Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proc Natl Acad Sci U S A. 2018;115:E11874-E11883. [PMID: 30482855 DOI: 10.1073/pnas.1807305115]

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Acyl-CoA Synthetase Medium-Chain Family Member 5-Mediated Fatty Acid Metabolism Dysregulation Promotes the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, with high incidence and mortality worldwide. Despite diagnostic and therapeutic advancements, HCC remains poorly responsive to treatment, with a poor prognosis. Understanding the molecular mechanisms driving HCC is crucial for developing effective therapies. Emerging evidence indicates that dysregulated fatty acid metabolism contributes to HCC. Acyl-CoA medium-chain synthetase 5 (ACSM5), involved in fatty acid metabolism, is down-regulated in HCC; however, its role is not well understood. This study was used to analyze ACSM5 expression in HCC patient samples and cell lines. The newly established ACSM5-overexpressing HCC cell lines, Huh7-ACSM5 and Hepa1-6-ACSM5, were used to investigate the effects and regulatory mechanisms of ACSM5. The results showed that ACSM5 was significantly down-regulated in HCC tumor tissues compared with non-tumor tissues. ACSM5 expression was regulated by DNA methylation, with a DNA methyltransferase 1 (DNMT1) inhibitor effectively increasing ACSM5 expression and reducing promoter region methylation. Overexpression of ACSM5 in Huh7 cells reduced fatty acid accumulation, decreased cell proliferation, migration, and invasion in vitro, and inhibited tumor growth in mouse xenografts. Furthermore, ACSM5 overexpression also decreased STAT3 phosphorylation, subsequently affecting downstream cytokine TGFB and FGF12 mRNA levels. These findings suggest that ACSM5 down-regulation contributes to HCC progression, providing insights into its oncogenic role and highlighting its potential as a biomarker and therapeutic target for HCC.

SLC13A3 is a major effector downstream of activated β-catenin in liver cancer pathogenesis

Activated Wnt/β-catenin pathway is a key genetic event in liver cancer development. Solute carrier (SLC) transporters are promising drug targets. Here, we identify SLC13A3 as a drug-targetable effector downstream of β-catenin in liver cancer. SLC13A3 expression is elevated in human liver cancer samples with gain of function (GOF) mutant CTNNB1, the gene encoding β-catenin. Activation of β-catenin up-regulates SLC13A3, leading to intracellular accumulation of endogenous SLC13A3 substrates. SLC13A3 is identified as a low-affinity transporter for glutathione (GSH). Silencing of SLC13A3 downregulates the leucine transporter SLC7A5 via c-MYC signaling, leading to leucine depletion and mTOR inactivation. Furthermore, silencing of SLC13A3 depletes GSH and induces autophagic ferroptosis in β-catenin-activated liver cancer cells. Importantly, both genetic inhibition of SLC13A3 and a small molecule SLC13A3 inhibitor suppress β-catenin-driven hepatocarcinogenesis in mice. Altogether, our study suggests that SLC13A3 could be a promising therapeutic target for treating human liver cancers with GOF CTNNB1 mutations.

Hedgehog components are overexpressed in a series of liver cancer cases

Liver cancers, including hepatocellular carcinoma (HCC), are the sixth most common cancer and the third leading cause of cancer-related death worldwide, representing a global public health problem. This study evaluated nine patients with HCC. Six of the cases involved hepatic explants, and three involved hepatic segmentectomy for tumor resection. Eight out of nine tumors were HCC, with one being a combined hepatocellular-cholangiocarcinoma tumor. Conventional markers of hepatocellular differentiation (Hep Par-1, arginase, pCEA, and glutamine synthetase) were positive in all patients, while markers of hepatic precursor cells (CK19, CK7, EpCAM, and CD56) were negative in most patients, and when positive, they were detected in small, isolated foci. Based on in silico analysis of HCC tumors from The Cancer Genome Atlas database, we found that Hedgehog (HH) pathway components (GLI1, GLI2, GLI3 and GAS1) have high connectivity values (module membership > 0.7) and are strongly correlated with each other and with other genes in biologically relevant modules for HCC. We further validated this finding by analyzing the gene expression of HH components (PTCH1, GLI1, GLI2 and GLI3) in our samples through qPCR, as well as by immunohistochemical analysis. Additionally, we conducted a chemosensitivity analysis using primary HCC cultures treated with a panel of 18 drugs that affect the HH pathway and/or HCC. Most HCC samples were sensitive to sunitinib. Our results offer a comprehensive view of the molecular landscape of HCC, highlighting the significance of the HH pathway and providing insight into focused treatments for HCC.

Comprehensive pan-cancer analysis of ACSS3 as a biomarker for prognosis and immunotherapy response

Background

ACSS3 (acyl-CoA synthetase short-chain family member 3) is found in numerous tissues and is linked to tumor cell type development and metastasis.

Methods

We conducted a comprehensive pan-cancer analysis of ACSS3. The TCGA (Cancer Genome Atlas), CPTAC (Clinical Proteomic Tumor Analysis Consortium), and HPA databases were used to ascertain the connection between ACSS3 and various types of tumors. Genes in the TCGA database would be identified using cBioPortal queries, and their transcriptome expression would then be verified using GEO data. ACSS3 expression and cellular localization in various tumor tissues of most cancer types were analyzed using single-cell sequencing data from the TISCH database. According to HPA and CPTAC databases, we analyzed and evaluated protein expression levels. Predictive analysis based on precise survival data of ACSS3 expression levels for 26 cancer types predicted using the TCGA database. Furthermore, we investigated the relationship between ACSS3 and immune microenvironments in different tumor tissues using the TIMER and TISCH databases. CellMiner, GDSC, and CTRP data would clarify the relationship between ACSS3 and drug resistance and explore the chemicals that affect ACSS3 expression. The final part of our study explored and validated the role ACSS3 played in glioma proliferation, migration, and invasion.

Results

ACSS3 is differentially expressed in various tumors and exhibits early diagnostic value. ACSS3 expression is associated with clinical features, and high ACSS expression anticipates a worse prognosis in multiple tumors and may impact drug sensitivity. The changes in the immunosuppressive microenvironment of gliomas are closely related to the upregulation of ACSS3.

