Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients.

PURPOSE: Despite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions. METHODS: Initially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells. RESULTS: We conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model's predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells. CONCLUSION: This study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis.

This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.

Development and validation of a multi-marker liquid bead array assay for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in single circulating tumor cells (CTCs).

Background: PIK3CA and ESR1 mutations are associated with progression and therapy resistance in metastatic breast cancer (MBC). CTCs are highly heterogeneous and their analysis at single cell level can provide unique information for mutational profiling and the existence of different sub-clones related to tumor progression. We have developed a novel multi-marker liquid bead array assay based on combination of an enzymatic mutation enrichment method, multiplex PCR-based assay, and liquid bead array technology for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in liquid biopsy samples. We focus on single CTCs, however the assay can be used for bulk CTC and ctDNA analysis. Materials and methods: Single CTCs were isolated from an ER+/HER2+ MBC patient from CellSearch® cartridges using the VyCAP Puncher System and subjected to whole genome amplification followed by nuclease-assisted minor-allele enrichment with probe-overlap (NaME-PrO) enrichment. The assay was validated for analytical sensitivity and specificity for the simultaneous detection of PIK3CA (E545K, E542K, H1047R, H1047L) and ESR1 (Y537S, Y537C, Y537N, D538G, L536H) mutations in single CTCs, while its clinical performance was evaluated on 22 single CTCs and three single white blood cells (WBCs). Results: The developed multi-marker liquid bead array assay is novel, highly specific and sensitive for both mutation panels. The assay can reliably detect mutation-allelic-frequencies (MAFs) as low as 0.1 %. The presence of PIK3CA and ESR1 mutations was detected in 13.6 % and 72.7 % of single CTCs, respectively. The developed assay is sample-saving since it requires only 2 µL of amplified DNA to check for nine hotspot PIK3CA and ESR1 mutations in a single cell. The developed liquid bead array assay (Luminex, US), based on a 96 microwell plate format, enables the simultaneous analysis of 96 single cells. Conclusions: The developed novel multi-marker liquid bead array assay for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in single CTCs is highly specific, highly sensitive, high-throughput, and sample-, cost-, and time-saving. This multi-marker liquid bead array assay can be extended to detect up to 100 mutations in many genes at once and can be applied for bulk CTC and ctDNA analysis.

[Molecular pathogenesis of adult T-cell leukemia/lymphoma].

Adult T-cell leukemia/lymphoma (ATLL) is an aggressive peripheral T-cell malignancy caused by human T-cell leukemia virus type-1 (HTLV-1) infection. Genetic alterations are thought to contribute to the pathogenesis of ATLL alongside HTLV-1 products such as Tax and HBZ. Several large-scale genetic analyses have delineated the entire landscape of somatic alterations in ATLL, which is characterized by frequent alterations in T-cell receptor/NF-κB pathways and immune-related molecules. Notably, up to one-fourth of ATLL patients harbor structural variations disrupting the 3'-UTR of the PD-L1 gene, which facilitate escape of tumor cells from anti-tumor immunity. Among these alterations, PRKCB and IRF4 mutations, PD-L1 amplification, and CDKN2A deletion are associated with poor prognosis in ATLL. More recently, several single-cell transcriptome and immune repertoire analyses have revealed phenotypic features of premalignant cells and tumor heterogeneity as well as virus- and tumor-related changes of the non-malignant hematopoietic pool in ATLL. Here we summarize the current understanding of the molecular pathogenesis of ATLL, focusing on recent progress made by genetic, epigenetic, and single-cell analyses. These findings not only provide a deeper understanding of the molecular pathobiology of ATLL, but also have significant implications for diagnostic and therapeutic strategies.

Dynamic Roles and Expanding Diversity of Adipose Tissue Macrophages in Obesity.

Adipose tissue macrophages (ATMs) are key regulators of adipose tissue (AT) inflammation and insulin resistance in obesity, and the traditional M1/M2 characterization of ATMs is inadequate for capturing their diversity in obese conditions. Single-cell transcriptomic profiling has revealed heterogeneity among ATMs that goes beyond the old paradigm and identified new subsets with unique functions. Furthermore, explorations of their developmental origins suggest that multiple differentiation pathways contribute to ATM variety. These advances raise concerns about how to define ATM functions, how they are regulated, and how they orchestrate changes in AT. This review provides an overview of the current understanding of ATMs and their updated categorization in both mice and humans during obesity. Additionally, diverse ATM functions and contributions in the context of obesity are discussed. Finally, potential strategies for targeting ATM functions as therapeutic interventions for obesity-induced metabolic diseases are addressed.

