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Schematic representation of biochemical pathways in HCC using consistently altered metabolic genes or their encoded proteins. Metabolic targets that are down-regulated are presented in green, with those up-regulated in red; metabolites are in black. Those we detected to be significantly altered at protein level are not in italics. *Predicts survival. **Predicts survival and varied with tumor size. ***Previously identified to be of clinical significance in HCC either as a drug target, biomarker, or prognostic indicator (Supplementary Table 1). Differentially expressed in 6 datasets, but did not reach selection threshold in 1 of the datasets. Within this axis are nucleotide biosynthesis targets PPAT, GART, PFAS, PAICS, ADSL, and ATIC, all of which are up-regulated (the nonitalicized names were detected at protein level). Steps from phosphoenolpyruvate (PEP) to Fructose-1,6-bisphosphate (F-1,6-P). 3-PG, 3-phoshoglycerate; a-KG, alpha ketoglutarate; ADS, adenylosuccinate; AMP, adenosine monophosphate; F-6-P, fructose-6-bisphosphate; G-6-P, glucose-6-phosphate; CTP,
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Background & Aims
Cancer cells rely on metabolic alterations to enhance proliferation and survival. Metabolic gene alterations that repeatedly occur in liver cancer are largely unknown. We aimed to identify metabolic genes that are consistently deregulated, and are of potential clinical significance in human hepatocellular carcinoma (HCC).
Methods...
Contexts in source publication
Context 1
... based on common knowledge of biochemical pathways, we attempted to map the portrait of liver cancer metabolism using the consistently altered genes or the corresponding proteins detected in our anal- ysis. The snapshot clearly depicted the suppression of serine biosynthetic pathway, urea cycle, and transamination as striking features of HCC (Figure 4). Also represented were targets in TCA cycle and NB, most of which were detected at protein level. ...
Context 2
... adenosine triphosphate-binding cassette; HCC, hepatocellular carcinoma; NAD, nicotinamide adenine dinucleotide; NCBI GEO, National Center for Biotechnology Information Gene Expression Omnibus; OXPHOS, oxidative phosphorylation; PPP, pentose phosphate pathway; REDOX, reduction-oxidation reaction; S.M., small molecule; TCA, tricarboxylic acid. 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 Targets in Liver Cancer 7 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 include HK2 and PKM (in glycolysis), GLS and GLUD1 (in glutaminolysis), CPS1 and ASL (in urea cycle), ACACA and FASN (in lipogenesis), HMGCS2 and SQLE (in cholestero- genesis), and PCK1 and FBP1 in gluconeogenesis (Figure 4, Supplementary Tables 1 and 2). Furthermore, we uncovered about 40 families of metabolic genes (mostly paralogues), whose members are frequently expressed in the opposite direction in HCC (Table 3). ...
Context 3
... we observed a mixture of both up-regulated and down-regulated genes in most of the metabolic pathways, as was previously noted in cancer. 19 For example, although glycolytic targets are predominantly up-regulated, ALDOB, ENO3, and PFKFB1 are down-regulated, as are some glucose transporters (Figure 4). Although it is unclear if HCC actu- ally require the down-regulation of these genes for optimal glycolysis, their suppressed expression may be beneficial for cancer cells. ...
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Citations
... Interestingly, several pathways identified by our PH method have been previously associated with HCC, or their association has been suggested by other computational approaches. For completeness, we provide a list: genetic information processing pathways (RNA polymerase [42], protein processing in endoplasmic reticulum [43], and base excision repair [44,45]), environmental information processing pathways (ABC transporters [46][47][48] and apelin signaling pathway [49,50]), a cellular processes pathway (p53 signaling pathway [51][52][53][54]), organismal systems pathways (IL-17 signaling pathway [55,56] and synaptic vesicle cycle [57]), and several metabolism pathways (riboflavin metabolism [58], citrate/TCA cycle [59][60][61], ascorbate and aldarate metabolism [62,63], drug metabolismcytochrome P450 [48,[64][65][66][67], glycine, serine, and threonine metabolism [48,64,68], primary bile acid biosynthesis [64], histidine metabolism [64], beta-alanine metabolism [64], tryptophan metabolism [54,64,69,70], pantothenate and CoA biosynthesis [58,[71][72][73], and porphyrin metabolism [58,74,75]). This suggests that our method may indeed be capable of detecting subtle, HCC-related changes from peripheral blood. ...
