Bioinformatic Deconstruction of Differentially Expressed Sequence Tags in Hepatocellular Carcinoma Based on Artificial Neural Network.

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 (P < 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.