Dissecting microRNA-regulated networks in hepatocellular carcinoma cell subtypes with different mutational profiles: Evidence from in vitro and in silico studies

Dissecting microRNA-regulated networks in hepatocellular carcinoma cell subtypes with different mutational profiles: Evidence from in vitro and in silico studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dissecting microRNA-regulated networks in hepatocellular carcinoma cell subtypes with different mutational profiles: Evidence from in vitro and in silico studies Shreyas Hulusemane Karunakara, Vijayalakshmi Bhat, Manju Moorthy, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4347735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) is a carcinoma of epithelial origin. While there are several factors, specific genetic and epigenetic landscapes define the initiation and progression of HCC. Genetic mutations, particularly missense mutations, often act as predictors of the onset of cancers, including HCC. Specifically, mutations associated with telomerase, TP53, and beta-catenin (CTNNB1) are among the three most commonly mutated genes in HCC. These genetic mutations define specific subtypes of HCC, exhibiting specific epigenetic expression patterns in terms of miRNA expression and the interactome. In our current study, we performed a differential expression analysis of multiple miRNAs among three different cell lines, HepG2, Huh7, and QGY7703, which exhibit different mutational patterns. This is the first study to characterize HCC cell lines based on miRNA expressions. We also identified the enriched pathways associated with the significantly differentially expressed miRNAs, bioinformatically predicted their targets, and characterized the interactomes. Additionally, we classified the small RNA sequencing data available from the publicly available dataset based on the mutational status of cancer samples and computed the overlaps of miRNAs exhibiting similar expression patterns consistent with the in vitro data, predicted the top hub genes and their associated pathways, and predicted their drug targets using an integrated bioinformatic approach. Hepatocellular carcinoma miRNAs TP53 CTNNB1 Hub genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Hepatocellular carcinoma (HCC) is a type of carcinoma affecting functional hepatocytes of epithelial origin (Feng et al. 2021; Qi et al. 2023). Multiple risk factors, such as unhealthy lifestyle (Schlageter et al. 2014), alcohol consumption (Tanai 2020), and the presence of viral infections (Chen et al. 2019; Jiang et al. 2021; De Ballista et al. 2021), support the causation of HCC. A vast majority of the reported HCCs present specific genetic and epigenetic landscapes associated with them. Genetically, somatic mutations in tumor suppressor genes (Di Benedetto et al. 2006; Long et al. 2019) and the activation of oncogenes reportedly act as drivers of cancer initiation and progression (Shen et al. 2026; Liang et al. 2021). Among these are mutations associated with telomerase reverse transcriptase (TERT) (50–60%), telomere length missense mutations associated with the tumor suppressor TP53 gene (3–40%), and the transcriptional regulator CTNNB1 (11–41%) (Zueman et al. 2015). Several other genes, including AXIN1, ARID1A , and CCND1 , also form a part of the mutation landscape associated with HCC (Zueman et al. 2015). TP53 is a major tumor suppressor gene commonly mutated in many cancers (Ruijs et al. 2010; Giacomelli et al. 2018; Marei et al. 2021). Mutation of this gene can result in both loss-of-function (Wang et al. 2024) and gain-of-function effects (Shi et al. 2023; Liu et al. 2023). Studies have reported that mutations in specific codons of TP53 can result in a mutant protein with a dominant negative effect on the wild-type protein, rendering the wild-type protein ineffective in its functions (Monti et al. 2011). Common TP53 mutations in HCC associated with a dominant negative effect include missense mutations at positions R249S (Han et al. 2020), Y220C (Paz et al. 2023), and R248W/Q (Yang et al. 2019). In in vitro -derived HCC cell line models, Huh7 