Exploring the molecular mechanisms of macrophages in islet transplantation using single-cell analysis.

Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

Elucidating T cell dynamics and molecular mechanisms in syngeneic and allogeneic islet transplantation through single-cell RNA sequencing.

Islet transplantation is a promising therapy for diabetes treatment. However, the molecular underpinnings governing the immune response, particularly T-cell dynamics in syngeneic and allogeneic transplant settings, remain poorly understood. Understanding these T cell dynamics is crucial for enhancing graft acceptance and managing diabetes treatment more effectively. This study aimed to elucidate the molecular mechanisms, gene expression differences, biological pathway alterations, and intercellular communication patterns among T-cell subpopulations after syngeneic and allogeneic islet transplantation. Using single-cell RNA sequencing, we analyzed cellular heterogeneity and gene expression profiles using the Seurat package for quality control and dimensionality reduction through t-SNE. Differentially expressed genes (DEGs) were analyzed among different T cell subtypes. GSEA was conducted utilizing the HALLMARK gene sets from MSigDB, while CellChat was used to infer and visualize cell-cell communication networks. Our findings revealed genetic variations within T-cell subpopulations between syngeneic and allogeneic islet transplants. We identified significant DEGs across these conditions, highlighting molecular discrepancies that may underpin rejection or other immune responses. GSEA indicated activation of the interferon-alpha response in memory T cells and suppression in CD4+ helper and γδ T cells, whereas TNFα signaling via NFκB was particularly active in regulatory T cells, γδ T cells, proliferating T cells, and activated CD8+ T cells. CellChat analysis revealed complex communication patterns within T-cell subsets, notably between proliferating T cells and activated CD8+ T cells. In conclusion, our study provides a comprehensive molecular landscape of T-cell diversity in islet transplantation. The insights into specific gene upregulation in xenotransplants suggest potential targets for improving graft tolerance. The differential pathway activation across T-cell subsets underscores their distinct roles in immune responses posttransplantation.

Corrigendum: Human IL-17 and TNF-α additively or synergistically regulate the expression of proinflammatory genes, coagulation-related genes, and tight junction genes in porcine aortic endothelial cells.

[This corrects the article DOI: 10.3389/fimmu.2022.857311.].

Integrative analysis of COL6A3 in lupus nephritis: insights from single-cell transcriptomics and proteomics.

Introduction: Lupus nephritis (LN), a severe complication of systemic lupus erythematosus (SLE), presents significant challenges in patient management and treatment outcomes. The identification of novel LN-related biomarkers and therapeutic targets is critical to enhancing treatment outcomes and prognosis for patients. Methods: In this study, we analyzed single-cell expression data from LN (n=21) and healthy controls (n=3). A total of 143 differentially expressed genes were identified between the LN and control groups. Then, proteomics analysis of LN patients (n=9) and control (SLE patients without LN, n=11) revealed 55 differentially expressed genes among patients with LN and control group. We further utilizes protein-protein interaction network and functional enrichment analyses to elucidate the pivotal role of COL6A3 in key signaling pathways. Its diagnostic value is evaluate through its correlation with disease progression and renal function metrics, as well as Receiver Operating Characteristic Curve (ROC) analysis. Additionally, immunohistochemistry and qPCR experiments were performed to validate the expression of COL6A3 in LN. Results: By comparison of single-cell and proteomics data, we discovered that COL6A3 is significantly upregulated, highlighting it as a critical biomarker of LN. Our findings emphasize the substantial involvement of COL6A3 in the pathogenesis of LN, particularly noting its expression in mesangial cells. Through comprehensive protein-protein interaction network and functional enrichment analyses, we uncovered the pivotal role of COL6A3 in key signaling pathways including integrin-mediated signaling pathways, collagen-activated signaling pathways, and ECM-receptor interaction, suggesting potential therapeutic targets. The diagnostic utility is confirmed by its correlation with disease progression and renal function metrics of the glomerular filtration rate. ROC analysis further validates the diagnostic value of COL6A3, with the area under the ROC values of 0.879 in the in-house cohort, and 0.802 and 0.915 in tubular and glomerular external cohort samples, respectively. Furthermore, immunohistochemistry and qPCR experiments were consistent with those obtained from the single-cell RNA sequencing and proteomics studies. Discussion: These results proved that COL6A3 is a promising biomarker and therapeutic target, advancing personalized medicine strategies for LN.

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Background: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. Methods: We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs. Results: Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. Conclusion: This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

Advancing diabetes treatment: the role of mesenchymal stem cells in islet transplantation.

