Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data

OBJECTIVE

To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.

METHODS

Using scRNA-seq data from PBMCs of AD patients and cognitively normal controls, we developed a deep learning framework that integrates autoencoders, classifiers, and discriminators. This approach analyzed gene expression across various immune cell types-including T cells, B cells, NK cells, and monocytes-by combining both differentially expressed genes (DEGs) and subtle genetic variations typically overlooked by conventional methods. Enrichment analyses were then conducted using Gene Ontology (GO), KEGG pathways, and protein-protein interaction (PPI) networks to assess the biological relevance of the identified genes.

RESULTS

Key genes, such as ZFP36L2, PNRC1, DUSP1, BTG1, YBX1, and CYBA, were identified as significant regulators of inflammation, apoptosis, and cell proliferation. Their overexpression in peripheral immune cells was linked to neuroinflammation, a critical factor in AD progression. Additionally, an observed overlap between aging-associated and AD-related genes reinforced the interconnected nature of these processes. The deep learning model achieved high precision, recall, and F1-scores across T cells, B cells, and NK cells, while Random Forest classifiers effectively managed constraints in monocyte data.

CONCLUSION

Combining scRNA-seq with deep learning provides a powerful non-invasive strategy for the early detection of AD by identifying novel blood-based biomarkers. This integrative approach not only enhances our understanding of immune regulation and neuroinflammatory pathways in AD but also paves the way for innovative diagnostic and therapeutic strategies.

Differential gene expression in PBMCs: Insights into the mechanism how pulmonary tuberculosis increases lung cancer risk

Pre-existing of pulmonary tuberculosis (PTB) poses increased lung cancer risk, yet the molecular mechanisms remain inadequately understood. This study sought to elucidate the potential mechanisms by performing comprehensive analyses of differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMCs) from patients with PTB, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). Microarray assays were employed to analyze the DEGs in PBMCs of these patients. The analyses revealed that, compared to healthy controls, the number of differentially expressed LncRNA in PBMCs from patients with PTB, LUAD, and LUSC were 801, 8,541, and 7,796, respectively. Similarly, the differentially expressed mRNA in PBMCs from patients with PTB, LUAD, and LUSC were 629, 4,865, and 4,438, respectively. These differentially expressed transcripts represent significant resources for the identifying diagnostic and differential diagnostic biomarkers for lung cancer and PTB. Pathways enriched by dysregulated mRNAs in patients with PTB, LUAD, and LUSC were identified through GO and KEGG pathway analyses. The results indicated that 9 pathways including the NOD-like receptor signaling pathway, pathways in cancer, and the MAPK signaling pathway were co-enriched across the PTB, LUAD, and LUSC groups, providing insights into the mechanisms by which PTB may increase the risk of cancer development and progression.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes

We assessed the reproducibility of differentially expressed genes (DEGs) in previously published Alzheimer's (AD), Parkinson's (PD), Schizophrenia (SCZ), and COVID-19 scRNA-seq studies. While transcriptional scores from DEGs of individual PD and COVID-19 datasets had moderate predictive power for case-control status of other datasets (AUC=0.77 and 0.75), genes from individual AD and SCZ datasets had poor predictive power (AUC=0.68 and 0.55). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power (AUC=0.88, 0.91, 0.78, and 0.62). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate BCAT1 as down-regulated in AD mouse oligodendrocytes. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

Exerkines mitigating Alzheimer's disease progression by regulating inflammation: Focusing on macrophage/microglial NLRP3 inflammasome pathway

