Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear.Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm.Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group.Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

BackgroundAlthough the roles of m6A modification in the immune responses to human diseases have been increasingly revealed, their roles in immune microenvironment regulation in coronary heart disease (CHD) are poorly understood.MethodsThe GSE20680 and GSE20681 datasets related to CHD were acquired from the Gene Expression Omnibus (GEO) database. A total of 30 m6A regulators were used to perform LASSO regression to identify the significant genes involved in CHD. Unsupervised clustering analysis was conducted using the m6A regulators to distinguish the m6A RNA methylation patterns in patients with CHD. The differentially expressed genes (DEGs) and biological characteristics, including GO and KEGG enrichment results, were assessed for the different m6A patterns to analyse the impacts of m6A regulators on CHD. Hub genes were identified, and subsequent microRNAs-mRNAs (miRNAs–mRNAs) and mRNAs-transcriptional factors (mRNA-TFs) interaction networks were constructed by the protein and protein interaction (PPI) network method using Cytoscape software. The infiltrating proportion of immune cells was assessed by ssGSEA and the CIBERSORT algorithm. Quantitative real-time PCR (qRT-PCR) was performed to detect the expression of the significant m6A regulators and hub genes.ResultsFour of 30 m6A regulators (HNRNPC, YTHDC2, YTHDF3, and ZC3H13) were identified to be significant in the development of CHD. Two m6A RNA methylation clusters were distinguished by unsupervised clustering analysis based on the expression of the 30 m6A regulators. A total of 491 genes were identified as DEGs between the two clusters. A PPI network including 308 mRNAs corresponding to proteins was constructed, and 30 genes were identified as hub genes that were enriched in the bioprocesses of peptide cross-linking, keratinocyte differentiation. Twenty-seven hub genes were found to be related to miRNAs, and seven hub genes were found to be related to TFs. Moreover, among the 30 hub genes, eight genes were found to be upregulated in CHD, and three were found to be downregulated in CHD compared to the normal people. The high m6A modification pattern was associated with a higher infiltrated abundance of immune cells.ConclusionOur findings demonstrated that m6A modification plays crucial roles in the diversity and complexity of the immune microenvironment in CHD.

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Purpose Abnormalities in the mitotic spindle have been linked to a variety of cancers. Data on their role in the onset, progression, and treatment of lung adenocarcinoma (LUAD) need to be explored. Methods The data were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Signatures Database (MSigDB), for the training cohort, external validation cohort, and the hallmark mitotic spindle gene set, respectively. Mitotic spindle genes linked to LUAD prognosis were identified and intersected with differentially expressed up-regulated genes in the training cohort. Nomogram prediction models were built based on least absolute shrinkage and selection operator (LASSO) regression, univariate cox, and multivariate cox analyses. The seven-gene immunological score was examined, as well as the correlation of immune checkpoints. The DLGAP5 and KIF15 expression in BEAS-2B, A549, H1299, H1975, and PC-9 cell lines was validated with western blot (WB). Results A total of 965 differentially expressed up-regulated genes in the training cohort intersected with 51 mitotic spindle genes associated with LUAD prognosis. Finally, the seven-gene risk score was determined and integrated with clinical characteristics to construct the nomogram model. Immune cell correlation analysis revealed a negative correlation between seven-gene expression with B cell, endothelial cell (excluding LMNB1), and T cell CD8 + (p < 0.05). However, the seven-gene expression was positively correlated with multiple immune checkpoints (p < 0.05). The expression of DLGAP5 and KIF15 were significantly higher in A549, H1299, H1975, and PC-9 cell lines than that in BEAS-2B cell line. Conclusion High expression of the seven genes is positively correlated with poor prognosis of LUAD, and these genes are promising as prospective immunotherapy targets.