Identification and integrative analysis of ACLY and related gene panels associated with immune microenvironment reveal prognostic significance in hepatocellular carcinoma

Development and Validation of a Novel Prognostic Nomogram Based on Platelet and CD8+T Cell Counts in Hepatocellular Carcinoma Patients with Portal Vein Tumor Thrombosis

Purpose

Portal vein tumor thrombosis (PVTT) is one of the hallmarks of advanced Hepatocellular carcinoma (HCC). Platelet (PLT) function parameters and CD8+T cells (CD8+Ts) play an important role in HCC progression and metastasis. This study is committed to establishing an efficient prognosis prediction model and exploring the combined effect of PLT and CD8+Ts on PVTT prognosis.

Patients and Methods

This retrospective study collected 932 HCC patients with PVTT from 2007 to 2017 and randomly divided them into a training cohort (n = 656) and a validation cohort (n = 276). We performed multivariable Cox and Elastic-net regression analysis, constructed a nomogram and used Kaplan-Meier survival curves to compare overall survival and progression-free survival rates in different substrata. Relationships between indicators involved were also analyzed.

Results

We found tumor number, size, treatment, PLT, γ-glutamyl transferase, alpha-fetoprotein, mean platelet volume, and CD8+Ts were related to the 5-year OS of patients with PVTT, and established a nomogram. The area under the receiver operating characteristic curve (AUCs) for predicting the 1-year OS rates were 0.767 and 0.794 in training and validation cohorts. The calibration curve and decision curve indicated its predictive consistency and strong clinical utility. We also found those with low PLT (<100*10^9/L) and high CD8+Ts (>320 cells/μL) had a better prognosis.

Conclusion

We established a well-performing prognostic model for PVTT based on platelet functional parameters and CD8+Ts, and found that PT-8 formed by PLT and CD8+Ts was an excellent predictor of the prognosis of PVTT.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes

Background and Aims

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC.

Methods

Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes.

Results

HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified.

Conclusions

This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

Lipid Metabolism as a Potential Target of Liver Cancer

Hepatocellular carcinoma (HCC) stands as a severe malignant tumor with a profound impact on overall health, often accompanied by an unfavorable prognosis. Despite some advancements in the diagnosis and treatment of this disease, improving the prognosis of HCC remains a formidable challenge. It is noteworthy that lipid metabolism plays a pivotal role in the onset, development, and progression of tumor cells. Existing research indicates the potential application of targeting lipid metabolism in the treatment of HCC. This review aims to thoroughly explore the alterations in lipid metabolism in HCC, offering a detailed account of the potential advantages associated with innovative therapeutic strategies targeting lipid metabolism. Targeting lipid metabolism holds promise for potentially enhancing the prognosis of HCC.

Progress on the molecular mechanism of portal vein tumor thrombosis formation in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, with poor prognosis and high mortality. Early-stage HCC has no obvious clinical symptoms, and most patients are already at an advanced stage when they are diagnosed. Portal vein tumor thrombus (PVTT) is the most common complication and a poor prognostic factor for HCC, which frequently leads to portal vein hypertension, ascites, gastrointestinal bleeding, and tumor metastasis. The formation of PVTT is related to the complex structure and hemodynamic changes of the portal vein and is closely related to changes at the cellular and molecular levels. The differentially-expressed genes (DEGs) between PVTT and primary tumor (PT) suggest that the two tissues may have different clonal origins. Epigenetic and proteomic analyses also suggest complex and diverse mechanisms for the formation of PVTT. In addition, the tumor microenvironment and energy metabolism pathways are interrelated in regulating the invasion and progression of PVTT. Aerobic glycolysis and the tumor immune microenvironment have been the focus of recent studies on PVTT. In this review, we summarize the mechanism of PVTT formation at the cellular and molecular levels to provide information to guide better prevention and treatment of PVTT in the clinic.

Acetyl-CoA metabolism in cancer

Few metabolites can claim a more central and versatile role in cell metabolism than acetyl coenzyme A (acetyl-CoA). Acetyl-CoA is produced during nutrient catabolism to fuel the tricarboxylic acid cycle and is the essential building block for fatty acid and isoprenoid biosynthesis. It also functions as a signalling metabolite as the substrate for lysine acetylation reactions, enabling the modulation of protein functions in response to acetyl-CoA availability. Recent years have seen exciting advances in our understanding of acetyl-CoA metabolism in normal physiology and in cancer, buoyed by new mouse models, in vivo stable-isotope tracing approaches and improved methods for measuring acetyl-CoA, including in specific subcellular compartments. Efforts to target acetyl-CoA metabolic enzymes are also advancing, with one therapeutic agent targeting acetyl-CoA synthesis receiving approval from the US Food and Drug Administration. In this Review, we give an overview of the regulation and cancer relevance of major metabolic pathways in which acetyl-CoA participates. We further discuss recent advances in understanding acetyl-CoA metabolism in normal tissues and tumours and the potential for targeting these pathways therapeutically. We conclude with a commentary on emerging nodes of acetyl-CoA metabolism that may impact cancer biology.

