Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

被引:13
|
作者
Tang, Pingfei [1 ]
Qu, Weiming [1 ]
Wang, Taoli [2 ]
Liu, Minji [1 ]
Wu, Dajun [1 ]
Tan, Lin [1 ]
Zhou, Hongbing [1 ]
机构
[1] Cent South Univ, Affiliated Zhuzhou Hosp, Zhuzhou Cent Hosp, Xiangya Med Coll,Dept Digest Dis, Zhuzhou, Peoples R China
[2] Cent South Univ, Affiliated Zhuzhou Hosp, Zhuzhou Cent Hosp, Xiangya Med Coll,Dept Pathol, Zhuzhou, Peoples R China
关键词
hypoxia; lncRNA (long non-coding RNA); hepatocellular carcinoma; prognostic signature; tumor immune microenvironment; immunotherapy response; CANCER; TUMOR; HIF-1-ALPHA; EXPRESSION; KNOWLEDGE; GENOMICS; STEMNESS; STRESS; GROWTH; CELLS;
D O I
10.3389/fgene.2021.785185
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Abstract Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8(+) T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.
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页数:23
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