In silico and cheminformatics prediction with experimental validation of an adipogenesis cocktail, sorafenib with rosiglitazone for HCC dedifferentiation

Abstract

Purpose

Hepatocellular carcinoma (HCC) resistance to sorafenib treatment and other treatment strategies causes a higher mortality rate in patients diagnosed with HCC.

Research question.

HCC often develops resistance to sorafenib treatment and other therapies, leading to increased mortality rates in diagnosed patients. Herein, we propose a combined therapeutic approach using rosiglitazone, a key factor in cellular differentiation, along with adipogenesis inducers such as dexamethasone, IBMX, and insulin. Additionally, we included sorafenib, a primary drug for liver cancer treatment, in this combination cocktail and carried out the differentiation process in the presence of sorafenib.

Results

Our study demonstrates that this combination induces the formation of adipocytes from HCC cells over several days under specific conditions and steps. Conclusion. findings suggest that supplementing sorafenib with rosiglitazone and adipogenesis inducers may potentially transform HCC cells into adipocyte-like cells. Fat could be “the good” in the story of liver cancer alleviation, demonstrating the role of rosiglitazone.

1. Introduction

1.1. Background

The most common type of primary liver cancer that arises from hepatocytes (the main cell type in the liver) is hepatocellular carcinoma (HCC), according to Kyoto Encyclopedia of Genes and Genomes (KEGG) https://www.genome.jp/kegg/ database (Last updated: January 1, 2024) https://www.genome.jp/kegg/kegg2.html KEGG Disease https://www.genome.jp/entry/H00048 HCC associated-risk factors are non-alcoholic fatty liver disease (NAFLD), metabolic dysfunction-associated steatotic liver disease (MASLD)¹, fibrotic/cirrhotic status², cirrhosis³, chronic viral hepatitis C (HCV), HCV-linked liver cirrhosis⁴ as well as certain genetic conditions.

Treatment options for HCC: The treatment options for HCC depend on the disease stage, the patient's liver function, as well as the patient's overall health status. Treatment modalities for HCC include surgical resection, liver transplantation, ablation techniques, trans-arterial chemoembolization (TACE), as well as targeted therapy depending on the disease stage, the patient liver function as well and the patient’s overall health status. HCC treatment modalities include surgical resection, liver transplantation, ablation techniques or trans-arterial chemoembolization (TACE) as well as targeted therapies such as Sorafenib, and immunotherapy.

Sorafenib according to KEGG Drug https://www.genome.jp/entry/D08524 is a targeted oral therapy drug that inhibits multiple signaling pathways (multi kinase inhibitor) involved in HCC growth and progression, tumor cell proliferation, and angiogenesis together with apoptosis induction. Sorafenib inhibits Raf serine/threonine kinases, vascular endothelial growth factor receptors 2 and 3 (VEGFR 2 and 3), and platelet-derived growth factor receptors (PDGFR-β) and fibroblast growth factor receptor-1 (FGFR-1).

https://www.genome.jp/pathway/map07045 + D08524. Moreover, Sorafenib (Nexavar®) per the DRUGBANK online has shown efficacy in extending HCC patients’ overall survival (OS). It is typically used as a first-line oral systemic therapy for patients with advanced HCC who are not eligible for curative treatments like surgery or liver transplantation https://go.drugbank.com/drugs/DB00398#identification-header. However, this anti-angiogenic drug, sorafenib, if being used for unresectable liver cancer treatment, may face the risk of resistance development and various side effects from activation of some resistant signaling pathways⁵. Therefore, finding other treatment options for such unresectable liver cancer cases is mandatory.

1.2. Problem definition

Despite a variety of HCC treatment techniques, still can’t cure the affected hepatocyte, completely, but tyrosine kinase inhibitors (TKIs) can prevent metastasis. Cancer invasion through the basement membrane (BM) and the interstitial extracellular matrix (ECM) requires the action of a series of proteolytic enzymes named matrix metalloproteinases (MMPs) as MMP-9⁶. Most clinical data show a correlation between MMPs expression with advanced tumor stage, invasion, metastasis, and shortened survival⁷ .

It should also be noted that changes in the endothelial adhesion molecules and complexes lead to cell proliferation, mobility, and differentiation alterations in various non-communicable diseases such as diabetes, cardiovascular diseases8, 9, and cancer.¹⁰ Endothelial-cadherin (E-cadherin), a transmembrane glycoprotein, mediates Ca²⁺-dependent cell–cell adhesion, through its intracytoplasmic interaction with β and α-catenin¹¹. α-catenin connects the cadherin–catenin complex to actin filament networks, increasing adhesive strength¹¹. A variety of cancers, such as breast and ovarian carcinoma, as well as HCC, showed reduced expression of E-cadherin, suggesting increased metastasis properties for these types of cancer¹² .

1.3. Hypothesis

An increased E-cadherin expression with decreased MMP-9 expression would lead to a decreased invasion and slowing metastasis and vice versa. Therefore, if there is a treatment option that would increase E-cadherin expression together with decreasing MMP-9 expression, then the tumor invasive state would be compacted positively.

Novel research published in 2019¹³ showed that adding the small thiazolidinedione class anti-diabetic drug molecule rosiglitazone, Avandia®, to anticancer drug(s) onto breast cancer cells, turned them into adipocytes, repressing tumor invasion and metastasis. Rosiglitazone binds to the peroxisome proliferators-activated receptor gamma (PPARγ) in fat cells to increase these cells' sensitivity to insulin, stimulating lipid uptake and adipogenesis¹⁴. Such research, together with the ability of rosiglitazone to induce adipogenesis in 3 T3-L1 fibroblast cell line¹⁵ clicked the idea of adding rosiglitazone to an anti-HCC treatment like sorafenib.

Combinatory therapeutic strategy of rosiglitazone with the adipogenesis inducers cocktail: dexamethasone/IBMX/insulin and sorafenib to induce adipogenesis in an attempt to repress hepatic metastasis in preclinical model.

The adipose tissue cells respond to the abnormal inflammatory state in blood, oxidative stress, inflammation, diabetes, obesity, metabolic syndrome, and apoptosis, apoptosis-related enzymes; granzymes¹⁶, and perforin,¹⁷ which can stimulate cancer cell proliferation, migration, and invasion. The adipose tissue, additionally, provides a physical scaffold for tumor growth and contributes to the formation of a pro-tumorigenic microenvironment (TME) milieu, participating in more tumor progression/resistance response to various environmental stressors present nowadays¹⁸ namely, climate changes and viral infections.

