Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis
<p>The signature exploration workflow. The workflow of generating the 8-gene signature associated with steroid hormone and prostate cancer progression.</p> "> Figure 2
<p>Progression-free interval (PFI) and overall survival (OS). Kaplan–Meier curves for (<b>A</b>) 5-year progression-free interval and (<b>B</b>) 5-year overall survival of the 8-gene signature. Patients were dichotomized into the “Low risk” group and the “High risk” group according to the 8-gene signature scores. The number of patients of the two risk groups in different following time in month were shown in the bottom tables of KM plots, respectively.</p> "> Figure 3
<p>Multivariate analysis for progression-free interval. Multivariate Cox regression of the 8-gene signature with clinical variables. Significance levels are annotated. Clinical factors such as gleason score, psa level, tumor TNM stage, and age at diagnosis were considered as confounding variables in the analysis. Both hazard ratios and 95% confidence intervals were shown in the forest plot and factors reached a significant level were plotted in red. *** <span class="html-italic">p</span>-value < 0.001.</p> "> Figure 4
<p>External validation. The expression of the 8-gene signature based on (<b>A</b>) PCS subtypes and (<b>B</b>) PAM50. The distributions of z-score transformed expression values in each group are shown in lollipop plot (<b>top</b>) and box plot (<b>bottom</b>). Higher expression of 8-gene signature in both aggressive subtypes (PCS1 and LumB) of two independent cohorts (PCS and PAM50) demonstrated the consistent results in external validation.</p> "> Figure 5
<p>The regulatory pathways. Functional annotation of 8 genes based on three databases in aspects of (<b>A</b>) steroid hormone-specific and (<b>B</b>) all functions containing more than 3 of 8 signature genes. The gene is illustrated as a filled grey circle. Databases are drawn as an empty triangle, rectangle, and diamond. The grey edge represents linkage between annotated gene and the corresponding function.</p> ">
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Collection and Preparation
2.2. Steroid Hormone-Related Gene Selection
2.3. Differentially Expression Analysis
2.4. Survival Analysis
2.5. Feature Selection and Signature Construction
2.6. Independent Datasets Validation
2.7. Functional Annotation
3. Results
3.1. Identification of Steroid Hormone Genes Associated with Disease Progression in Prostate Cancer
3.2. Identification of an Eight-Gene Signature Predicting PC Survival
3.3. Multivariate Cox Regression Analysis with Clinical Variables
3.4. Expression of the Eight-Gene Panel Based on External PC Cohort’s Validation
3.5. Functional Annotation of the Steroid Hormone Genes Associated with Prognosis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Differential Expression Analysis | Survival Analysis (PFI) | ||||
---|---|---|---|---|---|
Gene | Log2 Fold Change | Adjusted p-Value | HR | CI95 | p-Value |
CA2 | −4.48699 | 2.20 × 10−78 | 2.14 | 1.36–3.37 | 0.001038 |
CYP2E1 | −1.88521 | 1.31 × 10−25 | 1.55 | 1.01–2.38 | 0.043481 |
HSD17B3 | 1.32350 | 4.89 × 10−11 | 2.19 | 1.40–3.40 | 0.000527 |
SSTR3 | −1.21147 | 2.44 × 10−5 | 1.83 | 1.18–2.83 | 0.006554 |
SULT1E1 | −1.23635 | 9.17 × 10−6 | 1.94 | 1.24–3.01 | 0.003371 |
TUBB3 | 1.32113 | 2.14 × 10−10 | 2.27 | 1.45–3.54 | 0.000319 |
UCN | 2.16915 | 4.31 × 10−41 | 1.94 | 1.24–3.01 | 0.003137 |
UGT2B7 | −5.67669 | 1.25 × 10−54 | 1.64 | 1.07–2.52 | 0.023815 |
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Lai, Y.-L.; Liu, C.-H.; Wang, S.-C.; Huang, S.-P.; Cho, Y.-C.; Bao, B.-Y.; Su, C.-C.; Yeh, H.-C.; Lee, C.-H.; Teng, P.-C.; et al. Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis. Cancers 2022, 14, 1565. https://doi.org/10.3390/cancers14061565
Lai Y-L, Liu C-H, Wang S-C, Huang S-P, Cho Y-C, Bao B-Y, Su C-C, Yeh H-C, Lee C-H, Teng P-C, et al. Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis. Cancers. 2022; 14(6):1565. https://doi.org/10.3390/cancers14061565
Chicago/Turabian StyleLai, Yo-Liang, Chia-Hsin Liu, Shu-Chi Wang, Shu-Pin Huang, Yi-Chun Cho, Bo-Ying Bao, Chia-Cheng Su, Hsin-Chih Yeh, Cheng-Hsueh Lee, Pai-Chi Teng, and et al. 2022. "Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis" Cancers 14, no. 6: 1565. https://doi.org/10.3390/cancers14061565
APA StyleLai, Y. -L., Liu, C. -H., Wang, S. -C., Huang, S. -P., Cho, Y. -C., Bao, B. -Y., Su, C. -C., Yeh, H. -C., Lee, C. -H., Teng, P. -C., Chuu, C. -P., Chen, D. -N., Li, C. -Y., & Cheng, W. -C. (2022). Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis. Cancers, 14(6), 1565. https://doi.org/10.3390/cancers14061565