Recent research highlights the critical role of inflammation in accelerating amyloid beta and phosphorylated tubulin-associated protein tau cascade and Alzheimer's disease (AD) progression. Emerging evidence suggests that exercise influences AD by modulating inflammatory responses. We conducted a comprehensive search across multiple online databases. Our approach focused on previous and recent studies exploring the links among inflammation, AD, and the effects of exercise, specifically targeting research articles and books published in English. We pointed out that inflammation extends from the periphery to the central nervous system, facilitated by macrophage/microglial NLRP3 (nucleotide-binding domain, leucine rich-containing family, pyrin domain-containing protein 3) inflammasome signaling, which exacerbates classical AD mechanisms. Moreover, we provided further insights into the modulation of inflammasome signaling through exercise and exerkines, which may contribute to mitigating AD development. These insights deepen our understanding of AD mechanisms and offer the potential for identifying key therapeutic targets and biomarkers crucial for effective disease management and treatment. HIGHLIGHTS: Inflammation is potentially linked to the acceleration of classical Alzheimer's disease (AD) pathogenesis, including the pathways involving amyloid beta and phosphorylated tau, mediated by pro-inflammatory cytokines. Inflammation, initiated by the nucleotide-binding domain, leucine rich-containing family, pyrin domain-containing protein 3 (NLRP3) inflammasome signaling pathway within M1-type macrophages/microglia, may contribute to neuroinflammation and AD progression. Exercise has the potential to reduce inflammation and the development of AD by influencing NLRP3 inflammasome signaling via exerkines.

Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing

OBJECTIVES

Lung adenocarcinoma (LUAD) continues to pose a significant global health challenge. This research investigates cuproptosis and its association with LUAD progression. Employing various bioinformatics techniques, the study explores the heterogeneity of LUAD cells, identifies prognostic cuproptosis-related genes (CRGs), examines cell-to-cell communication networks, and assesses their functional roles.

METHODS

We downloaded single-cell RNA sequencing data from TISCH2 and bulk RNA sequencing data from TCGA for exploring LUAD cell heterogeneity. Subsequently, "CellChat" package was employed for intercellular communication network analysis, while weighted correlation network analysis was applied for identification of hub CRGs. Further, A cuproptosis related prognostic signature was constructed via LASSO regression, validated through survival analysis, nomogram development, and ROC curves. We assessed immune infiltration, gene mutations, and GSEA of prognostic CRGs. Finally, in vitro experiments were applied to validate CDC25C's role in LUAD.

RESULTS

We identified 15 clusters and nine cell type in LUAD. Malignant cells showed active communication and pathway enrichment in "oxidative phosphorylation" and "glycolysis". Meanwhile, prognostic hub CRGs including PFKP, CDC25C, F12, SIGLEC6, and NLRP7 were identified, with a robust prognostic signature. Immune infiltration, gene mutations, and functional enrichment correlated with prognostic CRGs. In vitro cell experiments have shown that CDC25C-deficient LUAD cell lines exhibited reduced activity.

CONCLUSION

This research reveals the heterogeneity of LUAD cells, identifies key prognostic CRGs, and maps intercellular communication networks, providing insights into LUAD pathogenesis. These findings pave the way for developing targeted therapies and precision medicine approaches.

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Machine learning and single-cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGFβ1/P-Smad2/3 signalling pathway in hypertrophic scar

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

Advances in Understanding and Managing Alzheimer's Disease: From Pathophysiology to Innovative Therapeutic Strategies

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder characterized by the presence of amyloid-β (Aβ) plaques and tau-containing neurofibrillary tangles, leading to cognitive and physical decline. Representing the majority of dementia cases, AD poses a significant burden on healthcare systems globally, with onset typically occurring after the age of 65. While most cases are sporadic, about 10% exhibit autosomal forms associated with specific gene mutations. Neurofibrillary tangles and Aβ plaques formed by misfolded tau proteins and Aβ peptides contribute to neuronal damage and cognitive impairment. Currently, approved drugs, such as acetylcholinesterase inhibitors and N-methyl D-aspartate receptor agonists, offer only partial symptomatic relief without altering disease progression. A promising development is using lecanemab, a humanized IgG1 monoclonal antibody, as an immune therapeutic approach. Lecanemab demonstrates selectivity for polymorphic Aβ variants and binds to large soluble Aβ aggregates, providing a potential avenue for targeted treatment. This shift in understanding the role of the adaptive immune response in AD pathogenesis opens new possibilities for therapeutic interventions aiming to address the disease's intricate mechanisms. This review aims to summarize recent advancements in understanding Alzheimer's disease pathophysiology and innovative therapeutic approaches, providing valuable insights for both researchers and clinicians.