... Interestingly, several pathways identified by our PH method have been previously associated with HCC, or their association has been suggested by other computational approaches. For completeness, we provide a list: genetic information processing pathways (RNA polymerase [42], protein processing in endoplasmic reticulum [43], and base excision repair [44,45]), environmental information processing pathways (ABC transporters [46][47][48] and apelin signaling pathway [49,50]), a cellular processes pathway (p53 signaling pathway [51][52][53][54]), organismal systems pathways (IL-17 signaling pathway [55,56] and synaptic vesicle cycle [57]), and several metabolism pathways (riboflavin metabolism [58], citrate/TCA cycle [59][60][61], ascorbate and aldarate metabolism [62,63], drug metabolismcytochrome P450 [48,[64][65][66][67], glycine, serine, and threonine metabolism [48,64,68], primary bile acid biosynthesis [64], histidine metabolism [64], beta-alanine metabolism [64], tryptophan metabolism [54,64,69,70], pantothenate and CoA biosynthesis [58,[71][72][73], and porphyrin metabolism [58,74,75]). This suggests that our method may indeed be capable of detecting subtle, HCC-related changes from peripheral blood. ...
... Interestingly, several pathways identified by our PH method have been previously associated with HCC, or their association has been suggested by other computational approaches. For completeness, we provide a list: genetic information processing pathways (RNA polymerase [42], protein processing in endoplasmic reticulum [43], and base excision repair [44,45]), environmental information processing pathways (ABC transporters [46][47][48] and apelin signaling pathway [49,50]), a cellular processes pathway (p53 signaling pathway [51][52][53][54]), organismal systems pathways (IL-17 signaling pathway [55,56] and synaptic vesicle cycle [57]), and several metabolism pathways (riboflavin metabolism [58], citrate/TCA cycle [59][60][61], ascorbate and aldarate metabolism [62,63], drug metabolismcytochrome P450 [48,[64][65][66][67], glycine, serine, and threonine metabolism [48,64,68], primary bile acid biosynthesis [64], histidine metabolism [64], beta-alanine metabolism [64], tryptophan metabolism [54,64,69,70], pantothenate and CoA biosynthesis [58,[71][72][73], and porphyrin metabolism [58,74,75]). This suggests that our method may indeed be capable of detecting subtle, HCC-related changes from peripheral blood. ...
Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically persistent homology (PH), to identify key pathways for early detection of hepatocellular carcinoma (HCC). Recognizing the limitations of current strategies for this purpose, we meticulously used PH to analyze RNA sequencing (RNA-seq) data from peripheral blood of both HCC patients and normal controls. This approach enabled us to gain nuanced insights by detecting significant differences between control and disease sample classes. By leveraging topological descriptors crucial for capturing subtle changes between these classes, our study identified 23 noteworthy pathways, including the apelin signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway. Subsequently, we performed a comparative analysis with a classical enrichment-based pathway analysis method which revealed both shared and unique findings. Notably, while the IL-17 signaling pathway was identified by both methods, the HCC-related apelin signaling and p53 signaling pathways emerged exclusively through our topological approach. In summary, our study underscores the potential of PH to complement traditional pathway analysis approaches, potentially providing additional knowledge for the development of innovative early detection strategies of HCC from blood samples.
... Cao et al. employed next-generation sequencing technology and machine learning approaches to discover that mutations at specific amino acid sites might cause HCC [32]. Previous research examined the expression of 2761 metabolism-related genes in HCC tissues and assessed the predictive value of particular genes in HCC prognosis [33]. Proteomic approaches, such as quantitative proteomic methods based on iTRAQ, have been utilized to identify diagnostic markers for HCC [34]. ...
Background:
Primary liver cancer (PLC) ranks third in terms of fatality rate among all malignant tumors worldwide. Proteomics and metabolomics have become widely utilized in identifying causes and diagnostic indicators of PLC. Nevertheless, in studies aiming to identify proteins/metabolites that experienced significant changes before PLC, the potential impact of reverse causation and confounding variables still needs to be fully addressed.
