UNASSIGNED: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear.
UNASSIGNED: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature.
UNASSIGNED: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples.
UNASSIGNED: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

UNASSIGNED: Clear cell renal cell carcinoma (ccRCC) is a renal cortical malignancy with a complex pathogenesis. Identifying ideal biomarkers to establish more accurate promising prognostic models is crucial for the survival of kidney cancer patients.
UNASSIGNED: Seurat R package was used for single-cell RNA-sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering, and differentially expressed genes analysis. Gene coexpression network analysis (WGCNA) was performed to identify the cytotoxicity-related module. The independent cytotoxicity-related risk model was established by the survival R package, and Kaplan-Meier (KM) survival analysis and timeROC with area under the curve (AUC) were employed to confirm the prognosis and effectiveness of the risk model. The risk and prognosis in patients suffering from ccRCC were predicted by establishing a nomogram. A comparison of the level of immune infiltration in different risk groups and subtypes using the CIBERSORT, MCP-counter, and TIMER methods, as well as assessment of drug sensitivity to conventional chemotherapeutic agents in risk groups using the pRRophetic package, was made.
UNASSIGNED: Eleven ccRCC subpopulations were identified by single-cell sequencing data from the GSE224630 dataset. The identified cytotoxicity-related T-cell cluster and module genes defined three cytotoxicity-related molecular subtypes. Six key genes (SOWAHB, SLC16A12, IL20RB, SLC12A8, PLG, and HHLA2) affecting prognosis risk genes were selected for developing a risk model. A nomogram containing the RiskScore and stage revealed that the RiskScore contributed the most and exhibited excellent predicted performance for prognosis in the calibration plots and decision curve analysis (DCA). Notably, high-risk patients with ccRCC demonstrate a poorer prognosis with higher immune infiltration characteristics and TIDE scores, whereas low-risk patients are more likely to benefit from immunotherapy.
UNASSIGNED: A ccRCC survival prognostic model was produced based on the cytotoxicity-related signature, which had important clinical significance and may provide guidance for ccRCC treatment.

UNASSIGNED: Hepatocellular carcinoma (HCC) poses a significant health burden globally, with high mortality rates despite various treatment options. Immunotherapy, particularly immune-checkpoint inhibitors (ICIs), has shown promise, but resistance and metastasis remain major challenges. Understanding the intricacies of the tumor microenvironment (TME) is imperative for optimizing HCC management strategies and enhancing patient prognosis.
UNASSIGNED: This study employed a comprehensive approach integrating multi-omics approaches, including single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (Bulk RNA-seq), and validation in clinical samples using spatial transcriptomics (ST) and multiplex immunohistochemistry (mIHC). The analysis aimed to identify key factors influencing the immunosuppressive microenvironment associated with HCC metastasis and immunotherapy resistance.
UNASSIGNED: HMGB2 is significantly upregulated in HCCTrans, a transitional subgroup associated with aggressive metastasis. Furthermore, HMGB2 expression positively correlates with an immunosuppressive microenvironment, particularly evident in exhausted T cells. Notably, HMGB2 expression correlated positively with immunosuppressive markers and poor prognosis in HCC patients across multiple cohorts. ST combined with mIHC validated the spatial expression patterns of HMGB2 within the TME, providing additional evidence of its role in HCC progression and immune evasion.
UNASSIGNED: HMGB2 emerges as a critical player of HCC progression, metastasis, and immunosuppression. Its elevated expression correlates with aggressive tumor behavior and poor patient outcomes, suggesting its potential as both a therapeutic target and a prognostic indicator in HCC management.

BACKGROUND: Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros.
RESULTS: BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms.
BACKGROUND: All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S.

Mass cytometry is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level, advancing clinical research in immune monitoring. Nevertheless, the vast data generated by cytometry by time-of-flight (CyTOF) poses a significant analytical challenge. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel toolkit for CyTOF data analysis. This framework incorporates BinaryClust, an in-house developed semi-supervised clustering tool that automatically identifies main cell types. BinaryClust outperforms existing clustering tools in accuracy and speed, as shown in benchmarks with two datasets of approximately 4 million cells, matching the precision of manual gating by human experts. Furthermore, ImmCellTyper offers various visualisation and analytical tools, spanning from quality control to differential analysis, tailored to users\' specific needs for a comprehensive CyTOF data analysis solution. The workflow includes five key steps: (1) batch effect evaluation and correction, (2) data quality control and pre-processing, (3) main cell lineage characterisation and quantification, (4) in-depth investigation of specific cell types; and (5) differential analysis of cell abundance and functional marker expression across study groups. Overall, ImmCellTyper combines expert biological knowledge in a semi-supervised approach to accurately deconvolute well-defined main cell lineages, while maintaining the potential of unsupervised methods to discover novel cell subsets, thus facilitating high-dimensional immune profiling.

