This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 174 genes and 14 clinical features across 194 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.
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1 gene correlated to 'Time to Death'.
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MSH6|MSH6-R-C
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2 genes correlated to 'AGE'.
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PCNA|PCNA-M-V , RAD51|RAD51-M-C
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5 genes correlated to 'HISTOLOGICAL.TYPE'.
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EGFR|EGFR_PY1173-R-C , CCNE2|CYCLIN_E2-R-C , NCOA3|AIB1-M-V , KIT|C-KIT-R-V , ARID1A|ARID1A-M-V
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2 genes correlated to 'PATHOLOGY.T'.
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BID|BID-R-C , DVL3|DVL3-R-V
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2 genes correlated to 'PATHOLOGY.N'.
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MAPK1 MAPK3|MAPK_PT202_Y204-R-V , STAT3|STAT3_PY705-R-V
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3 genes correlated to 'TUMOR.STAGE'.
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MAPK1 MAPK3|MAPK_PT202_Y204-R-V , STAT3|STAT3_PY705-R-V , CCND1|CYCLIN_D1-R-V
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1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.
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CCNE1|CYCLIN_E1-M-V
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No genes correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGICSPREAD(M)', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'TOBACCOSMOKINGHISTORYINDICATOR'.
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=1 | shorter survival | N=0 | longer survival | N=1 |
AGE | Spearman correlation test | N=2 | older | N=0 | younger | N=2 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=5 | ||||
PATHOLOGY T | Spearman correlation test | N=2 | higher pT | N=2 | lower pT | N=0 |
PATHOLOGY N | Spearman correlation test | N=2 | higher pN | N=0 | lower pN | N=2 |
PATHOLOGICSPREAD(M) | t test | N=0 | ||||
TUMOR STAGE | Spearman correlation test | N=3 | higher stage | N=1 | lower stage | N=2 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
STOPPEDSMOKINGYEAR | Spearman correlation test | N=0 | ||||
TOBACCOSMOKINGHISTORYINDICATOR | ANOVA test | N=0 | ||||
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=1 | higher yearoftobaccosmokingonset | N=1 | lower yearoftobaccosmokingonset | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0-173.8 (median=15.8) |
censored | N = 109 | |
death | N = 72 | |
Significant markers | N = 1 | |
associated with shorter survival | 0 | |
associated with longer survival | 1 |
Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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MSH6|MSH6-R-C | 0.5 | 0.0002128 | 0.037 | 0.34 |
Figure S1. Get High-res Image As an example, this figure shows the association of MSH6|MSH6-R-C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.000213 with univariate Cox regression analysis using continuous log-2 expression values.

Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 67.45 (9.5) |
Significant markers | N = 2 | |
pos. correlated | 0 | |
neg. correlated | 2 |
Table S4. Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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PCNA|PCNA-M-V | -0.3221 | 7.331e-06 | 0.00128 |
RAD51|RAD51-M-C | -0.2791 | 0.0001144 | 0.0198 |
Figure S2. Get High-res Image As an example, this figure shows the association of PCNA|PCNA-M-V to 'AGE'. P value = 7.33e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Table S5. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 49 | |
MALE | 145 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S6. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 30.29 (39) |
Significant markers | N = 0 |
Table S7. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'
HISTOLOGICAL.TYPE | Labels | N |
LUNG BASALOID SQUAMOUS CELL CARCINOMA | 3 | |
LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | 1 | |
LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | 1 | |
LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 189 | |
Significant markers | N = 5 |
Table S8. Get Full Table List of 5 genes differentially expressed by 'HISTOLOGICAL.TYPE'
ANOVA_P | Q | |
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EGFR|EGFR_PY1173-R-C | 1.552e-06 | 0.00027 |
CCNE2|CYCLIN_E2-R-C | 2.425e-05 | 0.0042 |
NCOA3|AIB1-M-V | 6.804e-05 | 0.0117 |
KIT|C-KIT-R-V | 0.0001128 | 0.0193 |
ARID1A|ARID1A-M-V | 0.0001154 | 0.0196 |
Figure S3. Get High-res Image As an example, this figure shows the association of EGFR|EGFR_PY1173-R-C to 'HISTOLOGICAL.TYPE'. P value = 1.55e-06 with ANOVA analysis.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.T'
PATHOLOGY.T | Mean (SD) | 1.99 (0.75) |
N | ||
T1 | 44 | |
T2 | 119 | |
T3 | 20 | |
T4 | 11 | |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
Table S10. Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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BID|BID-R-C | 0.2859 | 5.332e-05 | 0.00928 |
DVL3|DVL3-R-V | 0.2601 | 0.0002495 | 0.0432 |
Figure S4. Get High-res Image As an example, this figure shows the association of BID|BID-R-C to 'PATHOLOGY.T'. P value = 5.33e-05 with Spearman correlation analysis.