Conclusions

ACSS3 is a novel biomarker for forecasting different human cancer prognoses, as it can influence the biological process by modulating the immune microenvironment. ACSS3 is a critical prognostic factor for glioma and is related to its proliferation, migration, and invasion.

Genome-wide expression analysis in a Fabry disease human podocyte cell line

Fabry disease (FD) is an X-linked lysosomal disease caused by an enzyme deficiency of alpha-galactosidase A (α-gal A). This deficiency leads to the accumulation of glycosphingolipids in lysosomes, resulting in a range of clinical symptoms. The complex pathogenesis of FD involves lysosomal dysfunction, altered autophagy, and mitochondrial abnormalities. Omics sciences, particularly transcriptomic analysis, comprehensively understand molecular mechanisms underlying diseases. This study focuses on genome-wide expression analysis in an FD human podocyte model to gain insights into the underlying mechanisms of podocyte dysfunction. Human control and GLA-edited podocytes were used. Gene expression data was generated using RNA-seq analysis, and differentially expressed genes were identified using DESeq2. Principal component analysis and Spearman correlation have explored gene expression trends. Functional enrichment and Reporter metabolite analyses were conducted to identify significantly affected metabolites and metabolic pathways. Differential expression analysis revealed 247 genes with altered expression levels in GLA-edited podocytes compared to control podocytes. Among these genes, 136 were underexpressed, and 111 were overexpressed in GLA-edited cells. Functional analysis of differentially expressed genes showed their involvement in various pathways related to oxidative stress, inflammation, fatty acid metabolism, collagen and extracellular matrix homeostasis, kidney injury, apoptosis, autophagy, and cellular stress response. The study provides insights into molecular mechanisms underlying Fabry podocyte dysfunction. Integrating transcriptomics data with genome-scale metabolic modeling further unveiled metabolic alterations in GLA-edited podocytes. This comprehensive approach contributes to a better understanding of Fabry disease and may lead to identifying new biomarkers and therapeutic targets for this rare lysosomal disorder.

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability

BACKGROUND

Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking.

METHODS

In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque.

RESULTS

Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment.

CONCLUSIONS

This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

The Role of Metabolic Reprogramming in the Tumor Immune Microenvironment: Mechanisms and Opportunities for Immunotherapy in Hepatocellular Carcinoma

As one of the emerging hallmarks of tumorigenesis and tumor progression, metabolic remodeling is common in the tumor microenvironment. Hepatocellular carcinoma (HCC) is the third leading cause of global tumor-related mortality, causing a series of metabolic alterations in response to nutrient availability and consumption to fulfill the demands of biosynthesis and carcinogenesis. Despite the efficacy of immunotherapy in treating HCC, the response rate remains unsatisfactory. Recently, research has focused on metabolic reprogramming and its effects on the immune state of the tumor microenvironment, and immune response rate. In this review, we delineate the metabolic reprogramming observed in HCC and its influence on the tumor immune microenvironment. We discuss strategies aimed at enhancing response rates and overcoming immune resistance through metabolic interventions, focusing on targeting glucose, lipid, or amino acid metabolism, as well as systemic regulation.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

WNT/β-catenin signaling in hepatocellular carcinoma: The aberrant activation, pathogenic roles, and therapeutic opportunities

Hepatocellular carcinoma (HCC) is a liver cancer, highly heterogeneous both at the histopathological and molecular levels. It arises from hepatocytes as the result of the accumulation of numerous genomic alterations in various signaling pathways, including canonical WNT/β-catenin, AKT/mTOR, MAPK pathways as well as signaling associated with telomere maintenance, p53/cell cycle regulation, epigenetic modifiers, and oxidative stress. The role of WNT/β-catenin signaling in liver homeostasis and regeneration is well established, whereas in development and progression of HCC is extensively studied. Herein, we review recent advances in our understanding of how WNT/β-catenin signaling facilitates the HCC development, acquisition of stemness features, metastasis, and resistance to treatment. We outline genetic and epigenetic alterations that lead to activated WNT/β-catenin signaling in HCC. We discuss the pivotal roles of CTNNB1 mutations, aberrantly expressed non-coding RNAs and complexity of crosstalk between WNT/β-catenin signaling and other signaling pathways as challenging or advantageous aspects of therapy development and molecular stratification of HCC patients for treatment.

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.

Multiomics identifies metabolic subtypes based on fatty acid degradation allocating personalized treatment in hepatocellular carcinoma

BACKGROUND AND AIMS

Molecular classification is a promising tool for prognosis prediction and optimizing precision therapy for HCC. Here, we aimed to develop a molecular classification of HCC based on the fatty acid degradation (FAD) pathway, fully characterize it, and evaluate its ability in guiding personalized therapy.

APPROACH AND RESULTS

We performed RNA sequencing (RNA-seq), PCR-array, lipidomics, metabolomics, and proteomics analysis of 41 patients with HCC, in which 17 patients received anti-programmed cell death-1 (PD-1) therapy. Single-cell RNA sequencing (scRNA-seq) was performed to explore the tumor microenvironment. Nearly, 60 publicly available multiomics data sets were analyzed. The associations between FAD subtypes and response to sorafenib, transarterial chemoembolization (TACE), immune checkpoint inhibitor (ICI) were assessed in patient cohorts, patient-derived xenograft (PDX), and spontaneous mouse model ls. A novel molecular classification named F subtype (F1, F2, and F3) was identified based on the FAD pathway, distinguished by clinical, mutational, epigenetic, metabolic, and immunological characteristics. F1 subtypes exhibited high infiltration with immunosuppressive microenvironment. Subtype-specific therapeutic strategies were identified, in which F1 subtypes with the lowest FAD activities represent responders to compounds YM-155 and Alisertib, sorafenib, anti-PD1, anti-PD-L1, and atezolizumab plus bevacizumab (T + A) treatment, while F3 subtypes with the highest FAD activities are responders to TACE. F2 subtypes, the intermediate status between F1 and F3, are potential responders to T + A combinations. We provide preliminary evidence that the FAD subtypes can be diagnosed based on liquid biopsies.

