Department of Gastroenterology, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China.
Department of Gastroenterology, Jiujiang First People's Hospital, Jiujiang 332000, Jiangxi, China.
Contrast Media Mol Imaging. 2022 Oct 10;2022:6716324. doi: 10.1155/2022/6716324. eCollection 2022.
Traditional medical imaging methods for diagnosing hepatocellular carcinoma can only provide information for differential diagnosis in terms of morphology and blood supply of the lesion, and the determination of the nature of the lesion still relies on tissue biopsy. Although ultrasound or CT-guided biopsy has become an effective method for the diagnosis of liver cancer in recent years, the puncture has the possibility of tumor irritation, liver tumor rupture, or needle tract metastasis. In this paper, the use of bioinformatics method is to gradually screen potentially high-risk genes associated with HCC recurrence on a genome-wide scale would help to discover the key target molecules. The ANN method was used to establish a gene prediction model that can predict the recurrence and survival of HCC, so as to construct a tool to identify patients at risk of HCC recurrence. It provided a certain therapeutic basis for future clinical work, thereby improving the prognosis of patients with HCC. Using the "survfit" function of the "survival" package in the R language, the log-rank test (the log-rank test was a common method for comparing two survival curves) was performed on all genes with posthoc recurrence of hepatocellular carcinoma as the outcome event. Then, the BLAST tool (Basic Local Alignment Search Tool) was used to search the similarity of each hepatocellular carcinoma database to find out the genes with similar sequences to each hepatocellular carcinoma, so as to determine the function of each differentially expressed sequence tag. This paper found that the AUC of the ANN model was greater than that of the discriminant analysis model ( < 0.05). This paper promoted the development of new therapeutic measures for hepatocellular carcinoma and provided important theoretical guidance for human beings to fight cancer.
传统的医学影像学方法只能提供病变形态和血供方面的鉴别诊断信息，病变性质的确定仍依赖于组织活检。尽管近年来超声或 CT 引导下的活检已成为诊断肝癌的有效方法，但穿刺仍有肿瘤刺激、肝癌破裂或针道转移的可能。本文运用生物信息学方法，逐步筛选出全基因组范围内与 HCC 复发相关的潜在高危基因，有助于发现关键的靶分子。ANN 方法用于建立基因预测模型，可以预测 HCC 的复发和生存，从而构建一种识别 HCC 复发风险患者的工具。为未来的临床工作提供了一定的治疗基础，从而改善 HCC 患者的预后。使用 R 语言“survival”包中的“survfit”函数，以肝癌的术后复发作为结局事件，对所有肝癌复发的基因进行对数秩检验（对数秩检验是比较两条生存曲线的常用方法）。然后，使用 BLAST 工具（Basic Local Alignment Search Tool）搜索每个肝癌数据库之间的相似性，找出与每个肝癌具有相似序列的基因，从而确定每个差异表达序列标签的功能。本文发现 ANN 模型的 AUC 大于判别分析模型（<0.05）。本文推动了肝癌新治疗措施的发展，为人类抗癌提供了重要的理论指导。
