Genes and immunity最新文献

Gastric cancer (GC) is one of the most common and deadly malignancies in the world. Abnormal activation of hedgehog pathway is closely related to tumor development and progression. However, potential therapeutic targets for GC based on the hedgehog pathway have not been clearly identified. In the present study, we combined single-cell sequencing data and spatial transcriptomics to deeply investigate the role of hedgehog pathway in GC. Based on a comprehensive scoring algorithm, we found that fibroblasts from GC tumor tissues were characterized by a highly enriched hedgehog pathway. By analyzing the development process of fibroblasts, we found that CCND1 plays an important role at the end stage of fibroblast development, which may be related to the formation of tumor-associated fibroblasts. Based on spatial transcriptome data, we deeply mined the role of CCND1 in fibroblasts. We found that CCND1-negative and -positive fibroblasts have distinct characteristics. Based on bulk transcriptome data, we verified that highly infiltrating CCND1 + fibroblasts are a risk factor for GC patients and can influence the immune and chemotherapeutic efficacy of GC patients. Our study provides unique insights into GC and hedgehog pathways and also new directions for cancer treatment strategies.

Penile squamous cell carcinoma (PSCC) is becoming increasingly common and posing a severe threat to men's health, particularly in developing countries. The function of long non-coding RNAs (lncRNAs) in PSCC progression remains mysterious. Therefore, we explored the significance of lncRNAs in the competing endogenous RNA (ceRNA) network in PSCC tumor progression. The 5 healthy and 6 tumor tissue samples were subjected to lncRNA sequencing. Using miRcode, LncBase, miRTarBase, miRWalk, and TargetScan, we constructed a ceRNA network of differentially expressed lncRNAs, miRNAs, and mRNAs. Our analysis resulted in a ceRNA network consisting of 4 lncRNAs, 18 miRNAs, and 38 mRNAs, whose upstream regulators, the lncRNAs MIR205HG, MIAT, HCP5, and PVT1, were all elevated in PSCC. Immunohistochemical staining confirmed that cell proliferation-related genes TFAP2C, MKI67, and TP63, positively regulated by 4 lncRNAs, were considerably overexpressed in tumor tissues. Immune analysis revealed a significant upregulation in macrophage and exhausted T cell infiltration in PSCC. Our study identified a lncRNA-miRNA-mRNA ceRNA network for PSCC, revealing possible molecular mechanisms involved in the regulation of PSCC progression by key lncRNAs and their connections to the immunosuppressive tumor microenvironment. The ceRNA network provides a novel perspective for elucidating the pathogenesis of PSCC.

Chronicity of HBV infection is a complex process influenced by both viral and host factors. Understanding the complex interplay between HBV and cellular immunity is critical. In this study, we used bulk expression datasets for CHB liver tissue from GSE83148 and GSE84044, and scRNA-seq data of CHB liver samples from GSE182159 to find critical genes and immune cells accounted for CHB. We first identified DEGs closely associated with CHB by WGCNA and these genes were intricately linked to differentiation of Th2 cells, which were significantly higher in CHB and positively associated with ALT, AST, HBV-DNA, Scheuer grade and Scheuer stage. Among these DEGs, CST7 and DUSP5 highly expressed in CHB and positively associated with ALT, AST, HBV-DNA, Scheuer grade and Scheuer stage. Moreover, through scRNA-seq, we also found that CST7 and DUSP5 upregulated in Th2 cells and regulated differentiation of naive CD4+ T cells to Th2 cells. Finally, in-vitro studies also showed that HBV infection could significantly up-regulate DUSP5 and CST7 expression. This research strongly revealed that HBV could up-regulate CST7 and DUSP5 to drive differentiation of naive CD4+ T cells into Th2 cells and contribute to CHB, which may pave the way for immunotherapeutic interventions.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive analysis of the diverse types of PCD in lung adenocarcinoma (LUAD) is currently lacking. The study encompassed a total of 1481 genes associated with the regulation of 13 distinct PCD patterns. Ten machine learning algorithms were amalgamated into 101 combinations, from which the optimal algorithm was chosen to formulate an artificial intelligence-derived prognostic signature based on the average C-index across four multicenter cohorts. The established optimal cell death index (CDI) model emerged as an independent risk factor for overall survival, demonstrating robust and consistent performance. Notably, CDI exhibited significantly higher accuracy compared to traditional clinical variables and molecular features. It exhibited superior performance than other published models. By integrating CDI with relevant clinical features, a nomogram with excellent predictive performance was developed. LUAD patients with low CDI score had a higher immune modulators, TIDE scores and immune scores, indicating a better immunotherapy benefit. More importantly, we found that the regulation of antigen presentation is the crucial mechanism of PCD. SCG2 is a key molecule that inhibits the malignant progression of LUAD. CDI holds great potential as a robust and promising tool for enhancing clinical outcomes in patients with LUAD.