Methods:
This study thoroughly investigated the causal relationship between 4719 blood proteins, 21 amino acids, and the risk of PLC using the Mendelian randomization (MR) method. In addition, through a comprehensive analysis of the TCGA-LIHC cohort and GEO databases, we evaluated the differentially expressed genes (DEGs) related to serine metabolism in diagnosing and predicting the prognosis of patients with PLC.
Results:
A total of 63 proteins have been identified as connected to the risk of PLC. Additionally, there has been confirmation of a positive cause-effect between PLC and the concentration of serine. The integration of findings from both MR analyses determined that the protein associated with PLC risk exhibited a significant correlation with serine metabolism. Upon careful analysis of the TCGA-LIHC cohort, it was found that eight DEGs are linked to serine metabolism. After thoroughly validating the GEO database, two DEGs, TDO2 and MICB, emerged as potential biomarkers for diagnosing PLC.
Conclusions:
Two proteins involved in serine metabolism, MICB and TDO2, are causally linked to the risk of PLC and could potentially be used as diagnostic indicators.
... 14 The expression values of this gene were extracted from every data set by comparing two groups, for example, HCC and tumour background (non-HCC), then the value of expression of the gene in the microarray was calculated using GEO2R and analysed as described previously. 15 The following data sets were investigated: GSE89377 (HCC n = 40 vs. normal n = 13) 16 ; GSE60502 (HCC n = 18 vs. adjacent non-tumour n = 18) 17 and GSE14520 (HCC n = 221 vs. non-HCC n = 212). ...
Transforming growth factor (TGF)‐β and toll‐like receptors (TLRs) have been shown to independently modulate the proliferation of hepatocellular carcinoma (HCC). Since a direct cross‐talk between these two signalling pathways in HCC has not been clearly described before, we aimed here to explore the possibility of such interaction. A human HCC tissue array ( n = 20 vs. four control samples), human HCC samples ( n = 10) and steatohepatitis‐driven murine HCC samples (control, NASH and HCC; n = 6/group) were immunostained for TGFβR1, pSMAD2, TRAF6, IRAK1 and PCNA. The results were confirmed by immunoblotting. Effects of constant activation of the SMAD pathway by constitutive expression of ALK5 or knockdown of mediators of TLR signalling, IRAK1 and MyD88, on HCC proliferation, were investigated in the HCC cell line (HUH‐7) after treatment with TGFβ1 cytokine or TGFβR1 kinase inhibitor (LY2157299) using PCNA and MTS assay. TGFβR1 expression is decreased in human and murine HCC and associated with downregulated pSMAD2, but increased IRAK1, TRAF6 and PCNA staining. TGFβR1 kinase inhibition abolished the cytostatic effects of TGFβ1 and led to the induction of IRAK1, pIRAK1 and elevated mRNA levels of TLR‐9. Overexpression of ALK5 and knockdown of MyD88 or IRAK1 augmented the cytostatic effects of TGFβ1 on HUH‐7. In another epithelial HCC cell line, that is, HepG2, TGFβR1 kinase inhibitor similarly elevated cellular proliferation. There is a balance between the canonical SMAD‐driven tumour‐suppressing arm and the non‐canonical tumour‐promoting arm of TGFβ signalling. Disruption of this balance, by inhibition of the canonical pathway, induces HCC proliferation through TLR signalling.
... During liver cancer progression, typical hepatocyte metabolic functions, such as gluconeogenesis, bile acid (BA) metabolism, detoxication, and ureagenesis-ammonia, are diminished. This decline is accompanied by an increase in tumor malignancy [30,31], most likely due to the de-differentiation from functional hepatocytes to HCC cells. Recently, we identified two metabolic subtypes in 65 human liver cancer organoids through multi-omics profiling, which complements our understanding of HCC tissue metabolism. ...