Combined signature of G protein-coupled receptors and tumor microenvironment provides a prognostic and therapeutic biomarker for skin cutaneous melanoma

BACKGROUND

G protein-coupled receptors (GPCRs) have been shown to have an important role in tumor development and metastasis, and abnormal expression of GPCRs is significantly associated with poor prognosis of tumor patients. In this study, we analyzed the GPCRs-related gene (GPRGs) and tumor microenvironment (TME) in skin cutaneous melanoma (SKCM) to construct a prognostic model to help SKCM patients obtain accurate clinical treatment strategies.

METHODS

SKCM expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression analysis, LASSO algorithm, and univariate and multivariate cox regression analysis were used to screen prognosis-related genes (GPR19, GPR146, S1PR2, PTH1R, ADGRE5, CXCR3, GPR143, and OR2I1P) and multiple prognosis-good immune cells; the data set was analyzed according to above results and build up a GPR-TME classifier. The model was further subjected to immune infiltration, functional enrichment, tumor mutational load, immunotherapy prediction, and scRNA-seq data analysis. Finally, cellular experiments were conducted to validate the functionality of the key gene GPR19 in the model.

RESULTS

The findings indicate that high expression of GPRGs is associated with a poor prognosis in patients with SKCM, highlighting the significant role of GPRGs and the tumor microenvironment (TME) in SKCM development. Notably, the group characterized by low GPR expression and a high TME exhibited the most favorable prognosis and immunotherapeutic efficacy. Furthermore, cellular assays demonstrated that knockdown of GPR19 significantly reduced the proliferation, migration, and invasive capabilities of melanoma cells in A375 and A2058 cell lines.

CONCLUSION

This study provides novel insights for the prognosis evaluation and treatment of melanoma, along with the identification of a new biomarker, GPR19.

Identification of disulfidptosis-related genes and subgroups in Alzheimer's disease

Background

Alzheimer's disease (AD), a common neurological disorder, has no effective treatment due to its complex pathogenesis. Disulfidptosis, a newly discovered type of cell death, seems to be closely related to the occurrence of various diseases. In this study, through bioinformatics analysis, the expression and function of disulfidptosis-related genes (DRGs) in Alzheimer's disease were explored.

Methods

Differential analysis was performed on the gene expression matrix of AD, and the intersection of differentially expressed genes and disulfidptosis-related genes in AD was obtained. Hub genes were further screened using multiple machine learning methods, and a predictive model was constructed. Finally, 97 AD samples were divided into two subgroups based on hub genes.

Results

In this study, a total of 22 overlapping genes were identified, and 7 hub genes were further obtained through machine learning, including MYH9, IQGAP1, ACTN4, DSTN, ACTB, MYL6, and GYS1. Furthermore, the diagnostic capability was validated using external datasets and clinical samples. Based on these genes, a predictive model was constructed, with a large area under the curve (AUC = 0.8847), and the AUCs of the two external validation datasets were also higher than 0.7, indicating the high accuracy of the predictive model. Using unsupervised clustering based on hub genes, 97 AD samples were divided into Cluster1 (n = 24) and Cluster2 (n = 73), with most hub genes expressed at higher levels in Cluster2. Immune infiltration analysis revealed that Cluster2 had a higher level of immune infiltration and immune scores.

Conclusion

A close association between disulfidptosis and Alzheimer's disease was discovered in this study, and a predictive model was established to assess the risk of disulfidptosis subtype in AD patients. This study provides new perspectives for exploring biomarkers and potential therapeutic targets for Alzheimer's disease.