Integrative Bulk and Single-Cell Transcriptome Profiling of Telomere-Related Genes Reveals a Robust Prognostic Signature and Immunotherapeutic Landscape in Neuroblastoma

e20521 Background: Lung adenocarcinoma (LUAD), at the cellular level, has a high degree of intratumor heterogeneity. Advanced single-cell sequencing technologies have offered tools to analyze intratumor heterogeneity and identify the biomarkers, thereby aiding cancer diagnosis and prediction of the patient prognosis. Methods: From the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo) database, the single-cell RNA sequencing (scRNA-seq) data from two LUAD and two para-cancerous tissue samples were obtained. We performed a dimensionality reduction and unsupervised clustering to identify the different cell clusters within the tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, a weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, we classified the LUAD molecular subtypes according to the marker genes of these clusters. The Limma package ( www.bioconductor.org/packages/release/bioc/html/limma ) was used to screen for the differentially expressed genes (DEGs) between the subtypes. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses, the gene signature most significantly associated with the prognosis of LUAD patients was determined. Results: A total of 14 cell clusters belonging to 10 cell types in LUAD was identified. The turquoise module was found to be the most relevant to LUAD among all the modules; cluster 10 (C10) was found to be the most strongly associated with the turquoise module. In The Cancer Genome Atlas (TCGA), patients with LUAD were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD. Conclusions: 14 cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. In addition, two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients. Keywords: single-cell RNA sequencing, lung adenocarcinoma, intratumor heterogeneity, molecular subtypes, prognosis, five-gene signature.

Integrative analysis of bulk and single-cell RNA sequencing data reveals distinct subtypes of MAFLD based on N1-methyladenosine regulator expression

Endometriosis (EM) is a common gynecological disease. Though m6A RNA methylation has emerged as a key regulator in various physiological and pathological processes, its cell-type-specific function in EM remains unclear. We obtain batch and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. The “Seurat” package was used to annotate and label cell types in scRNA-seq data. After identifying m6A-associated modules with weighted gene co-expression network analysis (WGCNA), core biomarkers for EM were filtered and selected by conducting least absolute shrinkage and selection operator (LASSO), support vector machine–recursive feature elimination (SVM–RFE), and eXtreme Gradient Boosting (XGBoost). Subsequently, functional enrichment analysis was performed with the "clusterProfiler" package. Immune infiltration and pathway activity were assessed by single-sample gene set enrichment analysis (ssGSEA). Drug prediction was performed using DSigDB in Enrichr tool, and molecular docking was conducted with AutoDock Vina. Finally, quantitative real-time PCR (qRT-PCR) and Western blot were performed to detect gene expression in vitro, and cell migration and invasion were assessed by carrying out wound healing and Transwell assays. The scRNA-seq analysis revealed altered cellular composition in EM, characterized by increased proportions of epithelial and NK/T cells. m6A regulatory activity was significantly elevated in EM samples, particularly in immune cells, and was associated with neutrophil activation and RNA splicing pathways. Integrated analysis of WGCNA and machine learning identified EPCAM, DST, HSPH1, and NAP1L1 as core m6A-related genes. These four genes were correlated with specific immune cell subsets, including immature dendritic cells and effector memory CD4 + T cells, and were enriched in cell cycle-related oncogenic pathways. Drug prediction revealed that dronabinol and minocycline might be potential therapeutics for EM, with molecular docking confirming favorable binding affinities. Functionally, EPCAM was downregulated in endometriotic 12Z cells, and its overexpression significantly suppressed cell migration. This study revealed that m6A-related dysregulation contributed to EM pathogenesis through dysregulated immune responses and affected cell migration. The identified gene signature and candidate drugs provide novel insights for potential therapeutic strategies.