Diabetes mellitus, a prevalent global health challenge, significantly impacts societal and economic well-being. Islet transplantation is increasingly recognized as a viable treatment for type 1 diabetes that aims to restore endogenous insulin production and mitigate complications associated with exogenous insulin dependence. We review the role of mesenchymal stem cells (MSCs) in enhancing the efficacy of islet transplantation. MSCs, characterized by their immunomodulatory properties and differentiation potential, are increasingly seen as valuable in enhancing islet graft survival, reducing immune-mediated rejection, and supporting angiogenesis and tissue repair. The utilization of MSC-derived extracellular vesicles further exemplifies innovative approaches to improve transplantation outcomes. However, challenges such as MSC heterogeneity and the optimization of therapeutic applications persist. Advanced methodologies, including artificial intelligence (AI) and single-cell RNA sequencing (scRNA-seq), are highlighted as potential technologies for addressing these challenges, potentially steering MSC therapy toward more effective, personalized treatment modalities for diabetes. This review revealed that MSCs are important for advancing diabetes treatment strategies, particularly through islet transplantation. This highlights the importance of MSCs in the field of regenerative medicine, acknowledging both their potential and the challenges that must be navigated to fully realize their therapeutic promise.

Unraveling Key Factors for Hypoxia Tolerance in Contrasting Varieties of Cotton Rose by Comparative Morpho-physiological and Transcriptome Analysis.

The cotton rose (Hibiscus mutabilis) is a plant species commonly found in tropical and subtropical regions. It is remarkably resilient to waterlogging stress; however, the underlying mechanism behind this trait is yet unknown. This study used hypoxia-tolerant "Danbanhong" (DBH) and more hypoxia-sensitive "Yurui" (YR) genotypes and compared their morpho-physiological and transcriptional responses to hypoxic conditions. Notably, DBH had a higher number of adventitious roots (20.3) compared to YR (10.0), with longer adventitious roots in DBH (18.3 cm) than in YR (11.2 cm). Furthermore, the formation of aerenchyma was 3-fold greater in DBH compared to YR. Transcriptomic analysis revealed that DBH had more rapid transcriptional responses to hypoxia than YR. Identification of a greater number of differentially expressed genes (DEGs) for aerenchyma, adventitious root formation and development, and energy metabolism in DBH supported that DBH had better morphological and transcriptional adaptation than YR. DEG functional enrichment analysis indicated the involvement of variety-specific biological processes in adaption to hypoxia. Plant hormone signaling transduction, MAPK signaling pathway and carbon metabolism played more pronounced roles in DBH, whereas the ribosome genes were specifically induced in YR. These results show that effective multilevel coordination of adventitious root development and aerenchyma, in conjunction with plant hormone signaling and carbon metabolism, is required for increased hypoxia tolerance. This study provides new insights into the characterization of morpho-physiological and transcriptional responses to hypoxia in H. mutabilis, shedding light on the molecular mechanisms of its adaptation to hypoxic environments.

A brief review of the current status of pig islet xenotransplantation.

An estimated 1.5 million Americans suffer from Type I diabetes mellitus, and its incidence is increasing worldwide. Islet allotransplantation offers a treatment, but the availability of deceased human donor pancreases is limited. The transplantation of islets from gene-edited pigs, if successful, would resolve this problem. Pigs are now available in which the expression of the three known xenoantigens against which humans have natural (preformed) antibodies has been deleted, and in which several human 'protective' genes have been introduced. The transplantation of neonatal pig islets has some advantages over that of adult pig islets. Transplantation into the portal vein of the recipient results in loss of many islets from the instant blood-mediated inflammatory reaction (IBMIR) and so the search for an alternative site continues. The adaptive immune response can be largely suppressed by an immunosuppressive regimen based on blockade of the CD40/CD154 T cell co-stimulation pathway, whereas conventional therapy (e.g., based on tacrolimus) is less successful. We suggest that, despite the need for effective immunosuppressive therapy, the transplantation of 'free' islets will prove more successful than that of encapsulated islets. There are data to suggest that, in the absence of rejection, the function of pig islets, though less efficient than human islets, will be sufficient to maintain normoglycemia in diabetic recipients. Pig islets transplanted into immunosuppressed nonhuman primates have maintained normoglycemia for periods extending more than two years, illustrating the potential of this novel form of therapy.

Revolutionizing cancer immunotherapy in solid tumor: CAR engineering and single-cell sequencing insights.

The global increase in cancer incidence presents significant economic and societal challenges. While chimeric antigen receptor-modified T cell (CAR-T) therapy has demonstrated remarkable success in hematologic malignancies and has earned FDA approval, its translation to solid tumors encounters faces significant obstacles, primarily centered around identifying reliable tumor-associated antigens and navigating the complexities of the tumor microenvironment. Recent developments in single-cell RNA sequencing (scRNA-seq) have greatly enhanced our understanding of tumors by offering high-resolution, unbiased analysis of cellular heterogeneity and molecular patterns. These technologies have revolutionized our comprehension of tumor immunology and have led to notable progress in cancer immunotherapy. This mini-review explores the progress of chimeric antigen receptor (CAR) cell therapy in solid tumor treatment and the application of scRNA-seq at various stages following the administration of CAR cell products into the body. The advantages of scRNA-seq are poised to further advance the investigation of the biological characteristics of CAR cells in vivo, tumor immune evasion, the impact of different cellular components on clinical efficacy, the development of clinically relevant biomarkers, and the creation of new targeted drugs and combination therapy approaches. The integration of scRNA-seq with CAR therapy represents a promising avenue for future innovations in cancer immunotherapy. This synergy holds the potential to enhance the precision and efficacy of CAR cell therapies while expanding their applications to a broader range of malignancies.