CONCLUSIONS

We identified 3 FAD subtypes with unique clinical and biological characteristics, which could optimize individual cancer patient therapy and help clinical decision-making.

ACSS3 regulates the metabolic homeostasis of epithelial cells and alleviates pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal interstitial lung disease of unknown etiology. The emerging evidence demonstrates that metabolic homeostatic imbalance caused by repetitive injuries of the alveolar epithelium is the potential pathogenesis of IPF. Proteomic analysis identified that Acetyl-CoA synthetase short chain family member 3 (ACSS3) expression was decreased in IPF patients and mice with bleomycin-induced fibrosis. ACSS3 participated in lipid and carbohydrate metabolism. Increased expression of ACSS3 downregulated carnitine palmitoyltransferase 1A (CPT-1A) and resulted in the accumulation of lipid droplets, while enhanced glycolysis which led to an increase in extracellular lactic acid levels in A549 cells. ACSS3 increases the production of succinyl-CoA through propionic acid metabolism, and decreases the generation of acetyl-CoA and ATP in alveolar epithelial cells. Overexpression of Acss3 inhibited the excessive deposition of ECM and attenuated the ground-glass opacity which determined by micro-CT in vivo. In a nutshell, our findings demonstrate that ACSS3 decreased the fatty acid oxidation through CPT1A deficiency and enhanced anaerobic glycolysis, this metabolic reprogramming deactivate the alveolar epithelial cells by lessen mitochondrial fission and fusion, increase of ROS production, suppression of mitophagy, promotion of apoptosis, suggesting that ACSS3 might be potential therapeutic target in pulmonary fibrosis.

Metabolic reprogramming in the tumor microenvironment of liver cancer

The liver is essential for metabolic homeostasis. The onset of liver cancer is often accompanied by dysregulated liver function, leading to metabolic rearrangements. Overwhelming evidence has illustrated that dysregulated cellular metabolism can, in turn, promote anabolic growth and tumor propagation in a hostile microenvironment. In addition to supporting continuous tumor growth and survival, disrupted metabolic process also creates obstacles for the anticancer immune response and restrains durable clinical remission following immunotherapy. In this review, we elucidate the metabolic communication between liver cancer cells and their surrounding immune cells and discuss how metabolic reprogramming of liver cancer impacts the immune microenvironment and the efficacy of anticancer immunotherapy. We also describe the crucial role of the gut-liver axis in remodeling the metabolic crosstalk of immune surveillance and escape, highlighting novel therapeutic opportunities.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

RBM45 reprograms lipid metabolism promoting hepatocellular carcinoma via Rictor and ACSL1/ACSL4

Reprogramming of lipid metabolism during hepatocarcinogenesis is not well elucidated. Here, we aimed to explore pivotal RNA-binding motif proteins (RBMs) in lipid metabolism and their therapeutic potential in hepatocellular carcinoma (HCC). Through bioinformatic analysis, we identified RBM45 as a critical gene of interest among differentially expressed RBMs in HCC, with significant prognostic relevance. RBM45 influenced the malignant biological phenotype and lipid metabolism of HCC cells. Mechanically, RBM45 promotes de novo lipogenesis in HCC by directly targeting two key enzymes involved in long-chain fatty acid synthesis, ACSL1 and ACSL4. RBM45 also targets Rictor, which has been demonstrated to modulate lipid metabolism profoundly. RBM45 also aided lipid degradation through activating a key fatty acid β oxidation enzyme, CPT1A. Thus, RBM45 boosted lipid synthesis and decomposition, indicating an enhanced utility of lipid fuels in HCC. Clinically, body mass index was positively correlated with RBM45 in human HCCs. The combination of a PI3K/AKT/mTOR pathway inhibitor in vitro or Sorafenib in orthotopic liver cancer mouse models with shRBM45 has a more significant therapeutic effect on liver cancer than the drug alone. In summary, our findings highlight the versatile roles of RBM45 in lipid metabolism reprogramming and its therapeutic potential in HCC. Lipids induced RBM45 expression. In turn, RBM45 promoted the utility of lipid in HCCs through accelerating both de novo lipogenesis and fatty acid β oxidation, which required the participation of Rictor, a core component of mTORC2 that has been demonstrated to modulate lipid metabolism potently, as well as ACSL1/ACSL4, two key enzymes of long-chain fatty acid synthesis. When the first-line chemotherapy drug sorafenib is combined with a PI3K/AKT/mTOR pathway inhibitor (MK2206 is an AKT inhibitor, rapamycin is a mTOR inhibitor, and inhibiting RBM45 can significantly inhibit Rictor), cell cycle, proliferation, lipid metabolism reprogramming, and hepatocarcinogenesis can be significantly inhibited, while apoptosis can be significantly enhanced.

Aldolase A promotes cervical cancer cell radioresistance by regulating the glycolysis and DNA damage after irradiation

Radioresistance is the major obstacle that affects the efficacy of radiotherapy which is an important treatment for cervical cancer. By analyzing the databases, we found that aldolase A (ALDOA), which is a key enzyme in metabolic reprogramming, has a higher expression in cervical cancer patients and is associated with poor prognosis. We detected the expression of ALDOA in the constructed cervical cancer radioresistance (RR) cells by repetitive irradiation and found that it was upregulated compared to the control cells. Functional assays were conducted and the results showed that the knockdown of ALDOA in cervical cancer RR cells inhibited the proliferation, migration, and clonogenic abilities by regulating the cell glycolysis. In addition, downregulation of ALDOA enhanced radiation-induced apoptosis and DNA damage by causing G2/M phase arrest and further promoted radiosensitivity of cervical cancer cells. The functions of ALDOA in regulating tumor radiosensitivity were also verified by the mouse tumor transplantation model in vivo. Therefore, our study provides new insights into the functions of ALDOA in regulating the efficacy of radiotherapy and indicates that ALDOA might be a promising target for enhancing radiosensitivity in treating cervical cancer patients.