ALYREF associated with immune infiltration is a prognostic biomarker in hepatocellular carcinoma

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ALYREF is a potential prognostic marker for hepatocellular carcinoma.

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ALYREF affects the biological function of hepatocellular carcinoma cells.

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ALYREF is associated with immune infiltration in hepatocellular carcinoma.

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The model constructed based on ALYREF-related immune genes provides a reference for the evaluation of immunotherapy.

Background

Although ALYREF has been demonstrated to have a role in a number of malignancies, its role in hepatocellular carcinoma (HCC) has received little attention. Our objective was to research at the prognostic value, biological role and relevance of ALYREF to the immune system in HCC.

Methods

The expression of ALYREF and its relationship with clinical parameters of HCC patients were analyzed by liver cancer cohort (LIHC) of The Cancer Genome Atlas. The expression and prognosis were verified by immunohistochemistry experiments. Gene transfection, CCK-8, scratch healing, transwell invasion and flow cytometry were used to assess the molecular function of ALYREF in vitro. The TIMER and TISIDB online data portals were used to assess the relevance of ALYREF to immunization. Stepwise regression analysis of ALYREF-related immune genes in the LIHC training set was used to construct a prognostic risk prediction model. Also, construct a nomogram to predict patient survival. The testing set for internal verification.

Results

Knockdown of ALYREF changed the biological phenotypes of HCC cells, such as proliferation, apoptosis, and invasion. In addition, the expression of ALYREF in HCC affected the level of immune cell infiltration and correlated with the overall survival time of patients. The constructed immune prognostic model allows for a valid assessment of patients.

Conclusion

ALYREF is increased in HCC, has an impact on cellular function and the immune system, and might be used as a prognostic marker.

Fatty Acid Synthase Is the Key Regulator of Fatty Acid Metabolism and Is Related to Immunotherapy in Bladder Cancer

Fatty acid metabolism (FAM) genes are potentially useful for predicting prognosis and immunotherapy response in bladder cancer (BC). To examine this, we constructed a prognostic model and identified key FAM genes in BC. Using transcriptional expression profiles and clinical data of BC patients from public datasets and Changhai (CH) hospital, we built and validated a risk-score model based on 13 prognostic FAM genes. Differential gene expression identified fatty acid synthase (FASN) as central to fatty acid metabolism in BC. FASN was differentially expressed between normal and tumor tissue, and was related to survival. In the CH dataset, FASN independently predicted muscle-invasive BC. FASN differential expression was significantly related to immune-cell infiltration and patients with low FASN expression responded better to immune checkpoint inhibitor (ICI) treatment. SREBF1 was predicted as the most significant transcription factor for FASN. Competing endogenous RNA network analysis suggested that lncRNA AC107027.3 may upregulate FASN by competitively binding miR-27A-3p, thereby regulating the immunotherapy response in BC. Dasatinib and temsirolimus are potential FASN-targeting drugs. Our model efficiently predicted prognosis in BC. FASN is central to fatty acid metabolism, and a potential indicator and regulator of ICI treatment.

Deciphering Cell-Type-Specific Gene Expression Signatures of Cardiac Diseases Through Reconstruction of Bulk Transcriptomes

Cardiac diseases compose a fatal disease category worldwide. Over the past decade, high-throughput transcriptome sequencing of bulk heart tissues has widened our understanding of the onset and progression of cardiac diseases. The recent rise of single-cell RNA sequencing (scRNA-seq) technology further enables deep explorations of their molecular mechanisms in a cell-type-specific manner. However, due to technical difficulties in performing scRNA-seq on heart tissues, there are still few scRNA-seq studies on cardiac diseases. In this study, we demonstrate that an effective alternative could be cell-type-specific computational reconstruction of bulk transcriptomes. An integrative bulk transcriptome dataset covering 110 samples from 12 studies was first constructed by re-analysis of raw sequencing data derived from the heart tissues of four common cardiac disease mouse models (myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy). Based on the single-cell reference covering four major cardiac component cell types and 22 immune cell subtypes, for each sample, the bulk transcriptome was reconstructed into cellular compositions and cell-type-specific expression profiles by CIBERSORTx. Variations in the estimated cell composition revealed elevated abundances of fibroblast and monocyte during myocardial infarction, which were further verified by our flow cytometry experiment. Moreover, through cell-type-specific differential gene expression and pathway enrichment analysis, we observed a series of signaling pathways that mapped to specific cell type in diseases, like MAPK and EGFR1 signaling pathways in fibroblasts in myocardial infarction. We also found an increased expression of several secretory proteins in monocytes which may serve as regulatory factors in cardiac fibrosis. Finally, a ligand–receptor analysis identified key cell types which may serve as hubs in cellular communication in cardiac diseases. Our results provide novel clues for the cell-type-specific signatures of cardiac diseases that would promote better understanding of their pathophysiological mechanisms.