The evolution of data treatment tools in single-particle and single-cell ICP-MS analytics.

Single-particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) is one of the most powerful tools in the thriving field of nanomaterial analysis. Along the same lines, single-cell ICP-MS (sc-ICP-MS) has become an invaluable tool in the study of the variances of cell populations down to a per-cell basis. Their importance and application fields have been listed numerous times, across various reports and reviews. However, not enough attention has been paid to the immense and ongoing development of the tools that are currently available to the analytical community for the acquisition, and more importantly, the treatment of single-particle and single-cell-related data. Due to the ever-increasing demands of modern research, the efficient and dependable treatment of the data has become more important than ever. In addition, the field of single-particle and single-cell analysis suffers due to a large number of approaches for the generated data-with varying levels of specificity and applicability. As a result, finding the appropriate tool or approach, or even comparing results, can be challenging. This article will attempt to bridge these gaps, by covering the evolution and current state of the tools at the disposal of sp-ICP-MS users.

Mitochondrial ATP Synthesis and Proton Transport Synergistically Mitigate Oligodendrocyte Progenitor Cell Dysfunction Following Transient Middle Cerebral Artery Occlusion via the Pbx3/Dguok/Kif21b Signaling Pathway.

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.

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

Background: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor cells. These cells are crucial in driving tumor metastasis, recurrence, and resistance to treatment. The objective of this study was to pinpoint the essential regulatory genes associated with CSCs in prostate adenocarcinoma (PRAD) and assess their potential significance in the diagnosis, prognosis, and immunotherapy of patients with PRAD. Method: The study utilized single-cell analysis techniques to identify stem cell-related genes and evaluate their significance in relation to patient prognosis and immunotherapy in PRAD through cluster analysis. By utilizing diverse datasets and employing various machine learning methods for clustering, diagnostic models for PRAD were developed and validated. The random forest algorithm pinpointed HSPE1 as the most crucial prognostic gene among the stem cell-related genes. Furthermore, the study delved into the association between HSPE1 and immune infiltration, and employed molecular docking to investigate the relationship between HSPE1 and its associated compounds. Immunofluorescence staining analysis of 60 PRAD tissue samples confirmed the expression of HSPE1 and its correlation with patient prognosis in PRAD. Result: This study identified 15 crucial stem cell-related genes through single-cell analysis, highlighting their importance in diagnosing, prognosticating, and potentially treating PRAD patients. HSPE1 was specifically linked to PRAD prognosis and response to immunotherapy, with experimental data supporting its upregulation in PRAD and association with poorer prognosis. Conclusion: Overall, our findings underscore the significant role of stem cell-related genes in PRAD and unveil HSPE1 as a novel target related to stem cell.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Circulating Multiple Myeloma Cells (CMMCs) as Prognostic and Predictive Markers in Multiple Myeloma and Smouldering MM Patients.

In recent years, liquid biopsy has emerged as a promising alternative to the bone marrow (BM) examination, since it is a minimally invasive technique allowing serial monitoring. Circulating multiple myeloma cells (CMMCs) enumerated using CELLSEARCH® were correlated with patients' prognosis and measured under treatment to assess their role in monitoring disease dynamics. Forty-four MM and seven smouldering MM (SMM) patients were studied. The CMMC medians at diagnosis were 349 (1 to 39,940) and 327 (range 22-2463) for MM and SMM, respectively. In the MM patients, the CMMC count was correlated with serum albumin, calcium, ß2-microglobulin, and monoclonal components (p < 0.04). Under therapy, the CMMCs were consistently detectable in 15/40 patients (coMMstant = 1) and were undetectable or decreasing in 25/40 patients (coMMstant = 0). High-quality response rates were lower in the coMMstant = 1 group (p = 0.04), with a 7.8-fold higher risk of death (p = 0.039), suggesting that continuous CMMC release is correlated with poor responses. In four MM patients, a single-cell DNA sequencing analysis on residual CMMCs confirmed the genomic pattern of the aberrations observed in the BM samples, also highlighting the presence of emerging clones. The CMMC kinetics during treatment were used to separate the patients into two subgroups based on the coMMstant index, with different responses and survival probabilities, providing evidence that CMMC persistence is associated with a poor disease course.

Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status.

Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

Leveraging cell death patterns to predict metastasis in prostate adenocarcinoma and targeting PTGDS for tumor suppression.

Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

Inference of single-cell network using mutual information for scRNA-seq data analysis.