Semaglutide reduces tumor burden in the GAN diet-induced obese and biopsy-confirmed mouse model of NASH-HCC with advanced fibrosis

Non-alcoholic steatohepatitis (NASH) is emerging as a major cause of hepatocellular carcinoma (HCC), however, it is not resolved if compounds in late-stage clinical development for NASH may have additional therapeutic benefits in NASH-driven HCC (NASH-HCC). Here, we profiled monotherapy with semaglutide (glucagon-like-receptor-1 receptor agonist) and lanifibranor (pan-peroxisome proliferator-activated receptor agonist) in a diet-induced obese (DIO) mouse model of NASH-HCC. Disease progression was characterized in male C57BL/6 J mice fed the GAN (Gubra Amylin NASH) diet high in fat, fructose and cholesterol for 12-72 weeks (n = 15 per group). Other GAN DIO-NASH-HCC mice fed the GAN diet for 54 weeks and with biopsy-confirmed NASH (NAFLD Activity Score ≥ 5) and advanced fibrosis (stage F3) received vehicle (n = 16), semaglutide (30 nmol/kg, s.c., n = 15), or lanifibranor (30 mg/kg, p.o., n = 15) once daily for 14 weeks. GAN DIO-NASH-HCC mice demonstrated progressive NASH, fibrosis and HCC burden. Tumors presented with histological and molecular signatures of poor prognostic HCC. Consistent with clinical trial outcomes in NASH patients, both lanifibranor and semaglutide improved NASH while only lanifibranor reduced fibrosis in GAN DIO-NASH-HCC mice. Notably, only semaglutide reduced tumor burden in GAN DIO-NASH-HCC mice. In conclusion, the GAN DIO-NASH-HCC mouse is a clinical translational model of NASH-HCC. Semaglutide improves both NASH and tumor burden in GAN DIO-NASH-HCC mice, highlighting the suitability of this preclinical model for profiling novel drug therapies targeting NASH-HCC.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Recent progress in the development of nanomaterials targeting multiple cancer metabolic pathways: a review of mechanistic approaches for cancer treatment

Cancer is a very heterogeneous disease, and uncontrolled cell division is the main characteristic of cancer. Cancerous cells need a high nutrition intake to enable aberrant growth and survival. To do so, cancer cells modify metabolic pathways to produce energy and anabolic precursors and preserve redox balance. Due to the importance of metabolic pathways in tumor growth and malignant transformation, metabolic pathways have also been given promising perspectives for cancer treatment, providing more effective treatment strategies, and target-specific with minimum side effects. Metabolism-based therapeutic nanomaterials for targeted cancer treatment are a promising option. Numerous types of nanoparticles (NPs) are employed in the research and analysis of various cancer therapies. The current review focuses on cutting-edge strategies and current cancer therapy methods based on nanomaterials that target various cancer metabolisms. Additionally, it highlighted the primacy of NPs-based cancer therapies over traditional ones, the challenges, and the future potential.

Metabolic reprogramming and its clinical implication for liver cancer

Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated β-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.

MYC in liver cancer: mechanisms and targeted therapy opportunities

MYC, a major oncogenic transcription factor, regulates target genes involved in various pathways such as cell proliferation, metabolism and immune evasion, playing a critical role in the tumor initiation and development in multiple types of cancer. In liver cancer, MYC and its signaling pathways undergo significant changes, exerting a profound impact on liver cancer progression, including tumor proliferation, metastasis, dedifferentiation, metabolism, immune microenvironment, and resistance to comprehensive therapies. This makes MYC an appealing target, despite it being previously considered an undruggable protein. In this review, we discuss the role and mechanisms of MYC in liver physiology, chronic liver diseases, hepatocarcinogenesis, and liver cancer progression, providing a theoretical basis for targeting MYC as an ideal therapeutic target for liver cancer. We also summarize and prospect the strategies for targeting MYC, including direct and indirect approaches to abolish the oncogenic function of MYC in liver cancer.

The phospholipase PNPLA7 functions as a positive indicator in human colorectal and gastric cancers

Early diagnosis of gastrointestinal tumors remains a clinical challenge due to their insidious onset. Patatin-like phospholipase domain containing protein 7 (PNPLA7) has been shown to be associated with the occurrence and development of hepatocellular carcinoma. However, the expressions of PNPLA7 in colorectal and gastric cancers remain unclear. The online gene expression profiling interactive analysis and Kaplan-Meier Plotter databases were used for the analysis of the expression of PNPLA7 and the survival curve, respectively. The tumor tissues and their corresponding normal noncancerous tissues from colorectal cancer or gastric cancer patients were collected and quantitative real-time polymerase chain reaction assay was performed to evaluate the expression of related genes. PNPLA7 was significantly down-regulated in gastric and colorectal cancer tumor tissues compared to adjacent normal tissues. Receiver operating characteristic analysis showed that PNPLA7 could be used as a diagnostic marker for gastric and colorectal tumors. The overall survival of patients with high expression of PNPLA7 was also significantly higher than that of patients with low expression in stomach and rectum adenocarcinoma. Phospholipase PNPLA7 can be used as a positive diagnostic indicator for colorectal and gastric cancers.

Nrf2 Drives Hepatocellular Carcinoma Progression through Acetyl-CoA-Mediated Metabolic and Epigenetic Regulatory Networks

Correlations between the oxidative stress response and metabolic reprogramming have been observed during malignant tumor formation; however, the detailed mechanism remains elusive. The transcription factor Nrf2, a master regulator of the oxidative stress response, mediates metabolic reprogramming in multiple cancers. In a mouse model of hepatocellular carcinoma (HCC), through metabolic profiling, genome-wide gene expression, and chromatin structure analyses, we present new evidence showing that in addition to altering antioxidative stress response signaling, Nrf2 ablation impairs multiple metabolic pathways to reduce the generation of acetyl-CoA and suppress histone acetylation in tumors, but not in tumor-adjacent normal tissue. Nrf2 ablation and dysregulated histone acetylation impair transcription complex assembly on downstream target antioxidant and metabolic regulatory genes for expression regulation. Mechanistic studies indicate that the regulatory function of Nrf2 is low glucose dependent, the effect of which is demolished under energy refeeding. Together, our results implicate an unexpected effect of Nrf2 on acetyl-CoA generation, in addition to its classic antioxidative stress response regulatory activity, integrates metabolic and epigenetic programs to drive HCC progression.

