Oral squamous cell carcinoma (OSCC) has an extremely poor prognosis. Recent studies have suggested that mitophagy-related genes (MRGs) are closely correlated with the development and occurrence of cancer, but the role they play in oral cancer has not yet been explained.We conducted a comprehensive analysis of integrated single-cell and bulk RNA sequencing (RNA-seq) data retrieved from Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. Multiple methods were combined to provide a comprehensive understanding of the genetic expression patterns and biology of OSCC, such as analysis of pseudotime series, CellChat cell communication, immune infiltration, Gene Ontology (GO), LASSO Cox regression, gene set variation analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), Tumor Mutation Burden (TMB) and drug sensitivity assessments. The findings of this study demonstrated significantly greater activity of MRGs in NK cells than in other cells in OSCC. A reliable prognostic model was developed using 12 candidate genes strongly associated with mitochondrial autophagy. T stage, N stage and risk score were revealed as independent prognostic factors. Distinctively enriched pathways and immune cells were observed in different risk groups. Notably, low-risk patients were more responsive to chemotherapy. In addition, a nomogram model with excellent predictive ability was established by combining the risk scores and clinical features. The activity of MRGs suggest the potential for the development of new targeted therapies. The construction of a robust prognostic model also provides reference value for individualized prediction and clinical decision-making in patients with OSCC.

Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.

This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. The model was validated using the GSE65904 and GSE35640 datasets. Multi-omics analysis was conducted utilizing the maftools, limma, edgeR, ChAMP, and clusterProfiler packages. The examination of single-cell sequencing data revealed the presence of 8 cell types, with the transcription factors RFXAP, CLOCK, MGA, RBBP, and ZNF836 exhibiting notably high expression levels in CD8+ T cells as determined by the SCENIC package. Utilizing these transcription factors and their associated target genes, a prognostic model was developed through COX and LASSO analyses, incorporating the genes GPR171, FAM174A, and BPI. This study validated the model with independent datasets and conducted additional analysis involving multi-omics and immune infiltration to identify a more favorable prognosis for patients in the low-risk group. The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.

Extreme myopia (EM), defined as a spherical equivalent (SE) ≤ -10.00 diopters (D), is one of the leading causes of sight impairment. Known EM-associated variants only explain limited risk and are inadequate for clinical decision-making. To discover risk genes, we performed a whole-exome sequencing (WES) on 449 EM individuals and 9606 controls. We find a significant excess of rare protein-truncating variants (PTVs) in EM cases, enriched in the retrograde vesicle-mediated transport pathway. Employing single-cell RNA-sequencing (scRNA-seq) and a single-cell polygenic burden score (scPBS), we pinpointed PI16 + /SFRP4+ fibroblasts as the most relevant cell type. We observed that KDELR3 is highly expressed in scleral fibroblast and involved in scleral extracellular matrix (ECM) organization. The zebrafish model revealed that kdelr3 downregulation leads to elongated ocular axial length and increased lens diameter. Together, our study provides insight into the genetics of EM in humans and highlights KDELR3\'s role in EM pathogenesis.

BACKGROUND: Abdominal aortic aneurysm (AAA) is a complex vascular disorder characterized by the progressive dilation of the abdominal aorta, with a high risk of rupture and mortality. Understanding the cellular interactions and molecular mechanisms underlying AAA development is critical for identifying potential therapeutic targets.
METHODS: This study utilized datasets GSE197748, GSE164678 and GSE183464 from the GEO database, encompassing bulk and single-cell RNA sequencing data from AAA and control samples. We performed principal component analysis, differential expression analysis, and functional enrichment analysis to identify key pathways involved in AAA. Cell-cell interactions were investigated using CellPhoneDB, focusing on fibroblasts, vascular smooth muscle cells (VSMCs), and macrophages. We further validated our findings using a mouse model of AAA induced by porcine pancreatic enzyme infusion, followed by gene expression analysis and co-immunoprecipitation experiments.
RESULTS: Our analysis revealed significant alterations in gene expression profiles between AAA and control samples, with a pronounced immune response and cell adhesion pathways being implicated. Single-cell RNA sequencing data highlighted an increased proportion of pro-inflammatory macrophages, along with changes in the composition of fibroblasts and VSMCs in AAA. CellPhoneDB analysis identified critical ligand-receptor interactions, notably collagen type I alpha 1 chain (COL1A1)/COL1A2-CD18 and thrombospondin 1 (THBS1)-CD3, suggesting complex communication networks between fibroblasts and VSMCs. In vivo experiments confirmed the upregulation of these genes in AAA mice and demonstrated the functional interaction between COL1A1/COL1A2 and CD18.
CONCLUSIONS: The interaction between fibroblasts and VSMCs, mediated by specific ligand-receptor pairs such as COL1A1/COL1A2-CD18 and THBS1-CD3, plays a pivotal role in AAA pathogenesis.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

