Background: Few studies have analyzed the effect of matrix metalloproteinase (MMP) expression patterns on the tumor microenvironment (TME) during development of cervical cancer (CC). Methods: We elucidated the landscape and score of MMP expression in CC using single-cell RNA sequencing (scRNA-seq) and RNA sequencing datasets. Further, we aimed the MMPscore to probe the infiltration of immune cells. Further, MMP expression was measured by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Results: We found MMPs were cell-type specific expressed in diverse types of CC cells, regulating the relative pathways of CC progression. Two distinct MMP expression patterns that associated infiltrated tumor microenvironment (TME) were identified. We discovered MMP expression patterns can predict the stage of tumor, subtype, stromal activity in the TME, genetic variation, and patient outcome. Patients with high MMPscore benefited from significantly better treatment and clinical outcomes. Conclusion: These results indicate high MMPscore in diverse cell types may regulate immune response and improve the survival of patients with CC, which assist in developing more effective immunization strategies.

UNASSIGNED: The tumor microenvironment (TME) of colorectal cancer (CRC) mainly comprises immune cells, stromal cells, tumor cells, as well as the extracellular matrix (ECM), which holds a pivotal position. The ECM affects cancer progression, but its regulatory roles and predictive potential in CRC are not fully understood.
UNASSIGNED: We analyzed transcriptomes from CRC tumors and paired normal tissues to study ECM features. Up-regulated ECM components were examined through functional enrichment analysis, and single-cell sequencing identified cell types producing collagen, regulators, and secreted factors. Transcription factor analysis and cell-cell interaction studies were conducted to identify potential regulators of ECM changes. Additionally, a prognostic model was developed using TCGA-CRC cohort data, focusing on up-regulated core ECM components.
UNASSIGNED: Bulk RNA-seq analysis revealed a unique ECM pattern in tumors, with ECM abundance and composition significantly related to patient survival. Up-regulated ECM components were linked to various cancer-related pathways. Fibroblasts and non-fibroblasts interactions were crucial in forming the TME. Key potential regulators identified included ZNF469, PRRX2, TWIST1, and AEBP1. A prognostic model based on five ECM genes (THBS3, LAMB3, ESM1, SPRX, COL9A3) demonstrated strong associations with immune suppression and tumor angiogenesis.
UNASSIGNED: The ECM components were involved in various cell-cell interactions and correlated with tumor development and poor survival outcomes. The ECM prognostic model components could be potential targets for novel therapeutic interventions in colorectal cancer.

BACKGROUND: Neuroblastoma (NB) is the most common extracranial solid tumor in childhood and is closely related to the early development and differentiation of neuroendocrine (NE) cells. The disease is mainly represented by high-risk NB, which has the characteristics of high mortality and difficult treatment. The survival rate of high-risk NB patients is not ideal. In this article, we not only conducted a comprehensive study of NB through single-cell RNA sequencing (scRNA-seq) but also further analyzed cuproptosis, a new cell death pathway, in order to find clinical treatment targets from a new perspective.
METHODS: The Seurat software was employed to process the scRNA-seq data. This was followed by the utilization of GO enrichment analysis and GSEA to unveil pertinent enriched pathways. The inferCNV software package was harnessed to investigate chromosomal copy number variations. pseudotime analyses involved the use of Monocle 2, CytoTRACE, and Slingshot software. CellChat was employed to analyze the intercellular communication network for NB. Furthermore, PySCENIC was deployed to review the profile of transcription factors.
RESULTS: Using scRNA-seq, we studied cells from patients with NB. NE cells exhibited superior specificity in contrast to other cell types. Among NE cells, C1 PCLAF + NE cells showed a close correlation with the genesis and advancement of NB. The key marker genes, cognate receptor pairing, developmental trajectories, metabolic pathways, transcription factors, and enrichment pathways in C1 PCLAF + NE cells, as well as the expression of cuproptosis in C1 PCLAF + NE cells, provided new ideas for exploring new therapeutic targets for NB.
CONCLUSIONS: The results revealed the specificity of malignant NE cells in NB, especially the key subset of C1 PCLAF + NE cells, which enhanced our understanding of the key role of the tumor microenvironment in the complexity of cancer progression. Of course, cell death played an important role in the progression of NB, which also promoted our research on new targets. The scrutiny of these findings proved advantageous in uncovering innovative therapeutic targets, thereby bolstering clinical interventions.

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

BACKGROUND: Bisphenol A (BPA) is a common environmental pollutant, and its specific mechanisms in cancer development and its impact on the tumor immune microenvironment are not yet fully understood.
METHODS: Transcriptome data from osteosarcoma (OS) patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. BPA-related genes were identified through the Comparative Toxicogenomics Database (CTD), yielding 177 genes. Differentially expressed genes were analyzed using the GSE162454 dataset from the Tumor Immune Single Cell Hub 2 (TISCH2). We constructed the prognostic model using univariate Cox regression and LASSO analysis. The model was validated using the GSE16091 dataset. GO, KEGG, and GSEA analyses were performed to investigate the mechanisms of BPA-related genes.
RESULTS: A total of 15 BPA-related genes were identified as differentially expressed in OS. Univariate Cox regression and LASSO analysis identified four key prognostic genes (FOLR1, MYC, ESRRA, VEGFA). The prognostic model exhibited strong predictive performance with area under the curve (AUC) values of 0.89, 0.6, and 0.79 for predicting 1-, 2-, and 3-year survival, respectively. External validation using the GSE16091 dataset confirmed the model\'s high accuracy with AUC values exceeding 0.88. Our results indicated that the prognosis of the high-risk population is generally poorer, which may be associated with alterations in the tumor immune microenvironment. In the high-risk group, immune cells showed predominantly low expression levels, while immune checkpoint genes were significantly overexpressed, along with markedly elevated tumor purity. These findings revealed a correlation between upregulation of BPA-related genes and formation of an immunosuppressive microenvironment, leading to unfavorable patient outcomes.
CONCLUSIONS: Our study highlighted the significant association of BPA with OS biology, particularly in its potential role in modulating the tumor immune microenvironment. We offered a fresh insight into the influence of BPA on cancer development, thus providing valuable insights for future clinical interventions and treatment strategies.

