Plaat1l1 controls feeding induced NAPE biosynthesis and contributes to energy balance regulation in zebrafish.

Dysregulation of energy balance leading to obesity is a significant risk factor for cardiometabolic diseases such as diabetes, non-alcoholic fatty liver disease and atherosclerosis. In rodents and several other vertebrates, feeding has been shown to induce a rapid rise in the intestinal levels of N-acyl-ethanolamines (NAEs) and the chronic consumption of a high fat diet abolishes this rise. Administering NAEs to rodents consuming a high fat diet reduces their adiposity, in part by reducing food intake and enhancing fat oxidation, so that feeding-induced intestinal NAE biosynthesis appears to be critical to appropriate regulation of energy balance. However, the contribution of feeding-induced intestinal NAE biosynthesis to appropriate energy balance remains poorly understood in part because there are multiple enzymes that can contribute to NAE biosynthesis and the specific enzyme(s) that are responsible for feeding-induced intestinal NAE biosynthesis have not been identified. The rate-limiting step in the intestinal biosynthesis of NAEs is formation of their immediate precursors, the N-acyl-phosphatidylethanolamines (NAPEs), by phosphatidylethanolamine N-acyltransferases (NATs). At least six NATs are found in humans and multiple homologs of these NATs are found in most vertebrate species. In recent years, the fecundity and small size of zebrafish (Danio rerio), as well as their similarities in feeding behavior and energy balance regulation with mammals, have led to their use to model key features of cardiometabolic disease. We therefore searched the Danio rerio genome to identify all NAT homologs and found two additional NAT homologs besides the previously reported plaat1, rarres3, and rarres3l, and used CRISPR/cas9 to delete these two NAT homologs (plaat1l1 and plaat1l2). While wild-type fish markedly increased their intestinal NAPE levels in response to a meal after fasting, this response was completely ablated in plaat1l1-/-fish. Furthermore, plaat1l1-/- fish fed a standard flake diet had increased weight gain and glucose intolerance compared to wild-type fish. The results support a critical role for feeding-induced NAPE and NAE biosynthesis in regulating energy balance and suggest that restoring this response in obese animals could potentially be used to treat obesity and cardiometabolic disease.

ErbB3 is required for hyperaminoacidemia-induced pancreatic α cell hyperplasia.

Blood amino acid levels are maintained in a narrow physiological range. The pancreatic α cells have emerged as the primary aminoacidemia regulator through glucagon secretion to promote hepatic amino acid catabolism. Interruption of glucagon signaling disrupts the liver-α cells axis leading to hyperaminoacidemia, which triggers a compensatory rise in glucagon secretion and α cell hyperplasia. The mechanisms of hyperaminoacidemia-induced α cell hyperplasia remain incompletely understood. Using a mouse α cell line and in vivo studies in zebrafish and mice, we found that hyperaminoacidemia-induced α cell hyperplasia requires ErbB3 signaling. In addition to mechanistic target of rapamycin complex 1, another ErbB3 downstream effector signal transducer and activator of transcription 3 also plays a role in α cell hyperplasia. Mechanistically, ErbB3 may partner with ErbB2 to stimulate cyclin D2 and suppress p27 via mechanistic target of rapamycin complex 1 and signal transducer and activator of transcription 3. Our study identifies ErbB3 as a new regulator for hyperaminoacidemia-induced α cell proliferation and a critical component of the liver-α cells axis that regulates aminoacidemia.

NAC1 transcriptional activation of LDHA induces hepatitis B virus immune evasion leading to cirrhosis and hepatocellular carcinoma development.

