Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis.

Background: Sepsis is a syndrome marked by life-threatening organ dysfunction and a disrupted host immune response to infection. PANoptosis is a recent conceptual development, which emphasises the interconnectedness among multiple programmed cell deaths in various diseases. Nevertheless, the role of PANoptosis in sepsis is still unclear. Methods: We utilized the GSE65682 dataset to identify PANoptosis-related genes (PRGs) and associated immune characteristics in sepsis, classified sepsis samples based on PRGs using the ConsensusClusterPlus method and applied the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify cluster-specific hub genes. Based on PANoptosis -specific DEGs, we compared results from machine learning models and the best-performing model was selected. Predictive efficiency was validated through external dataset, nomogram, survival analysis, quantitative real-time PCR, and western blot. Results: The expression levels of PRGs were generally dysregulated in sepsis patients compared with normal samples, and higher PRGs expression correlated with increased immune cell infiltration. In addition, two distinct PANoptosis-related clusters were defined, and functional analysis indicated that DEGs associated with these clusters were primarily linked to immune-related pathways. The SVM model was selected as best-performing model, with lower residuals and the highest area under the curve (AUC = 0.967), which was then validated in an external dataset (AUC = 0.989) and through in vivo experiments. Additional validation through nomogram and survival analysis further confirmed its substantial predictive efficacy. Conclusion: Our findings exposed the intricate association between PANoptosis and sepsis, offering important insights on sepsis diagnosis and potential therapeutic targets.

Development and validation of a leukocyte-associated immunoglobulin-like receptor-1 prognostic signature for lower-grade gliomas.

OBJECTIVE: Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1), is an immunosuppressive receptor, widely expressed by immune cells, but the part of LAIR-1 in glioma progression remains unclear. The purpose of this study was to explore the relationship between LAIR-1 expression and the development of lower-grade glioma (LGG) using publicly available data sets. METHODS: We took advantage of The Cancer Genome Atlas (TCGA) to analyze the expression of LAIR-1 in patients with LGG. Second, Kaplan-Meier methods and univariate and multivariate Cox regression analyses were used to examine the clinical significance of LAIR-1 expression in combination with CGGA databases, and then receiver operating characteristic curve analysis was used to verify the prognostic utility of LAIR-1. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore the function of LAIR-1. Analysis of the correlation with immune infiltration was conducted using the ESTIMATE algorithm and single sample gene set enrichment analysis. RESULTS: Our results showed that LAIR-1 expression to be positively correlated with malignant clinicopathologic features of LGG. Univariate analysis and multivariate analysis revealed that overexpression of LAIR-1 was correlated with a worse prognosis in patients. A nomogram model combining LAIR-1 was more useful in guiding clinical diagnosis, and functional enrichment analysis showed that malignant development of glioma was closely affiliated with the tumor immune microenvironment. CONCLUSION: These results indicate that LAIR 1 is a latent marker for determining the prognosis of LGG patients. LAIR 1 may also participate a critical part in TIME of LGG by regulating the infiltration of immune cells, suggesting that LAIR 1 might be used as a therapeutic target to regulate the antitumor immune response.

Viral oncogenes, viruses, and cancer: a third-generation sequencing perspective on viral integration into the human genome.

The link between viruses and cancer has intrigued scientists for decades. Certain viruses have been shown to be vital in the development of various cancers by integrating viral DNA into the host genome and activating viral oncogenes. These viruses include the Human Papillomavirus (HPV), Hepatitis B and C Viruses (HBV and HCV), Epstein-Barr Virus (EBV), and Human T-Cell Leukemia Virus (HTLV-1), which are all linked to the development of a myriad of human cancers. Third-generation sequencing technologies have revolutionized our ability to study viral integration events at unprecedented resolution in recent years. They offer long sequencing capabilities along with the ability to map viral integration sites, assess host gene expression, and track clonal evolution in cancer cells. Recently, researchers have been exploring the application of Oxford Nanopore Technologies (ONT) nanopore sequencing and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) sequencing in cancer research. As viral integration is crucial to the development of cancer via viruses, third-generation sequencing would provide a novel approach to studying the relationship interlinking viral oncogenes, viruses, and cancer. This review article explores the molecular mechanisms underlying viral oncogenesis, the role of viruses in cancer development, and the impact of third-generation sequencing on our understanding of viral integration into the human genome.