Department of Biostatistics, Harbin Medical University, Harbin, Heilongjiang, China.
Cancer Med. 2020 Dec;9(24):9219-9235. doi: 10.1002/cam4.3526. Epub 2020 Nov 24.
Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA-seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA-miRNA-mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool-based ceRNA network, an HCC-specific ceRNA network was obtained, and lncRNA-miRNA-mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer-related pathways. Four HCC-specific lncRNA-miRNA-mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK-AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK-AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1-year, 3-year, and 5-year survival was 0.777 (0.657-0.865), 0.722 (0.640-0.848), and 0.630 (0.528-0.823), and 0.751 (0.664-0.870), 0.773 (0.707-0.849), and 0.734 (0.638-0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC.
肝细胞癌（HCC）是一种具有高发病率和预后不良的异质性恶性肿瘤。探索潜在的机制和有效的预后指标有助于临床管理和优化治疗。从癌症基因组图谱（TCGA）中检索了 HCC 的 RNA-seq 和临床表型数据，并进行了差异表达分析。然后，构建了一个差异长非编码 RNA-miRNA-mRNA 调控网络，并进一步鉴定和验证了关键基因。通过将该网络与基于在线工具的 ceRNA 网络整合，获得了 HCC 特异性的 ceRNA 网络，并提取了 lncRNA-miRNA-mRNA 调控轴。进一步获得与预后相关的 RNA，并建立多变量 Cox 回归模型来识别预后标志和列线图。结果，鉴定出 198 个差异表达长非编码 RNA、120 个差异表达 microRNA 和 2827 个差异表达 mRNA，从差异网络中鉴定出的 30 个关键基因富集在四个癌症相关途径中。提取了四个 HCC 特异性的 lncRNA-miRNA-mRNA 调控轴，并发现 SNHG11、CRNDE、MYLK-AS1、E2F3 和 CHEK1 与 HCC 预后相关。多变量 Cox 回归分析确定了一个由 CRNDE、MYLK-AS1 和 CHEK1 组成的用于 HCC 总生存期（OS）的预后标志。建立了一个包含预后标志和病理分期的列线图，并显示出一些净临床获益。预后标志和列线图对 1 年、3 年和 5 年生存率的 AUC 分别为 0.777（0.657-0.865）、0.722（0.640-0.848）和 0.630（0.528-0.823），0.751（0.664-0.870）、0.773（0.707-0.849）和 0.734（0.638-0.845）。这些结果为研究 HCC 的潜在生物标志物和治疗靶点提供了线索。此外，获得的 30 个关键基因和 4 个调控轴可能还有助于阐明 HCC 的潜在机制。
