This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 548 genes and 14 clinical features across 299 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.
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1 gene correlated to 'AGE'.
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HSA-MIR-125A
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7 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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HSA-LET-7G , HSA-MIR-499 , HSA-MIR-26B , HSA-MIR-491 , HSA-MIR-3653 , ...
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3 genes correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-29A , HSA-MIR-181A-2 , HSA-MIR-490
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13 genes correlated to 'PATHOLOGICSPREAD(M)'.
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HSA-MIR-26A-1 , HSA-MIR-326 , HSA-MIR-628 , HSA-MIR-106A , HSA-MIR-16-1 , ...
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2 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.
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HSA-MIR-3926-1 , HSA-MIR-651
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1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.
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HSA-MIR-125A
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No genes correlated to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', and 'STOPPEDSMOKINGYEAR'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=7 | higher score | N=0 | lower score | N=7 |
HISTOLOGICAL TYPE | ANOVA test | N=3 | ||||
PATHOLOGY T | Spearman correlation test | N=0 | ||||
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=13 | ||||
TUMOR STAGE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
STOPPEDSMOKINGYEAR | Spearman correlation test | N=0 | ||||
TOBACCOSMOKINGHISTORYINDICATOR | ANOVA test | N=2 | ||||
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=1 | higher yearoftobaccosmokingonset | N=0 | lower yearoftobaccosmokingonset | N=1 |
Time to Death | Duration (Months) | 0-173.8 (median=13.1) |
censored | N = 176 | |
death | N = 107 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 67.9 (8.5) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-125A | 0.2317 | 6.409e-05 | 0.0351 |
GENDER | Labels | N |
FEMALE | 78 | |
MALE | 221 | |
Significant markers | N = 0 |
7 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 27.08 (39) |
Significant markers | N = 7 | |
pos. correlated | 0 | |
neg. correlated | 7 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-LET-7G | -0.6441 | 7.83e-07 | 0.000429 |
HSA-MIR-499 | -0.7751 | 1.278e-06 | 0.000699 |
HSA-MIR-26B | -0.5845 | 1.287e-05 | 0.00702 |
HSA-MIR-491 | -0.5612 | 3.334e-05 | 0.0182 |
HSA-MIR-3653 | -0.5561 | 4.064e-05 | 0.0221 |
HSA-MIR-196B | -0.5554 | 4.176e-05 | 0.0227 |
HSA-MIR-26A-1 | -0.5443 | 6.364e-05 | 0.0345 |
HISTOLOGICAL.TYPE | Labels | N |
LUNG BASALOID SQUAMOUS CELL CARCINOMA | 5 | |
LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | 1 | |
LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | 1 | |
LUNG SMALL CELL SQUAMOUS CELL CARCINOMA | 1 | |
LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 291 | |
Significant markers | N = 3 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-29A | 4.365e-08 | 2.37e-05 |
HSA-MIR-181A-2 | 3.446e-06 | 0.00187 |
HSA-MIR-490 | 2.818e-05 | 0.0153 |
PATHOLOGY.T | Mean (SD) | 1.97 (0.75) |
N | ||
T1 | 72 | |
T2 | 181 | |
T3 | 30 | |
T4 | 16 | |
Significant markers | N = 0 |
PATHOLOGY.N | Mean (SD) | 0.49 (0.71) |
N | ||
N0 | 183 | |
N1 | 85 | |
N2 | 25 | |
N3 | 4 | |
Significant markers | N = 0 |
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 260 | |
M1 | 3 | |
MX | 30 | |
Significant markers | N = 13 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-26A-1 | 4.012e-08 | 2.2e-05 |
HSA-MIR-326 | 7.437e-07 | 0.000407 |
HSA-MIR-628 | 1.388e-06 | 0.000758 |
HSA-MIR-106A | 1.851e-05 | 0.0101 |
HSA-MIR-16-1 | 2.169e-05 | 0.0118 |
HSA-MIR-361 | 2.295e-05 | 0.0125 |
HSA-MIR-1277 | 2.516e-05 | 0.0136 |
HSA-MIR-320E | 4.3e-05 | 0.0233 |
HSA-MIR-3615 | 4.625e-05 | 0.025 |
HSA-MIR-1180 | 6.116e-05 | 0.033 |
TUMOR.STAGE | Mean (SD) | 1.7 (0.81) |
N | ||
Stage 1 | 153 | |
Stage 2 | 83 | |
Stage 3 | 57 | |
Stage 4 | 3 | |
Significant markers | N = 0 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 10 | |
YES | 289 | |
Significant markers | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 53.22 (33) |
Significant markers | N = 0 |
STOPPEDSMOKINGYEAR | Mean (SD) | 1997.69 (11) |
Significant markers | N = 0 |
2 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.
TOBACCOSMOKINGHISTORYINDICATOR | Labels | N |
CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | 146 | |
CURRENT REFORMED SMOKER FOR > 15 YEARS | 67 | |
CURRENT SMOKER | 70 | |
LIFELONG NON-SMOKER | 11 | |
Significant markers | N = 2 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-3926-1 | 4.108e-05 | 0.0225 |
HSA-MIR-651 | 5.48e-05 | 0.03 |
YEAROFTOBACCOSMOKINGONSET | Mean (SD) | 1958.7 (12) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-125A | -0.2853 | 3.7e-05 | 0.0203 |
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Expresson data file = LUSC-TP.miRseq_RPKM_log2.txt
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Clinical data file = LUSC-TP.clin.merged.picked.txt
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Number of patients = 299
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Number of genes = 548
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Number of clinical features = 14
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
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
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
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.