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Aortic dissection (AD) represents a life-threatening cardiovascular condition with a high mortality rate. Extensive research has implicated inflammation as a pivotal factor in the development of aortic dissection. Lactylation, a process implicated in various inflammatory responses, play a critical role in several cardiovascular diseases. However, the specific role of lactylation-related genes in the pathogenesis of AD remains largely unexplored. We downloaded and integrated two AD-related datasets (GSE 52093 and GSE 98770) from the GEO database. Subsequently, we pinpointed hub genes associated with lactylation, conducted a comprehensive analysis of their functional implications, and examine the correlation between their expression levels and immune infiltration. Furthermore, we utilized single-cell sequencing data to compare the lactylation levels across various immune cell types between AD patients and healthy individuals. Our analysis identified three hub genes (CALM1, PARP1, and PTBP1) that are strongly associated with lactylation in AD. Importantly, we found a robust correlation between the expression levels of these hub genes and the extent of immune cell infiltration. Single-cell sequencing data further highlighted marked differences in lactylation levels among diverse immune cell types between AD patients and healthy individuals. Notably, the lactylation levels of immune cells in the aortic tissues of AD patients were significantly elevated. In clinical sample validation, the expression of CALM1, but not PARP1 and PTBP1, showed significant differences between the two groups. Our study unveils significant differences in lactylation levels within the immune cells of aortic tissue between AD patients and healthy individuals. Moreover, we provide experimental validation that the lactylation-related gene CALM1 may serve as a promising biomarker for the diagnosis of AD, offering new insights into the pathogenesis and potential diagnostic approaches for this deadly condition.

In this study, we try to find the pathogenic role of immune-related genes in the bone marrow microenvironment of AML. Through WGCNA, seven modules were obtained, among which the turquoise module containing 1793 genes was highly correlated with the immune infiltration score. By unsupervised clustering, the turquoise module was divided into two clusters: the intersection of clinically significant genes in the TCGA and DEGs to obtain 178 genes for mutation analysis, followed by obtaining 17 genes with high mutation frequency. Subsequently, these 17 genes were subjected to LASSO regression analysis to construct a riskscore model of 8 hub genes. The TIMER database, ImmuCellAI portal website, and ssGSEA elucidate that the hub genes and risk scores are closely related to immune cell infiltration into the bone marrow microenvironment. In addition, we also validated the relative expression levels of hub genes using the TCGA database and GSE114868, and additional expression levels of hub genes in AML cell lines in vitro . Therefore, we constructed an immune infiltration-related gene model that identify 8 hub genes with good risk stratification and predictive prognosis for AML.

Purpose: Atherosclerosis is still a global public problem with increasing incidence rate and mortality. It has been found that gender factors play an important role in the progression of atherosclerosis. However, few people explore gender related atherosclerosis at the level of genes and immune cells. The purpose of this study was to determine genetic and immune cell differences between male and female samples. Patients and Methods: This study aims to identify differential genes between male and female samples in the GSE43292 dataset. The focus will be on identifying immune-related genes (IRGs) among these differentially expressed genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis will be employed to explore the enrichment of IRGs in biological processes, molecular functions, cellular components, and pathways. Furthermore, a protein-protein interaction (PPI) network for the IRGs will be constructed using Cytoscape software. To estimate the degree of immune cell infiltration, single-sample gene set enrichment analysis (ssGSEA) will be conducted. Moreover, the identified IRGs will be validated using GSE28829 dataset. Finally, we validated in atherosclerotic mice. Results: Seven IRGs (CCL13, IL1RN, FPR2, S100A8, CCL19, CXCL1, CXCL8) were identified as being overexpressed in male atherosclerosis. GO and KEGG analysis revealed that these IRGs are primarily enriched in inflammatory response pathways, cytokine signaling pathways, and cytokine- cytokine receptor interactions. Notably, when compared to females, there was a significant infiltration of immune cells in male specimens. Importantly, all seven IRGs demonstrated high diagnostic value in GSE28829 dataset. The use of animal samples supports our results. Conclusion: This study demonstrates the effectiveness of seven IRGs and reveal sex differences in atherosclerosis. Notably, there is a significant presence of immune cells within the atherosclerotic plaque of men compared to women. These findings have potential implications for the development of personalized treatment approaches targeting gender-related atherosclerosis. Keywords: atherosclerosis, immune related genes, sex related atherosclerosis, bioinformatics, immune infiltration