Interestingly, the adipose tissue via released adiponectin has been shown to have an anti-inflammatory and anti-proliferative property⁹ potentially inhibiting tumor growth, therefore, any treatment approach to stimulate adiponectin release or augment its effect would have beneficial anti-cancer effect(s).

The interplay between the adipose tissue and cancer cells is being researched nowadays to better use the mechanisms underlying the interactions between adipocytes, adipokines, cancer cells, immune cells, various signaling molecules and pathways, tumor suppressors vs oncogenes19, 20, genetics and epigenetics20, 21 apoptosis and autophagy. Where, cancer resistance and metastasis to be, hopefully, more controllable, as transcription factors would bind to specific DNA sequences to regulate gene expression. Chromatin architecture can influence the accessibility of these binding sites, affecting the ability of transcription factors to bind and activate genes involved in dedifferentiation²² .

To identify potential therapeutic targets and develop strategies to modulate the TME and, moreover, the tumor immune microenvironment (TIME) for an improved cancer treatment outcome will fulfill the sustainable development goals (SDGs) SDG#3 for “Better Health”. Additionally, repurposing drugs is an option; innovative therapeutic modalities, regenerative cells, and drug combinations for synergetic effect given as nano-formula or within exosome cargo are extensively studied currently.

Adipogenesis-inducers promote preadipocyte differentiation into mature adipocytes. These inducers are growth factors (GF) and/or hormones together with small molecule drugs to activate specific signaling pathways involved in adipocytes differentiation.. Adipogenesis-inducers²³ are the insulin (INS) hormone, dexamethasone, and isobutylmethylxanthine (IBMX).

Insulin²⁴ is involved in preadipocyte differentiation into mature adipocytes through insulin-like growth factors (IGFs) signaling pathways. These pathways activate the expression of adipogenic transcription factors (TFs) and promote the accumulation of lipid droplets in the differentiating cells.

Dexamethasone is a synthetic glucocorticoid that has been shown to induce adipocyte differentiation in various cell types, enhancing the expression of the key TF master regulator of adipocyte differentiation, peroxisome proliferator-activated receptor-gamma (PPARG or PPAR-γ) and CCAAT enhancer-binding proteins (C/EBPs) are main regulators of adipogenesis leading to the transformation into adipocyte-like characteristics²⁵. PPARG activation promotes the expression of genes involved in adipogenesis and lipid storage.

IBMX is a small molecule phosphodiesterase inhibitor (PDI) that increases intracellular levels of cyclic adenosine monophosphate (cAMP) which in turn activates protein kinase A (PKA) to phosphorylate and activate TFs involved in adipogenesis²⁴. If IBMX is used for HCC treatment, might induce HCC cell lines differentiation into adipocyte-functioning cells; a hypothesis worth exploration.

To enhance the impact of PPAR-γ on HCC cancer cells, promoting their differentiation into adipocytes, we have introduced an additional PPAR-γ agonist drug to the existing cocktail. Specifically, we included Rosiglitazone, as detailed by Ishay-Ronen, Diepenbruck et al. (2019) and Mostafa et al. in 2015. Rosiglitazone (Avandia®) https://go.drugbank.com/drugs/DB00412 is an oral small molecule medication used to treat type 2 diabetes (T2D), that belongs to the thiazolidinediones (TZDs) drugs class, which function as PPAR-γ agonists (activator). PPARG according to GeneCards The Human Gene Database https://www.genecards.org/cgi-bin/carddisp.pl?gene = PPARG#:∼:text = PPARG%20(Peroxisome%20Proliferator%20Activated%20Receptor,and%20Gene%20expression%20(Transcription) is a nuclear receptor (NR) involved in glucose and lipid metabolism with potential role in adipogenesis and adipocyte differentiation. Activation of PPAR-γ can promote the differentiation of preadipocytes (immature fat cells) into mature adipocytes (fat cells) and enhance adipogenesis. This later effect is mediated by the upregulation of genes involved in adipocyte differentiation and lipid storage as well as improving insulin sensitivity (IS) and regulating glucose metabolism in cells.

Sorafenib, rosiglitazone, dexamethasone, IBMX and insulin all were mixed in a combination and used to treat HCC cells to trigger the de-differentiation process²³. Sorafenib is to arrest cell division and promote a dedifferentiated state via inhibition of cell proliferation and differentiation. Rosiglitazone acts by activating PPARγ, induce the expression of genes involved in adipocyte differentiation and potentially contribute to a de-differentiated phenotype¹³. Dexamethasone influence various cellular processes, including cell proliferation, differentiation, and survival. In this context, dexamethasone might contribute to the overall dedifferentiation process by modulating gene expression and cellular signaling pathways. Finally, BMX is to increase intracellular cAMP levels, which activates the protein kinase A (PKA) pathway, to influence various cellular processes, including cell proliferation, differentiation, and metabolism. All of these act in combination to insulin, used to support survival and metabolic activity of HCC dedifferentiating cells²⁴ .

1.4. Research Statement

Inducing the dedifferentiation of HCC cell lines into adipocytes could be considered a hepatoprotective measure, as it reduces or suppresses the metastatic potential and invasive capabilities of cancer cells. This approach holds great promise as a strategy for liver cancer therapy.

1.5. Research aim

In our current protocol, we investigated the efficacy of a regenerative treatment utilizing adipogenesis inducers for potential relevance in treating HCC. We incorporated sorafenib and rosiglitazone into the adipogenesis inducer cocktail to assess their distinct impacts on cancer cells and adipogenesis. We are exploring a drug combination that could potentially hinder HCC differentiation while stimulating adipogenesis in an in vitro model.

This aim will be fulfilled via several objectives.

• (i)studying the effect of adding rosiglitazone to sorafenib onto 2 different HCC cell lines, with and without the adipogenesis cocktail,
• (ii)determine the role of such combination in stimulating de-differentiation of HCC cell lines into adipocytes, detected by Oil red O stain for the presence of lipid droplets inside adipocytes
• (iii)the final objective, to determine whether such combination would, hopefully, decrease HCC invasion and/or metastasis. These objectives will be examined in silico and validated experimentally.

2. Methodology and results

2.1. In silico Analysis/Bioinformatics databases search

Gene function annotation and pathway enrichment analysis (Accessed on March 28th, 2024).

KEGG HCChttps://www.genome.jp/pathway/hsa05225 + H00048.

https://www.genome.jp/kegg-bin/show_pathway?hsa05225.

However, for NAFLD presented in Fig. 1A.

Fig. 1.