IMPLICATIONS

This study highlights that Nrf2 integrates metabolic and epigenetic regulatory networks to dictate tumor progression and that Nrf2 targeting is therapeutically exploitable in HCC treatment.

Tumor steatosis and glutamine synthetase expression in patients with advanced hepatocellular carcinoma receiving atezolizumab plus bevacizumab therapy

AIM

The anti-programmed death-ligand 1 antibody atezolizumab and vascular endothelial growth factor-neutralizing antibody bevacizumab in combination (Atezo + Bev) have become the first-line therapy in advanced hepatocellular carcinoma (HCC). Distinct types of tumor immune microenvironment (TIME) and their associations with specific molecular subclasses and driver gene mutations have been identified in HCC; however, these insights are mainly based on surgically resected early-stage tumors. The current study aimed to reveal the biology and TIME of advanced HCC and their significance in predicting clinical outcomes of Atezo + Bev therapy.

METHODS

Thirty-three patients with advanced HCC who were scheduled for treatment with Atezo + Bev therapy were included in this study. Pretreatment tumor biopsy, pre- and posttreatment diffusion-weighted magnetic resonance imaging (MRI) with nine b values (0-1500 s/mm2 ), and other clinicopathologic factors were analyzed.

RESULTS

Compared with resectable HCC, advanced HCC was characterized by higher proliferative activity, a higher frequency of Wnt/β-catenin-activated HCC, and lower lymphocytic infiltration. Prognostically, two metabolism-related factors, histopathologically determined tumor steatosis and/or glutamine synthetase (GS) expression, and MRI-determined tumor steatosis, were the most significant prognostic indicators for progression-free survival (PFS) and overall survival after Atezo + Bev therapy. Furthermore, changes in the pre- and posttreatment true diffusion coefficients on MRI, which might reflect changes in TIME after treatment, were significantly associated with better PFS.

CONCLUSIONS

The biology and TIME of HCC were strikingly different in advanced HCC compared with those of surgically resected HCC. Two metabolism-related factors, pathologically determined tumor steatosis and/or GS expression, and MRI-determined tumor steatosis, were found to be the most significant prognostic indicators for Atezo + Bev therapy in advanced HCC.

The role of CXCL8 and CCNB1 in predicting hepatocellular carcinoma in the context of cirrhosis: implications for early detection and immune-based therapies

BACKGROUND

Cirrhosis is a serious condition characterized by the replacement of healthy liver tissue with scar tissue, which can progress to liver failure if left untreated. Hepatocellular carcinoma (HCC) is a concerning complication of cirrhosis. It can be challenge to identify individuals with cirrhosis who are at high risk of developing HCC, particularly in the absence of known risk factors.

METHODS

In this study, statistical and bioinformatics methods were utilized to construct a protein-protein interaction network and identify disease-related hub genes. We analyzed two hub genes, CXCL8 and CCNB1, and developed a mathematical model to predict the likelihood of developing HCC in individuals with cirrhosis. We also investigated immune cell infiltration, functional analysis under ontology terms, pathway analysis, distinct clusters of cells, and protein-drug interactions.

RESULTS

The results indicated that CXCL8 and CCNB1 were associated with the development of cirrhosis-induced HCC. A prognostic model based on these two genes was able to predict the occurrence and survival time of HCC. In addition, the candidate drugs were also discovered based on our model.

CONCLUSION

The findings offer the potential for earlier detection of cirrhosis-induced HCC and provide a new instrument for clinical diagnosis, prognostication, and the development of immunological medications. This study also identified distinct clusters of cells in HCC patients using UMAP plot analysis and analyzed the expression of CXCL8 and CCNB1 within these cells, indicating potential therapeutic opportunities for targeted drug therapies to benefit HCC patients.

Participation of protein metabolism in cancer progression and its potential targeting for the management of cancer

Cancer malignancies may broadly be described as heterogeneous disorders manifested by uncontrolled cellular growth/division and proliferation. Tumor cells utilize metabolic reprogramming to accomplish the upregulated nutritional requirements for sustaining their uncontrolled growth, proliferation, and survival. Metabolic reprogramming also called altered or dysregulated metabolism undergoes modification in normal metabolic pathways for anabolic precursor's generation that serves to continue biomass formation that sustains the growth, proliferation, and survival of carcinogenic cells under a nutrition-deprived microenvironment. A wide range of dysregulated/altered metabolic pathways encompassing different metabolic regulators have been described; however, the current review is focused to explain deeply the metabolic pathways modifications inducing upregulation of proteins/amino acids metabolism. The essential modification of various metabolic cycles with their consequent outcomes meanwhile explored promising therapeutic targets playing a pivotal role in metabolic regulation and is successfully employed for effective target-specific cancer treatment. The current review is aimed to understand the metabolic reprogramming of different proteins/amino acids involved in tumor progression along with potential therapeutic perspective elucidating targeted cancer therapy via these targets.

Intratumoral microbial heterogeneity affected tumor immune microenvironment and determined clinical outcome of HBV-related HCC

BACKGROUND AND AIMS

The intratumoral microbiome has been reported to regulate the development and progression of cancers. We aimed to characterize intratumoral microbial heterogeneity (IMH) and establish microbiome-based molecular subtyping of HBV-related HCC to elucidate the correlation between IMH and HCC tumorigenesis.

APPROACH AND RESULTS

A case-control study was designed to investigate microbial landscape and characteristic microbial signatures of HBV-related HCC tissues adopting metagenomics next-generation sequencing. Microbiome-based molecular subtyping of HCC tissues was established by nonmetric multidimensional scaling. The tumor immune microenvironment of 2 molecular subtypes was characterized by EPIC and CIBERSORT based on RNA-seq and verified by immunohistochemistry. The gene set variation analysis was adopted to explore the crosstalk between the immune and metabolism microenvironment. A prognosis-related gene risk signature between 2 subtypes was constructed by the weighted gene coexpression network analysis and the Cox regression analysis and then verified by the Kaplan-Meier survival curve.IMH demonstrated in HBV-related HCC tissues was comparably lower than that in chronic hepatitis tissues. Two microbiome-based HCC molecular subtypes, defined as bacteria- and virus-dominant subtypes, were established and significantly correlated with discrepant clinical-pathologic features. Higher infiltration of M2 macrophage was detected in the bacteria-dominant subtype with to the virus-dominant subtype, accompanied by multiple upregulated metabolism pathways. Furthermore, a 3-gene risk signature containing CSAG4 , PIP4P2 , and TOMM5 was filtered out, which could predict the clinical prognosis of HCC patients accurately using the Cancer Genome Atlas data.

