A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma
BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e855866f69404aa8ad11b6fa8394d6d9 |
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| Sumario: | BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation. |
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