Signaling pathway according to KEGG (A) NAFLD and (B) PPAR.https://www.kegg.jp/kegg-bin/search_pathway_text?map=map&keyword=adipose+tissue&mode=1&viewImage=trueandhttps://www.genome.jp/kegg-bin/show_pathway?hsadd03320 respectively.

https://www.kegg.jp/kegg-bin/search_pathway_text?map = map&keyword = adipose + tissue&mode = 1&viewImage = true represents a spectrum ranging from simple steatosis, with the involvement of D-Glucose, fatty acid, INS, insulin receptor substrat-1 and −2 (IRS1 and IRS2), AKT, and PPAR signaling pathway.

via THE HUMAN PROTIEN ATLAS²⁶.

The adipose tissue-specific proteome.

https://www.proteinatlas.org/humanproteome/tissue/adipose + tissue.

34 group enriched genes are there in the adipose tissue. While only 2 tissue_category_rna:strictly adipose tissue, liver; group enriched Proteoglycan 4 (PRG4) and TNF superfamily 14 (TNFSF14).

https://www.proteinatlas.org/search/tissue_category_rna:strictly + adipose + tissue,liver;group + enriched + AND + show_columns:groupenriched.

The liver tissue enriched genes are 263 genes.

https://www.proteinatlas.org/search/tissue_category_rna:liver;tissue + enriched + AND + show_columns:groupenriched and from Diseases database https://diseases.jensenlab.org/ for gene-disease association from text mining.

Disease genes and drug targets in KEGG pathways maps.

Homo sapiens (human) PPAR signaling pathway (Fig. 1B) + Disease/drug.

PPARs are nuclear hormone receptors activated by fatty acids and their derivatives. PPAR has three subtypes (PPARalpha, beta/delta, and gamma) showing different expression patterns in vertebrates. Each is encoded in a separate gene and binds fatty acids and eicosanoids. PPARalpha plays a role in the clearance of circulating or cellular lipids via the regulation of gene expression involved in lipid metabolism in liver and skeletal muscle. PPARbeta/delta is involved in lipid oxidation and cell proliferation. PPARG promotes adipocyte differentiation to enhance blood glucose uptake.

https://www.genome.jp/kegg-bin/show_pathway?hsadd03320.

PPARG Expression Profile Enrichment-Analyses in the liver hepatocellular carcinoma (LIHC) via the open-access platform The University of ALabama at Birmingham CANcer data analysis Portal UALCAN database mining https://ualcan.path.uab.edu/ from the TCGA database²⁷. Used to compare the relative transcriptional levels of PPARG between tumor and para-cancerous tissues, as well as the correlation of PPARG gene mRNA levels with pathological features. In this study, UALCAN was employed to compare the association between the tumor and normal tissues transcriptional levels of PPARG https://ualcan.path.uab.edu/cgi-bin/Pan-cancer.pl?genenam = PPARG and OS not the disease-free survival (DFS) as was found non-significant (Fig. 2A).

Fig. 2.

2A. R graph output showing the Effect of PPARG expression level on LIHC patient overall survival. 2B. PPARG Gene Effect scores derived from CRISPR knockout screens published by Broad's Achilles and Sanger's SCORE projects. Negative scores imply cell growth inhibition and/or death following gene knockout. https://ualcan.path.uab.edu/cgi-bin/ualcan-depmap-res.pl?genenam=PPARG&ctype=LIHC [Scores are normalized such that nonessential genes have a median score of 0 and independently identified common essentials have a median score of −1.] 2C. Expression pattern of input genes in LIHC that are positively correlated with PPARg in LIHChttps://ualcan.path.uab.edu/cgi-bin/TCGAExHeatMap5KK.pl?cantype = LIHC&correlFile = PPARG%23 %23CWr33Rd3o.

Gene effect score for PPARG in liver cancer cell lines DepMap (Fig. 2B).

https://ualcan.path.uab.edu/cgi-bin/ualcan-depmap-res.pl?genenam = PPARG&ctype = LIHC.

Genes positively correlated to PPARG in LIHC Heatmap (Fig. 2C).

https://ualcan.path.uab.edu/cgi-bin/TCGAExHeatMap5KK.pl?cantype = LIHC&correlFile = PPARG%23 %23CWr33Rd3o.

Enrichment Analysis by STRING version 12.0²⁸Protein-Protein Interaction (PPI) (Fig. 2D)https://string-db.org/cgi/input?sessionId = bJwDmfBXDNHh&input_page_active_form = multiple_identifiers.

Implementing well-known classification systems such as Gene Ontology (GO), KEGG, and hierarchical clustering of the association network itself. Protein-based co-expression analysis STRING v.11 resource https://string-db.org/ restricted to one dataset imported as is: namely the ProteomeHD dataset. Search was done with multiple proteins by name.

https://string-db.org/cgi/network?taskId = bcVeS18izJ5q&sessionId = bmcXI3uaKG3l.

2D. PPARG interacting proteins retrieved from full STRING network, confidence interactive network [ADIPOR1-PPARG-CDH1].

https://string-db.org/cgi/network?taskId = bVnlKqAK6oc9&sessionId = bmcXI3uaKG3l and https://string-db.org/cgi/network?taskId = bq9AkfiAnxDb&sessionId = bmcXI3uaKG3l.

Where, PPARG interacts with CCAAT/enhancer-binding protein beta (CEBPB) TF regulating the expression of genes involved in immune and inflammatory responses, and of a significant role in adipogenesis. With RETN Resistin, a hormone that suppresses insulin’s ability to stimulate glucose uptake into adipose cells (by similarity).

CDH1, Cadherin-1, and E-Cadherin are calcium-dependent epithelial cell adhesion proteins. Cadherins contribute to the sorting of heterogeneous cell types and are involved in mechanisms regulating cell–cell adhesions, mobility, and proliferation of epithelial cells. E-cadherin has a potent invasive suppressor role and is a ligand for integrin alpha-E/beta-7.

Adiponectin receptor protein 1 or 2; Receptor for ADIPOQ, an essential hormone secreted by adipocytes that regulates glucose and lipid metabolism. Required for normal glucose and fat homeostasis and maintaining a normal body weight. ADIPOQ-binding activates a signaling cascade that leads to increased AMPK activity and, ultimately to increased fatty acid oxidation, increased glucose uptake, and decreased gluconeogenesis. It has a high affinity for globular adiponectin and a low affinity for full-length adiponectin (by similarity). It belongs to the ADIPOR family.