Table S11. Basic characteristics of clinical feature: 'PATHOLOGY.N'
PATHOLOGY.N | Mean (SD) | 0.48 (0.73) |
N | ||
N0 | 124 | |
N1 | 50 | |
N2 | 15 | |
N3 | 4 | |
Significant markers | N = 2 | |
pos. correlated | 0 | |
neg. correlated | 2 |
Table S12. Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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MAPK1 MAPK3|MAPK_PT202_Y204-R-V | -0.3066 | 1.444e-05 | 0.00251 |
STAT3|STAT3_PY705-R-V | -0.2705 | 0.0001416 | 0.0245 |
Figure S5. Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'PATHOLOGY.N'. P value = 1.44e-05 with Spearman correlation analysis.

Table S13. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 175 | |
MX | 16 | |
Significant markers | N = 0 |
Table S14. Basic characteristics of clinical feature: 'TUMOR.STAGE'
TUMOR.STAGE | Mean (SD) | 1.68 (0.77) |
N | ||
Stage 1 | 98 | |
Stage 2 | 57 | |
Stage 3 | 36 | |
Significant markers | N = 3 | |
pos. correlated | 1 | |
neg. correlated | 2 |
Table S15. Get Full Table List of 3 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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MAPK1 MAPK3|MAPK_PT202_Y204-R-V | -0.2807 | 8.402e-05 | 0.0146 |
STAT3|STAT3_PY705-R-V | -0.2737 | 0.0001274 | 0.022 |
CCND1|CYCLIN_D1-R-V | 0.271 | 0.0001495 | 0.0257 |
Figure S6. Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'TUMOR.STAGE'. P value = 8.4e-05 with Spearman correlation analysis.

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S16. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 8 | |
YES | 186 | |
Significant markers | N = 0 |
Table S17. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'
NUMBERPACKYEARSSMOKED | Mean (SD) | 51.71 (33) |
Significant markers | N = 0 |
Table S18. Basic characteristics of clinical feature: 'STOPPEDSMOKINGYEAR'
STOPPEDSMOKINGYEAR | Mean (SD) | 1996.38 (12) |
Significant markers | N = 0 |
No gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.
Table S19. Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'
TOBACCOSMOKINGHISTORYINDICATOR | Labels | N |
CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | 91 | |
CURRENT REFORMED SMOKER FOR > 15 YEARS | 48 | |
CURRENT SMOKER | 39 | |
LIFELONG NON-SMOKER | 10 | |
Significant markers | N = 0 |
Table S20. Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'
YEAROFTOBACCOSMOKINGONSET | Mean (SD) | 1958.43 (12) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
Table S21. Get Full Table List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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CCNE1|CYCLIN_E1-M-V | 0.3419 | 7.311e-05 | 0.0127 |
Figure S7. Get High-res Image As an example, this figure shows the association of CCNE1|CYCLIN_E1-M-V to 'YEAROFTOBACCOSMOKINGONSET'. P value = 7.31e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

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Expresson data file = LUSC-TP.rppa.txt
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Clinical data file = LUSC-TP.clin.merged.picked.txt
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Number of patients = 194
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Number of genes = 174
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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.