Atherosclerosis (AS) is a common cardiovascular disease worldwide. The mitochondrial unfolded protein response (UPRmt) is a defense mechanism that enhances protein folding and degradation to maintain mitochondrial function and cellular homeostasis under stress. Research suggests a strong link between mitochondrial dysfunction and AS, particularly related to oxidative stress and inflammation. However, the exact relationship between UPRmt and AS is unclear. Identifying biomarkers associated with UPRmt is crucial for improving AS diagnosis and treatment. Microarray datasets related to AS were retrieved from the Gene Expression Omnibus (GEO) database. After integrating these datasets and eliminating batch effects, we obtained 101 AS and 67 control samples. Based on the expression levels of UPRmt-related genes (MRGs), the samples were classified into two subtypes and subjected to differential analysis, weighted correlation network analysis, and immune infiltration analysis. A predictive model was built using 12 machine learning algorithms to identify hub genes associated with UPRmt. Additionally, single-cell RNA-seq data and the CellChat algorithm were used to explore intercellular communication mechanisms mediated by these hub genes in AS. Mendelian randomization analysis was performed to identify biomarkers linked to AS. Molecular simulation techniques assessed the therapeutic potential of Iloprost. Finally, the expression and distribution of core genes were analyzed by RT-qPCR, Western blot, and immunofluorescence. We identified seven hub genes at the intersection of UPRmt dysregulation and atherosclerosis. These genes showed consistent differential expression across cohorts and formed coherent mitochondria-stress modules. Their expression correlated with multiple immune-cell infiltration scores, including macrophage and T-cell signatures, and with inflammatory mediators. A classifier based on the seven-gene panel distinguished atherosclerotic from non-atherosclerotic samples across external datasets and remained robust after accounting for clinical covariates. Experimental assays confirmed altered expression of selected genes and their modulation under mitochondrial stress. Molecular simulation suggested that Iloprost can bind to the APOC1 protein's active pocket. ARHGAP25, CYTH4, ITGB7, APOC1, WDFY4, MARCO and PLCB2 are pivotal genes intimately linked to AS and the UPRmt. They potentially play crucial roles in mitochondrial dysfunction and immune regulation. As such, these genes may be promising biomarkers and therapeutic targets for AS.

Introduction The PKHD1 (Polycystic Kidney and Hepatic Disease 1) gene is essential for producing fibrocystin or polyductin, which is crucial in various cellular functions. Mutations in PKHD1 have been found to be involved in the development and progression of colorectal cancer (CRC). Along with APC, TP53, and KRAS, PKHD1 is one of the most frequently mutated genes in CRC. PKHD1 expression is governed by the Wnt/PCP pathway, often dysregulated in CRC. Targeting this pathway, crucial for CRC progression, could unveil potential therapeutic strategies for colon cancer treatment. Methods This study examined an in‐house dataset of 3702 colon cancer samples, analyzing mutation landscapes, clinical features, tumor mutational burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN) score. For the survival analysis of PKHD1 patients, survival data of 436 colon adenocarcinoma samples were obtained from TCGA dataset. Additionally, 433 samples from TCGA with RNA‐seq data were used for the assessment of immune cell infiltration and gene set enrichment analysis. Results Polycystic Kidney and Hepatic Disease 1 mutation was detected in 424 colon cancer patients from our in‐house cohort and was associated with increased TMB, higher MSI, and lower CIN score. Importantly, within the TCGA dataset, PKHD1 mutations were identified as an independent prognostic factor, not merely correlated with established prognostic biomarkers, and were associated with poorer overall survival outcomes. In terms of immune response, these mutations correlated with increased enrichment scores for 12 immune cell types, including B cell plasma, macrophages, and naive CD4+ T cells. Additionally, interferon alpha and interferon‐gamma gene sets were significantly down‐regulated in patients with PKHD1 mutations (FDA q‐value < 0.1). Conclusions Overall, these findings suggest that PKHD1 may be a potential biomarker for the prognosis of colon cancer and provide some insight for personalized immunotherapy. In this study of 3702 samples, mutations in the PKHD1 gene, frequently observed in colorectal cancer (CRC), were correlated with increased tumor mutational burden and microsatellite instability, but decreased chromosomal instability. These mutations also serve as an independent prognostic biomarker, associated with poorer overall survival outcomes in TCGA COAD patients. These critical findings reinforce the potential of PKHD1 as a pivotal biomarker for colon cancer prognosis and may facilitate the emergence of personalized immunotherapy strategies.

Breast cancer accounts for the highest incidence of tumors in women. Immune infiltrating of the tumor microenvironment positively correlates with the overall survival of breast cancer patients. PLAT can affect the development of many cancers, but its mechanism in breast cancer is unclear. We assessed the correlation between PLAT and immune infiltrating in breast cancer based on the TCGA database. The expression and DNA methylation of PLAT in breast cancer with different clinical characteristics was tested by Wilcoxon signed rank test and displayed by box plot. Sequentially, Kaplan-Meier plot was employed to compare the difference in overall survival rates between patients with different expressed levels. Univariate and multivariate Cox regression analyses were used to validate whether PLAT is an independent prognostic factor of breast cancer. After that, GO, KEGG, and gene-set enrichment analysis were employed to do functional enrichment analysis. Finally, TIMER, TISIDB database, and ssGSEA algorithm were used to assess the correlation between PLAT expression and various immune characteristics. The correlation between PLAT expression and DNA methylation was examined by Pearson correlation coefficient. PLAT displays differential expression levels in breast cancer patients with various clinical characteristics. As an independent protective factor for breast cancer, PLAT may significantly correlate with the immune status of breast cancer by adjusting many immune molecules and affecting the immune infiltration in the tumor microenvironment. DNA methylation of PLAT downregulates the gene expression and affects the prognosis of breast cancer. PLAT can be considered a potential biomarker to predict breast cancer prognosis and might contribute to the development of immunological treatment strategies.

Skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) is the most lethal skin cancer with increasing incidence. Regulated cell death plays an important role in tumorigenesis and serves as an important target for almost all treatment strategies. Cuproptosis is the most recently identified copper-dependent regulated cell death form that relies on mitochondria respiration. However, its role in tumorigenesis remains unknown. The correlation of cuproptosis-related genes with tumor prognosis is far to be understood, either. In the present study, we explored the correlation between cuproptosis-related genes with the prognosis of melanoma by accessing and analyzing a public database and found 11 out 12 genes were upregulated in melanoma tissues and three genes (LIPT1, PDHA1, and SLC31A1) have predictive value for the prognosis. The subgroup of melanoma patients with higher cuproptosis-related gene expression showed longer overall survival than those with lower gene expression. We chose LIPT1 for further exploration. LIPT1 expression was increased in melanoma biopsies and was an independent favorable prognostic indicator for melanoma patients. Moreover, LIPT1 expression was positively correlated with PD-L1 expression and negatively associated with Treg cell infiltration. The melanoma patients with higher LIPT1 expression showed longer overall survival than those with lower LIPT1 expression after receiving immunotherapy, indicating the prognostic predictive value of LIPT1. Finally, a pan-cancer analysis indicated that LIPT1 was differentially expressed in diverse cancers as compared to normal tissues and correlated with the expression of multiple immune checkpoints, especially PD-L1. It could serve as a favorable prognosis indicator in some cancer types. In conclusion, our study demonstrated the prognostic value of cuproptosis-related genes, especially LIPT1, in melanoma, and revealed the correlation between LIPT1 expression and immune infiltration in melanoma, thus providing new clues on the prognostic assessment of melanoma patients and providing a new target for the immunotherapy of melanoma.

Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet. Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues. Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes in public datasets and the Xiangya in-house dataset with decent efficiency and showed better performance when compared with 95 published signatures. The TIIClnc signature was strongly correlated to immune characteristics, including microsatellite instability, tumor mutation burden, and interferon γ, and exhibited a more active immunologic process. Furthermore, the TIIClnc signature predicted superior immunotherapy response in multiple datasets across cancer types. Notably, the positive correlation between the TIIClnc signature and CD8, PD-1, and PD-L1 was verified in the Xiangya in-house dataset. Conclusions: The TIIClnc signature enabled a more precise selection of the LGG population who were potential beneficiaries of immunotherapy.Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet. Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues. Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes in public datasets and the Xiangya in-house dataset with decent efficiency and showed better performance when compared with 95 published signatures. The TIIClnc signature was strongly correlated to immune characteristics, including microsatellite instability, tumor mutation burden, and interferon γ, and exhibited a more active immunologic process. Furthermore, the TIIClnc signature predicted superior immunotherapy response in multiple datasets across cancer types. Notably, the positive correlation between the TIIClnc signature and CD8, PD-1, and PD-L1 was verified in the Xiangya in-house dataset. Conclusions: The TIIClnc signature enabled a more precise selection of the LGG population who were potential beneficiaries of immunotherapy.

Background Understanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor–immune interactions in cancers. Results We analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER . Conclusions We develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.

IntroductionOsteoarthritis (OA) is a degenerative joint disease marked by chronic inflammation, extracellular matrix degradation, and dysregulated cell death. The roles of apoptosis-, autophagy-, and ferroptosis-related genes in OA pathogenesis remain unclear.MethodsIntegrated bioinformatics analyses were conducted on public GEO datasets to identify apoptosis-autophagy-ferroptosis-related genes (AAFRGs). TLR3 and GLUL were identified using LASSO, random forest, and SVM-RFE algorithms. Immune infiltration analysis, immunohistochemistry, and functional assays in human chondrocytes were performed, and ACLT-induced rat OA models were used for in vivo validation.ResultsTLR3 was upregulated and associated with pro-inflammatory immune cells, while GLUL was downregulated and correlated with anti-inflammatory signatures. TLR3 knockdown reduced inflammation, apoptosis, and aberrant mineralization, partially restoring extracellular matrix integrity. GLUL overexpression promoted cellular homeostasis. In rats, TLR3 inhibition and PRP treatment decreased pro-inflammatory cytokines (IL-1β, TNF-α), reduced matrix-degrading enzymes (MMP3, MMP13), and restored GLUL and IL-10 levels.Discussion/ConclusionTLR3 and GLUL orchestrate inflammatory responses and homeostatic imbalance in OA, representing potential biomarkers and therapeutic targets for diagnosis and intervention.