The main important predicted functional partners related to PPARG are ANGPTL6; Angiopoietin-related protein 6; May play a role in the wound healing process, promote epidermal proliferation, remodeling and regeneration, and promote the chemotactic activity of endothelial cells and induce neovascularization, ADIG; Adipogenin; Plays a role in stimulating adipocyte differentiation and development; Belongs to the adipogenin family, MTA3; Metastasis-associated protein MTA3; Plays a role in maintenance of the normal epithelial architecture through the repression of SNAI1 transcription in a histone deacetylase-dependent manner, and thus the regulation of E-cadherin levels, ADIRF; Adipogenesis regulatory factor; Plays a role in fat cell development; promotes adipogenic differentiation and stimulates transcription initiation of master adipogenesis factors like PPARG and CEBPA at early stages of preadipocyte differentiation, and ZNF467; Zinc finger protein 467; Transcription factor that promotes adipocyte differentiation.

2.2. Cheminformatics analysis

Preparing Rosiglitazone Chemical Structure (Table 1). The isomeric chemical molecular structures processing the simplified molecular input line entry specification (SMILES) canonicalization, and the structure 2D as well as 3D, were obtained for the drug, using either MOL-Instincts Chemical Database based on Quantum Chemistry and Quantitative Structure-Property Relationships (QSAR) for chemical properties information.

Table 1.

Rosiglitazone 2D and 3D chemical structures as well as the simplified molecular input line entry specification (SMILES) canonicalization.

Compound		Rosiglitazone
DrugBank	ID	https://go.drugbank.com/drugs/DB00412 DB00412
Human	Metabolome db	https://hmdb.ca/metabolites/HMDB0005031
Canonical	SMILES	CN(CCOc1ccc(C[C@H]2SC(=O)NC2 = O)cc1)c1ccccn1
Chemical	structure
3D	Geometry

https://search.molinstincts.com/search/searchTextForm.ce or ZINC20 docking platform https://zinc.docking.org/ or PubChem https://pubchem.ncbi.nlm.nih.gov/.

https://biosig.lab.uq.edu.au/pkcsm/prediction_single/adme_1674867000.65 The 3D geometry obtained from.

https://www.molinspiration.com/cgi-bin/galaxy?smiles = CN%28CCOc1ccc%28C%5BC%40H%5D2SC%28 %3DO%29NC2%3DO%29 cc1%29c1ccccn1.

Physico-chemical Properties Prediction platform calculated the main descriptors of the investigated drugs (Table 2).

Table 2.

Main physico-chemical descriptors of the investigated drugs molecular properties.

Molecule/ Target	Total polar surface area TPSA (Å)	# of acceptor/donor atoms for H-bonds (N, O)	logP (o/w)	Mwt. (g/mol)	Lipinski's rule of five
Rosiglitazone/ PPARG	71.53	nON; 6/ nOHNH; 1	2.491	357.435	Yes

Using either ZINC20 or using the pkCSM²⁹ Web tool using graph-based signatures https://biosig.lab.uq.edu.au/pkcsm/prediction (Accessed on Jan 28th, 2023) cheminformatics platforms, the octanol–water partition coefficient (LogP), topological polar surface area (TPSA), molecular weight (Mwt.), number of acceptors and donors of hydrogen bonds (H-bonds).

https://zinc.docking.org/substances/ZINC000000968330/ based on the canonical SMILES. Accessed Jan. 28th, 2023. [o/w; Octanol-Water Partition Coefficient, Mwt.; Molecular weight.].

Drug-likeness through Biological Activities and Bioactivity Score (Table 3).

Table 3.

Predicted biological activity of the rosiglitazone using the 2023 Molinspiration Cheminformatics bioactivity score v2022.08.

	GPCR	NR	Ion channel	Kinase	Protease	Enzyme
Compound	ligand		modulator	inhibitor
Rosiglitazone	0.15	0.35	−0.65	−0.61	−0.21	−0.07

Biological properties were evaluated using the online server Molinspiration Cheminformatics https://www.molinspiration.com/cgi-bin/properties?textMode = 1 (Accessed on 28th of Jan., 2023).

https://www.molinspiration.com/cgi-bin/galaxy?smiles = CN%28CCOc1ccc%28C%5BC%40H%5D2SC%28 %3DO%29NC2%3DO%29 cc1%29c1ccccn1 Accessed Jan. 28th, 2023. [GPCR; G protein-coupled receptor (GPCR) superfamily, NR; nuclear receptor.].

Obtaining the Drug Target Genes via either ChEMBL library based on ChEMBL 20 in eukaryotes https://zinc.docking.org/substances/ZINC000000968330/activities/?sort = -affinity Accessed Jan. 28th, 2023. Or the Similarity Ensemble Approach (SEA)³⁰ https://sea.bkslab.org/ relates proteins based on the set-wise chemical similarity among their ligands (Table 4) search_49416358-b3ce-4b03-af26-48ff5b5b27ee and https://sea.bkslab.org/jobs/search_49416358-b3ce-4b03-af26-48ff5b5b27ee for rosiglitazone. Accessed on Jan. 29th, 2023. This is used to rapidly search large compound databases and build cross-target similarity maps (Fig. 3A).

Table 4.

Rosiglitazone observations gene activities from ChEMBL library based on ChEMBL 20 in eukaryotes and SEA Search server predictions for targets.

	Gene Name	Class	pKi (L.E.)	P-Value
Rosiglitazone	AGTR1	membrane receptor / GPCR-A	9.64 (0.54)	NA
	PPARG (agonist)	Transcription factor / NR	8.62 (0.48)	9.03E-44
	PPARA		8.40 (0.47)	1.69E-13
	MAOB	enzyme / reductase	6.08 (0.34)	NA
	TBXAS1	enzyme / p450	5.45 (0.31)	NA
	PPARD	Transcription factor / NR	5.44 (0.30)	5.63E-06
	CA2	enzyme / lyase	5.39 (0.30)	NA
	ABCB11	Transporter / NTPase	5.19 (0.29)	3.28E-21
	RARG	Transcription factor / NR	5.00 (0.28)	0.0009832

Fig. 3.

3A. Percentage distribution of the macromolecule targets of the small drug Rosiglitazone obtained via the Swiss institute of Bioinformatics (SIB) 2022 powered by ChemAxon SwissTargetPrediction. Accessed Jan. 29th, 2023.

https://zinc.docking.org/substances/ZINC000000968330/activities/?sort = -affinity.

search_49416358-b3ce-4b03-af26-48ff5b5b27ee.

and https://sea.bkslab.org/jobs/search_49416358-b3ce-4b03-af26-48ff5b5b27ee Accessed on Jan. 29th, 2023. [AGTR1, angiotensin II receptor, PPARG, A, D; Peroxisome proliferator-activated receptor gamma, alpha, delta, MAOB; Amine oxidase [flavin-containing] B, TBXAS1; Thromboxane-A synthase, CA2; Carbonic anhydrase 2, ABCB11; Bile salt export pump, RARG; Retinoic acid receptor gamma, NR; nuclear receptor, NA; not available, L.E.; ligand efficiency.].

https://www.swisstargetprediction.ch/result.php?job = 1308602366&organism = Homo_sapiens.

