Objectives To investigate the different CT

Background. Tuberculosis (TB) is a serious chronic bacterial infection caused by Mycobacterium tuberculosis (MTB). It is one of the deadliest diseases in the world and a heavy burden for people all over the world. However, the hub genes involved in the host response remain largely unclear. Methods. The data set GSE11199 was studied to clarify the potential gene network and signal transduction pathway in TB. The subjects were divided into latent tuberculosis and pulmonary tuberculosis, and the distribution of differentially expressed genes (DEGs) was analyzed between them using GEO2R. We verified the enriched process and pathway of DEGs by making use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The construction of protein-protein interaction (PPI) network of DEGs was achieved through making use of the Search Tool for the Retrieval of Interacting Genes (STRING), aiming at identifying hub genes. Then, the hub gene expression level in latent and pulmonary tuberculosis was verified by a boxplot. Finally, through making use of Gene Set Enrichment Analysis (GSEA), we further analyzed the pathways related to DEGs in the data set GSE11199 to show the changing pattern between latent and pulmonary tuberculosis. Results. We identified 98 DEGs in total in the data set GSE11199, 91 genes upregulated and 7 genes downregulated included. The enrichment of GO and KEGG pathways demonstrated that upregulated DEGs were mainly abundant in cytokine-mediated signaling pathway, response to interferon-gamma, endoplasmic reticulum lumen, beta-galactosidase activity, measles, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, etc. Based on the PPI network, we obtained 4 hub genes with a higher degree, namely, CTLA4, GZMB, GZMA, and PRF1. The box plot showed that these 4 hub gene expression levels in the pulmonary tuberculosis group were higher than those in the latent group. Finally, through Gene Set Enrichment Analysis (GSEA), it was concluded that DEGs were largely associated with proteasome and primary immunodeficiency. Conclusions. This study reveals the coordination of pathogenic genes during TB infection and offers the diagnosis of TB a promising genome. These hub genes also provide new directions for the development of latent molecular targets for TB treatment.

The objective of this study is to form a cancer stem cell index-based model to stratify HCC risk and predict survival. After screening the Tumor Genome Atlas (TCGA) of liver and normal liver tissue samples, we obtained differentially expressed genes (DEGs). We employed a weighted correlation network analysis (WGCNA) and differentially expressed genes were studied in HCC to find the modules most associated with cancer stem cells (mRNAsi). At the same time, gene ontology and Kyoto Genome Encyclopedia (KEGG) were used for functional annotation and combined with LASSO, univariate, and multivariate COX regression analyses, a prediction model of key module genes of cancer stem cells was developed. The model’s clinical efficacy was measured using the C index, calibration curve, multiindex ROC curve, and clinical decision curve. WGCNA found that black modules were most correlated with tumour stem cell index. Seven genes (CSDC2, GNA14, LGI2, MMRN1, PDE2A, SELP, and STK32B) were filtered by univariate, LASSO, and multivariate Cox regression analyses to establish the primary HCC model. The survival analysis and ROC curve in the TCGA training and validation cohort showed good performance. The independent prognostic factor of primary HCC was risk score, according to univariate and multivariate Cox regression analyses. It is found that the stem cell index model of 7 genes could predict factors independently, indicating that signatures of the stem cell will play a significant role in liver cancer survival prediction and risk stratification.