Ribonuclease 1 Enhances Antitumor Immunity against Breast Cancer by Boosting T cell Activation

Vδ1 T Cells Integrated in Full-Thickness Skin Equivalents: A Model for the Role of Human Skin-Resident γδT Cells

Vδ1 T cells are a subpopulation of γδT cells found in human dermis. Much less is known regarding their role and function in skin health and disease than regarding the roles of murine skin-resident γδT cells. In this study, we report the successful integration of Vδ1 T cells into long-term fibroblast-derived matrix skin equivalents. We isolated Vδ1 T cells from human blood, where they are rare, and established conditions for the integration and maintenance of the freshly isolated Vδ1 T cells in the skin equivalents. Plated on top of the dermal equivalents, almost all Vδ1 T cells migrated into the dermal matrix where they exerted their influence on both the dermal equivalents and the epithelium. Vδ1 T cells contributed to epidermal differentiation of HaCaT cells as indicated by histology, expression of epidermal differentiation markers, and RNA-sequencing expression profile. When complemented with the carcinoma-derived SCC13 cells instead of HaCaT, our data suggest a role for Vδ1 T cells in slowing growth of the tumor cells, as indicated by reduced stratification and changes in gene expression profiles. Together, we demonstrate the successful establishment of human Vδ1 T cell-competent skin equivalents and skin carcinoma equivalents and provide evidence for molecular and functional consequences of the Vδ1 T cells on their respective environment.

Immune and inflammatory insights in atherosclerosis: development of a risk prediction model through single-cell and bulk transcriptomic analyses

Background

Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of specific genes implicated in atherosclerosis.

Result

This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target.

Conclusion

Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies.

RNASE4 promotes malignant progression and chemoresistance in hypoxic glioblastoma via activation of AXL/AKT and NF-κB/cIAPs signaling pathways

Glioblastoma (GBM) is the most malignant brain tumor frequently characterized by a hypoxic microenvironment. In this investigation, we unveiled unprecedented role of Ribonuclease 4 (RNASE4) in GBM pathogenesis through integrative methodologies. Leveraging The Cancer Genome Atlas (TCGA) dataset and clinical specimens from normal brain tissues, low- and high-grade gliomas, alongside rigorous in vitro and in vivo functional analyses, we identified a consistent upregulation of RNASE4 correlating with advanced GBM pathological stages and poor clinical survival outcomes. Functional assays corroborated the pivotal influences of RNASE4 on key tumorigenic processes such as cell proliferation, migration, invasion, stemness properties and temozolomide (TMZ) resistance. Further, Gene Set Enrichment Analysis (GSEA) illuminated the involvement of RNASE4 in modulating epithelial-mesenchymal transition (EMT) via activation of AXL, AKT and NF-κB signaling pathways. Furthermore, recombinant human RNASE4 (hRNASE4)-mediated NF-κB activation through IκBα phosphorylation and degradation could result in the upregulation of inhibitors of apoptosis proteins (IAPs), such as cIAP1, cIAP2, and SURVIVIN. Notably, treating RNASE4-induced TMZ-resistant cells with the SURVIVIN inhibitor YM-155 significantly restored cellular sensitivity to TMZ therapy. Herein, this study positions RNASE4 as a potent prognostic biomarker and therapeutic target, offering new insights into molecular pathogenesis of GBM and new avenues for future therapeutic interventions.

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.

METHODS

Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.

RESULTS

By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.

CONCLUSIONS

This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Characterize molecular signatures and establish a prognostic signature of gastric cancer by integrating single-cell RNA sequencing and bulk RNA sequencing

Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

Breast Cancer Treatment Strategies Targeting the Tumor Microenvironment: How to Convert "Cold" Tumors to "Hot" Tumors

Breast cancer characterized as "cold tumors" exhibit low levels of immune cell infiltration, which limits the efficacy of conventional immunotherapy. Recent studies have focused on strategies using nanotechnology combined with tumor microenvironment modulation to transform "cold tumors" into "hot tumors". This approach involves the use of functionalized nanoparticles that target and modify the tumor microenvironment to promote the infiltration and activation of antitumor immune cells. By delivering immune activators or blocking immunosuppressive signals, these nanoparticles activate otherwise dormant immune responses, enhancing tumor immunogenicity and the therapeutic response. These strategies not only promise to increase the response rate of breast cancer patients to existing immunotherapies but also may pave new therapeutic avenues, providing a new direction for the immunotherapy of breast cancer.