3B. Rosigliatzone IC₅₀ calculation using with dfferent concentrations (0 μM, 5 μM, 10 μM, 20 μM, 30 μM, 40 μM, 50 μM, 60 μM, 80 μM, and 100 μM) on Hep3B cells and HepG2 (3C) viability. The graph plots the perecnatge of viable cells on the y-axes vs rosiglitazone conc.μM on the x-axes. The percentage of HCC cell lines viable cells was decreased by 50 % after treatment with rosiglitazone doses higher than 50 μM and 40 μM and using the equations (y = 95.67e^-0.011x) and (y = 85.74e^-0.014x) to calculate IC₅₀ for both cell lines that were obtained as 58 μM for HepB3 and 41 μM for HepG2.

3D. HepG2 cell line morphology upon applying Rosiglitazone different concentrations ¼ IC₅₀, ½ IC₅₀, IC₅₀, 2xIC₅₀; 5 μM, 10 μM, 20 μM, 41 μM, and 80 μM, respectively, in comparison to the control mock cell line (free media with no drugs).

2.3. Experimental validation of the hypothesis examined

2.3.1. Chemicals, Reagents, Buffers, Stain, Antibodies, and Kits

Antibodies were purchased from Santa Cruz Biotechnology (Santa Cruz, USA), and chemicals from Sigma-Aldrich (St. Louis, MO, USA). Nitrocellulose blotting membrane, Second antibody: anti-goat, Second antibody: anti-donkey. Proteinase inhibitor cocktail ROCH Chemicals, Protein ladder standard pre-stained from 10 to 250KDa, Wst-1 proliferation assay from Santa Cruise, Colorimetric Western Blotting Kit, TMB Substrate, Cocktail proteinase inhibitor. Phosphate buffered saline (PBS), trypsin-EDTA, BCA, RIPA lysis buffer, Oil Red O powder (Sigma-Aldrich St. Louis, MO, USA), isopropanol, formalin, paraformaldehyde.

2.3.2. Drugs

From Sigma-Aldrich (St. Louis, MO, USA) purchased: Sorafenib powder (Code: SML2653-5MG) https://www.scientificlabs.ie/product/-nocategory-/SML2653-5MG.

Rosiglitazone pure powder (cat. #R2408-10MG) https://www.scientificlabs.ie/product/R2408-10MG# and IBMX (cat. #17018-100MG) https://www.scientificlabs.ie/product/enzyme-inhibitors/I7018-100MG (Scientific Laboratory Supplies (SLS), Nottingham, England). Human insulin crystals were a gift from Medical Union Pharmaceuticals (MUP) company, Egypt. Dexamethasone from G biosciences supplied as ready 1 ml 10 mM solution Dexamethasone (cat #API-04).

2.3.3. Cells, Culture, and media treatment

HCC cells, media, and criteria for HepG2 and Hep3B as appear in Table 5.

Table 5.

Criteria and media used for HCC HepG2 and Hep3B cell lines.

	Cells
Criteria	HepG2	Hep3B
ATCC https://www.atcc.org/	Hep G2 [HEPG2] HB-8065	Hep 3B2.1–7 [Hep 3B, Hep-3B, Hep3B] HB-8064 ™
Culture media	Dulbecco's modified Eagle's medium containing 10 % fetal calf serum
p53 expression	wild type	null/mutant
PPARγ expression	high	low

Differentiation medium used is 90 % Dulbecco’s Modified Eagle’s Medium (DMEM) with 10 % Fetal Bovine Serum (FBS), 100 U/ml penicillin and 100 µg/ml streptomycin. Insulin solution (10 mg/mL insulin in 25 mM HEPES, pH 8.2, Bioreagent, sterile filtered to be suitable for cell culture).

Cell culture grade DMSO for sparingly soluble powdered reagents was used. All reagents were cell culture grade and sterile.

2.4. In vitro Experiments

2.4.1. WST-1 cell proliferation/viability assay for rosiglitazone using HepG2 and Hep

Both HepG2 and Hep3B cells were seeded 5 x 10⁴ cells/well in a 96-well microtiter plate in a final volume of 100 μL culture medium and incubated for 24 hrs. Rosiglitazone dissolved in free media was added with different concentrations (0 μM, 5 μM, 10 μM, 20 μM, 30 μM, 40 μM, 50 μM, 60 μM, 80 μM, and 100 μM) and incubated for 48 hrs.

10 μL of WST-1 reagent was added, shacked for 1 min on a plate shaker, and incubated for 4 hrs. The change in WST-1 dye color was measured by a microplate reader with a test wavelength at 440 nm and a reference wavelength at 600 nm. The IC₅₀ was calculated after measuring the cell viability from absorbance measured by using the cell viability (%) equation³¹

$$\mathbf{\%}\mathit{V}\mathit{i}\mathit{a}\mathit{b}\mathit{i}\mathit{l}\mathit{i}\mathit{t}\mathit{y}=\frac{\mathit{M}\mathit{e}\mathit{a}\mathit{n}\mathit{O}\mathit{D}(\mathit{S}\mathit{a}\mathit{m}\mathit{p}\mathit{l}\mathit{e})}{\mathit{M}\mathit{e}\mathit{a}\mathit{n}\mathit{O}\mathit{D}(\mathit{B}\mathit{l}\mathit{a}\mathit{n}\mathit{k})}\mathit{x}100$$

The IC₅₀ was calculated from the standard curve in Fig. 3B and C with the exponential equations (y = 95.67e^-0.011x) and (y = 85.74e^-0.014x) for Hep3B and HepG2 to be 58 μM and 41 μM, respectively.³² .

After identifying Rosiglitazone IC₅₀, morphological assessment of HepG2 cells after their de-differentiation was photographed (Fig. 3D).

HepG2 cells morphology before (mock control cell group) and after treatment with rosiglitazone different concentrations during WST1 experiment; 5 μM, 10 μM, 20 μM, 41 μM, 80 μM, representing ¼ IC₅₀, ½ IC₅₀, IC₅₀, 2xIC₅₀. Where, 5 μM, 10 μM, and 20 μM rosiglitazone doses showed viable cells as the mock group, but the IC₅₀ and its doubled dose 80 μM showed a lower percentage of viable cells than other groups (Fig. 3D), to determine whether the IC50 value of rosiglitazone is capable of inducing adipogenesis, independently of its cytotoxic effects as demonstrated in the morphology photos captured.

