Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines. Li Ming,Wang Ping,Zhang Ning,Guo Lu,Feng Yuan-Ming Cancer gene therapy Triple-negative breast cancer (TNBC), colon adenocarcinoma (COAD), ovarian cancer (OV), and glioblastoma multiforme (GBM) are common malignant tumors, in which significant challenges are still faced in early diagnosis, treatment, and prognosis. Therefore, further identification of genes related to those malignant tumors is of great significance for the improvement of management of the diseases. The database of the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository was used as the data source of gene expression profiles in this study. Malignant tumors genes were selected using a feature selection algorithm of maximal relevance and minimal redundancy (mRMR) and the protein-protein interaction (PPI) network. And finally selected 20 genes as potential related genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the potential related genes, and different tumor-specific genes and similarities and differences between network modules and pathways were analyzed. Further, using the potential cancer-related genes found above in this study as features, a support vector machine (SVM) model was developed to predict high-risk malignant tumors. As a result, the prediction accuracy reached more than 85%, indicating that such a model can effectively predict the four types of malignant tumors. It is demonstrated that such genes found above in this study indeed play important roles in the differentiation of the four types of malignant tumors, providing basis for future experimental biological validation and shedding some light on the understanding of new molecular mechanisms related to the four types of tumors. 10.1038/s41417-019-0143-5

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods. International journal of biological macromolecules Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key role in lipid metabolism and inflammation. PLA2G1B has selective substrate specificity. In this paper, the recombinant protein molecular structure of PLA2G1B was studied and novel therapeutic interventions were designed to disrupt PLA2G1B activity and impede tumor growth by targeting specific regions or residues in its structure. Construct protein-protein interaction networks and core genes using R's "STRING" program. LASSO, SVM-RFE and RF algorithms identified important genes associated with lung cancer. 282 deg were identified. Enrichment analysis showed that these genes were mainly related to adhesion and neuroactive ligand-receptor interaction pathways. PLA2G1B was subsequently identified as developing a preventative feature. GSEA showed that PLA2G1B is closely related to α-linolenic acid metabolism. Through the analysis of LASSO, SVM-RFE and RF algorithms, we found that PLA2G1B gene may be a preventive gene for lung cancer. 10.1016/j.ijbiomac.2024.133918

A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma. Journal of hepatocellular carcinoma Purpose:The response to transarterial chemoembolization (TACE) varies among individuals with hepatocellular carcinoma (HCC). This study aimed to identify a biomarker for predicting TACE response in HCC patients and to investigate its correlations with the tumor microenvironment and pre-TACE radiomics features. Patients and Methods:GSE104580 data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis and machine learning algorithms were used to identify genes for constructing the TACE failure signature (TFS). TFS scores were then calculated for HCC patients in The Cancer Genome Atlas (TCGA) cohort. After obtaining images from The Cancer Imaging Archive (TCIA), tumor labeling and radiomics feature extraction, the Rad-score model was generated. Correlation analysis was performed between the TFS score and the Rad-score. CIBERSORT, ssGSEA and TME analysis were performed to explore differences in the immune landscape among distinct risk groups. The immunotherapy response was compared between different groups. Results:ADH1C, CXCL11, EMCN, SPARCL1 and LIN28B were selected and incorporated into the TFS, which demonstrated satisfactory performance in predicting TACE response. Patients in the high TFS score group had poorer overall survival (OS) than those in the low TFS score group. The Rad-score model was constructed using six radiomics features, and the Rad-score was significantly correlated with hub gene expression and the TFS score. The high-TFS group was also characterized by an immunosuppressive tumor microenvironment and exhibited unfavorable responses to immunotherapy with PD-1 and CTLA-4 checkpoint inhibitors. Conclusion:This study established a transcriptomic biomarker for predicting the efficacy of TACE that correlates with radiomics features on pretreatment imaging, tumor immune microenvironment characteristics, and the efficacy of immunotherapy and targeted therapy in HCC patients. 10.2147/JHC.S480540