Our study aimed to elucidate the molecular mechanisms underlying NAC1 (nucleus accumbens associated 1) transcriptional regulation of LDHA and its role in HBV immune evasion, thus contributing to the development of cirrhosis and hepatocellular carcinoma (HCC). Utilizing public datasets, we performed differential gene expression and weighted gene co-expression network analysis (WGCNA) on HBV-induced cirrhosis/HCC data. We identified candidate genes by intersecting differentially expressed genes with co-expression modules. We validated these genes using the TCGA database, conducting survival analysis to pinpoint key genes affecting HBV-HCC prognosis. We also employed the TIMER database for immune cell infiltration data and analyzed correlations with identified key genes to uncover potential immune escape pathways. In vitro, we investigated the impact of NAC1 and LDHA on immune cell apoptosis and HBV immune evasion. In vivo, we confirmed these findings using an HBV-induced cirrhosis model. Bioinformatics analysis revealed 676 genes influenced by HBV infection, with 475 genes showing differential expression in HBV-HCC. NAC1 emerged as a key gene, potentially mediating HBV immune escape through LDHA transcriptional regulation. Experimental data demonstrated that NAC1 transcriptionally activates LDHA, promoting immune cell apoptosis and HBV immune evasion. Animal studies confirmed these findings, linking NAC1-mediated LDHA activation to cirrhosis and HCC development. NAC1, highly expressed in HBV-infected liver cells, likely drives HBV immune escape by activating LDHA expression, inhibiting CD8 + T cells, and promoting cirrhosis and HCC development.

Animal Models for Understanding the Mechanisms of Beta Cell Death during Type 2 Diabetes Pathogenesis.

Type 2 diabetes (T2D) has become a worldwide epidemic, primarily driven by obesity from overnutrition and sedentariness. Recent results reveal there is heterogeneity in both pathology and treatment responses in T2D patients. Therefore, a variety of T2D animal models are necessary to obtain a mechanistic understanding of distinct disease processes. T2D results from insufficient insulin, either due to beta cell loss or inborn deficiency. Although decreases in beta cell mass can occur through loss of identity or cell death, in this review, we will highlight the T2D animal models that display beta cell death, including the Zucker Diabetic Fatty Rat, sand rat, db/db mouse, and a novel diabetic zebrafish model, the Zebrafish Muscle Insulin-Resistant (zMIR) fish. Procuring a mechanistic understanding of different T2D progression trajectories under a variety of contexts is paramount for developing and testing more individualized treatments.

Unintended findings: Therapeutic effects of hormones or gamma globulins on Lentiform Fork sign in 3 diabetic uremic patients: Case report/case series.

INTRODUCTION: The lentiform fork sign (LFS) is a unique magnetic resonance imaging (MRI) finding characterized by a bright hyperintense rim delineating the lentiform nucleus as a fork associated with metabolic acidosis in end-stage renal disease. PATIENT CONCERNS: We report 3 cases of LFS in diabetic uremic patients. In one case of uremia, intensive hemodialysis treatment was not effective. Given our poor understanding of LFS, it was regarded as bilateral basal ganglia pathology, and pulse hormone and gamma globulins therapy was initiated. The patient neurological symptoms improved, and the pathological signs on imaging subsided. Based on our experience with the first LFS case, 2 diabetic uremic cases presenting with LFS were successfully treated with hormone or gamma globulin pulse therapy in addition to intensive hemodialysis. DIAGNOSIS: Based on the clinical manifestations, past medical history and MRI imaging changes of the 3 cases reported here, the diagnosis of LFS was established. INTERVENTIONS: Our experience from these 3 cases suggests that hormone supplementation and gamma globulin therapy may be indicated for treating LFS. LESSONS: Our findings highlight that in diabetic uremic dialysis patients with neurological symptoms, LFS should be suspected. The clinical manifestations, past medical history and MRI imaging findings are essential for diagnosing LFS. Hormone supplementation and gamma globulin therapy may be the effective treatment for LFS.

Pancreatic islet α cell function and proliferation requires the arginine transporter SLC7A2.