2.4.2. Cancer cell lines differentiation protocol

Group 1: Control (Mock) group; HCC cell lines either HepG2 or Hep3B received media free of any drugs,

Then, to induce cancer cell differentiation, the adipogenesis inducers (0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone)³³ and rosiglitazone were added to HCC cell lines. Rosiglitazone reported dose to induce cellular differentiation is 2 µM³⁴, but herein, we will use double this dose 4 µM (1/10 IC₅₀), to examine if higher rosiglitazone dose will increase the rate and number of differentiated cancer cells to adipocytes or not.

Sorafenib reported IC₅₀ is 6 µM35, 36, 37 was applied to the following group:

Group 2A: HCC cell lines either HepG2 or Hep3B treated with 6 µM Sorafenib, 2 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone.

Group 2B: HCC cell lines either HepG2 or Hep3B treated with 6 µM Sorafenib, 4 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone.

Now we will try the same group 2 doses without Sorafenib.

Group 3A: HCC cell lines either HepG2 or Hep3B treated with 2 µM Rosiglitazone and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone.

Group 3B: HCC cell lines, either HepG2 or Hep3B, treated with 4 µM Rosiglitazone and 0.5 µM IBMX, 0.86 µM Insulin, and 1 µM Dexamethasone.

After this initial treatment, all groups received media containing just insulin (0.86 µM) for another 48hrs and 2 more passages³⁴ .

2.4.3. Adipocytes-formation Confirmation

Oil Red O staining was used to confirm cell differentiation with the formation of adipocytes13, 15.

HepG2 or Hep3B cells were washed with PBS (1x) once, followed by cells fixation using formalin (10 %) for 30 min. followed by washing with distilled water. 60 % isopropanol was added to cells for only 5 min. then removed.

2 ml of Oil Red O working solution was added to cells to cover wells, incubated at room temperature for 20 min. Excess Oil Red O was removed, and cells were washed with distilled water 2 to 5 times, then finally washed with PBS (1x).

HepG2 or Hep3B cells were checked for red-stained adipocytes under an inverted microscope (Optika, Italy) with a magnification power of 400x. Digital images were taken using a Kodak microscopic digital Camera, as shown in Fig. 4, where signs of morphological changes in adipocytes were confirmed by more intense staining (and more magnification is in the supplementary Fig. S1).

Fig. 4.

HCC cell lines either Hep3B or HepG2 photographs examined microscopically using an inverted microscope for signs of morphological changes into adipocytes. Cells were stained with Oil Red O working solution. Magnification power x400. Control (Mock) group; HCC cell lines either HepG2 or Hep3B treated with free media, Group 2A: HCC cell lines, either HepG2 or Hep3B treated with 6 µM Sorafenib, 2 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone, Group 2B: HCC cell lines either HepG2 or Hep3B treated with 6 µM Sorafenib, 4 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone, Group 3A: HCC cell lines either HepG2 or Hep3B treated with 2 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone, and Group 3B: HCC cell lines either HepG2 or Hep3B treated with 4 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone. Magnification power is 400x. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

2.4.4. HCC cells groups preparation for Western blot analysis

HepG2 cells were seeded into 6 wells plates, incubated at 37^◦C and 5 % CO₂ and after confluence, in addition to the control mock group 1 (Con),

Rosiglitazone group (R1): cells treated with rosiglitazone conc. 41 μM,

Group 4B: HCC cell line treated with 6 µM Sorafenib, 4 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone,

Sorafenib group (Sora1): cells treated with sorafenib IC₅₀ conc. 6 μM to evaluate the impact of sorafenib's IC₅₀ value on its own,

Group 5B: HCC cell line treated with 4 µM Rosiglitazone, 0.5 µM IBMX, 0.86 µM Insulin, and 1 µM Dexamethasone.

Groups were incubated at 37^◦C and 5 % CO₂ for 48 hrs. After 48 h of treatment, cells were prepared for protein extraction.

2.4.4.1. Cell lysis, protein extraction, and quantification

Cells were prepared for protein extraction by removal of media using cold PBS (1x). Cells were collected using a cold cell scraper and kept on ice, centrifuged at 40,000 rpm for 10 min.³⁷. The supernatant was removed by cold PBS (1x) added to the pellets and mixed. The pellet suspended in PBS was recentrifuged at 4000 rpm for 5 min. According to the pellet size, we added 60–––300 µl RIPA lysis buffer (Thermo Fisher Scientific) and incubated for 1 hr. on ice, followed by cold centrifugation for 30 min. The supernatant containing the cells’ extracted protein was used for protein quantification using a bicinchoninic acid (BCA) assay³⁸ .

Protein quantification using BCA Assay.

Using a 96-well plate, added 10 µl from the groups protein lysate samples into the plate wells, 10 µl of bovine serum albumin (BSA) standard reagent (#23209, Thermo fisher, Inc.) in serial dilutions from 125 to 2000 µg/ml, added 200 µl of the working reagent in the appropriate wells.

The protein concentration for each group was calculated using standard curve plotting the average blank-corrected at 562 nm measurement for each BSA standard vs. its concentration in µg/mL using the equation $X=\frac{Y-C}{M}$.³⁸ .

2.4.4.2. Western blotting

Western blot was performed for HepG2-treated groups (results are illustrated in Fig. 5).

Fig. 5.

Alteration of adipogenesis markers in HepG2-treated cells appearing as ADIPOR1 and E-cadherin. The control mock group 1 (Con), Rosiglitazone group (R1): cells treated with rosiglitazone conc. 41 μM, Group 4B: HCC cell line treated with 6 µM Sorafenib, 4 µM Rosiglitazone, 0.5 µM IBMX, 0.86 µM Insulin, and 1 µM Dexamethasone, Sorafenib group (Sora1): cells treated with sorafenib IC₅₀ conc. 6 μM to evaluate the impact of sorafenib's IC₅₀ value on its own, Group 5B: HCC cell line treated with 4 µM Rosiglitazone, and 0.5 µM IBMX, 0.86 µM Insulin and 1 µM Dexamethasone. [KDa: kilo Dalton.] Lysates were immunoblotted with an anti-ADIPOR1 antibody and anti-E-cadherin antibody. Representative immunoblot of ADIPOR1 protein, Adipo-R1 (49 kDa) and E-cad (125 kDa). Actin served as loading control.

The amount needed from each group into the gel was defined according to BCA calculation, so samples were prepared and boiled for separation with 3 µl of 2-mercaptomethanol (Sigma-Aldrich Corp. St. Louis, MO USA), 3 drops distilled water, and an equal amount of loading buffer (Bio-Rad Laboratories) was added so the total is less than 30 µl.