cells carry a missense mutation, Y220C (Tseng et al. 2022). The CTNNB1 gene is yet another gene that is commonly mutated in HCC (Zueman et al. 2015). This gene encodes beta-catenin, which is a prominent transcription factor involved in the canonical WNT signalling cascade (Xu et al. 2022). Particularly in cancers, upon activation of frizzled receptors by the Wnt family of proteins, the destruction complex (DC) is degraded, and beta-catenin in the cytosol is stabilized and translocated into the nucleus, resulting in the active transcription of genes involved in cell proliferation, migration, and survival (Jung and Park et al. 2020). In HCC, CTNNB1 commonly has missense mutations at the S45P (Kumar et al. 2023), T41A (Oversoe et al. 2021; Tao et al. 2021), and G34E (Suarez et al. 2015) positions. The HepG2 cell line is known to carry a deletion in exons 3 and 4 of the CTNNB1 gene (Crippa et al. 2017). MicroRNAs are regulatory RNAs critical for the regulation of multiple genes (Saliminejad et al. 2019). Aberrant expression of these small RNAs has been well-studied in different cancers, including HCC (Kabekkodu et al. 2020; Liu et al. 2021). Many of these miRNAs, along with their target genes, comprise complex interactomes that define cellular cascades that define cancer development and progression. In our study, we first attempted to elucidate and characterize the epigenetic landscape in terms of the expression of several miRNAs in three different HCC cell lines, identified their mRNA targets, and performed enrichment analysis to identify the potential pathways regulated by these miRNA‒mRNA networks. We then compared the differential miRNA expression analysis in clinical samples from the TCGA-LIHC dataset with CTNNB1 and TP53 missense mutations to understand the expression patterns of miRNAs and to determine whether there was any overlap with our in vitro data. We then bioinformatically identified the hub gene partners of these miRNAs and characterized them in the CTNNB1 and TP53 mutational HCC subtypes using in silico tools. 2. Methods and Methodology 2.1. Cell culture and RNA isolation The hepatocellular carcinoma cell lines HepG2, Huh7, and QGY7703 were used in the study. HepG2 cells were maintained in minimal essential medium. Huh7 (TP53 mutant) cells were maintained in Dulbecco’s modified Eagle’s medium-F12. QGY7703 cells were maintained in high-glucose Dulbecco’s modified Eagle’s medium. All three cell lines were supplemented with 10% fetal bovine serum, L-glutamine, and 100 U/ml penicillin-streptomycin antibiotic cocktail and maintained at 37°C in 5% CO 2 until they were 80% confluent. Total cellular RNA was isolated using TRIzol reagent, and the quality of the isolated RNA was assessed on a 2% denaturing agarose gel. Quantification of total RNA was performed using a Nanodrop spectrophotometer, and quantification of total cellular miRNA was performed using a Qubit RNA HS assay (Invitrogen, Cat #Q32855) kit, and quantitatively analysed on an Agilent 2100 Bioanalyzer Pico chip. All the experiments were conducted in biological duplicates. 2.2. Differential miRNA expression analysis We used the nCounter Human v3 miRNA Expression Assay (NS_H_miR_v3B) and the nCounter Analysis System (NanoString Technologies) for miRNA expression analysis according to the manufacturer’s protocol. The miRNA expression assay has unique barcodes for 799 endogenous miRNA targets, housekeeping genes [beta-actin (ACTB), beta-2-microglobulin (B2M), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), ribosomal protein L19 (RPL19), and ribosomal protein lateral stalk subunit P0 (RPLP0)], and spike-in miRNAs from across species. These included Arabidopsis thaliana miR-159, Caenorhabditis elegans miRs-248 and 254, Oryza sativa miRs-414 and 442, and specific positive and negative controls for monitoring ligation as well as overall assay efficiency. For quality control in terms of imaging, the limit of detection, positive control, binding density, and ligation was assessed using the raw output miRNA data in Reporter Code Count (RCC) format using nSolver analysis software (NanoString