CONCLUSIONS

Microbiome-based molecular subtyping demonstrated IMH of HBV-related HCC was correlated with a disparity in clinical-pathologic features and tumor microenvironment (TME), which might be proposed as a biomarker for prognosis prediction of HCC.

SL-scan identifies synthetic lethal interactions in cancer using metabolic networks

Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies.

Hepatocellular Carcinoma Genetic Classification

ABSTRACT

Hepatocellular carcinoma (HCC) represents a significant global burden, with management complicated by its heterogeneity, varying presentation, and relative resistance to therapy. Recent advances in the understanding of the genetic, molecular, and immunological underpinnings of HCC have allowed a detailed classification of these tumors, with resultant implications for diagnosis, prognostication, and selection of appropriate treatments. Through the correlation of genomic features with histopathology and clinical outcomes, we are moving toward a comprehensive and unifying framework to guide our diagnostic and therapeutic approach to HCC.

Emerging therapies in cancer metabolism

Metabolic reprogramming in cancer is not only a biological hallmark but also reveals treatment vulnerabilities. Numerous metabolic molecules have shown promise as treatment targets to impede tumor progression in preclinical studies, with some advancing to clinical trials. However, the intricacy and adaptability of metabolic networks hinder the effectiveness of metabolic therapies. This review summarizes the metabolic targets for cancer treatment and provides an overview of the current status of clinical trials targeting cancer metabolism. Additionally, we decipher crucial factors that limit the efficacy of metabolism-based therapies and propose future directions. With advances in integrating multi-omics, single-cell, and spatial technologies, as well as the ability to track metabolic adaptation more precisely and dynamically, clinicians can personalize metabolic therapies for improved cancer treatment.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Comprehensive Analysis and Drug Modulation of Human Endogenous Retrovirus in Hepatocellular Carcinomas

BACKGROUND

Human endogenous retroviruses (HERVs) play an important role in the development of cancer and many diseases. Here, we comprehensively explored the impact of HERVs on hepatocellular carcinomas (HCCs).

METHODS

We employed Telescope to identify HERVs and quantify their expression in the total RNA sequencing data obtained from 254 HCC samples, comprising 254 tumor tissues and 34 matched normal tissues.

RESULTS

In total, 3357 locus-specific activations of HERVs were differentially expressed, and 180 were correlated with patient survival. Using these 180 HERVs for classification, we found four subgroups with survival correlation. Higher expression levels of the 180 HERVs were correlated with poorer survival, while age, AFP, some mutations, and copy and structural variants differed among subgroups. The differential expression of host genes in high expression of these 180 HERVs primarily involved the activation of pathways related to immunity and infection, lipid and atherosclerosis, MAPK and NF-kB signaling, and cytokine-cytokine receptor interactions. Conversely, there was a suppression of pathways associated with RNA processing, including nucleocytoplasmic transport, surveillance and ribosome biogenesis, and transcriptional misregulation in cancer pathways. Almost all genes involved in HERV activation restriction, KRAB zinc finger proteins, RNA nucleocytoplasmic transport, stemness, HLA and antigen processing and presentation, and immune checkpoints were overexpressed in cancerous tissues, and many over-expressed HERV-related nearby genes were correlated with high HERV activation and poor survival. Twenty-three immune and stromal cells showed higher expression in non-cancerous than cancerous tissues, and seven were correlated with HERV activation. Small-molecule modulation of alternative splicing (AS) altered the expression of survival-related HERVs and their activation-related genes, as well as nearby genes.

CONCLUSION

Comprehensive and integrated approaches for evaluating HERV expression and their correlation with specific pathways have the potential to provide new companion diagnostics and therapeutic strategies for HCC.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Cell line-specific features of 3D chromatin organization in hepatocellular carcinoma

Liver cancer, particularly hepatocellular carcinoma (HCC), poses a significant global threat to human lives. To advance the development of innovative diagnostic and treatment approaches, it is essential to examine the hidden features of HCC, particularly its 3D genome architecture, which is not well understood. In this study, we investigated the 3D genome organization of four HCC cell lines-Hep3B, Huh1, Huh7, and SNU449-using in situ Hi-C and assay for transposase-accessible chromatin sequencing. Our findings revealed that HCC cell lines had more long-range interactions, both intra-and interchromosomal, compared to human mammary epithelial cells (HMECs). Unexpectedly, HCC cell lines displayed cell line-specific compartmental modifications at the megabase (Mb) scale, which could potentially be leveraged in determining HCC subtypes. At the sub-Mb scale, we observed decreases in intra-TAD (topologically associated domain) interactions and chromatin loops in HCC cell lines compared to HMECs. Lastly, we discovered a correlation between gene expression and the 3D chromatin architecture of SLC8A1, which encodes a sodium-calcium antiporter whose modulation is known to induce apoptosis by comparison between HCC cell lines and HMECs. Our findings suggest that HCC cell lines have a distinct 3D genome organization that is different from those of normal and other cancer cells based on the analysis of compartments, TADs, and chromatin loops. Overall, we take this as evidence that genome organization plays a crucial role in cancer phenotype determination. Further exploration of epigenetics in HCC will help us to better understand specific gene regulation mechanisms and uncover novel targets for cancer treatment.

Cell metabolism-based therapy for liver fibrosis, repair, and hepatocellular carcinoma

Progression of chronic liver injury to fibrosis, abnormal liver regeneration, and HCC is driven by a dysregulated dialog between epithelial cells and their microenvironment, in particular immune, fibroblasts, and endothelial cells. There is currently no antifibrogenic therapy, and drug treatment of HCC is limited to tyrosine kinase inhibitors and immunotherapy targeting the tumor microenvironment. Metabolic reprogramming of epithelial and nonparenchymal cells is critical at each stage of disease progression, suggesting that targeting specific metabolic pathways could constitute an interesting therapeutic approach. In this review, we discuss how modulating intrinsic metabolism of key effector liver cells might disrupt the pathogenic sequence from chronic liver injury to fibrosis/cirrhosis, regeneration, and HCC.