10 % SDS-PAGE was used, and then the gel was transferred to polyvinylidene difluoride membranes¹³The membrane was treated with 3 % BSA rotated for 1 h, then with the concentration of 1:1000, added the 1ry antibody (5 ml Tris-buffered saline with 0.1 % Tween® 20 (TBST) + 5 µl 1ry Ab) incubated overnight at 4˚C. The measured antibodies were E-Cadherin and ADIPOR1 (Santa Cruz Biotechnology, INC.). After washing the membrane using TBST, a secondary antibody was added to target the transferred proteins. Visualizing these proteins was done in a dark room for 15 min the membrane was incubated with a solution containing 3,3′,5,5′-tetramethylbenzidine (TMB) (Sigma-Aldrich Corp. St. Louis, MO USA). This solution changed color in the presence of the protein-antibody complexes. The membranes were then stripped using blot stripping buffer (Thermo Scientific Restore™, Thermo Scientific, lL, USA). Subsequently, to guarantee uniform protein loading among all samples, the samples were exposed to an anti-beta-actin antibody (42–44 KDa)^39,37 The E-cadherin and Adiponectin receptor 1 (ADIPOR1) markers were used to differentiate between HCC and if dedifferentiation state of the cells occurred. During carcinogenesis, before treatment, the E-Cadherin level in HCC state was very low which indicates losing of cell – cell adhesion that in turn means metastasis and continuous cell proliferation.¹² On the other hand, ADIPOR1 is only detected in the de- differentiated cells only, as these receptors denotes the presences of an adipose tissue⁹.

3. Discussion

The innovative approach to treating HCC involves the use of a hepatic anti-cancer vaccine⁴⁰ which is a type of immunotherapy that aims to stimulate the immune system to recognize and target cancer cells specifically in the liver. The vaccine is designed to elicit an immune response against specific liver cancer cells, particularly T cells, involving cytotoxic cells that have a role in HCC¹⁹ as well as leukocytes-associated antigens⁴¹. Moreover, the vaccine is designed to recognize and target specific tumor antigens expressed by HCC cells. By doing so, the vaccine can selectively destroy cancer cells and potentially prevent tumor recurrence or progression. These vaccines can target specific tumor antigens or stimulate a broad immune response against liver cancer cells. However, the development and effectiveness of hepatic cancer vaccines are actively researched, and several clinical trials (CTs) have been conducted to assess their safety and efficacy in HCC patients.

Moreover, cell-based regenerative therapy⁴² as potential therapeutic implications may vary depending on the type of cancer and the individual patient’s (epi)genetic makeup. Therefore, understanding the molecular characteristics of individual tumors and identifying predictive molecular markers for response-to-differentiation (R2D or R-to-D) therapy is crucial for personalized medicine (PM) treatment approaches.

Rosiglitazone in various low concentrations (less than the IC50) has no influence on HepG2 cell morphology. The morphology was recorded for rosiglitazone without sorafenib to confirm that rosiglitazone alone is not able to elicit morphological changes to HCC cell lines, but when using the adipogenesis inducers and rosiglitazone as a cocktail, they were able to de-differentiate HCC cells into adipocytes with obvious morphological changes from HCC cells to adipocytes.

The adipose tissue has been implicated in promoting tumor growth and metastasis in breast cancer (BC) and colorectal cancer (CRC). However, the relationship between the adipose tissue and HCC is complex and context-dependent. Per, adipocytes release adipokines such as leptin⁴³, fractalkine and apelin⁴⁴, FTO⁴⁵, lipocalins46, 47, chemerin and omentin⁴⁸, and various pro-inflammatory or anti-inflammatory cytokines like interleukins (IL); IL6, IL10, IL18¹⁶, IL28B⁴⁹, and C-reactive protein⁵⁰ .

Differentiation therapy³⁴ is an approach that aims to induce cancer cells to differentiate into more mature and less aggressive cell types. The rationale behind this strategy is to promote a less proliferative and less invasive phenotype in cancer cells, potentially leading to reduced tumor growth and improved treatment outcomes. The concept of inducing differentiation of HCC cells into adipocyte-like cells is an interesting one, as it would be promising that inducing HCC cells to differentiate or de-differentiate into a less aggressive phenotype may have an anti-tumor effect.

Per, HCC is primarily a liver cancer that arises from hepatocytes, the main cell type in the liver, rather than adipocytes. This presents a challenge for the current tested hypothesis. However, it is worth noting that considering the adipose tissue presence in the TME can influence cancer progression and treatment response in various types of cancer. Adipocytes and adipose tissue secretions can contribute to inflammation, promote angiogenesis, and affect tumor growth in certain contexts.

One clinical trial registered to clinicaltrials.gov examined “rosiglitazone in HCC” when searching for glitazones|carcinoma or glitazones|HCC or rosiglitazone|carcinoma or rosiglitazone|HCC.

https://clinicaltrials.gov/search?cond = Hepatocellular%20Carcinoma&intr = rosiglitazone.

NCT Number; NCT04114136; Anti-PD-1 mAb Plus Metabolic Modulator in Solid Tumor Malignancies, Study URL; https://clinicaltrials.gov/study/NCT04114136, Study Status; RECRUITING with no Study Results yet. Conditions; Melanoma|NSCLC|Hepatocellular Carcinoma|Urothelial Cancer|Gastric Adenocarcinoma|HNSCC|Esophageal Adenocarcinoma|Microsatellite Instability-High Solid Malignant Tumor. Interventions; DRUG: Nivolumab or Pembrolizumab (dependent upon approved indication) |DRUG: Metformin|DRUG: Rosiglitazone. Sex; ALL, Age; ADULT, OLDER_ADULT, Phases; PHASE2, Enrollment; 72. Study Type; INTERVENTIONAL, Study Design; Allocation: RANDOMIZED|Intervention Model: PARALLEL|Masking: NONE|Primary Purpose: TREATMENT.

To investigate the effect of sorafenib and rosiglitazone on the HCC cells, HepG2 and Hep3B cells were treated with them using several IC₅₀ concentrations. Both drugs were found to have an inhibitory effect on both cell lines. In other words, using sorafenib IC₅₀ (6 µM) and rosiglitazone IC₅₀ 41 µM on HepG2 and 58 µM on Hep3B. These data support that both drugs decrease HCC cancer cell viability.