Corrigendum: Single-cell RNA sequencing reveals the role of phosphorylation-related genes in hepatocellular carcinoma stem cells.

[This corrects the article DOI: 10.3389/fcell.2021.734287.].

FACS-based genome-wide CRISPR screens define key regulators of DNA damage signaling pathways.

DNA damage-activated signaling pathways are critical for coordinating multiple cellular processes, which must be tightly regulated to maintain genome stability. To provide a comprehensive and unbiased perspective of DNA damage response (DDR) signaling pathways, we performed 30 fluorescence-activated cell sorting (FACS)-based genome-wide CRISPR screens in human cell lines with antibodies recognizing distinct endogenous DNA damage signaling proteins to identify critical regulators involved in DDR. We discovered that proteasome-mediated processing is an early and prerequisite event for cells to trigger camptothecin- and etoposide-induced DDR signaling. Furthermore, we identified PRMT1 and PRMT5 as modulators that regulate ATM protein level. Moreover, we discovered that GNB1L is a key regulator of DDR signaling via its role as a co-chaperone specifically regulating PIKK proteins. Collectively, these screens offer a rich resource for further investigation of DDR, which may provide insight into strategies of targeting these DDR pathways to improve therapeutic outcomes.

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm.

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis.

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.

Construction of endothelial cell-related and autophagy-related prognostic models for hepatocellular carcinoma based on single-cell data.

BACKGROUND: We aimed to develop an autophagy-related prognostic model with single-cell RNA sequencing (ScRNA-Seq) data for hepatocellular carcinoma (HCC) patients. METHODS: ScRNA-Seq datasets of HCC patients were analyzed by Seurat. The expression of genes involved in canonical and noncanonical autophagy pathways in scRNA-seq data was also compared. Cox regression was applied to construct an AutRG risk prediction model. Subsequently, we examined the characteristics of AutRG high-risk and low-risk group patients. RESULTS: Six major cell types (hepatocytes, myeloid cells, T/NK cells, B cells, fibroblast cells, and endothelial cells) were identified in the scRNA-Seq dataset. The results showed that most of the canonical and noncanonical autophagy genes were highly expressed in hepatocytes, with the exception of MAP 1LC3B, SQSTM1, MAP 1LC3A, CYBB, and ATG3. Six AutRG risk prediction models originating from different cell types were constructed and compared. The AutRG prognostic signature (GAPDH, HSP90AA1, and TUBA1C) in endothelial cells had the best overall performance for predicting the overall survival of HCC patients, with 1-year, 3-year, and 5-year AUCs equal to 0.758, 0.68, and 0.651 in the training cohort and 0.760, 0.796, and 0.840 in the validation cohort, respectively. The different tumor mutation burden, immune infiltration, and gene set enrichment characteristics of the AutRG high-risk and low-risk group patients were identified. CONCLUSION: We constructed an endothelial cell-related and autophagy-related prognostic model of HCC patients using the ScRNA-Seq dataset for the first time. This model demonstrated the good calibration ability of HCC patients and provided a new understanding of the evaluation of prognosis.

Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis.

One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.

The transcriptome profile of RPE cells by the fullerenol against hydrogen peroxide stress.

Age-related macular degeneration (AMD) causes central vision impairment with increased incidence. In the pathogenesis of AMD, reactive oxygen species (ROS) are associated with RPE cell apoptosis. H2O2 is an oxidative toxicant and is used to establish the AMD in vitro model. However, the mechanisms of ROS in H2O2-induced AMD are still unclear. Fullerenol, a promising antioxidant of nanomaterials, protects RPE cells from ROS attack. In addition to working as a scavenger, little is known about the antioxidant mechanism of fullerenol in RPE cells. In this study, transcriptome sequencing was performed to examine the global changes in mRNA transcripts induced by H2O2 in human ARPE-19 cells. Moreover, we comprehensively investigated the protective effects of fullerenol against H2O2-induced oxidative injury by RNA sequencing. Gene Ontology enrichment analysis showed that those pathways related to the release of positive regulation of DNA-templated transcription and negative regulation of apoptotic process were affected. Finally, we found that 12 hub genes were related to the oxidative-protection function of fullerenol. In summary, H2O2 affected these hub genes and signaling pathways to regulate the senescence of RPE cells. Moreover, fullerenol is a potent nanomaterial that protects the RPE and would be a promising approach for AMD prevention.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis.

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.