Liver Hepatocellular Carcinoma: Correlation between mRNAseq expression and clinical features<BR>(primary solid tumor cohort)

Liver Hepatocellular Carcinoma: Correlation between mRNAseq expression and clinical features
(primary solid tumor 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 17848 genes and 7 clinical features across 58 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

8 genes correlated to 'GENDER'.

XIST|7503 , TSIX|9383 , EIF1AX|1964 , ZFY|7544 , KDM5C|8242 , ...

6 genes correlated to 'LYMPH.NODE.METASTASIS'.

DTX3L|151636 , CCDC111|201973 , CASP3|836 , MID2|11043 , ACY3|91703 , ...

1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

SLC16A11|162515

5 genes correlated to 'NEOPLASM.DISEASESTAGE'.

SGEF|26084 , CCDC111|201973 , DTX3L|151636 , CASP3|836 , ACY3|91703

No genes correlated to 'Time to Death', 'AGE', and 'DISTANT.METASTASIS'.

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
GENDER	t test	N=8	male	N=4	female	N=4
DISTANT METASTASIS	ANOVA test	N=0
LYMPH NODE METASTASIS	ANOVA test	N=6
COMPLETENESS OF RESECTION	ANOVA test	N=1
NEOPLASM DISEASESTAGE	ANOVA test	N=5

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)	0.1-83.6 (median=14.4)
	censored	N = 30
	death	N = 23

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	60.46 (14)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

8 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	22
	MALE	36

	Significant markers	N = 8
	Higher in MALE	4
	Higher in FEMALE	4

List of 8 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 8 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
XIST\|7503	-14.14	3.305e-15	5.9e-11	0.9867
TSIX\|9383	-9.09	3.501e-09	6.25e-05	0.9825
EIF1AX\|1964	-6.78	1.294e-08	0.000231	0.8927
ZFY\|7544	11.97	1.974e-08	0.000352	0.9917
KDM5C\|8242	-6.36	4.144e-08	0.000739	0.8889
NLGN4Y\|22829	9.48	1.093e-07	0.00195	0.9806
PRKY\|5616	9.21	6.029e-07	0.0108	0.9838
TERF1\|7013	5.36	1.996e-06	0.0356	0.8472

Figure S1. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 3.31e-15 with T-test analysis.

Clinical variable #4: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S5. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'


DISTANT.METASTASIS	Labels	N
	M0	38
	M1	1
	MX	19

	Significant markers	N = 0

Clinical variable #5: 'LYMPH.NODE.METASTASIS'

6 genes related to 'LYMPH.NODE.METASTASIS'.

Table S6. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	39
	N1	1
	NX	17

	Significant markers	N = 6

List of 6 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S7. Get Full Table List of 6 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

	ANOVA_P	Q
DTX3L\|151636	1.281e-09	2.29e-05
CCDC111\|201973	1.715e-09	3.06e-05
CASP3\|836	8.973e-09	0.00016
MID2\|11043	7.176e-07	0.0128
ACY3\|91703	1.912e-06	0.0341
PARP14\|54625	2.261e-06	0.0403

Figure S2. Get High-res Image As an example, this figure shows the association of DTX3L|151636 to 'LYMPH.NODE.METASTASIS'. P value = 1.28e-09 with ANOVA analysis.

Clinical variable #6: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S8. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	41
	R1	7
	R2	1
	RX	6

	Significant markers	N = 1

List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S9. Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
SLC16A11\|162515	1.553e-08	0.000277

Figure S3. Get High-res Image As an example, this figure shows the association of SLC16A11|162515 to 'COMPLETENESS.OF.RESECTION'. P value = 1.55e-08 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

5 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S10. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	22
	STAGE II	11
	STAGE IIIA	12
	STAGE IIIB	3
	STAGE IIIC	1
	STAGE IVB	1

	Significant markers	N = 5

List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S11. Get Full Table List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
SGEF\|26084	3.771e-08	0.000673
CCDC111\|201973	4.61e-08	0.000823
DTX3L\|151636	1.093e-07	0.00195
CASP3\|836	1.623e-07	0.0029
ACY3\|91703	2.521e-07	0.0045

Figure S4. Get High-res Image As an example, this figure shows the association of SGEF|26084 to 'NEOPLASM.DISEASESTAGE'. P value = 3.77e-08 with ANOVA analysis.

Methods & Data

Input

Expresson data file = LIHC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 58
Number of genes = 17848
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

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

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

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.

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

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[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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