To investigate the ability of sorafenib with rosiglitazone and the adipogenesis inducers (IBMX, INS, DEXA) to de-differentiate the HCC cells into adipocytes, we performed the differentiation protocol using HepG2 and Hep3B. Where, both cell lines treated with sorafenib, rosiglitazone IBMX, INS, DEXA and cells treated with rosiglitazone IBMX, INS, DEXA without sorafenib, both, turned into adipocytic cells confirmed by detecting the presence of Oil Red O staining as shown in Fig. 4. However, the percentage of adipocytes stained is more in both groups 2B and 3B groups where we used higher rosiglitazone concentration (4 µM).

This differentiation was confirmed by western blot, confirming the bioinformatics results of PPARG interacting proteins retrieved from the STRING network [ADIPOR1-PPARG-CDH1].

https://string-db.org/cgi/network?taskId = bVnlKqAK6oc9&sessionId = bmcXI3uaKG3l.

Where PPARG interacts with CDH1, Cadherin-1, and E-Cadherin, a calcium-dependent epithelial cell adhesion protein, contributes to the sorting of heterogeneous cell types, involved in mechanisms regulating cell–cell adhesions, mobility, and proliferation of epithelial cells. E-cadherin has a potent invasive suppressor role, a ligand for integrin alpha-E/beta-7. And, PPARG interacts with adiponectin receptor protein 1; receptor for ADIPOQ, an essential hormone secreted by adipocytes that regulates glucose and lipid metabolism; ADIPOQ-binding activates a signaling cascade that leads to increased AMPK activity and ultimately to an increased fatty acid oxidation, increased glucose uptake and decreased gluconeogenesis, belongs to the ADIPOR family.

We used the Hep3B cell line with low PPARG levels for the western blot. Since HepG2 cells express high levels of PPARG and can show apoptosis in response to the p53 effect we conducted the western blot using HepG2 cells to confirm the expression of PPARG, ADIPOR1, and E-cadherin (Fig. 5).

The Western blot to be performed on Hep3b with low PPARG expression levels presents a challenge in inducing the de-differentiation of these HCC cells into functional adipocytic cells. The goal is to compare the levels of PPARG, Adiponectin or its R1 receptor, and E-cadherin between the control and treated groups.

Western blotting performed showed that both Adiponectin receptor1 and PPAR-γ were only highly expressed in groups treated with the adipogenesis inducers; the cells that were differentiated into adipocytes compared to the control mock group and other groups treated with sorafenib IC₅₀ alone and groups treated with rosiglitazone IC₅₀ alone. The data obtained support our hypothesis that adding sorafenib with rosiglitazone to the adipogenesis inducers as a cocktail are able to differentiate HCC cells into functional adipocytes while hepatic cancer cells are losing their carcinogenicity.

Expert opinion regarding some challenges could be facing this Experimental proposal evidence and/or further Therapeutic implications.

Studies on inducing adipocyte-like differentiation in HCC cells using adipogenesis-inducer cocktails are relatively limited. While some studies have reported successful differentiation of BC cell lines into adipocyte-like cells using this cocktail, the phenotype's differentiation efficiency and stability may vary in HCC. The specific cocktail concentration optimization and the experimental conditions needed, exposure durations of the adipogenesis-inducing agents, for such hypothesis examination, are carefully optimized currently and evaluated in our lab to ensure an efficient and reproducible differentiation of HCC cells into adipocyte-like cells. However, more challenge lies in the heterogeneity of HCC, as different cell lines and patient-derived tumors may exhibit varying responses to PPAR-γ and adipogenesis induction. The molecular characteristics, genetic alterations, and TME/TIME may influence the differentiation potential of HCC cells. Inducing adipocyte-like differentiation in HCC cells could potentially alter the TME by modulating adipokines secretion and other factors, which may affect tumor-stromal interactions and immune responses.

Adipocytes and their secreted factors can impact tumor growth, invasion, and therapeutic responses. Understanding how the newly differentiated cells interact with the TME and whether these interactions affect HCC progression is critical. It is important to carefully evaluate the functional consequences of adipocyte-like differentiation in HCC cells. This includes assessing changes in cell proliferation, migration, invasion, metabolic characteristics, and gene expression profiles to determine the impact on HCC progression and therapeutic responses. it's important to assess whether the differentiated cells exhibit reduced proliferation, altered metabolism, or changes in other cancer-related characteristics.

Inducing differentiation of HCC cells into adipocyte-like cells using an adipogenesis-inducer cocktail with PPARG agonist and multitarget signaling pathways inhibitor is a novel potential therapeutic approach. Current experimental research is ongoing to optimize and evaluate the functional consequences and validate it preclinically using an in vivo model.

The current in vitro experimental validation showed that high doses of rosiglitazone alone have an inhibitory effect on both HCC cell lines. Furthermore, the addition of rosiglitazone to sorafenib and adipogenesis inducers IBMX, INS, and DEXA effectively transformed cancer cells into fat cells without retaining their carcinogenic activity.

In summary, while sorafenib has shown effectiveness in treating HCC in various studies, there is a pressing need for new treatment approaches to combat this aggressive and resistant cancer. Combining sorafenib with additional drugs has demonstrated success in transforming HCC cell lines into adipocytes.

These data suggest that PPARγ may be of an important role for the regulation of hepatic ADIPOR1 protein and E-Cadherin, indicating new interactions between PPARγ, ADIPOR1, and E-Cadherin for the regulation of HCC dedifferentiation.

Treatment of HepG2 cells with a PPARγ agonist and adipogenesis inducers had led to the induction of ADIPOR1 and E-Cadherin via nuclear translocation of the PPARγ nuclear receptor, resulting in an increase in one adipogenesis protein or receptor levels.

4. Future perspective

It is essential to validate the approach in additional preclinical models such as xenograft models and/or in vivo tumor models to evaluate the therapeutic potential and safety of inducing adipocyte-like differentiation in HCC. It is important to determine whether the differentiated cells demonstrate reduced proliferation, altered metabolism, or changes in other cancer-related characteristics, or not.

Recommendation

The possibility of adding more repurposed drugs or vitamins that modulate the TME like vitamin E or D⁵¹.

“Declarations”.

All authors edited and reviewed the manuscript. All authors approved the current authorship. All authors have read and approved manuscript submission in the current form. The authors declare that all data were generated in-house and that no paper mill was used.

Consent to Publish All authors have read the manuscript and agreed to publish.

CRediT authorship contribution statement

Aya Anis: Data curation, Investigation, Project administration, Writing – review & editing. Ahmed M. Mostafa: Conceptualization, Supervision, Writing – review & editing. Mariam S. Kerema: Investigation, Methodology, Resources. Nadia M. Hamdy: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Ahmed S. Sultan: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article: