This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 541 genes and 14 clinical features across 321 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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HSA-MIR-181C , HSA-MIR-582
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8 genes correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-194-2 , HSA-MIR-194-1 , HSA-MIR-369 , HSA-MIR-215 , HSA-MIR-192 , ...
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5 genes correlated to 'PATHOLOGICSPREAD(M)'.
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HSA-MIR-628 , HSA-MIR-424 , HSA-MIR-140 , HSA-MIR-148B , HSA-MIR-3607
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7 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.
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HSA-LET-7F-1 , HSA-MIR-105-1 , HSA-MIR-130B , HSA-MIR-1-2 , HSA-MIR-105-2 , ...
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No genes correlated to 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'YEAROFTOBACCOSMOKINGONSET'.
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 | ||
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Time to Death | Cox regression test | N=2 | shorter survival | N=1 | longer survival | N=1 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=8 | ||||
PATHOLOGY T | Spearman correlation test | N=0 | ||||
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=5 | ||||
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=7 | ||||
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0-224 (median=10) |
censored | N = 205 | |
death | N = 80 | |
Significant markers | N = 2 | |
associated with shorter survival | 1 | |
associated with longer survival | 1 |
Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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HSA-MIR-181C | 0.55 | 1.51e-05 | 0.0082 | 0.34 |
HSA-MIR-582 | 1.32 | 6.285e-05 | 0.034 | 0.63 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-181C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.51e-05 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 65.26 (9.9) |
Significant markers | N = 0 |
Table S4. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 177 | |
MALE | 144 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 73.46 (33) |
Significant markers | N = 0 |
Table S6. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'
HISTOLOGICAL.TYPE | Labels | N |
LUNG ACINAR ADENOCARCINOMA | 10 | |
LUNG ADENOCARCINOMA MIXED SUBTYPE | 72 | |
LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 198 | |
LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | 3 | |
LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | 14 | |
LUNG CLEAR CELL ADENOCARCINOMA | 2 | |
LUNG MICROPAPILLARY ADENOCARCINOMA | 2 | |
LUNG MUCINOUS ADENOCARCINOMA | 1 | |
LUNG PAPILLARY ADENOCARCINOMA | 13 | |
LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | 1 | |
MUCINOUS (COLLOID) ADENOCARCINOMA | 5 | |
Significant markers | N = 8 |
Table S7. Get Full Table List of 8 genes differentially expressed by 'HISTOLOGICAL.TYPE'
ANOVA_P | Q | |
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HSA-MIR-194-2 | 1.569e-07 | 8.49e-05 |
HSA-MIR-194-1 | 1.576e-07 | 8.51e-05 |
HSA-MIR-369 | 9.529e-06 | 0.00514 |
HSA-MIR-215 | 1.277e-05 | 0.00687 |
HSA-MIR-192 | 3.665e-05 | 0.0197 |
HSA-MIR-377 | 4.972e-05 | 0.0267 |
HSA-MIR-376C | 5.206e-05 | 0.0279 |
HSA-MIR-154 | 5.768e-05 | 0.0308 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-194-2 to 'HISTOLOGICAL.TYPE'. P value = 1.57e-07 with ANOVA analysis.
![](V5ex.png)
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T'
PATHOLOGY.T | Mean (SD) | 1.9 (0.76) |
N | ||
T1 | 91 | |
T2 | 184 | |
T3 | 26 | |
T4 | 17 | |
Significant markers | N = 0 |
Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N'
PATHOLOGY.N | Mean (SD) | 0.56 (0.78) |
N | ||
N0 | 192 | |
N1 | 65 | |
N2 | 53 | |
N3 | 1 | |
Significant markers | N = 0 |
Table S10. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 224 | |
M1 | 15 | |
MX | 74 | |
Significant markers | N = 5 |
Table S11. Get Full Table List of 5 genes differentially expressed by 'PATHOLOGICSPREAD(M)'
ANOVA_P | Q | |
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HSA-MIR-628 | 2.109e-06 | 0.00114 |
HSA-MIR-424 | 1.087e-05 | 0.00587 |
HSA-MIR-140 | 3.098e-05 | 0.0167 |
HSA-MIR-148B | 5.761e-05 | 0.031 |
HSA-MIR-3607 | 6.29e-05 | 0.0338 |
Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'PATHOLOGICSPREAD(M)'. P value = 2.11e-06 with ANOVA analysis.
![](V8ex.png)
Table S12. Basic characteristics of clinical feature: 'TUMOR.STAGE'
TUMOR.STAGE | Mean (SD) | 1.77 (0.94) |
N | ||
Stage 1 | 167 | |
Stage 2 | 70 | |
Stage 3 | 62 | |
Stage 4 | 16 | |
Significant markers | N = 0 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S13. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 19 | |
YES | 302 | |
Significant markers | N = 0 |
Table S14. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'
NUMBERPACKYEARSSMOKED | Mean (SD) | 39.86 (26) |
Significant markers | N = 0 |
Table S15. Basic characteristics of clinical feature: 'STOPPEDSMOKINGYEAR'
STOPPEDSMOKINGYEAR | Mean (SD) | 1993.53 (14) |
Significant markers | N = 0 |
7 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.
Table S16. Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'
TOBACCOSMOKINGHISTORYINDICATOR | Labels | N |
CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | 105 | |
CURRENT REFORMED SMOKER FOR > 15 YEARS | 85 | |
CURRENT SMOKER | 71 | |
LIFELONG NON-SMOKER | 47 | |
Significant markers | N = 7 |
Table S17. Get Full Table List of 7 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'
ANOVA_P | Q | |
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HSA-LET-7F-1 | 4.001e-06 | 0.00216 |
HSA-MIR-105-1 | 4.421e-06 | 0.00239 |
HSA-MIR-130B | 5.462e-06 | 0.00294 |
HSA-MIR-1-2 | 1.58e-05 | 0.0085 |
HSA-MIR-105-2 | 4.135e-05 | 0.0222 |
HSA-MIR-135B | 4.173e-05 | 0.0224 |
HSA-MIR-99A | 5.965e-05 | 0.0319 |
Figure S4. Get High-res Image As an example, this figure shows the association of HSA-LET-7F-1 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 4e-06 with ANOVA analysis.
![](V13ex.png)
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Expresson data file = LUAD-TP.miRseq_RPKM_log2.txt
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Clinical data file = LUAD-TP.clin.merged.picked.txt
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Number of patients = 321
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Number of genes = 541
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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.