ObjectiveThis study aimed to elucidate the mechanistic role of M2 tumor-associated macrophages in esophageal cancer (EC) progression and to construct an M2 macrophage–related gene signature for prognostic prediction.MethodsIntegrated analyses of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data of EC were performed. scRNA-seq data were processed with Seurat and annotated using SingleR. Immune infiltration was evaluated through ssGSEA and CIBERSORT. Weighted gene coexpression network analysis (WGCNA) was used to identify M2 macrophage–associated modules, and candidate genes were intersected with differentially expressed genes (DEGs). Functional enrichment analyses were performed, and a prognostic risk model was established through multivariate Cox regression analysis. The functional roles of key genes were validated through in vitro and in vivo experiments.ResultsTwelve cell types were identified by scRNA-seq, with macrophages representing the predominant immune population. M2 macrophages formed the major immunosuppressive subtype and were negatively associated with patient survival. WGCNA and DEG analysis identified 25 M2-related genes, from which a four-gene prognostic signature (SPINK5, A2ML1, IL1RN, IL36G) was constructed. The model effectively stratified EC patients into distinct risk groups with significantly different survival outcomes. Further in vitro experiments demonstrated that silencing IL1RN and IL36G in macrophages markedly suppressed the malignant phenotypes of esophageal cancer cells. Complementary in vivo experiments provided additional evidence, further indicating that IL1RN and IL36G play important functional roles in overall tumor progression.ConclusionA four-gene M2 macrophage–related prognostic model provides reliable prediction of clinical outcomes in EC. Among these genes, IL1RN and IL36G function as key regulators whose silencing inhibits M2 polarization and attenuates tumor proliferation, invasion, migration, and epithelial–mesenchymal transition.

Spinal cord injury (SCI) significantly impacts patients, with mitochondrial dysfunction playing a critical role in its pathology. Identifying mitochondria-related genes may offer new therapeutic and prognostic insights. RNA sequencing data from the GEO database were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses were conducted, and weighted gene coexpression network analysis (WGCNA) alongside machine learning algorithms was used to identify key mitochondria-related genes. Immune infiltration was assessed using the EPIC algorithm, and single-cell RNA sequencing (scRNA-seq) data were analyzed for cellular diversity. A total of 2566 upregulated and 2634 downregulated genes were identified in SCI versus control samples. GO and KEGG enrichment analyses revealed these DEGs were primarily involved in oxidative stress, mitochondrial function, and immune pathways, including necroptosis and T cell receptor signaling. Then, 1578 genes with the strongest correlation to SCI were selected by WGCNA. By integrating DEGs, WGCNA module genes, and mitochondria-related genes, 76 candidate genes were obtained and used to construct a PPI network. Six hub genes (NDUFB3, SLC25A24, SLC25A40, GSTZ1, MAOA, and MRPL12) were identified by machine learning, all showing strong diagnostic potential (AUC > 0.77). Immune infiltration analysis indicated reduced B and T cell infiltration and increased macrophage activity in SCI samples. scRNA-seq analysis further revealed higher expression of NDUFB3 in dendritic cells and MAOA in pro-B cells, suggesting their involvement in immune regulation and mitochondrial dysfunction. These six genes represent potential biomarkers and therapeutic targets for SCI, providing insights into its molecular mechanisms and immune response.

The incidence and mortality rates of lung cancer are exceptionally high. Many patients are diagnosed with early stage lung cancer but experience rapid recurrence post-surgery. Many research studies have shown that the unfavorable prognosis of patients may be associated with micro-metastasis in the lymph nodes. Our research aimed to develop a nomogram to predict the prognosis of lung adenocarcinoma (LUAD). Single-cell RNA sequencing (scRNA-seq) data were analyzed to identify 11 cell clusters. Patterns of incoming and outgoing signals were identified across the entire cell population. A weighted gene co-expression network analysis (WGCNA) was conducted to uncover critical genes in LUAD. The intersecting marker genes were used to construct the prognostic model. scRNA-seq data were analyzed to identify 19 cell clusters. We identified 3,464 marker genes from the scRNA-seq dataset, 1,994 differentially expressed genes from the bulk RNA sequencing (RNA-seq) dataset, and 1,863 genes associated with a key module identified by the WGCNA. After performing the intersection, univariate Cox, and least absolute shrinkage and selection operator analyses, a prognostic model was established based on the expression levels of 13 signature genes. Subsequent functional experiments confirmed the role of selected regulated genes. Through the integration of scRNA-seq data and bulk RNA-seq data, we developed an innovative model to predict the prognosis of patients. The risk score was found to be a significant independent predictor and clinical-pathological features of LUAD.

