Lung Adenocarcinoma: Correlation between miRseq expression and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

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

Summary

Testing the association between 539 genes and 15 clinical features across 223 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

5 genes correlated to 'GENDER'.

HSA-MIR-146A , HSA-MIR-195 , HSA-MIR-1976 , HSA-MIR-99A , HSA-MIR-1254

4 genes correlated to 'HISTOLOGICAL.TYPE'.

HSA-MIR-194-1 , HSA-MIR-194-2 , HSA-MIR-215 , HSA-MIR-192

2 genes correlated to 'PATHOLOGICSPREAD(M)'.

HSA-MIR-3681 , HSA-MIR-3124

1 gene correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

HSA-LET-7F-1

No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NEOADJUVANT.THERAPY', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'YEAROFTOBACCOSMOKINGONSET'.

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=5	male	N=1	female	N=4
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
HISTOLOGICAL TYPE	ANOVA test	N=4
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	ANOVA test	N=2
TUMOR STAGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0
NEOADJUVANT THERAPY	t test	N=0
NUMBERPACKYEARSSMOKED	Spearman correlation test	N=0
STOPPEDSMOKINGYEAR	Spearman correlation test	N=0
TOBACCOSMOKINGHISTORYINDICATOR	ANOVA test	N=1
YEAROFTOBACCOSMOKINGONSET	Spearman correlation 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)	0-224 (median=13.9)
	censored	N = 133
	death	N = 62

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	65.93 (9.7)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	125
	MALE	98

	Significant markers	N = 5
	Higher in MALE	1
	Higher in FEMALE	4

List of 5 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 5 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
HSA-MIR-146A	-4.45	1.415e-05	0.00763	0.6696
HSA-MIR-195	-4.38	1.852e-05	0.00996	0.6537
HSA-MIR-1976	-4.16	4.771e-05	0.0256	0.6559
HSA-MIR-99A	-4.07	6.503e-05	0.0349	0.6642
HSA-MIR-1254	4.05	8.07e-05	0.0432	0.6696

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-146A to 'GENDER'. P value = 1.42e-05 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	67.06 (40)
	Significant markers	N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

4 genes related to 'HISTOLOGICAL.TYPE'.

Table S6. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	LUNG ACINAR ADENOCARCINOMA	5
	LUNG ADENOCARCINOMA MIXED SUBTYPE	54
	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	136
	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	3
	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	10
	LUNG MICROPAPILLARY ADENOCARCINOMA	2
	LUNG MUCINOUS ADENOCARCINOMA	1
	LUNG PAPILLARY ADENOCARCINOMA	8
	MUCINOUS (COLLOID) ADENOCARCINOMA	4

	Significant markers	N = 4

List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7. Get Full Table List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
HSA-MIR-194-1	1.057e-07	5.7e-05
HSA-MIR-194-2	1.093e-07	5.88e-05
HSA-MIR-215	2.696e-06	0.00145
HSA-MIR-192	1.98e-05	0.0106

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-194-1 to 'HISTOLOGICAL.TYPE'. P value = 1.06e-07 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	1.94 (0.79)
		N
	T1	60
	T2	130
	T3	17
	T4	15

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.59 (0.81)
		N
	N0	132
	N1	43
	N2	41
	N3	1

	Significant markers	N = 0

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

2 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	159
	M1	8
	MX	51

	Significant markers	N = 2

List of 2 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11. Get Full Table List of 2 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

	ANOVA_P	Q
HSA-MIR-3681	2.237e-06	0.00121
HSA-MIR-3124	1.076e-05	0.00579

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-3681 to 'PATHOLOGICSPREAD(M)'. P value = 2.24e-06 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S12. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	1.77 (0.93)
		N
	Stage 1	115
	Stage 2	47
	Stage 3	47
	Stage 4	9

	Significant markers	N = 0

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S13. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	15
	YES	208

	Significant markers	N = 0

Clinical variable #11: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S14. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	36
	YES	187

	Significant markers	N = 0

Clinical variable #12: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S15. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'


NUMBERPACKYEARSSMOKED	Mean (SD)	40.04 (27)
	Significant markers	N = 0

Clinical variable #13: 'STOPPEDSMOKINGYEAR'

No gene related to 'STOPPEDSMOKINGYEAR'.

Table S16. Basic characteristics of clinical feature: 'STOPPEDSMOKINGYEAR'


STOPPEDSMOKINGYEAR	Mean (SD)	1991.76 (14)
	Significant markers	N = 0

Clinical variable #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

One gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S17. Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'


TOBACCOSMOKINGHISTORYINDICATOR	Labels	N
	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	69
	CURRENT REFORMED SMOKER FOR > 15 YEARS	67
	CURRENT SMOKER	45
	LIFELONG NON-SMOKER	32

	Significant markers	N = 1

List of one gene differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S18. Get Full Table List of one gene differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

	ANOVA_P	Q
HSA-LET-7F-1	3.951e-05	0.0213

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-LET-7F-1 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 3.95e-05 with ANOVA analysis.

Clinical variable #15: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S19. Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'


YEAROFTOBACCOSMOKINGONSET	Mean (SD)	1961.99 (13)
	Significant markers	N = 0

Methods & Data

Input

Expresson data file = LUAD.miRseq_RPKM_log2.txt
Clinical data file = LUAD.clin.merged.picked.txt
Number of patients = 223
Number of genes = 539
Number of clinical features = 15

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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