PI3K/AKT/mTOR Signaling Pathway in HPV-Driven Head and Neck Carcinogenesis: Therapeutic Implications

High-risk human papillomaviruses (HR-HPVs) are the causal agents of cervical, anogenital and a subset of head and neck carcinomas (HNCs). Indeed, oropharyngeal cancers are a type of HNC highly associated with HR-HPV infections and constitute a specific clinical entity. The oncogenic mechanism of HR-HPV involves E6/E7 oncoprotein overexpression for promoting cell immortalization and transformation, through the downregulation of p53 and pRB tumor suppressor proteins, among other cellular targets. Additionally, E6/E7 proteins are involved in promoting PI3K/AKT/mTOR signaling pathway alterations. In this review, we address the relationship between HR-HPV and PI3K/AKT/mTOR signaling pathway activation in HNC with an emphasis on its therapeutic importance.

A key driver to promote HCC: Cellular crosstalk in tumor microenvironment

Liver cancer is the third greatest cause of cancer-related mortality, which of the major pathological type is hepatocellular carcinoma (HCC) accounting for more than 90%. HCC is characterized by high mortality and is predisposed to metastasis and relapse, leading to a low five-year survival rate and poor clinical prognosis. Numerous crosstalk among tumor parenchymal cells, anti-tumor cells, stroma cells, and immunosuppressive cells contributes to the immunosuppressive tumor microenvironment (TME), in which the function and frequency of anti-tumor cells are reduced with that of associated pro-tumor cells increasing, accordingly resulting in tumor malignant progression. Indeed, sorting out and understanding the signaling pathways and molecular mechanisms of cellular crosstalk in TME is crucial to discover more key targets and specific biomarkers, so that develop more efficient methods for early diagnosis and individualized treatment of liver cancer. This piece of writing offers insight into the recent advances in HCC-TME and reviews various mechanisms that promote HCC malignant progression from the perspective of mutual crosstalk among different types of cells in TME, aiming to assist in identifying the possible research directions and methods in the future for discovering new targets that could prevent HCC malignant progression.

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

Decreased propionyl-CoA metabolism facilitates metabolic reprogramming and promotes hepatocellular carcinoma

BACKGROUND & AIMS

Alterations of multiple metabolites characterize distinct features of metabolic reprograming in hepatocellular carcinoma (HCC). However, the role of most metabolites, including propionyl-CoA (Pro-CoA), in metabolic reprogramming and hepatocarcinogenesis remains elusive. In this study, we aimed to dissect how Pro-CoA metabolism affects these processes.

METHODS

TCGA data and HCC samples were used to analyze ALDH6A1-mediated Pro-CoA metabolism and its correlation with HCC. Multiple metabolites were assayed by targeted mass spectrometry. The role of ALDH6A1-generated Pro-CoA in HCC was evaluated in HCC cell lines as well as xenograft nude mouse models and primary liver cancer mouse models. Non-targeted metabolomic and targeted energy metabolomic analyses, as well as multiple biochemical assays, were performed.

RESULTS

Decreases in Pro-CoA and its derivative propionyl-L-carnitine due to ALDH6A1 downregulation were tightly associated with HCC. Functionally, ALDH6A1-mediated Pro-CoA metabolism suppressed HCC proliferation in vitro and impaired hepatocarcinogenesis in mice. The aldehyde dehydrogenase activity was indispensable for this function of ALDH6A1, while Pro-CoA carboxylases antagonized ALDH6A1 function by eliminating Pro-CoA. Mechanistically, ALDH6A1 caused a signature enrichment of central carbon metabolism in cancer and impaired energy metabolism: ALDH6A1-generated Pro-CoA suppressed citrate synthase activity, which subsequently reduced tricarboxylic acid cycle flux, impaired mitochondrial respiration and membrane potential, and decreased ATP production. Moreover, Pro-CoA metabolism generated 2-methylcitric acid, which mimicked the inhibitory effect of Pro-CoA on citrate synthase and dampened mitochondrial respiration and HCC proliferation.

CONCLUSIONS

The decline of ALDH6A1-mediated Pro-CoA metabolism contributes to metabolic remodeling and facilitates hepatocarcinogenesis. Pro-CoA, propionyl-L-carnitine and 2-methylcitric acid may serve as novel metabolic biomarkers for the diagnosis and treatment of HCC. Pro-CoA metabolism may provide potential targets for development of novel strategies against HCC.

IMPACT AND IMPLICATIONS

Our study presents new insights on the role of propionyl-CoA metabolism in metabolic reprogramming and hepatocarcinogenesis. This work has uncovered potential diagnostic and predictive biomarkers, which could be used by physicians to improve clinical practice and may also serve as targets for the development of therapeutic strategies against HCC.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Pharmacological targeting of MTHFD2 suppresses NSCLC via the regulation of ILK signaling pathway

Lung cancer is the most common cause of cancer related deaths worldwide with the highest mortality rate. Non-small cell lung cancer (NSCLC) accounts for about 85 % of lung cancers. Mitochondrial methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a bifunctional enzyme and is the most differentially expressed metabolic enzyme in various tumors including lung cancer. However, little is known about how MTHFD2 functions in NSCLC. Integrin-linked kinase (ILK) signaling plays key a role in tumor progression including metastasis, proliferation and migration. Here, we show that MTHFD2 inhibition results in suppression of cell growth, migration, invasion and epithelial-mesenchymal transition (EMT) in NSCLC. Microarray analysis suggests that MTHFD2 is positively associated with ILK signaling based on western blotting results. In addition, the phosphorylation of AMPKα plays an essential role in MTHFD2 regulation of ILK signaling. Further, the small-molecule compound C18 inhibits MTHFD2 with great efficiency. C18 blocks MTHFD2/ILK signaling pathway and restrains cell growth, migration, invasion, and EMT of NSCLC and induces apoptosis. In brief, our study found that the positive impact of MTHFD2 is mediated via ILK signaling pathway in NSCLC. Thus, blocking MTHFD2 represents a promising therapeutic strategy against NSCLC clinically.

