An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma.
Rui ZhuWenna GuoXin-Jian XuLiu-Cun ZhuPublished in: Computational and mathematical methods in medicine (2020)
Growing evidence suggests that the superiority of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) could act as biomarkers for cancer prognosis. However, the prognostic marker for hepatocellular carcinoma with high accuracy and sensitivity is still lacking. In this research, a retrospective, cohort-based study of genome-wide RNA-seq data of patients with hepatocellular carcinoma was carried out, and two protein-coding genes (GTPBP4, TREM-1) and one lncRNA (LINC00426) were sorted out to construct an integrative signature to predict the prognosis of patients. The results show that both the AUC and the C-index of this model perform well in TCGA validation dataset, cross-platform GEO validation dataset, and different subsets divided by gender, stage, and grade. The expression pattern and functional analysis show that all three genes contained in the model are associated with immune infiltration, cell proliferation, invasion, and metastasis, providing further confirmation of this model. In summary, the proposed model can effectively distinguish the high- and low-risk groups of hepatocellular carcinoma patients and is expected to shed light on the treatment of hepatocellular carcinoma and greatly improve the patients' prognosis.
Keyphrases
- signaling pathway
- end stage renal disease
- cell proliferation
- ejection fraction
- rna seq
- prognostic factors
- mental health
- machine learning
- dna methylation
- single cell
- electronic health record
- young adults
- cell migration
- small molecule
- replacement therapy
- patient reported
- network analysis
- lymph node metastasis
- papillary thyroid