The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis
Introduction. Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to dev...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Canadian Journal of Gastroenterology and Hepatology |
| Online Access: | http://dx.doi.org/10.1155/2021/5212953 |
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| author | Samer Tohme Hamza O Yazdani Amaan Rahman Sanah Handu Sidrah Khan Tanner Wilson David A Geller Richard L Simmons Michele Molinari Christof Kaltenmeier |
| author_facet | Samer Tohme Hamza O Yazdani Amaan Rahman Sanah Handu Sidrah Khan Tanner Wilson David A Geller Richard L Simmons Michele Molinari Christof Kaltenmeier |
| author_sort | Samer Tohme |
| collection | DOAJ |
| description | Introduction. Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods. The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results. 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan–Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38–4.80), 2 points: 3.91 (2.11–7.24), and 3 points: 5.09 (2.70–9.58), p < 0.01). Conclusion. Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients. |
| format | Article |
| id | doaj-art-26c53fa847c641028b9b052b1d574e79 |
| institution | Kabale University |
| issn | 2291-2797 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Canadian Journal of Gastroenterology and Hepatology |
| spelling | doaj-art-26c53fa847c641028b9b052b1d574e792025-02-03T01:21:46ZengWileyCanadian Journal of Gastroenterology and Hepatology2291-27972021-01-01202110.1155/2021/5212953The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort AnalysisSamer Tohme0Hamza O Yazdani1Amaan Rahman2Sanah Handu3Sidrah Khan4Tanner Wilson5David A Geller6Richard L Simmons7Michele Molinari8Christof Kaltenmeier9Department of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryDepartment of SurgeryIntroduction. Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods. The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results. 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan–Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38–4.80), 2 points: 3.91 (2.11–7.24), and 3 points: 5.09 (2.70–9.58), p < 0.01). Conclusion. Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.http://dx.doi.org/10.1155/2021/5212953 |
| spellingShingle | Samer Tohme Hamza O Yazdani Amaan Rahman Sanah Handu Sidrah Khan Tanner Wilson David A Geller Richard L Simmons Michele Molinari Christof Kaltenmeier The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis Canadian Journal of Gastroenterology and Hepatology |
| title | The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis |
| title_full | The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis |
| title_fullStr | The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis |
| title_full_unstemmed | The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis |
| title_short | The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis |
| title_sort | use of machine learning to create a risk score to predict survival in patients with hepatocellular carcinoma a tcga cohort analysis |
| url | http://dx.doi.org/10.1155/2021/5212953 |
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