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A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics

BACKGROUND: Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and wi...

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Autores principales: Wang, Jin, Ding, Zhi-Wen, Chen, Kuang, Liu, Yan-Zhe, Li, Nan, Hu, Ming-Gen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675478/
https://www.ncbi.nlm.nih.gov/pubmed/34911488
http://dx.doi.org/10.1186/s12885-021-09047-1
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author Wang, Jin
Ding, Zhi-Wen
Chen, Kuang
Liu, Yan-Zhe
Li, Nan
Hu, Ming-Gen
author_facet Wang, Jin
Ding, Zhi-Wen
Chen, Kuang
Liu, Yan-Zhe
Li, Nan
Hu, Ming-Gen
author_sort Wang, Jin
collection PubMed
description BACKGROUND: Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI. METHODS: In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients. RESULTS: Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients’ HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10. CONCLUSION: We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09047-1.
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spelling pubmed-86754782021-12-20 A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics Wang, Jin Ding, Zhi-Wen Chen, Kuang Liu, Yan-Zhe Li, Nan Hu, Ming-Gen BMC Cancer Research BACKGROUND: Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI. METHODS: In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients. RESULTS: Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients’ HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10. CONCLUSION: We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09047-1. BioMed Central 2021-12-16 /pmc/articles/PMC8675478/ /pubmed/34911488 http://dx.doi.org/10.1186/s12885-021-09047-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Jin
Ding, Zhi-Wen
Chen, Kuang
Liu, Yan-Zhe
Li, Nan
Hu, Ming-Gen
A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title_full A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title_fullStr A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title_full_unstemmed A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title_short A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics
title_sort predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based tcga database genomics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675478/
https://www.ncbi.nlm.nih.gov/pubmed/34911488
http://dx.doi.org/10.1186/s12885-021-09047-1
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