Interrupting glucagon signaling decreases gluconeogenesis and the fractional extraction of amino acids by liver from blood resulting in lower glycemia. The resulting hyperaminoacidemia stimulates α cell proliferation and glucagon secretion via a liver-α cell axis. We hypothesized that α cells detect and respond to circulating amino acids levels via a unique amino acid transporter repertoire. We found that Slc7a2ISLC7A2 is the most highly expressed cationic amino acid transporter in α cells with its expression being three-fold greater in α than ß cells in both mouse and human. Employing cell culture, zebrafish, and knockout mouse models, we found that the cationic amino acid arginine and SLC7A2 are required for α cell proliferation in response to interrupted glucagon signaling. Ex vivo and in vivo assessment of islet function in Slc7a2-/- mice showed decreased arginine-stimulated glucagon and insulin secretion. We found that arginine activation of mTOR signaling and induction of the glutamine transporter SLC38A5 was dependent on SLC7A2, showing that both's role in α cell proliferation is dependent on arginine transport and SLC7A2. Finally, we identified single nucleotide polymorphisms in SLC7A2 associated with HbA1c. Together, these data indicate a central role for SLC7A2 in amino acid-stimulated α cell proliferation and islet hormone secretion.

Fact or Artifact in Inferior ST Elevation-Avoiding Misdiagnosis and Missed Diagnosis.

This case report describes a patient in their 60s with a history of hypertension who experienced sudden chest pain radiating to the back and sweating for 2 hours without relief.

Identification of a novel diagnosis model based on 5 hub genes for chronic thromboembolic pulmonary hypertension.

BACKGROUND: As a type of precapillary pulmonary hypertension, chronic thromboembolic pulmonary hypertension (CTEPH) results from incomplete pulmonary embolism resolution. In this study, we aimed to determine biomarker genes for predicting the prognosis of CTEPH. METHOD: RNAseq of CTEPH was collected from the public database, namely Gene Expression Omnibus (GEO), including GSE84538 and GSE188938, which combined a dataset (GSE). Differentially expressed genes (DEG) or miRNA (DEM) were identified by limma package. Functional enrichment analysis was performed by the WebGestaltR package. Then, the miRNA-mRNA network was presented by Cytoscape, and the protein-protein interactions (PPI) network was constructed by STRING. MCODE was mined by mature MCODE algorithm. Immune infiltration analysis was conducted by ESTIMATER and ssGSEA analysis. A diagnosis model was established by SVM algorithm. RESULT: In the GSE dataset, CTEPH samples had a lower GOBP_RESPONSE_TO_OXIDATIVE_STRESS score. A total of 628 DEGs and 31 DEMs were identified between CTEPH and normal samples. Afterward, DEGs were intersected with genes, which correlated with the GOBP_RESPONSE_TO_OXIDATIVE_STRESS score. A 26 DEMs-152 DEGs network was constructed, and a PPI network was established based on 152 DEGs to find 149 target genes. From the above 149 target genes, 3 modules were extracted to obtain 15 core targets. Finally, 5 hub genes were obtained by the intersection of 15 core targets and genes in MCODE2. A total of 5 hub genes were positively correlated with most immune cell scores as well as GOBP_RESPONSE_TO_OXIDATIVE_STRESS. It was found that a diagnosis model based on 5 hub genes had a well diagnostic ability for CTEPH. CONCLUSION: We identified 5 hub genes associated with oxidative stress. It can be concluded that they may be beneficial in diagnosing CTEPH.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes.

