Dataset Information

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

SUBMITTER: Xie S

PROVIDER: S-EPMC10408211 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

Xie Shicheng S Zhong Jinwei J Zhang Zhongjing Z Huang Weiguo W Lin Xiaoben X Pan Yating Y Kong Xiuyan X Xia Hongping H Yu Zhijie Z Ni Haizhen H Xia Jinglin J

Cancer cell international 20230807 1

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by corre ...[more]

PMID: 37550755

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Project description:Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model. Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR. Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group. Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.

Project description:Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high morbidity and mortality worldwide. Angiogenesis is essential for HCC progression and metastasis. Some angiogenesis-related genes promote this process, whereas other antiangiogenic genes inhibit HCC growth and metastasis. Therefore, finding new potential biomarkers for HCC prognosis prediction and treatment is essential. Public RNAseq and clinical data from TCGA and GEO database, download angiogenesis-related genes from the GeneCards, MSigDB database, through the single factor analysis of Cox, LASSO build risk score-Cox regression analysis model and external validation verified from the GEO. Cox regression analysis, Kaplan Meier (KM) curve, ROC curve, and decision-curve analysis will be used to evaluate and examine the risk score prediction effect of the model. GSVA analysis was used to assess the variation of gene sets between groups, and ClBERSOFT, ESTIMATE, and TIMER databases were used to analyze the immune infiltration in the single-cell level analysis of gene expression differences between cells. Finally, in the three pairs of HCC tissues and tissue adjacent to carcinoma by real-time fluorescent quantitative PCR (qRT_PCR) and western blotting (WB) to evaluate angiogenesis-related genes (ATP2A3 AEBP1 PNMA1, PLAT) expression level in HCC, and AEBP1 was knocked out in HCCLM3 cells, which is to study AEBP1 biological function in HCC. We established a prognostic risk assessment model based on 13 significant genes associated with HCC prognosis by Cox analysis and LASSO-Cox regression analysis. The median was used to divide these patients into high-risk and low-risk groups, and the prognosis of the high-risk group was worse than that of the low-risk group. Through the multivariate Cox regression analysis, it was found that the risk score was an independent predictor of overall survival (OS). The GSVA analysis suggested that the predicted high-risk population showed higher activity in the purine, pyrimidine, and riboflavin metabolic pathways. Compared with the low-risk group, the tumor microenvironment in the high-risk group showed a reduction in the number of cells promoting anti-tumor immunity and an increase in the number of cells inhibiting anti-tumor immunity, as well as a reduction in overall immune infiltration and matrix components. On the single-cell level, it was confirmed that the key genes (AEBP1, ATP2A3, PLAT, and PNMA1) expressed differently between liver cancer and adjacent tissue cell groups. Finally, qRT_PCR and WB results showed that ATP2A3, AEBP1, PNMA1, and PLAT were highly expressed in liver cancer tissue compared to adjacent tissue, and the proliferation, migration, and invasion of HCCLM3 cells were inhibited after knocking out AEBP1. We constructed novel risk score models as prognostic biomarkers for HCC, which has the potential to guide the development of more personalized treatment strategies for HCC patients. In addition, AEBP1 is a potential therapeutic target for HCC.

Dataset Information

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

Publications

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

Similar Datasets

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