In the realm of this study, obtaining a comprehensive understanding of ischemic brain injury and its molecular foundations is of paramount importance. Our study delved into single-cell data analysis, with a specific focus on sub-celltypes and differentially expressed genes in the aftermath of ischemic injury. Notably, we observed a significant enrichment of the \"ATP METABOLIC PROCESS\" and \"ATP HYDROLYSIS ACTIVITY\" pathways, featuring pivotal genes such as Pbx3, Dguok, and Kif21b. A remarkable finding was the consistent upregulation of genes like Fabp7 and Bcl11a within the MCAO group, highlighting their crucial roles in regulating the pathway of mitochondrial ATP synthesis coupled proton transport. Furthermore, our network analysis unveiled pathways like \"Neuron differentiation\" and \"T cell differentiation\" as central in the regulatory processes of sub-celltypes. These findings provide valuable insights into the intricate molecular responses and regulatory mechanisms that govern brain injury. The shared differentially expressed genes among sub-celltypes emphasize their significance in orchestrating responses post-ischemic injury. Our research, viewed from the perspective of a medical researcher, contributes to the evolving understanding of the molecular landscape underlying ischemic brain injury, potentially paving the way for targeted therapeutic strategies and improved patient outcomes.

This study unveils the pivotal roles of taurine metabolic reprogramming and its implications in the development and progression of Abdominal Aortic Aneurysm (AAA). Leveraging an integrated approach that combines single-cell RNA sequencing (scRNA-seq) and Weighted Gene Co-expression Network Analysis (WGCNA), our research investigates the intricate transcriptional and gene expression dynamics crucial to AAA. Our findings uniquely link metabolic shifts to the integrity of the extracellular matrix (ECM) and the functionality of smooth muscle cells (SMCs), key elements in the pathology of AAA. Utilizing scRNA-seq data from a mouse model (GSE152583 dataset), we identified critical alterations in cellular composition during AAA progression, particularly highlighting shifts in fibroblasts and inflammatory cells. Concurrently, WGCNA of human AAA tissue samples has outlined distinct gene expression patterns correlated with disease severity and progression, offering comprehensive insights into both molecular and cellular disease mechanisms. Moreover, this study introduces innovative metabolic profiling techniques to identify differential metabolites in AAA, integrating extensive metabolomic analyses with pathway enrichment strategies. This novel approach has pinpointed potential biomarkers and therapeutic targets, notably within taurine metabolism pathways, crucial for crafting non-surgical interventions. By merging state-of-the-art bioinformatics with thorough molecular analysis, our study not only enhances the understanding of AAA\'s complex pathophysiology but also catalyzes the development of targeted therapeutic strategies. This research represents a significant advancement in the molecular characterization of AAA, with substantial implications for its future diagnosis and treatment strategies.

Rationale: Understanding the immune mechanisms associated with liver transplantation (LT), particularly the involvement of tissue-resident memory T cells (TRMs), represents a significant challenge. Methods: This study employs a multi-omics approach to analyse liver transplant samples from both human (n = 17) and mouse (n = 16), utilizing single-cell RNA sequencing, bulk RNA sequencing, and immunological techniques. Results: Our findings reveal a comprehensive T cell-centric landscape in LT across human and mouse species, involving 235,116 cells. Notably, we found a substantial increase in CD8+ TRMs within rejected grafts compared to stable ones. The elevated presence of CD8+ TRMs is characterised by a distinct expression profile, featuring upregulation of tissue-residency markers (CD69, CXCR6, CD49A and CD103+/-,), immune checkpoints (PD1, CTLA4, and TIGIT), cytotoxic markers (GZMB and IFNG) and proliferative markers (PCNA and TOP2A) during rejection. Furthermore, there is a high expression of transcription factors such as EOMES and RUNX3. Functional assays and analyses of cellular communication underscore the active role of CD8+ TRMs in interacting with other tissue-resident cells, particularly Kupffer cells, especially during rejection episodes. Conclusions: These insights into the distinctive activation and interaction patterns of CD8+ TRMs suggest their potential utility as biomarkers for graft rejection, paving the way for novel therapeutic strategies aimed at enhancing graft tolerance and improving overall transplant outcomes.

