Department of Mathematics, Shanghai University, Shanghai 200444, China.
School of Life Sciences, Shanghai University, Shanghai 200444, China.
Comput Math Methods Med. 2020 Nov 3;2020:8872329. doi: 10.1155/2020/8872329. eCollection 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.
越来越多的证据表明,长非编码 RNA(lncRNA)和信使 RNA(mRNA)的优越性可以作为癌症预后的生物标志物。然而,具有高准确性和灵敏度的肝细胞癌的预后标志物仍然缺乏。在这项研究中,对肝细胞癌患者的全基因组 RNA-seq 数据进行了回顾性、队列研究,并筛选出两个蛋白编码基因(GTPBP4、TREM-1)和一个 lncRNA(LINC00426),构建了一个综合特征来预测患者的预后。结果表明,该模型在 TCGA 验证数据集、跨平台 GEO 验证数据集以及按性别、分期和分级划分的不同亚组中的 AUC 和 C-index 均表现良好。表达模式和功能分析表明,模型中包含的所有三个基因都与免疫浸润、细胞增殖、侵袭和转移有关,为该模型提供了进一步的确认。总之,所提出的模型可以有效地区分肝细胞癌患者的高低危组,有望为肝细胞癌的治疗提供新的思路,并大大改善患者的预后。