... Taken together, systems biological approaches in metabolic signature deconvolution can illuminate metabolic heterogeneity and identify potential metabolic targets for PLC (Fig. 1). Systemic analysis focusing on metabolic gene expressions and non-targeted metabolic profiling has shown that aerobic glycolysis, lipid metabolism, and amino acid metabolism are the main metabolic alterations in HCC tissues [31,37]. Cancer cells often face hypoxic and hypo-nutrient environments, necessitating metabolic rearrangement to satisfy energy demands and biomass synthesis. ...
... Conversely, high serum cholesterol levels are linked with better patient outcomes by inhibiting tumor metastasis [59], implying that cholesterol distribution and homeostasis significantly influence HCC tumorigenesis (Fig. 1). Moreover, numerous studies indicate enhanced amino acid metabolism in liver tumors compared to non-tumor tissues [1,31,37,60,61]. Sustained urea cycle repression in liver cancer shifts metabolism from arginine production to pyrimidine biosynthesis. ...
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.
... [42][43][44] It has been shown that most enzymes of the PPP are upregulated in HCC. 45 In particular, the expression of glucose-6-phosphate dehydrogenase (G6PDH), the rate-limiting enzyme of the pathway, is highly upregulated in HCC and is associated with migration, invasion, poor prognosis, and chemoresistance. 46 G6PDH also undergoes O-GlcNAcylation mediated by HBP (another rewired metabolic process in HCC, detailed in the following subsection), enhancing its activity and stability and thus augmenting its tumorigenic potency in HCC. ...
... In another study, 634 genes were consistently altered in HCC and are potentially relevant targets for onward studies in preclinical and clinical contexts. 45 Of these, there are 284 upregulated genes involved mainly in glucose metabolism, thus offering valuable therapeutic targets for HCC. However, HCC has biocomplexity and heterogeneity, which results in therapeutic challenges and added advantages for its survival. ...
Hepatocellular carcinoma (HCC) is the primary form of liver cancer. It causes ∼ 800 000 deaths per year, which is expected to increase due to increasing rates of obesity and metabolic dysfunction associated steatotic liver disease (MASLD). Current therapies include immune checkpoint inhibitors, tyrosine kinase inhibitors, and monoclonal antibodies, but these therapies are not satisfactorily effective and often come with multiple side effects and recurrences. Metabolic reprogramming plays a significant role in HCC progression and is often conserved between tumor types. Thus, targeting rewired metabolic pathways could provide an attractive option for targeting tumor cells alone or in conjunction with existing treatments. Therefore, there is an urgent need to identify novel targets involved in cancer-mediated metabolic reprogramming in HCC. In this review, we provide an overview of molecular rewiring and metabolic reprogramming of glucose metabolism in HCC to understand better the concepts that might widen the therapeutic window against this deadly cancer.
... For instance, ACSL1 is up-regulated in colorectal cancer and estrogen receptor (ER)-negative, ER-positive and HER2-positive breast cancer subtypes, and high ACSL1 expression in these patients' tumor samples is linked to unfavorable prognosis [12,[16][17][18][19]. In parallel, ACSL1 has been previously reported to be down-regulated in non-small cell lung cancer (NSCLC) and liver cancer, with its tumor-suppressive effects in NSCLC having been demonstrated by Chen, W.C et al. [19,20]. The differential ACSL1 expression in different tumors suggests that ACSL1 has diverse effects on tumorigenesis, which is worth studying. ...
Background
Clear cell renal cell carcinoma (ccRCC) is the dominant subtype of kidney cancer. Dysregulation of long-chain acyl-CoA synthetase 1 (ACSL1) is strongly implicated in undesirable results in varieties of cancers. Nevertheless, the dysregulation and associated multi-omics characteristics of ACSL1 in ccRCC remain elusive.
Methods
We probed the mRNA and protein profiles of ACSL1 in RCC using data from the Cancer Genome Atlas, Gene Expression Omnibus, the Human Protein Atlas (HPA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and verified them in our patient cohort and RCC cell lines. Correlations between ACSL1 expression and clinicopathological features, epigenetic modification and immune microenvironment characteristics were analyzed to reveal the multi-omics profile associated with ACSL1.