Rationale: Immunosuppressive tumor microenvironment (iTME) plays an important role in carcinogenesis, and some macrophage subsets are associated with iTME generation. However, the sub-population characterization of macrophages in oral carcinogenesis remains largely unclear. Here, we investigated the immunosuppressive status with focus on function of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in oral carcinogenesis. Methods: We built a single cell transcriptome atlas from 3 patients simultaneously containing oral squamous cell carcinoma (OSCC), precancerous oral leukoplakia (preca-OLK) and paracancerous tissue (PCA). Through single-cell RNA sequencing and further validation using multicolor immunofluorescence staining and the in vitro/in vivo experiments, the immunosuppressive cell profiles were built and the role of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in the malignant transformation of oral leukoplakia was evaluated. Results: The iTME formed at preca-OLK stage, as evidenced by increased exhausted T cells, Tregs and some special subsets of macrophages and fibroblasts. Macro-IDO1 was predominantly enriched in preca-OLK and OSCC, distributed near exhausted T cells and possessed tumor associated macrophage transformation potentials. Functional analysis revealed the established immunosuppressive role of Macro-IDO1 in preca-OLK and OSCC: enriching the immunosuppression related genes; having an established level of immune checkpoint score; exerting strong immunosuppressive interaction with T cells; positively correlating with the CD8-exhausted. The immunosuppression related gene expression of macrophages also increased in preca-OLK/OSCC compared to PCA. The use of the IDO1 inhibitor reduced 4NQO induced oral carcinogenesis in mice. Mechanistically, IFN-γ-JAK-STAT pathway was associated with IDO1 upregulation in OLK and OSCC. Conclusions: These results highlight that Macro-IDO1-enriched in preca-OLK possesses a strong immunosuppressive role and contributes to oral carcinogenesis, providing a potential target for preventing precancerous legions from transformation into OSCC.

UNASSIGNED: Inflammatory Bowel Diseases (IBDs), encompassing Ulcerative Colitis (UC) and Crohn\'s Disease (CD), are chronic, recurrent inflammatory conditions of the gastrointestinal tract. The microRNA (miRNA) -mRNA regulatory network is pivotal in the initiation and progression of IBDs. Although individual studies provide valuable insights into miRNA mechanisms in IBDs, they often have limited scope due to constraints in population diversity, sample size, sequencing platform variability, batch effects, and potential researcher bias. Our study aimed to construct comprehensive miRNA-mRNA regulatory networks and determine the cellular sources and functions of key miRNAs in IBD pathogenesis.
UNASSIGNED: To minimize potential bias from individual studies, we utilized a text mining-based approach on published scientific literature from PubMed and PMC databases to identify miRNAs and mRNAs associated with IBDs and their subtypes. We constructed miRNA-mRNA regulatory networks by integrating both predicted and experimentally validated results from DIANA, Targetscan, PicTar, Miranda, miRDB, and miRTarBase (all of which are databases for miRNA target annotation). The functions of miRNAs were determined through gene enrichment analysis of their target mRNAs. Additionally, we used two large-scale single-cell RNA sequencing datasets to identify the cellular sources of miRNAs and the association of their expression levels with clinical status, molecular and functional alternation in CD and UC.
UNASSIGNED: Our analysis systematically summarized IBD-related genes using text-mining methodologies. We constructed three comprehensive miRNA-mRNA regulatory networks specific to IBD, CD, and UC. Through cross-analysis with two large-scale scRNA-seq datasets, we determined the cellular sources of the identified miRNAs. Despite originating from different cell types, hsa-miR-142, hsa-miR-145, and hsa-miR-146a were common to both CD and UC. Notably, hsa-miR-145 was identified as myofibroblast-specific in both CD and UC. Furthermore, we found that higher tissue repair and enhanced glucose and lipid metabolism were associated with hsa-miR-145 in myofibroblasts in both CD and UC contexts.
UNASSIGNED: This comprehensive approach revealed common and distinct miRNA-mRNA regulatory networks in CD and UC, identified cell-specific miRNA expressions (notably hsa-miR-145 in myofibroblasts), and linked miRNA expression to functional alterations in IBD. These findings not only enhance our understanding of IBD pathogenesis but also offer promising diagnostic biomarkers and therapeutic targets for clinical practice in managing IBDs.