Spearman correlation test | N=0 |  |  |  |
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY N STAGE | t test | N=3 | class1 | N=2 | class0 | N=1
PATHOLOGY M STAGE | ANOVA test | N=0 |  |  |  |
GENDER | t test | N=0 |  |  |  |
COMPLETENESS OF RESECTION | ANOVA test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0-113 (median=14.6)
| censored | N = 66
| death | N = 52
| Significant markers | N = 0

Clinical variable #2: 'AGE'

No miR related to 'AGE'.

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

AGE | Mean (SD) | 61.45 (14)
| Significant markers | N = 0

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S3. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 46
| STAGE II | 26
| STAGE III | 2
| STAGE IIIA | 29
| STAGE IIIB | 3
| STAGE IIIC | 5
| STAGE IV | 1
| STAGE IVA | 1
| STAGE IVB | 1
| Significant markers | N = 0

Clinical variable #4: 'PATHOLOGY.T.STAGE'

No miR related to 'PATHOLOGY.T.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.97)
|  | N
| 1 | 49
| 2 | 29
| 3 | 38
| 4 | 7
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY.N.STAGE'

3 miRs related to 'PATHOLOGY.N.STAGE'.

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 80
| class1 | 3
| Significant markers | N = 3
| Higher in class1 | 2
| Higher in class0 | 1

List of 3 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

Table S6. Get Full Table List of 3 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

| T(pos if higher in 'class1') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-29B-1 | 5.64 | 2.895e-07 | 0.000105 | 0.7458
HSA-MIR-200C | -5.11 | 3.95e-06 | 0.00143 | 0.75
HSA-MIR-29B-2 | 4.78 | 2.653e-05 | 0.00958 | 0.7208

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-29B-1 to 'PATHOLOGY.N.STAGE'. P value = 2.9e-07 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

No miR related to 'PATHOLOGY.M.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 98
| M1 | 2
| MX | 23
| Significant markers | N = 0

Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

Table S8. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 46
| MALE | 77
| Significant markers | N = 0

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

No miR related to 'COMPLETENESS.OF.RESECTION'.

Table S9. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 98
| R1 | 10
| R2 | 1
| RX | 9
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = LIHC-TP.miRseq_RPKM_log2.txt

- Clinical data file = LIHC-TP.merged_data.txt

- Number of patients = 123

- Number of miRs = 545

- Number of clinical features = 8

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

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] 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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