Results
ACSL1 was down-regulated in ccRCC tissues compared to adjacent normal tissues. Lower expression of ACSL1 was linked to unfavorable pathological parameters and prognosis. The dysregulation of ACSL1 was greatly ascribed to CpG island-associated methylation modification. The ACSL1 high-expression subgroup had enriched fatty acid metabolism-related pathways and high expression of ferroptosis-related genes. In contrast, the ACSL1 low-expression subgroup exhibited higher immune and microenvironment scores, elevated expression of immune checkpoints PDCD1, CTLA4, LAG3, and TIGIT, and higher TIDE scores. Using data from the GDSC database, we corroborated that down-regulation of ACSL1 was associated with higher sensitivity towards Erlotinib, Pazopanib, and PI3K-Akt-mTOR-targeted therapeutic strategies.
Conclusion
Taken together, our findings point to ACSL1 as a biomarker for prognostic prediction of ccRCC, identifying the tumor microenvironment (TME) phenotype, and even contributing to treatment decision-making in ccRCC patients.
... In HCC, β-catenin mutations have been shown to drive CPT2 expression and an increase in FAO [17]. Additionally, transcriptomics of HCC tissues has identified molecular patterns of metabolism-related gene expression, which have even proposed metabolism-based molecular classifications of HCC [19][20][21]. Altogether, the current state of the literature suggests that metabolism is intimately linked with cancer onset and progression. ...
... Together with other LC/MS studies of HCC, the main overlapping metabolites considered as altered pathways in liver tumors include alanine, arginine, lactate, succinate, NADH, and NADP metabolites [26,27]. Likewise, convincing evidence of the molecular analysis suggests metabolic reprogramming in HCC [19][20][21]. As such, collaboration within the research community on HCC, with a multi-omics approach, is fundamental in the identification of holistic metabolism-based HCC classifications. ...
Background:
Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming.
Methods:
HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS).
Results:
Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS.
Conclusions:
Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.
... As an extracellular matrix component, ECM2 is involved in the regulation of cell proliferation and differentiation [8]. Recent studies have found an association between ECM2 and colon adenocarcinoma, hepatocellular carcinoma, and cervical cancer [9][10][11]. Additionally, ECM2 has been considered to be associated with idiopathic pulmonary fibrosis and pulmonary artery hypertension [12,13]. ...
Objective:
Glioma is the most prevalent and fatal intracranial malignant tumor. Extracellular matrix protein 2 (ECM2) has rarely been studied in gliomas. Therefore, we explored the role of ECM2 in lower-grade gliomas (LGGs).
Methods:
The RNA-seq and clinicopathology data were obtained from the TCGA database. The immunohistochemical (IHC) staining was used to verify the expression of ECM2. Functional enrichment analyses, immune-related analyses, drug sensitivity, and mutation profile analyses were further conducted. Cox regression and Kaplan-Meier curves were utilized for survival analyses, while four external datasets were used to validate the prognostic role of ECM2. Furthermore, qRT-PCR, CCK-8, wound healing, and transwell assays were performed to confirm the function of ECM2 in gliomas.
Results:
The study found a significant upregulation of ECM2 expression with increasing glioma grades and a significant association between ECM2 expression and tumor immune infiltration. Cox regression verified the prognostic role of ECM2 in LGG patients (HR = 1.656, 95%CI = 1.055-2.600, p = 0.028). High ECM2 expression was significantly associated with poor outcome (p < 0.001). Four external datasets validated its prognostic value. After the knockdown of ECM2, the functional experiments showed a significant decrease in proliferation, migration, and invasion in glioma cell lines.
Conclusion:
The study suggested the potential of ECM2 as a novel immune-associated prognostic biomarker and therapeutic target for glioma patients.
... Moreover, the pan-IP6K inhibitor TNP [N2-(m-trifluoro benzyl), N6-(p-nitrobenzyl)purine] [23,24] ameliorates these diseases [25]. IP6K1 has been implicated in human liver disease, as hepatic IP6K1 is upregulated in NASH, alcoholic cirrhosis [21], and hepatocellular carcinoma patients [26]. Furthermore, whole-body and hepatocyte-specific Ip6k1 deletion protects mice from Western-diet-induced NAFLD/NASH [21]. ...