The immune-metabolic crosstalk between CD3+C1q+TAM and CD8+T cells associated with relapse-free survival in HCC

Introduction

Although multiple targeted treatments have appeared, hepatocellular carcinoma (HCC) is still one of the most common causes of cancer-related deaths. The immunosuppressive tumor microenvironment (TME) is a critical factor in the oncogenesis and progression of HCC. The emerging scRNA-seq makes it possible to explore the TME at a high resolution. This study was designed to reveal the immune-metabolic crosstalk between immune cells in HCC and provide novel strategies to regulate immunosuppressive TME.

Method

In this study, we performed scRNA-seq on paired tumor and peri-tumor tissues of HCC. The composition and differentiation trajectory of the immune populations in TME were portrayed. Cellphone DB was utilized to calculate interactions between the identified clusters. Besides, flow cytometry, RT-PCR and seahorse experiments were implemented to explore potential metabolic and epigenetic mechanisms of the inter-cellular interaction.

Result

A total of 19 immune cell clusters were identified and 7 were found closely related to HCC prognosis. Besides, differentiation trajectories of T cells were also presented. Moreover, a new population, CD3+C1q+ tumor-associated macrophages (TAM) were identified and found significantly interacted with CD8+ CCL4+T cells. Compared to the peri-tumor tissue, their interaction was attenuated in tumor. Additionally, the dynamic presence of this newly found cluster was also verified in the peripheral blood of patients with sepsis. Furthermore, we found that CD3+C1q+TAM affected T cell immunity through C1q signaling-induced metabolic and epigenetic reprogramming, thereby potentially affecting tumor prognosis.

Conclusion

Our study revealed the interaction between CD3+C1q+TAM and CD8+ CCL4+T cells and may provide implications for tackling the immunosuppressive TME in HCC.

Integrated genomic analyses of hepatocellular carcinoma

BACKGROUND

Genomic alterations play important roles in the development of cancer. We explored the impact of protein-coding genes and transcriptomic changes on clinical and molecular alterations in Taiwanese hepatocellular carcinoma (HCC) patients.

METHODS

We analyzed 147 whole-exome sequencing and 100 RNA sequencing datasets of HCC and compared them with The Cancer Genome Atlas (TCGA)-Liver Hepatocellular Carcinoma cohort and develop a panel of 81 apoptosis-related genes for molecular classification.

RESULTS

TERT (50%), TP53 (25%), CTNNB1 (14%), ARID1A (12%), and KMT2C (11%) were the most common genetic alterations of cancer-related genes. ALDH2 and KMT2C mutated at much higher frequencies in our cohort than in TCGA, whereas CTNNB1 was found only in 14% of our Taiwanese patients. A high germline mutation rate of ALDH2 in the APOBEC mutational signature and herb drug-related aristolochic acid-associated signature was also observed. Groups A and B of HCC were identified when we used apoptosis-related genes for molecular classification. The latter group, which had poorer survival outcomes, had significantly more aDC, CD4+ Tem, macrophages M2, NKT, plasma cells, and Th1 cells, and less CD4+ memory T cells, CD8+ Tcm, cDC, iDC, and Th2 cells, as well as more inter-chromosome fusion genes. Metatranscriptomic analysis revealed 54 cases of HBV infection. Moreover, we found that the main target gene of HBV integration is ALB.

CONCLUSIONS

Unique genomic alterations were observed in our Taiwanese HCC patients. Molecular classification using apoptosis-related genes could lead to new therapeutic approaches for HCC.

The Role of PI3K/AKT/mTOR Signaling in Hepatocellular Carcinoma Metabolism

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths in the world. Metabolic reprogramming is considered a new hallmark of cancer, but it remains unclearly described in HCC. The dysregulation of the PI3K/AKT/mTOR signaling pathway is common in HCC and is, therefore, a topic of further research and the concern of developing a novel target for liver cancer therapy. In this review, we illustrate mechanisms by which this signaling network is accountable for regulating HCC cellular metabolism, including glucose metabolism, lipid metabolism, amino acid metabolism, pyrimidine metabolism, and oxidative metabolism, and summarize the ongoing clinical trials based on the inhibition of the PI3K/AKT/mTOR pathway in HCC.

Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq

PURPOSE

Cancer cells maintain cell growth, division, and survival through altered energy metabolism. However, research on metabolic reprogramming in lung adenocarcinoma (LUAD) is limited METHODS: We downloaded TCGA and GEO sequencing data. Consistent clustering with the ConsensusClusterPlus package was employed to detect the scores for four metabolism-related pathways. The LUAD samples in the TCGA dataset were clustered with ConsensusClusterPlus, and the optimal number of clusters was determined according to the cumulative distribution function (CDF). The cell score for each sample in the TCGA dataset was calculated using the MCPcounter estimate function of the MCPcounter package.

RESULTS

We identified two subtypes by scoring the samples based on the 4 metabolism-related pathways and cluster dimensionality reduction. The prognosis of cluster B was obviously poorer than that of cluster A in patients with LUAD. The analysis of single-nucleotide variation (SNV) data showed that the top 15 genes in the four metabolic pathways with the most mutations were TKTL2, PGK2, HK3, EHHADH, GLUD2, PKLR, TKTL1, HADHB, CPT1C, HK1, HK2, PFKL, SLC2A3, PFKFB1, and CPT1A. The IFNγ score of cluster B was significantly higher than that of cluster A. The immune T-cell lytic activity score of cluster B was significantly higher than that of cluster A. We further identified 5 immune cell subsets from single-cell sequencing data. The top 5 marker genes of B cells were IGHM, JCHAIN, IGLC3, IGHA1, and IGKC. The C0 subgroup of monocytes had a higher pentose phosphate pathway (PPP) score than the C6 subgroup.

CONCLUSIONS

Metabolism-related subtypes could be potential biomarkers in the prognosis prediction and treatment of LUAD.

A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma

Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and 𝛼-fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity.