Aim This study aimed to analyze the involvement of hub genes in hepatocellular carcinoma. Methods Four series were used in this study: GSE45267, GSE84402, and GSE101685 from GPL570 platform in the Gene Expression Omnibus and the other from The Cancer Genome Atlas. The gene audition was completed using R software and Venn diagrams. The outcome, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes preliminary analyses of differentially expressed genes were performed using the R software. A string image was obtained using the Search Tool for the Retrieval of Interacting Genes. The protein–protein interaction network was examined using Cytoscape software. The corrplot package was used to analyze the correlation of genes. Human Protein Atlas was used to confirm the protein levels. Univariate Cox regression was used to analyze whether these genes were related to survival. UALCAN was used to confirm the effect of these genes on patient survival. Results A total of 107 differentially expressed genes from 491 patients with hepatocellular carcinoma and 119 normal individuals were selected in this study. Cytoscape revealed 25 central nodes from the 107 genes. CCNB1, CDK1, CCNA2, PTTG1, and CDC20 were selected based on the cell cycle pathway. A significant correlation was found among the 6 DEGs. The transcription levels and protein levels of these genes were verified in cells and human tissue samples. The overall survival for these genes was analyzed using univariate Cox regression and UALCAN. Conclusion CCNB1, CDK1, CDC20, PTTG1, CCNA2, and TTK were overexpressed and correlated in hepatocellular carcinoma cells and tumors. The results might help explore the prognosis and diagnostic markers of HCC.

In this study, we constructed the ferroptosis-related genes diagnostic and prognostic models. We analyzed the relationship between ferroptosis and tumor mutational burden in hepatocellular carcinoma (HCC). Eighty-four ferroptosis-related genes were analyzed by Cox regression and the least absolute shrinkage and selection operator method. Seven genes (SLC7A11, ACSL3, ACACA, SLC1A5, G6PD, ACSL6, and VDAC2) were used to construct models. The reliability of the model was verified by using the data from the ICGC database. Differential genes in high and low-risk groups revealed enrichment of many immune features by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The degree of ferroptosis was negatively correlated with tumor mutational burden (i.e., the higher the degree of ferroptosis, the lower the tumor mutational burden). The tumor mutational burden was negatively correlated with survival. We also found that ALB, TP53, and DOCK2 may be a bridge between ferroptosis and tumor mutational burden. The reported models and the relationship with tumor mutational burden indicate new possibilities for individualized treatment of HCC patients.

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is a leading cause of cancer-related death worldwide. This study aimed to establish a reliable prognostic model for HCC using histological grades and the expression levels of related genes. The histological grade of a tumor provides prognostic information. The expression data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) database. We employed the univariate and multivariate Cox regression analyses, as well as the least absolute shrinkage and selection operator (LASSO) regression to establish the prognostic model. After verification of the proposed model using data downloaded from the International Cancer Genome Consortium (ICGC) database, we found that the model was highly reliable, and it was revealed that the prognosis in the high-risk group was significantly worse than that in the low-risk group. Next, we explored the correlation of RiskScore with patients’ clinicopathological characteristics, and we found that the RiskScore could be used as an independent prognostic factor, which further confirmed the reliability of our model. In summary, the proposed model could accurately predict the prognosis of HCC patients, assisting clinicians to study the roles of different histological grades of HCC.

This paper presents a novel approach of radar station intelligence quality evaluation which based on fuzzy Backpropagation neural network (BPNN). Firstly, the index system of the radar station intelligence quality evaluation is established according to the analysis of the process, the characteristics, and the main influencing factors of the radar station intelligence production. And then the factor set, comment set and the membership matrix are structured, the fuzzy BPNN for evaluating the quality of the radar station intelligence is designed referring to the index system. Finally, the experiment shows that the accuracy and stability can be improved effectively by using fuzzy BPNN to evaluate the radar station intelligence quality

Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.