Technologies) V 4.0. Furthermore, for each raw count, normalization was performed using geometric means of positive controls and the top 100 highly expressed miRNAs. HepG2 vs Huh7, HepG2 vs QGY7703, and Huh7 vs QGY7703 were the comparison groups considered for differential miRNA expression analysis. The differential expression in terms of foldchange between the groups was calculated using the build ratio utility of the nSolver, whose significance was calculated using the Student unpaired t-test. All the differentially expressed miRNAs were considered significantly up/downregulated if the threshold fold changes were ≥ + 1.5 (upregulated) or ≤ -1.5 (downregulated) with p ≤ 0.05 and if the geometric mean expression was greater than or equal to the average counts of the negative controls provided in the assay panel. 2.3. Characterization of differentially expressed miRNAs The differentially upregulated and downregulated miRNAs identified from each of the comparison groups were subjected to overrepresentation analysis (ORA) using the miRNA Enrichment Analysis and Annotation tool (miEAA 2.0: https://ccb-compute2.cs.uni-saarland.de/mieaa2/ ). The ORAs included an FDR (Benjamini–Hochberg adjustment), the minimum number of required hits per subcategory was set to 1, and the significance level was set to 0.05. ORA was performed against the REACTOME (mirPathdb) database to identify the possible pathways regulated by differentially expressed miRNAs. 2.4. Target prediction and functional enrichment analysis For the statistically significant miRNA features (p < 0.05), we used the miRTarBase v.9.0 database (Hsu et al. 2011; Huang et al. 2020) to identify the experimentally validated mRNA targets. These targets were then queried for Gene Ontology (GO) enrichment in terms of biological process (BP), cellular component (CC), and molecular function (MF) along with REACTOME pathways using the ClusterProfiler R package (Wu et al. 2021). 2.5. Enrichment of miRNA‒mRNA-pathway networks The validated mRNA targets of the differentially expressed miRNAs (up- and downregulated) were then subjected to enrichment using MIERNTURNET ( http://userver.bio.uniroma1.it/apps/mienturnet/ ) to identify the prominent miRNA‒mRNA target interactions. These interactions were visualized as a functional enrichment network against the REACTOME pathway database using the ClueGO v2.5.10 ( 10 ) plugin in Cytoscape (Bindea et al. 2009). Functionally grouped networks were connected based on kappa scores (≥ 0.4), with the color of the nodes representing the involvement of either the target of upregulated miRNAs (red) or downregulated miRNAs (green). Pathways with p ≤ 0.05 were visualized within the network. 2.6. Analysis of miRNA expression in different mutational subtypes of HCC in TCGA Prominent driver mutations in HCC include mutations in the genes CTNNB1 and TP53 . The cell lines chosen for our study, namely, HepG2 and Huh7, carry CTNNB1 deletion and TP53 m missense mutation, respectively. We were interested in understanding the representations of miRNA signatures in clinical samples of HCC patients with these two mutations and whether they exhibited an expression pattern similar to that of in vitro observations. The small RNA sequencing dataset containing the mature miRNA expression values (log2 reads/kb of transcript/million or RPKM + 1) for 420 normal and HCC samples from the TCGA-LIHC dataset was downloaded from the UCSC Xena database (Goldman et al. 2020). The accession details of patients with CTNNB1 and TP53 mutations were downloaded separately from cBioportal ( http://www.cbioportal.org/public-portal ). Patients exhibiting both mutations were excluded from further analysis. The mature miRNA expression details were then manually sorted using the accession details obtained from the TCGA-LIHC dataset. An unpaired t-test (p < 0.05) was used to determine the significantly differentially expressed miRNAs between i-mutated and TP53 -mutated HCC subtypes using Graph Pad Prism v. 9.5.0. Further experimentally validated target genes of these miRNAs were obtained from miRTarBase. A protein-protein interactome