BACKGROUND: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging. RESULTS: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property. CONCLUSIONS: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

Comprehensive analysis of hypoxia-related genes in diagnosis and immune infiltration in acute myocardial infarction: based on bulk and single-cell RNA sequencing data.

Background: Hypoxia has been found to cause cellular dysfunction and cell death, which are essential mechanisms in the development of acute myocardial infarction (AMI). However, the impact of hypoxia-related genes (HRGs) on AMI remains uncertain. Methods: The training dataset GSE66360, validation dataset GSE48060, and scRNA dataset GSE163956 were downloaded from the GEO database. We identified hub HRGs in AMI using machine learning methods. A prediction model for AMI occurrence was constructed and validated based on the identified hub HRGs. Correlations between hub HRGs and immune cells were explored using ssGSEA analysis. Unsupervised consensus clustering analysis was used to identify robust molecular clusters associated with hypoxia. Single-cell analysis was used to determine the distribution of hub HRGs in cell populations. RT-qPCR verified the expression levels of hub HRGs in the human cardiomyocyte model of AMI by oxygen-glucose deprivation (OGD) treatment in AC16 cells. Results: Fourteen candidate HRGs were identified by differential analysis, and the RF model and the nomogram based on 8 hub HRGs (IRS2, ZFP36, NFIL3, TNFAIP3, SLC2A3, IER3, MAFF, and PLAUR) were constructed, and the ROC curves verified its good prediction effect in training and validation datasets (AUC = 0.9339 and 0.8141, respectively). In addition, the interaction between hub HRGs and smooth muscle cells, immune cells was elucidated by scRNA analysis. Subsequently, the HRG pattern was constructed by consensus clustering, and the HRG gene pattern verified the accuracy of its grouping. Patients with AMI could be categorized into three HRG subclusters, and cluster A was significantly associated with immune infiltration. The RT-qPCR results showed that the hub HRGs in the OGD group were significantly overexpressed. Conclusion: A predictive model of AMI based on HRGs was developed and strongly associated with immune cell infiltration. Characterizing patients for hypoxia could help identify populations with specific molecular profiles and provide precise treatment.

Acute and persistent responses after H5N1 vaccination in humans.

To gain insight into how an adjuvant impacts vaccination responses, we use systems immunology to study human H5N1 influenza vaccination with or without the adjuvant AS03, longitudinally assessing 14 time points including multiple time points within the first day after prime and boost. We develop an unsupervised computational framework to discover high-dimensional response patterns, which uncover adjuvant- and immunogenicity-associated early response dynamics, including some that differ post prime versus boost. With or without adjuvant, some vaccine-induced transcriptional patterns persist to at least 100 days after initial vaccination. Single-cell profiling of surface proteins, transcriptomes, and chromatin accessibility implicates transcription factors in the erythroblast-transformation-specific (ETS) family as shaping these long-lasting signatures, primarily in classical monocytes but also in CD8+ naive-like T cells. These cell-type-specific signatures are elevated at baseline in high-antibody responders in an independent vaccination cohort, suggesting that antigen-agnostic baseline immune states can be modulated by vaccine antigens alone to enhance future responses.

Composition Tuning of Semi-Open Cell Carriers via Phase Freeze-Shrink Self-Molding.

Extracellular matrix (ECM)-mimicking microsized cell carriers featuring a semi-isolated chamber facilitate the study of cellular heterogeneity as well as intercellular communication. However, the semiopen shaping of the designated gel mixture remains unattainable with current methods. We report an oil-phase freeze-shrink self-molding mechanism for generating size- and composition-tunable cradle-shaped microgels (microcradles) from water-in-oil droplets. The universality of this shape transition principle is demonstrated with six types of polysaccharides dispersed in a poly(ethylene glycol) diacrylate (PEGDA) or methacrylate gelatin (GelMA) matrix. By doping the microcradles with the major ECM component, hyaluronic acid sodium, we demonstrate a label-free selective culture of CD44 receptor-rich cells and the formation of cell spheroids within 3 days. This cryo-induced cradle-shaping strategy enables the functionalization of microcarriers for selective cell culture, thereby allowing them to be used for intercellular communication, drug delivery, and the construction of structural units for osteogenesis and 3D printing.