Regenerative capacities and strategies vary dramatically across animals, as well as between cell types, organs, and with age. In recent years, high-throughput single-cell transcriptomics and other single-cell profiling technologies have been applied to many animal models to gain an understanding of the cellular and molecular mechanisms underlying regeneration. Here, we review recent single-cell studies of regeneration in diverse contexts and summarize key concepts that have emerged. The immense regenerative capacity of some invertebrates, exemplified by planarians, is driven mainly by the differentiation of abundant adult pluripotent stem cells, whereas in many other cases, regeneration involves the reactivation of embryonic or developmental gene-regulatory networks in differentiated cell types. However, regeneration also differs from development in many ways, including the use of regeneration-specific cell types and gene regulatory networks.

BACKGROUND: Myelodysplastic syndrome (MDS) is a complicated hematopoietic malignancy characterized by bone marrow (BM) dysplasia with symptoms like anemia, neutropenia, or thrombocytopenia. MDS exhibits considerable heterogeneity in prognosis, with approximately 30% of patients progressing to acute myeloid leukemia (AML). Single cell RNA-sequencing (scRNA-seq) is a new and powerful technique to profile disease landscapes. However, the current available scRNA-seq datasets for MDS are only focused on CD34+ hematopoietic progenitor cells. We argue that using entire BM cell for MDS studies probably will be more informative for understanding the pathophysiology of MDS.
METHODS: Five MDS patients and four healthy donors were enrolled in the study. Unsorted cells from BM aspiration were collected for scRNA-seq analysis to profile overall alteration in hematopoiesis.
RESULTS: Standard scRNA-seq analysis of unsorted BM cells successfully profiles deficient hematopoiesis in all five MDS patients, with three classified as high-risk and two as low-risk. While no significant increase in mutation burden was observed, high-risk MDS patients exhibited T-cell activation and abnormal myelogenesis at the stages between hematopoietic stem and progenitor cells (HSPC) and granulocyte-macrophage progenitors (GMP). Transcriptional factor analysis on the aberrant myelogenesis suggests that the epigenetic regulator chromatin structural protein-encoding gene HMGA1 is highly activated in the high-risk MDS group and moderately activated in the low-risk MDS group. Perturbation of HMGA1 by CellOracle simulated deficient hematopoiesis in mouse Lineage-negative (Lin-) BM cells. Projecting MDS and AML cells on a BM cell reference by our newly developed MarcoPolo pipeline intuitively visualizes a connection for myeloid leukemia development and abnormalities of hematopoietic hierarchy, indicating that it is technically feasible to integrate all diseased bone marrow cells on a common reference map even when the size of the cohort reaches to 1,000 patients or more.
CONCLUSIONS: Through scRNA-seq analysis on unsorted cells from BM aspiration samples of MDS patients, this study systematically profiled the development abnormalities in hematopoiesis, heterogeneity of risk, and T-cell microenvironment at the single cell level.

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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【中文题目：单细胞RNA测序技术
在肺癌肿瘤微环境研究中的进展】 【中文摘要：免疫微环境对肿瘤的发生发展起着关键作用。近年来，随着高通量测序技术的飞速发展，研究人员对肿瘤微环境中的免疫细胞组成及其功能有了更深入的了解。然而，传统的群体测序技术难以解析单个细胞层面的异质性，限制了对肿瘤微环境复杂性的全面理解。单细胞RNA测序技术的兴起，为揭示肺癌免疫微环境的异质性带来了新的机遇。当前以T细胞为中心的免疫治疗在临床中容易出现免疫原性耐药或者免疫相关性肺炎等影响预后的副作用，其关键因素是肿瘤微环境中免疫细胞与肿瘤细胞的相互作用发生了变化。而单细胞RNA测序技术可以从细胞间互作、拟时序分析等角度揭示肿瘤微环境中不同亚群间的起源与作用，进而发现新的细胞亚群或新生生物标志物，为揭示免疫治疗的耐药及疗效监测等提供新的途径。该综述系统回顾了单细胞RNA测序技术在揭示肺癌特别是免疫治疗后肺癌微环境异质性方面的最新研究进展，为促进肺癌免疫治疗的精准化与个体化提供参考。
】 【中文关键词：单细胞RNA测序；肺肿瘤；免疫治疗；肿瘤异质性；肿瘤微环境】.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.
METHODS: The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.
RESULTS: An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.
CONCLUSIONS: The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.