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung disease influenced by epigenetic modifications, particularly RNA methylation. Emerging evidence also suggests that autophagy plays a crucial role in immune cell infiltration and is implicated in COPD progression. This study aimed to investigate key RNA methylation regulators and explore the roles of RNA methylation and autophagy in COPD pathogenesis. We analyzed tissue-based bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) datasets from COPD and non-COPD patients, sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between COPD and non-COPD samples, and protein–protein interaction networks were constructed. Univariate logistic regression identified shared genes between DEGs and RNA methylation gene sets. Functional enrichment analyses, including Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were performed. Weighted gene co-expression network analysis (WGCNA) and immune infiltration analysis were conducted. Integration with scRNA-seq data further elucidated changes in immune cell composition, and cell communication analysis assessed interactions between macrophages and other immune cells. AddModuleScore analysis quantified RNA methylation and autophagy effects. Finally, a COPD mouse model was used to validate the expression of critical RNA methylation genes (FTO and IGF2BP2) in lung macrophages via RT-qPCR and flow cytometry. As revealed, we identified 13 RNA methylation-related genes enriched in translation and methylation processes. GSEA and GSVA revealed significant enrichment of these genes in immune and autophagy pathways. WGCNA analysis pinpointed key hub genes linking RNA methylation and autophagy. Integrated scRNA-seq analysis demonstrated a marked reduction of macrophages in COPD, with FTO and IGF2BP2 emerging as critical RNA methylation regulators. Macrophages with elevated RNA methylation and autophagy scores had increased interactions with other immune cells. In COPD mouse models, decreased expression of FTO and IGF2BP2 in lung macrophages was validated. Taken together, this study highlights the significant roles of RNA methylation in relation to autophagy pathways in the context of COPD. We identified key RNA methylation-related hub genes, such as FTO and IGF2BP2, which were found to have decreased expression in COPD macrophages. These findings provide novel genetic insights into the epigenetic mechanisms of COPD and suggest potential avenues for developing diagnostic and therapeutic strategies.

Diabetic nephropathy (DN) is a prevalent and serious complication of diabetes, characterized by high incidence and significant morbidity. Despite growing evidence that the tricarboxylic acid (TCA) cycle plays a crucial role in DN progression, the diagnostic potential of TCA-related genes has yet to be fully explored. This study began by analyzing the GSE131882 dataset to reveal the expression patterns of TCA-related genes in various renal cell types and to identify genes that differ in expression between high and low subgroups. The GSE30122 dataset was then examined to identify genes with differential expression in DN. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were applied to pinpoint TCA-related gene modules. Following this, multiple machine learning techniques were employed to analyze the TCA gene set that showed differential expression at both cellular and sample levels, allowing us to identify the hub genes. A diagnostic model was constructed, with its effectiveness validated through ROC analysis. The immune landscape of DN was assessed using ssGSEA. GeneMANIA and NetworkAnalyst were also utilized to predict genes with similar functions, as well as miRNAs and transcription factors (TFs) that may regulate these diagnostic genes. Finally, single-cell RNA sequencing (scRNA-seq) data confirmed the expression patterns of these genes. Two TCA-related genes, HPGD and G6PC, were identified as potential diagnostic markers for DN. ROC analysis demonstrated that these genes and their predictive model exhibited strong diagnostic performance in both training and validation cohorts. Immune landscape analysis revealed a more active immune microenvironment in DN patients compared to controls. Additionally, 59 miRNAs and 15 TFs were predicted to regulate the expression of HPGD and G6PC, along with 20 functionally related genes. scRNA-seq data highlighted that HPGD and G6PC are predominantly expressed in glomerular and proximal tubular cells. Two reliable TCA-related biomarkers were pinpointed, potentially advancing early diagnosis and management of DN.