was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database ( https://string-db.org/ ). The top 10 hub genes were identified using the maximum clique centrality algorithm through the Cytoscape-CytoHubba plugin. Over-representation analysis was performed for the hub genes against REACTOME, BIOCARTA, and WIKIPATHWAYS datasets using the Molecular Signatures Database ( https://www.gsea-msigdb.org/gsea/index.jsp ). The gene signatures and their regulated pathways were visualized as a chord diagram using SRPlot ( http://www.bioinformatics.com.cn/en?keywords=chord ) to understand the association of the hub genes with the molecular pathways. Possible druggable gene targets were then identified using the Drug-gene-interaction database (DGIdb: https://www.dgidb.org/ ) (Cannon et al. 2024). Pandrug analysis was also carried out to prioritize and characterize the drugs available for HCC management (Jimenez et al. 2023). 2.7. Statistical analysis All the statistical analyses were performed by using R (version 4.0.2, https://www.r-project.org/ ). The results were considered statistically significant if p was less than 0.05. 3. Results 3.1. Specific miRNAs are differentially expressed in different HCC cells We compared the differentially expressed miRNAs between three different HCC cell lines. Approximately 799 miRNA targets were quantified. The comparison groups for differential expression analyses included HepG2 vs Huh7 cells, QGY7703 vs HepG2 cells, and QGY7703 vs Huh7 cells. nCounter-miRNA analysis revealed specific miRNA signatures that were differentially upregulated and downregulated in different HCC cell lines. We observed 13 miRNAs to be downregulated and 28 miRNAs to be upregulated in HepG2 vs Huh7 cells (Fig. 1 A.). Differential miRNA expression analysis between QGY-7703 and HepG2 cells revealed a total of 14 downregulated miRNAs and 12 upregulated miRNAs (Fig. 1 B.). Similarly, the comparison between QGY-7703 and Huh7 cells revealed 10 downregulated miRNAs and 34 upregulated miRNAs (Fig. 1 C.). The top 10 upregulated and downregulated miRNAs in each comparison are represented in Fig. 1 D-F. The expression details of these miRNAs in each of the comparisons and their fold changes are summarized in Table 1 . ORA revealed the top 20 downregulated and upregulated pathways associated with the differentially expressed miRNAs in each of the compared groups (Fig. 2 .). Interestingly, we observed that RAS signalling, EGFR signalling, and VEGFA signalling were upregulated in the HepG2 vs Huh7 comparison (Fig. 2 A.). Compared with those in Huh7 cells, the expression of the RUNX family and NOTCH genes, CCNE1 and ACdk2-mediated G1/S transition, and IGFR-2 activation were downregulated, and oncogene-induced senescence was the prominent pathway with upregulated expression (Fig. 2 B.). Likewise, the QGY7703 vs HepG2 comparison revealed activation of WNT signalling, reduction of PTEN and NOTCH expression, and reduced apoptosis as enriched pathways (Fig. 2 C.). 3.2. Analysis of predicted miRNA targets in different HCC cell lines To understand the possible functional roles and regulatory functions of the differentially expressed miRNAs in each of the cell lines, we predicted the validated mRNA targets using miRTarBase. The targets identified for each of the differentially expressed miRNAs are listed in Supplementary Table 1 . Functional enrichment in terms of gene ontology (BP, CC, MF, and pathway enrichment) of upregulated and downregulated targets revealed the top 20 annotations of the target genes in terms of HepG2 vs Huh7 (Fig. 3 A & 3 B.), QGY7703 vs Huh7 (Fig. 3 C & 3 D.), and QGY7703 vs HepG2 (Fig. 3 E & 3 F.). 