Sepsis remains a significant global health burden and contributor to mortality, yet the precise molecular mechanisms underlying the immune response are not fully elucidated. To gain insight into this issue, we performed a comprehensive analysis using a variety of techniques including bulk RNA sequencing, single-cell RNA sequencing, and enzyme-linked immunosorbent assay (ELISA). We performed enrichment analysis of differentially expressed genes in sepsis and healthy individuals by utilizing Gene Ontology (GO) analysis and indicated significant enrichment of immune-related response. Following Weighted Gene Co-Expression Network Analysis (WGCNA) and protein-protein interaction analysis (PPI) were used to identify key immune-related hub genes and validated by ELISA to show that NLRC4 is highly expressed in sepsis. Additionally, an analysis of scRNA-seq data from newly diagnosed sepsis, sepsis diagnosis at 6 hours, and healthy samples demonstrates a significant increase in both the expression levels and proportions of NLRC4 in sepsis monocytes and neutrophils. In addition, using pySCENIC we identified upstream transcription factors that regulate NLRC4. Our study provides valuable insights into the identification of NLRC4 in peripheral blood as a potential candidate gene for the diagnosis and treatment of sepsis.

Oxidative stress (OS) is crucial in idiopathic pulmonary fibrosis (IPF) pathogenesis, with its genes potentially acting as both causes and consequences of the disease. We identified OS-related genes from GeneCards and performed a meta-analysis on pulmonary transcriptome datasets to discover differentially expressed genes (DEGs) related to OS in IPF. We integrated this data with the largest available IPF GWAS summaries, expression quantitative trait loci (eQTLs), and DNA methylation QTLs (mQTLs) from blood. This approach aimed to identify blood OS genes and regulatory elements linked to IPF risk, incorporating the latest pulmonary eQTLs and bronchoalveolar lavage fluid microbial QTLs (bmQTLs) for a comprehensive view of gene-lung microbiota interactions through SMR and colocalization analyses. Sensitivity analyses were conducted using two additional mendelian randomization (MR) methods. Meta-analysis revealed 1090 differentially expressed OS genes between IPF patients and controls. Integration with IPF GWAS, eQTL, and mQTL data identified key genes and regulatory elements involved in IPF pathogenesis, highlighting the role of specific genes such as KCNMA1 and SLC22A5 in modulating IPF risk through epigenetic mechanisms. Colocalization analysis further identified potential interactions between gene expression and lung microbiota. Our findings elucidate the complex interplay between OS genes and IPF, suggesting potential therapeutic targets and highlighting the importance of considering epigenetic and microbial interactions in the disease's etiology and progression.

Based on favorable outcomes and decreased propensity for lymph node and distant metastasis, multiple ground-glass nodules (GGNs) are now predominantly recognized as early-stage primary independent lung cancer. In this study, we discuss a case involving a patient with reoperative multifocal GGNs who was ultimately diagnosed with early multiple intrapulmonary metastases and multifocal primary lung cancers. This patient exhibited multisite epidermal growth factor receptor (EGFR) mutations, including the classical L858R, exon 19 deletion and the rare V834L variant. Despite a high tumor burden and the presence of various EGFR driver mutations, the patient experienced prolonged dormancy and exceptionally slow lesion growth, even without any systemic treatment. Our research indicates that the patient's immune response against the tumor remained robust throughout the disease course. Furthermore, we found that pathways associated with integrin-mediated cell extracellular matrix adhesion played a role in activating her innate immune responses and regulating tumor dormancy. Our findings suggest that the interplay between cancer cell mutations and the tumor microenvironment (TME) phenotype during tumor evolution contributed to this patient's prolonged survival. Integrating these aspects for lung tumor stratification is expected to improve predictions of growth potential and aid in clinical decision making.

Apolipoprotein E (ApoE) plays a crucial role in iron homeostasis in the body, while macrophages are the principal cells responsible for handling iron in mammals. However, it is unknown whether ApoE can affect the functional subtypes and the iron handling capacity of splenic macrophages (SM). Here, we investigated the effects of ApoE deficiency (ApoE^-/-) on the polarization and iron content of SM and its potential mechanisms. ApoE^-/- was found to induce a significant increase in the expressions of M1 marker genes CD86, IL-1β, IL-6, IL-12, TNF-α and iNOS and a reduction in M2 marker genes CD206, Arg-1, IL-10 and Ym-1 in SM of mice aged 28 weeks, Meanwhile, ApoE^-/- caused a significant increase in iron content and expression of ferritin, transferrin receptor 1 (TfR1), iron regulatory protein 1 (IRP1) and heme oxygenase-1 (HO-1) and a reduction in ferroportin1 (Fpn1) in spleen and/or SM of mice aged 28 weeks. It was concluded that ApoE^-/- can increase iron content through increased iron uptake mediated by TfR/ IRPs and decreased iron release mediated by Fpn1, leading to polarization of the SM to M1 phenotype.

Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to avoid overdiagnosis and overtreatment. Taking full advantage of scRNA-Seq data to resolve the tumor heterogeneities, we explored the overall landscape of LUAD microenvironment. Utilizing the stage-specific tumor cell markers, we have developed highly accurate diagnostic and prognostic models with elevated sensitivity and specificity. The diagnostic model, developed through random forest algorithms with a thirteen-gene signature, achieved an accuracy of 96.4% and an AUC of 0.993. These metrics were further demonstrated by benchmarking with available models and scoring systems in independent cohorts. Concurrently, the prognostic model, formulated via Cox regression with a six-gene signature, effectively predicted overall survival, with elevated risk scores associated with increased fractions of cancer-associated fibroblasts, and higher likelihood of immune escape and T-cell exclusion. Subsequently, two nomograms were developed to predict survival and drug responses, facilitating their integration into clinical practice. Overall, this study underscores the potential of our models for efficient, rapid, and cost-effective diagnosis and prognosis of LUAD, adaptable to multiple expression profiling platforms and quantification methods.