Objective This study aimed to identify inflammatory subtypes of asthma using peripheral blood transcriptomics and to derive candidate biomarkers supported by multi-level validation. Methods This study integrated peripheral blood bulk RNA sequencing (bulk RNA-seq; GSE69683) and single-cell RNA sequencing (GSE172495) data. The bulk RNA-seq dataset was preprocessed using standard procedures, and differentially expressed genes (DEGs) were identified with the limma package, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) enrichment analyses. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify inflammation-related gene modules, which were then integrated with DEGs for consensus clustering to define molecular subtypes. Key genes were selected using both Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine–Recursive Feature Elimination (SVM-RFE) algorithms, and their expression patterns were mapped at the single-cell level. Finally, gene expression changes were validated by quantitative real-time PCR (qRT-PCR) in a lipopolysaccharide (LPS)–stimulated inflammatory model of phorbol 12-myristate 13-acetate (PMA)–induced THP-1-derived macrophage-like cells. Results A total of 317 DEGs were enriched in immune-related pathways. WGCNA and consensus clustering identified two molecular subtypes and six key genes (MMP9, NFIL3, STXBP5, OLAH, SRPK1, FAR2). Single-cell profiling mapped these genes to specific immune cells, and qRT-PCR confirmed the upregulation of MMP9 (p < .001) and NFIL3 (p < .01) in inflammatory conditions. Conclusion Peripheral blood transcriptomics may facilitate the classification of asthma inflammatory subtypes, and the identified biomarkers hold potential for noninvasive phenotyping and precision management.

Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. This study aimed to identify and characterize key biomarkers associated with the progression and treatment response of MetS-related EOLCC. An in-hospital cohort was utilized to assess the clinical implications of primary tumor location in early-onset colorectal cancer (EOCRC). Differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) were employed to identify genes potentially associated with MetS-related EOLCC. Functional enrichment analyses were conducted to explore the underlying mechanisms. Candidate biomarkers were screened using random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Survival relevance, expression profiles, and diagnostic performance were analyzed to identify key biomarkers. Treatment responses were evaluated, and potential therapeutic compounds were identified through molecular docking. Single-cell RNA sequencing (scRNA-seq) data and in vitro experiments were used to validate gene expression and functional characteristics. The in-hospital cohort revealed a higher proportion of EOLCC among EOCRC patients. Using the edgeR package and WGCNA, we identified coexpressed genes common to both EOLCC and MetS, significantly enriched in pathways associated with stromal remodeling and metabolic regulation. Machine learning algorithms highlighted three candidate biomarkers. Among them, only CD151 was associated with prognosis and advanced disease stage. CD151 was strongly correlated with stromal remodeling and chemoresistance. Additionally, potential therapeutic compounds targeting MetS-related EOLCC were identified via molecular docking. scRNA-seq analysis confirmed the expression and functional patterns of CD151, particularly in tumor cells. The bioinformatics results were further validated through quantitative real-time PCR (qRT-PCR), western blotting, and immunohistochemical (IHC) staining. This study identified CD151 as a key biomarker in MetS-related EOLCC, offering valuable insights into prognosis, tumor biology, and personalized treatment strategies. CD151 may serve as a reference for future research and clinical applications targeting this disease subtype.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Metabolic reprogramming and immune dysregulation are increasingly recognized as pivotal contributors to RA pathogenesis. However, a comprehensive understanding of metabolism-related genes that act as key regulators of RA progression and their impact on the immune microenvironment is lacking.We obtained RA mRNA expression profiles and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus. Weighted Gene Co-expression Network Analysis identified RA-associated gene modules, followed by functional enrichment (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis) and Gene Set Variation Analysis. Four machine learning algorithms (Least Absolute Shrinkage and Selection Operator, Random Forest, Support Vector Machine-Recursive Feature Elimination, and Boruta) were applied to select diagnostic biomarkers. Model performance was validated using Receiver Operating Characteristic curves. Immune infiltration was assessed via CIBERSORT and Single-sample Gene Set Enrichment Analysis. Consensus clustering identified RA subtypes, and scRNA-seq data were analyzed using CellChat to characterize cellular profiles and intercellular interactions.Four robust metabolism-related biomarkers, ACSL4, ARG1, GALNT4, and ST3GAL6, were identified and validated across datasets, demonstrating strong diagnostic performance. The model stratified RA patients into two subtypes with distinct immune infiltration patterns. Single-cell analysis revealed increased CD4 T cells and B cells proportions in RA, with enhanced migration inhibitory factor (MIF) signaling and upregulated metabolic pathways. Regulatory networks (Competing Endogenous RNA, Transcription Factor) and single-gene GSEA highlighted the roles of hub genes in immune and metabolic processes.This study provides a comprehensive analysis of metabolism-related genes in RA, identifying four diagnostic biomarkers. The integration of single-cell transcriptomics offers novel insights into RA pathogenesis and suggests potential biomarkers and therapeutic targets for precision medicine.

