An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism
Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on T...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.749313/full |
| _version_ | 1819004713832546304 |
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| author | Hao Zhang Renzheng Liu Lin Sun Xiao Hu |
| author_facet | Hao Zhang Renzheng Liu Lin Sun Xiao Hu |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma. |
| first_indexed | 2024-12-20T23:41:16Z |
| format | Article |
| id | doaj.art-b5e3186a04644f96b886e2b7a762a67f |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-20T23:41:16Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-b5e3186a04644f96b886e2b7a762a67f2022-12-21T19:23:04ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-11-01810.3389/fmolb.2021.749313749313An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA MechanismHao Zhang0Renzheng Liu1Lin Sun2Xiao Hu3Department of Hepatobiliary Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Hepatobiliary Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of ICU, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Hepatobiliary Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, ChinaLiver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.https://www.frontiersin.org/articles/10.3389/fmolb.2021.749313/fulllncRNAmodelprognosishepatocellular carcinomaceRNA |
| spellingShingle | Hao Zhang Renzheng Liu Lin Sun Xiao Hu An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism Frontiers in Molecular Biosciences lncRNA model prognosis hepatocellular carcinoma ceRNA |
| title | An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism |
| title_full | An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism |
| title_fullStr | An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism |
| title_full_unstemmed | An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism |
| title_short | An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism |
| title_sort | lncrna model for predicting the prognosis of hepatocellular carcinoma patients and cerna mechanism |
| topic | lncRNA model prognosis hepatocellular carcinoma ceRNA |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.749313/full |
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