Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer
BackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized g...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.863094/full |
| _version_ | 1817986090059956224 |
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| author | Leqi Zhou Yue Yu Rongbo Wen Kuo Zheng Siyuan Jiang Xiaoming Zhu Jinke Sui Haifeng Gong Zheng Lou Liqiang Hao Guanyu Yu Wei Zhang |
| author_facet | Leqi Zhou Yue Yu Rongbo Wen Kuo Zheng Siyuan Jiang Xiaoming Zhu Jinke Sui Haifeng Gong Zheng Lou Liqiang Hao Guanyu Yu Wei Zhang |
| author_sort | Leqi Zhou |
| collection | DOAJ |
| description | BackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.MethodsPrognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.ResultsA total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.ConclusionA credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients. |
| first_indexed | 2024-04-14T00:05:00Z |
| format | Article |
| id | doaj.art-858dddf687bf479191c9c7a54c835598 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-14T00:05:00Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-858dddf687bf479191c9c7a54c8355982022-12-22T02:23:33ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-05-011210.3389/fonc.2022.863094863094Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal CancerLeqi ZhouYue YuRongbo WenKuo ZhengSiyuan JiangXiaoming ZhuJinke SuiHaifeng GongZheng LouLiqiang HaoGuanyu YuWei ZhangBackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.MethodsPrognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.ResultsA total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.ConclusionA credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.https://www.frontiersin.org/articles/10.3389/fonc.2022.863094/fullcolorectal cancerrisk scoreoverall survivalrecurrence-free survivalprognostic signature |
| spellingShingle | Leqi Zhou Yue Yu Rongbo Wen Kuo Zheng Siyuan Jiang Xiaoming Zhu Jinke Sui Haifeng Gong Zheng Lou Liqiang Hao Guanyu Yu Wei Zhang Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer Frontiers in Oncology colorectal cancer risk score overall survival recurrence-free survival prognostic signature |
| title | Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer |
| title_full | Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer |
| title_fullStr | Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer |
| title_full_unstemmed | Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer |
| title_short | Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer |
| title_sort | development and validation of an 8 gene signature to improve survival prediction of colorectal cancer |
| topic | colorectal cancer risk score overall survival recurrence-free survival prognostic signature |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.863094/full |
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