Cigarette smoking is considered as a major risk factor for esophageal carcinoma (ESCA) patients. Neutrophil activation plays a key role in cancer development and progression. However, the relationship between cigarette smoking and neutrophils in ESCA patients remained unclear. Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data were obtained from public databases. Uniform manifold approximation and projection (UMAP) was used to perform downscaling and clustering based on scRNA-seq data. The module genes associated with smoking in ESCA patients were filtered by weighted gene co-expression network analysis (WGCNA). Using the "AUCell" package, the enrichment of different cell subpopulations and gene collections were assessed. "CellChat" and "CellphoneDB" were used to infer the probability and significance of ligand-receptor interactions between different cell subpopulations. WGCNA was performed to screened module genes associated with smoking in ESCA patients from MEdarkquosie, MEturquoise, and MEgreenyellow. Next, eight cell clusters were identified, and using the AUCell score, we determined that neutrophil clusters were more active in the gene modules associated with smoking in ESCA patients. Two neutrophil subtypes, Neutrophils 1 and Neutrophils 2, exhibited greater enrichment in inflammatory response regulation, intercellular adhesion, and regulation of T cell activation. Furthermore, we found that neutrophils may pass through AMPT-(ITGA5 + ITGB1) and ICAM1-AREG in order to promote the development of ESCA, and that the expression levels of the receptor genes insulin-degrading enzyme and ITGB1 were significantly and positively correlated with cigarette smoking per day. Combining smoking-related gene modules and scRNA-seq, the current findings revealed the heterogeneity of neutrophils in ESCA and a tumor-promoting role of neutrophils in the tumor microenvironment of smoking ESCA patients.

According to the World Health Organization (WHO), gastric cancer (GC) is the fourth leading cause of tumor-related mortality globally and one of the most prevalent malignant tumors. To better understand the role of tumor-infiltrating B cells (TIBs) in GC, this work used single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data to identify candidate hub genes. Both scRNA-Seq and bulk RNA-Seq data for stomach adenocarcinoma (STAD) were obtained from the GEO and TCGA databases, respectively. Using scRNA-seq data, the FindNeighbors and FindClusters tools were used to group the cells into distinct groups. Immune cell clusters were sought in the massive RNA-seq expression matrix using the single-sample gene set enrichment analysis (ssGSEA). The expression profiles were used in Weighted Gene Coexpression Network Analysis (WGCNA) to build TCGA's gene coexpression networks. Next, univariate Cox regression, LASSO regression, and Kaplan-Meier analyses were used to identify hub genes in scRNA-seq data from sequential B-cell analyses. Finally, we examined the correlation between the hub genes and TIBs utilizing the TISIDB database. We confirmed the immune-related markers in clinical validation samples using reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). 15 cell clusters were classified in the scRNA-seq database. According to the WGCNA findings, the green module is most associated with cancer and B cells. The intersection of 12 genes in two separate datasets (scRNA and bulk) was attained for further analysis. However, survival studies revealed that increased C-X-C motif chemokine receptor 4 (CXCR4) expression was linked to worse overall survival. CXCR4 expression is correlated with active, immature, and memory B cells in STAD were identified. Finally, RT-PCR and IHC assays verified that in GC, CXCR4 is overexpressed, and its expression level correlates with TIBs. We used scRNA-Seq and bulk RNA-Seq to study STAD's cellular composition. We found that CXCR4 is highly expressed by TIBs in GC, suggesting that it may serve as a hub gene for these cells and a starting point for future research into the molecular mechanisms by which these immune cells gain access to tumors and potentially identify therapeutic targets.

BackgroundHead and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, and larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity and aggressive behavior, leading to high mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL and certain types of HNSCC. The purpose of this study is to elucidate the molecular and immune interplay between HNSCC and DLBCL using bioinformatics and machine learning (ML) to identify shared biomarkers and potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) were identified using the “limma” package in R from the HNSCC dataset in The Cancer Genome Atlas (TCGA) database, and relevant modules were selected through weighted gene co-expression network analysis (WGCNA) from a DLBCL dataset in the Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses were conducted using Gene Ontology (GO), Disease Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks and ML algorithms were employed to screen for biomarkers. The prognostic value of these biomarkers was evaluated using Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analyses. The Human Protein Atlas (HPA) database facilitated the examination of messenger RNA (mRNA) and protein expressions. Further analyses of mutations, immune infiltration, drug predictions, and pan-cancer impacts were performed. Additionally, single-cell RNA sequencing (scRNA-seq) data analysis at the cell type level was conducted to provide deeper insights into the tumor microenvironment.ResultsFrom 2,040 DEGs and 1,983 module-related genes, 85 shared genes were identified. PPI analysis with six algorithms proposed 21 prospective genes, followed ML examination yielded 16 candidates. Survival and ROC analyses pinpointed four hub genes—ACACB, MMP8, PAX5, and TNFAIP6—as significantly associated with patient outcomes, demonstrating high predictive capabilities. Evaluations of mutations and immune infiltration, coupled with drug prediction and a comprehensive cancer analysis, highlighted these biomarkers’ roles in tumor immune response and treatment efficacy. The scRNA-seq data analysis revealed an increased abundance of fibroblasts, epithelial cells and mononuclear phagocyte system (MPs) in HNSCC tissues compared to lymphoid tissues. MMP8 showed higher expression in five cell types in HNSCC tissues, while TNFAIP6 and PAX5 exhibited higher expression in specific cell types.ConclusionsLeveraging bioinformatics and ML, this study identified four pivotal genes with significant diagnostic capabilities for DLBCL and HNSCC. The survival analysis corroborates their diagnostic accuracy, supporting the development of a diagnostic nomogram to assist in clinical decision-making.

Nerve injury triggers complex molecular responses involving immune activation and neuronal damage, yet the key regulatory genes and their mechanisms remain poorly understood. Here, we integrated multi-transcriptomic datasets and machine learning to identify and validate novel biomarkers of nerve injury and elucidate their functional roles. The RNA-seq data and the single-cell transcriptome data of nerve injury and sham-surgery samples were sourced from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and three machine learning algorithms were used to identify hub genes associated with nerve injury. The expression patterns and diagnostic value of these hub genes were validated in independent datasets. The correlation between these genes and immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, single-cell RNA sequencing (scRNA-seq) data were used to investigate the cell-specific expression patterns of the hub genes in neural cells. Seven nerve injury-related genes were identified via WGCNA and three machine learning methods, of which Atf3, Bin2, Fcgr2b, and Ucn exhibited robust diagnostic performance (AUC > 0.7) across validation cohorts. Functional enrichment implicated these genes in neuroinflammation, neuronal fate commitment, and JAK-STAT/NF-κB signaling. Immune infiltration analysis correlated their expression with M2 macrophage polarization and CD4+ T cell depletion, while scRNA-seq highlighted cell-specific patterns: Atf3 and Ucn were neuron-enriched, whereas Fcgr2b and Bin2 predominated in macrophages/NK cells. Moreover, Fcgr2b promoted the outgrowth of neurites in PC12 cells. Our study unveils Atf3, Bin2, Fcgr2b, and Ucn as critical nerve injury biomarkers with dual roles in neuroimmune crosstalk, offering novel insights into therapeutic targeting for nerve repair. Moreover, Fcgr2b may be involved in neurite outgrowth after nerve injury.