Characterizing neuroinflammation and identifying prenatal diagnostic markers for neural tube defects through integrated multi-omics analysis

BACKGROUND

Neural Tube Defects (NTDs) are congenital malformations of the central nervous system resulting from the incomplete closure of the neural tube during early embryonic development. Neuroinflammation refers to the inflammatory response in the nervous system, typically resulting from damage to neural tissue. Immune-related processes have been identified in NTDs, however, the detailed relationship and underlying mechanisms between neuroinflammation and NTDs remain largely unclear. In this study, we utilized integrated multi-omics analysis to explore the role of neuroinflammation in NTDs and identify potential prenatal diagnostic markers using a murine model.

METHODS

Nine public datasets from Gene Expression Omnibus (GEO) and ArrayExpress were mined using integrated multi-omics analysis to characterize the molecular landscape associated with neuroinflammation in NTDs. Special attention was given to the involvement of macrophages in neuroinflammation within amniotic fluid, as well as the dynamics of macrophage polarization and their interactions with neural cells at single-cell resolution. We also used qPCR assay to validate the key TFs and candidate prenatal diagnostic genes identified through the integrated analysis in a retinoic acid-induced NTDs mouse model.

RESULTS

Our analysis indicated that neuroinflammation is a critical pathological feature of NTDs, regulated both transcriptionally and epigenetically within central nervous system tissues. Key alterations in gene expression and pathways highlighted the crucial role of STATs molecules in the JAK-STAT signaling pathway in regulating NTDs-associated neuroinflammation. Furthermore, single-cell resolution analysis revealed significant polarization of macrophages and their interaction with neural cells in amniotic fluid, underscoring their central role in mediating neuroinflammation associated with NTDs. Finally, we identified a set of six potential prenatal diagnostic genes, including FABP7, CRMP1, SCG3, SLC16A10, RNASE6 and RNASE1, which were subsequently validated in a murine NTDs model, indicating their promise as prospective markers for prenatal diagnosis of NTDs.

CONCLUSIONS

Our study emphasizes the pivotal role of neuroinflammation in the progression of NTDs and underlines the potential of specific inflammatory and neural markers as novel prenatal diagnostic tools. These findings provide important clues for further understanding the underlying mechanisms between neuroinflammation and NTDs, and offer valuable insights for the future development of prenatal diagnostics.

Targeting ALK averts ribonuclease 1-induced immunosuppression and enhances antitumor immunity in hepatocellular carcinoma

Tumor-secreted factors contribute to the development of a microenvironment that facilitates the escape of cancer cells from immunotherapy. In this study, we conduct a retrospective comparison of the proteins secreted by hepatocellular carcinoma (HCC) cells in responders and non-responders among a cohort of ten patients who received Nivolumab (anti-PD-1 antibody). Our findings indicate that non-responders have a high abundance of secreted RNase1, which is associated with a poor prognosis in various cancer types. Furthermore, mice implanted with HCC cells that overexpress RNase1 exhibit immunosuppressive tumor microenvironments and diminished response to anti-PD-1 therapy. RNase1 induces the polarization of macrophages towards a tumor growth-promoting phenotype through activation of the anaplastic lymphoma kinase (ALK) signaling pathway. Targeting the RNase1/ALK axis reprograms the macrophage polarization, with increased CD8+ T- and Th1- cell recruitment. Moreover, simultaneous targeting of the checkpoint protein PD-1 unleashes cytotoxic CD8+ T-cell responses. Treatment utilizing both an ALK inhibitor and an anti-PD-1 antibody exhibits enhanced tumor regression and facilitates long-term immunity. Our study elucidates the role of RNase1 in mediating tumor resistance to immunotherapy and reveals an RNase1-mediated immunosuppressive tumor microenvironment, highlighting the potential of targeting RNase1 as a promising strategy for cancer immunotherapy in HCC.