Background: DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. Methods: The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. Results: A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). Conclusions: In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

Single-cell RNA sequencing reveals the mediatory role of cancer-associated fibroblast PTN in hepatitis B virus cirrhosis-HCC progression.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are essential stromal components in the tumor microenvironment of hepatocellular carcinoma (HCC). Hepatitis B virus (HBV) infection induces pathological changes such as liver fibrosis/cirrhosis and HCC. The aim of this research was to explore the novel mediators of CAFs to modulate HBV cirrhosis-HCC progression. METHODS: The single-cell transcriptome data of HCC were divided into subsets, and the significant subset related to fibrotic cells, along with biological function, and clinical information of HCC was revealed by integrated data analyses. The cell communication, cells communicated weight analysis of signaling pathways, and key genes in signaling pathways analysis of significant CAFs subclasses were conducted to discover the novel gene of CAFs. Bioinformatics, vitro and HBV transfection assays were used to verify the novel gene is an important target for promoting the progression HBV cirrhosis-HCC progression. RESULTS: Fibroblasts derived from HCC single-cell data could be separated into three cell subclasses (CAF0-2), of which CAF2 was associated with the HCC clinical information. Fibroblasts have opposite developmental trajectories to immune B cells and CD8 + T cells. CAF0-2 had strong interaction with B cells and CD8 + T cells, especially CAF2 had the highest interaction frequency and weight with B cells and CD8 + T cells. Moreover, PTN participated in CAF2-related pathways involved in the regulation of cell communication, and the interactions among CAF2 and PTN contributed the most to B cells and CD8 + T cells. Furthermore, the genes of PTN, SDC1, and NCL from PTN signaling were highest expression in CAF2, B cells, and CD8 + T cells, respectively, and the interaction of PTN- SDC1 and PTN- NCL contributed most to the interaction of CAF2- B cells and CAF2-CD8 + T cells. Bioinformatics and vitro experiments confirm PTN was upregulated in HCC and promoted the proliferation of tumor cells, and HBV infection could initiate PTN to perform cirrhosis-HCC progression. CONCLUSION: Our findings revealed CAF was associated with hepatocarcinogenesis, and the functional importance of B cells and CD8 + T cells in modulating CAF in HCC. Importantly, PTN maybe a novel mediator of CAF to mediate HBV cirrhosis-HCC progression.

Optimized protocol for live imaging of overnutrition-elicited interactions between immune cells and ß cells in zebrafish.

Here, we provide an optimized protocol to observe the interactions between infiltrating immune cells and islet ß cells using live imaging. This protocol is useful for the characterization of cell-cell interactions and for the direct visualization of immune cell migration to the principal pancreatic islet during islet inflammation. We describe the preparation of zebrafish transgenic lines and detail steps for setting up the fish for live confocal imaging. For more details on the use and execution of this protocol, please refer to Yang et al. (2022).1.

Hyperaminoacidemia induces pancreatic α cell proliferation via synergism between the mTORC1 and CaSR-Gq signaling pathways.

Glucagon has emerged as a key regulator of extracellular amino acid (AA) homeostasis. Insufficient glucagon signaling results in hyperaminoacidemia, which drives adaptive proliferation of glucagon-producing α cells. Aside from mammalian target of rapamycin complex 1 (mTORC1), the role of other AA sensors in α cell proliferation has not been described. Here, using both genders of mouse islets and glucagon receptor (gcgr)-deficient zebrafish (Danio rerio), we show α cell proliferation requires activation of the extracellular signal-regulated protein kinase (ERK1/2) by the AA-sensitive calcium sensing receptor (CaSR). Inactivation of CaSR dampened α cell proliferation, which was rescued by re-expression of CaSR or activation of Gq, but not Gi, signaling in α cells. CaSR was also unexpectedly necessary for mTORC1 activation in α cells. Furthermore, coactivation of Gq and mTORC1 induced α cell proliferation independent of hyperaminoacidemia. These results reveal another AA-sensitive mediator and identify pathways necessary and sufficient for hyperaminoacidemia-induced α cell proliferation.

Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification.

High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment.

Insulin secretion assays in an engineered MIN6 cell line.