Stroke is a major cause of adult disability worldwide, often involving disruption of the blood-brain barrier (BBB). Repairing the BBB is crucial for stroke recovery, and pericytes, essential components of the BBB, are potential intervention targets. Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a treatment for functional impairments after stroke, with potential effects on BBB integrity. However, the underlying mechanisms remain unclear. In this study using a transient middle cerebral artery occlusion (tMCAO) rat model, we investigated the impact of rTMS on post-stroke BBB. Through single-cell sequencing (ScRNAs), we observed developmental relationships among pericytes, endothelial cells, and vascular smooth muscle cells, suggesting the differentiation potential of pericytes. A distinct subcluster of pericytes emerged as a potential therapeutic target for stroke. Additionally, our results revealed enhanced cellular communication among these cell types, enriching signaling pathways such as IGF, TNF, NOTCH, and ICAM. Analysis of differentially expressed genes highlighted processes related to stress, differentiation, and development. Notably, rTMS intervention upregulated Reck in vascular smooth muscle cells, implicating its role in the classical Wnt signaling pathway. Overall, our bioinformatics findings suggest that rTMS may modulate BBB permeability and promote vascular regeneration following stroke. This might happen through 20 Hz rTMS promoting pericyte differentiation into vascular smooth muscle cells, upregulating Reck, then activating the classical Wnt signaling pathway, and facilitating vascular regeneration and BBB stability.

Macrophage-based cell therapeutics is an emerging modality to treat cancer and repair tissue damage. A reproducible manufacturing and engineering process is central to fulfilling their therapeutic potential. Here, we establish a robust macrophage-manufacturing platform (Mo-Mac) and demonstrate that macrophage functionality can be enhanced by N1-methylpseudouridine (m1Ψ)-modified mRNA. Using single-cell transcriptomic analysis as an unbiased approach, we found that >90% cells in the final product were macrophages while the rest primarily comprised T cells, B cells, natural killer cells, promyelocytes, promonocytes, and hematopoietic stem cells. This analysis also guided the development of flow-cytometry strategies to assess cell compositions in the manufactured product to meet requirements by the National Medical Products Administration. To modulate macrophage functionality, as an illustrative example we examined whether the engulfment capability of macrophages could be enhanced by mRNA technology. We found that efferocytosis was increased in vitro when macrophages were electroporated with m1Ψ-modified mRNA encoding CD300LF (CD300LF-mRNA-macrophage). Consistently, in a mouse model of acute liver failure, CD300LF-mRNA-macrophages facilitated organ recovery from acetaminophen-induced hepatotoxicity. These results demonstrate a GMP-compliant macrophage-manufacturing process and indicate that macrophages can be engineered by versatile mRNA technology to achieve therapeutic goals.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE\'s efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Adamantinomatous craniopharyngioma (ACP) is a clinically aggressive tumor without effective treatment method. Previous studies proposed a paracrine tumorigenesis model, in which oncogenic β-catenin induces senescence in pituitary stem cells and the senescent cells lead the formation of paracrine tumors through secretion of pro-tumorigenic factors. However, there lacks characterization on senescent cells in ACPs. Here, we profiled 12 ACPs with single-cell RNA and TCR-sequencing to elucidate the cellular atlas in ACPs and 3 of them were also subject to spatial sequencing to localize different subpopulations of the tumor cells. In total, we obtained the transcriptome profiles of 70,682 cells. Tumor cells, which were unambiguously identified through the cellular mutation status of the driver CTNNB1 mutations, were clustered into 6 subsets. The whorl-like cluster (WC) cells show distinct molecular features from the other tumor cells and the palisading epithelium (PE) cells consists of a proliferating subset. Other than typical PE and WC, we identified two novel subpopulations of the tumor cells. In one subpopulation, the cells express a high level of cytokines, e.g., FDCSP and S100A8/A9, and are enriched with the senescence-associated secretory phenotype (SASP) factors. Hematoxylin and eosin staining reveals that these SASP cells lack an ordered structures and their nuclei are elongated. In the other subpopulation, the cell sizes are small and they are tightly packed together with an unusual high density expressing a high level of mitochondrial genes (median 10.9%). These cells are the origin of the tumor developmental trajectories revealed by RNA velocity and pseudo-time analysis. Single-cell RNA and TCR analysis reveals that some ACPs are infiltrated with clonally expanded cytotoxic T cells. We propose a hypothesis that WC and PE are formed via different negative regulation mechanisms of the overactivated WNT/β-catenin signaling which provides a new understanding on the tumorigenesis of ACPs. The study lays a foundation for future studies on targeting senescent cells in ACPs with senolytic compounds or other therapeutic agents.

BACKGROUND: This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.
METHODS: Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.
RESULTS: In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.
CONCLUSIONS: A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.