Anti-PD-L1 blockade facilitates antitumor effects of radiofrequency ablation by improving tumor immune microenvironment in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a complex disease with advanced presentation that significantly affects survival rates. Therefore, novel therapeutic strategies are needed. In this study, we investigate the tumor microenvironment (TME) in HCC by analyzing 13 HCC samples at single cell level. We identified key cell populations, including CD8 + T cells, Tregs, M1/M2 macrophages, and CD4 + memory T cells, and explored their roles and interactions. Our research revealed an early enrichment of CD8 + T cells, which could potentially lead to their exhaustion and facilitate tumor progression. We also investigated the impact of percutaneous radiofrequency ablation (RFA) on the immune microenvironment. Using a dual tumor mouse model, we demonstrated that RFA induces necrosis, enhancing antigen presentation and altering immune responses. Our results indicate that RFA increases PD-L1 expression in residual liver tissue, suggesting potential immune escape mechanisms. Furthermore, the combination of RFA and anti-PD-L1 therapy in the mouse model resulted in significant improvements in immune modulation. This included increased CD8 + T cell efficacy and decreased Treg infiltration. This combination shows promise as an approach to counteract HCC progression by altering the immune landscape. This study highlights the critical interaction within the TME of HCC and suggests the possibility of improving patient outcomes by targeting immune evasion mechanisms through combined therapeutic strategies.

Heterogeneity in Dental Tissue-Derived MSCs Revealed by Single-Cell RNA-seq.

Mesenchymal stromal cells (MSCs) are multipotent, progenitor cells that reside in tissues across the human body, including the periodontal ligament (PDL) and gingiva. They are a promising therapeutic tool for various degenerative and inflammatory diseases. However, different heterogeneity levels caused by tissue-to-tissue and donor-to-donor variability, and even intercellular differences within a given MSCs population, restrict their therapeutic potential. There are considerable efforts to decipher these heterogeneity levels using different "omics" approaches, including single-cell transcriptomics. Previous studies applied this approach to compare MSCs isolated from various tissues of different individuals, but distinguishing between donor-to-donor and tissue-to-tissue variability is still challenging. In this study, MSCs were isolated from the PDL and gingiva of 5 periodontally healthy individuals and cultured in vitro. A total of 3,844 transcriptomes were generated using single-cell mRNA sequencing. Clustering across the 2 different tissues per donor identified PDL- and gingiva-specific and tissue-spanning MSCs subpopulations with unique upregulated gene sets. Gene/pathway enrichment and protein-protein interaction (PPI) network analysis revealed differences restricted to several cellular processes between tissue-specific subpopulations, indicating a limited tissue-of-origin variability in MSCs. Gene expression, pathway enrichment, and PPI network analysis across all donors' PDL- or gingiva-specific subpopulations showed significant but limited donor-to-donor differences. In conclusion, this study demonstrates tissue- and donor-specific variabilities in the transcriptome level of PDL- and gingiva-derived MSCs, which seem restricted to specific cellular processes. Identifying tissue-specific and tissue-spanning subpopulations highlights the intercellular differences in dental tissue-derived MSCs. It could be reasonable to control MSCs at a single-cell level to ensure their properties before transplantation.

Pan-Cancer Single-Cell Analysis Revealing the Heterogeneity of Cancer-Associated Fibroblasts in Skin Tumors.

BACKGROUND: Cancer-Associated Fibroblasts (CAFs) constitute a heterogeneous group of cells critical for the remodeling of the tumor microenvironment (TME). Given their significant impact on tumor progression, particularly in skin cancers, a deeper understanding of their characteristics and functions is essential. METHODS: This study employed a single-cell transcriptomic analysis to explore the diversity of CAFs within three major types of skin cancer: basal cell carcinoma, melanoma, and head and neck squamous cell carcinoma. We applied analytical techniques, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), pseudotime tracking, metabolic profiling, and stemness assessment to delineate and define the functional attributes of identified CAF subgroups. RESULTS: Our analysis successfully delineated nine distinct CAF subgroups across the studied tumor types. Of particular interest, we identified a novel CAF subtype, designated as C0, exclusive to basal cell carcinoma. This subtype exhibits phenotypic traits associated with invasive and destructive capabilities, significantly correlating with the progression of basal cell carcinoma. The identification of this subgroup provides new insights into the role of CAFs in cancer biology and opens avenues for targeted therapeutic strategies. CONCLUSION: A pan-cancer analysis was performed on three cancers, BCC, MA, and HNSCC, focusing on tumor fibroblasts in TME. Unsupervised clustering categorized CAF into nine subpopulations, among which the C0 subpopulation had a strong correspondence with BCC-CAF and an invasive- destructive-related phenotype.

Machine learning and single-cell analysis identify the mitophagy-associated gene TOMM22 as a potential diagnostic biomarker for intervertebral disc degeneration.

Background: Mitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated. Methods: The GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR. Results: This study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples. Conclusions: Our findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.