Obesity and nonalcoholic fatty liver disease (NAFLD) are global health concerns, and thus, drugs for the long-term treatment of these diseases are urgently needed. We previously discovered that the inositol pyrophosphate biosynthetic enzyme IP6K1 is a target in diet-induced obesity (DIO), insulin resistance, and NAFLD. Moreover, high-throughput screening (HTS) assays and structure−activity relationship (SAR) studies identified LI-2242 as a potent IP6K inhibitor compound. Here, we tested the efficacy of LI-2242 in DIO WT C57/BL6J mice. LI-2242 (20 mg/kg/BW daily, i.p.) reduced body weight in DIO mice by specifically reducing the accumulation of body fat. It also improved glycemic parameters and reduced hyperinsulinemia. LI-2242-treated mice displayed reduced the weight of various adipose tissue depots and an increased expression of metabolism- and mitochondrial-energy-oxidation-inducing genes in these tissues. LI-2242 also ameliorated hepatic steatosis by reducing the expression of genes that enhance lipid uptake, lipid stabilization, and lipogenesis. Furthermore, LI-2242 enhances the mitochondrial oxygen consumption rate (OCR) and insulin signaling in adipocytes and hepatocytes in vitro. In conclusion, the pharmacologic inhibition of the inositol pyrophosphate pathway by LI-2242 has therapeutic potential in obesity and NAFLD.
... Our 12-gene TSG panel comprises: (1) regulators of cell cycle, e.g., CDKN2A [13,14]; (2) metabolic enzymes such as BCO2 [37,38] (a regulator of lycopene metabolism), CPS1 [39] (a liver-specific, intramitochondrial, rate-limiting enzyme in the urea cycle), PTGR1 [40] (a bifunctional enzyme that inactivates leukotrienes and prostaglandins), and PSAT1 [41,42] (a phosphoserine aminotransferase); (3) regulators of stemness, such as HHIP [43][44][45][46][47][48][49], which is a suppressor of the Hedgehog signaling pathway involved in embryonic development and tumorigenicity; (4) pro-apoptotic factors including the metallothioneins MT1E [50,51] and MT1M [52][53][54], which act as a surveillance systems for carcinogens-caused cellular damage; immune-regulators, such as the transmembrane protein TMEM106A [55,56] (an activator of the MAPK and NF-κB signaling pathways implicated in the pro-inflammatory and anti-tumoral M1-type macrophage polarization); and (5) negative regulators of migration, invasion, angiogenesis, and metastasis, such as the liver specific miR-122-5p [57,58] (a post-transcriptional regulator of genes involved in TNF and Notch signaling pathways), PZP [59-61] (a proteinase inhibitor), and TTC36 [62][63][64] (a regulator of the Wnt-β-catenin pathway). Thus, while we focused on a limited array of phenotypic assays, the multiplexing approach has great potential to reprogram multiple aspects of HCC pathobiology, which could potentially facilitate a more comprehensive and multifactorial reprogramming of the HCC phenotype, including the tumor microenvironment. ...
Background
Epigenetic silencing of tumor suppressor genes (TSGs) is a key feature of oncogenesis in hepatocellular carcinoma (HCC). Liver-targeted delivery of CRISPR-activation (CRISPRa) systems makes it possible to exploit chromatin plasticity, by reprogramming transcriptional dysregulation.
Results
Using The Cancer Genome Atlas HCC data, we identify 12 putative TSGs with negative associations between promoter DNA methylation and transcript abundance, with limited genetic alterations. All HCC samples harbor at least one silenced TSG, suggesting that combining a specific panel of genomic targets could maximize efficacy, and potentially improve outcomes as a personalized treatment strategy for HCC patients. Unlike epigenetic modifying drugs lacking locus selectivity, CRISPRa systems enable potent and precise reactivation of at least 4 TSGs tailored to representative HCC lines. Concerted reactivation of HHIP, MT1M, PZP, and TTC36 in Hep3B cells inhibits multiple facets of HCC pathogenesis, such as cell viability, proliferation, and migration.
Conclusions
By combining multiple effector domains, we demonstrate the utility of a CRISPRa toolbox of epigenetic effectors and gRNAs for patient-specific treatment of aggressive HCC.