Insulin secretion from pancreatic beta cells is crucial for maintaining glucose homeostasis. The murine insulinoma derived MIN6 cell line is commonly used as a model for insulin secretion studies. However, its glucose responsiveness wanes with passaging, and insulin secretion is traditionally measured by expensive and time-consuming RIA or ELISA. We have developed a MIN6 subclone (MIN6-6) that allows for high throughput assay of insulin secretion in both population and single cells. In addition, MIN6-6 also expresses Cas9, permitting genome wide CRISPR screen of insulin secretion using a pooled sgRNA library. Here we provide methods for assaying insulin secretion both in bulk and in single cells in MIN6-6 cells, as well as for CRISPR screen of insulin secretion.•A highly glucose responsive beta cell reporter line (MIN6-6) with multiple engineered functionalities.•Allows for CRISPR/Cas9 mutagenesis, quantification of bulk insulin secretion by a straightforward nanoLuc assay and visualization of intracellular insulin granules.•Allows for en masse quantification of insulin granule exocytosis in individual cells under multiple conditions.

Classification and Prognostic Characteristics of Hepatocellular Carcinoma Based on Glycolysis Cholesterol Synthesis Axis.

Background: Liver hepatocellular carcinoma (LIHC) is among the most frequent causes of cancer-related death across the world with a considerably poor prognosis. The current study targeted providing a new type of LIHC from the perspective of the glycolysis/cholesterol synthesis axis, predicting its prognostic characteristics, and exploring the potential role and mechanism of the glycolysis/cholesterol synthesis axis in the occurrence and development of LIHC. Methods: Based on the two expression profile data and clinical information of LIHC in The Cancer Genome Atlas (TCGA) database and hepatocellular carcinoma database (HCCDB), as well as glycolysis/cholesterol-related genes from the Molecular Signatures Database (MSigDB), unsupervised consistent clustering method was used to identify molecular subtypes. In addition, the differential genes were identified by limma package, and then the gene set was enriched, analyzed, and annotated by WebGestaltR package. At the same time, the immune infiltration analysis of tumor samples was carried out using the ESTIMATE to evaluate the tumor immune score of the samples. Finally, the differences in clinical characteristics among molecular subtypes were measured using univariate and multivariate Cox analyses. Results: According to the median standardized expression levels of glycolysis/cholesterol production genes, samples were divided into four groups (molecular subtypes): Quiescent group, Glycolysis group, Cholesterol group, and Mixed group. Significant prognostic differences were observed among the four groups. In both TCGA and HCCDB18 datasets, the prognosis of subtype Mixed was the worst, while Quiescent had a good prognosis. Cell cycle and oncogenic pathways were significantly enriched in the Mixed group. In addition, glycolysis and cholesterol production gene expressions were related to the prognostic LIHC subtype classification genes' expression levels. Conclusion: Metabolic classification regarding glycolysis and cholesterol production pathways provided new insights into the biological aspects of LIHC molecular subtypes and might help to develop personalized therapies for unique tumor metabolic profiles.

Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma.

BACKGROUND: Dysregulation of lipid metabolism is implicated in the progression of hepatocellular carcinoma (HCC). We therefore investigated the molecular characteristics of lipid-metabolism-related genes in HCC. METHODS: Multi-dimensional bioinformatics analysis was conducted to comprehensively identify the lipid metabolism-related genes (LMRGs) from public databases, as well as the clinical information, immune features, and biological characteristics of HCC. The IMGR were then used to classify HCC into molecular phenotypes. Six lipid metabolism-related genes sets with the potential to predict the prognosis of HCC patients were identified. RESULTS: A total of 770 HCC patients with complete clinical information and corresponding 776 LMRGs were downloaded from 3 databases. Univariate cox and non-negative matrix factorization analyses were used to classify HCC patients into 2 clusters. This molecular classification was associated with overall survival, clinical characteristics, and immune cells. The biological function of the differentially expressed LMRGs in the 2 clusters showed the genes associated with tumor-related metabolism pathways. A combination of multivariate/univariate cox regression and least absolute shrinkage and selection operator analyses were conducted to build a 6 LMRGs signature (6-IS) to predict the prognosis of HCC. The 6-IS signature was found to be an independent prognostic factor. Performance of the 6-IS prognostic signature was verified in a validation set and compared with an external data set. Results revealed the 6-IS signature could effectively predict the prognosis of patients with HCC. CONCLUSIONS: This study provides new insights into the role of LMRG in the pathogenesis of HCC and presents a novel prognostic signature 6-IS monitoring the clinical course of HCC.

The Association between Immune Subgroups and Gene Modules for the Clinical, Cellular, and Molecular Characteristic of Hepatocellular Carcinoma.

The heterogeneity of hepatocellular carcinoma (HCC) is related to immune cell infiltration and genetic aberrations in the tumor microenvironment. This study aimed to identify the novel molecular typing of HCC according to the genetic and immune characteristics, to obtain accurate clinical management of this disease. We performed consensus clustering to divide 424 patients into different immune subgroups and assessed the reproducibility and efficiency in two independent cohorts with 921 patients. The associations between molecular typing and molecular, cellular, and clinical characteristics were investigated by a multidimensional bioinformatics approach. Furthermore, we conducted graph structure learning-based dimensionality reduction to depict the immune landscape to reveal the interrelation between the immune and gene systems in molecular typing. We revealed and validated that HCC patients could be segregated into 5 immune subgroups (IS1-5) and 7 gene modules with significantly different molecular, cellular, and clinical characteristics. IS5 had the worst prognosis and lowest enrichment of immune characteristics and was considered the immune cold type. IS4 had the longest overall survival, high immune activity, and antitumorigenesis, which were defined as the immune hot and antitumorigenesis types. In addition, immune landscape analysis further revealed significant intraclass heterogeneity within each IS, and each IS represented distinct clinical, cellular, and molecular characteristics. Our study provided 5 immune subgroups with distinct clinical, cellular, and molecular characteristics of HCC and may have clinical implications for precise therapeutic strategies and facilitate the investigation of immune mechanisms in HCC.

Identification and Characterization of Genes Related to the Prognosis of Hepatocellular Carcinoma Based on Single-Cell Sequencing.

The heterogeneity of hepatocellular carcinoma (HCC) highlights the importance of precision therapy. In recent years, single-cell RNA sequencing has been used to reveal the expression of genes at the single-cell level and comprehensively study cell heterogeneity. This study combined big data analytics and single-cell data mining to study the influence of genes on HCC prognosis. The cells and genes closely related to the HCC were screened through single-cell RNA sequencing (71,915 cells, including 34,414 tumor cells) and big data analysis. Comprehensive bioinformatics analysis of the key genes of HCC was conducted for molecular classification and multi-dimensional correlation analyses, and a prognostic model for HCC was established. Finally, the correlation between the prognostic model and clinicopathological features was analyzed. 16,880 specific cells, screened from the single-cell expression profile matrix, were divided into 20 sub-clusters. Cell typing revealed that 97% of these cells corresponded to HCC cell lines, demonstrating the high specificity of cells derived from single-cell sequencing. 2,038 genes with high variability were obtained. The 371 HCC samples were divided into two molecular clusters. Cluster 1 (C1) was associated with tumorigenesis, high immune score, immunotherapy targets (PD-L1 and CYLA-4), high pathological stage, and poor prognosis. Cluster 2 (C2) was related to metabolic and immune function, low immune score, low pathological stage, and good prognosis. Seven differentially expressed genes (CYP3A4, NR1I2, CYP2C9, TTR, APOC3, CYP1A2, and AFP) identified between the two molecular clusters were used to construct a prognostic model. We further validated the correlation between the seven key genes and clinical features, and the established prognostic model could effectively predict HCC prognosis. Our study identified seven key genes related to HCC that were used to construct a prognostic model through single-cell sequencing and big data analytics. This study provides new insights for further research on clinical targets of HCC and new biomarkers for clinical application.