3.3. Functional interactome analysis reveals signature pathways regulated by gene targets and their interactomes The reduced list of mRNA targets obtained from MIERNTURNET of differentially expressed miRNAs was used for generating functional pathway interactomes and to understand the interactions between different cellular pathways. Compared with those in Huh7 cells, cytokine signalling, ALK signalling, and SHC-related events mediated by IGF1R were significantly downregulated in HepG2 cells (Fig. 4 A.). Similarly, MAPK3-activated pathways, provirus integration, sulfide oxidation pathways, RUNX3-regulated WNT signalling, and vitamin B1 metabolism were downregulated in QGY7703 cells compared with Huh7 cells (Fig. 4 B.). Additionally, it was predicted that, compared with HepG2 cells, QGY7703 cells would exhibit a decrease in the expression of RUNX3 , which is commonly involved in the maintenance of cell morphology and migration, and an increase in WNT signalling (Fig. 4 C.). 3.4. The miRNA‒mRNA landscape in HCC patients with different mutational statuses: details from public datasets Genetically, HepG2 and Huh7 cells carry mutations in the CTNNB1 and TP53 genes, respectively. We were interested in understanding the miRNA landscape in tumor samples of the publicly available TCGA-LIHC dataset carrying mutations in the CTNNB1 and TP53 genes. A total of 78 samples exhibited CTNNB1 mutations, and 51 samples had at least one missense mutation in the TP53 tumor suppressor gene. Differential miRNA expression analysis revealed that at least 9 miRNAs were differentially upregulated or downregulated, which was consistent with our comparison of HepG2 and Huh7 cells. These miRNAs included miR-885-5p, miR-424-5p, miR-130-5p, miR-296-5p, miR-382-5p, miR-181b5p, miR-181d-5p, miR-520d-3p, and miR-328-5p (Fig. 5 A.). The top 10 hub genes identified for these miRNAs included MYC, CCND1, STAT3, BCL2, PTEN, MTOR, CASP3, HIF1A, H3C4 , and ESR1 (Fig. 5 B.). ORA revealed HCV infection and HCC as one of the enriched pathways. Other enriched pathways involving these hub genes included the involvement of androgen receptors in prostate cancer, EGFR resistance, IL3 and IL24 signalling, CKAP4 signalling, and gastrin signalling (Fig. 5 C.). Drug-gene interaction analysis revealed a total of 207 drug interactions with ESR1 , 145 drug interactions with PTEN , 144 targets with HIF1A , 142 drug interactions with MTOR , 76 drug interactions with BCL2 , 69 drug interactions with MYC , 38 drug interactions with CASP3 , 28 drug interactions with CCND1 , and 29 drug interactions with STAT3 ( Supplementary Table 2 ). Pancancer drug analysis revealed that HIF1A/PTEN-targeting drugs, such as sorafenib, and PTEN-targeting drugs, such as pembrolizumab and nivolumab, are FDA-approved drugs for HCC management ( Supplementary Table 3 ). 4. Discussion Hepatocellular carcinoma (HCC) is a complex disease characterized by genomic and epigenomic changes. Each tumor represents a specific mutation profile in specific gene families that can determine the composition of the tumor and dictate its nature in terms of the tumor cell’s ability to remain undifferentiated, migrate, and metastasize (Waarta et al. 2022). Epi/genome-wide studies have been useful in deciphering the association of drug response with the expression profiles of tumor cells with specific mutations (Chiu et al. 2019; Li et al. 2020). Thus, both genetic and epigenetic tumor profiles not only shape the nature of tumor cells but also predict the response and outcomes of clinical intervention. In our study, we used cell line models comprising three HCC cell lines, namely, HepG2, Huh7, and QGY7703, to understand the miRNA landscape present in these three cancer cell lines. With the use of specific probes targeting approximately 800 miRNAs, we quantified mature miRNAs that were differentially upregulated or downregulated in each of the cell lines. The differentially expressed miRNAs in HepG2 vs Huh7 cells were associated with the upregulation of the RAS, EGFR, and VEGFA signalling pathways. Furthermore, pathway enrichment analysis revealed downregulation of genes in the RUNX and NOTCH families and upregulation of kinases involved in G1/S transition in QGY7703 cells compared with Huh7 cells. Interestingly, RUNX and NOTCH are well-known tumor suppressors in HCC (Zhu et al. 2021; Krajinović et al. 2023). Downregulation of these two genes in QGY7703 cells may be critical for determining the rigor and growth of QGY7703 cells, which are faster-growing cell lines than Huh7 cells. Furthermore, our bioinformatic analysis revealed that, compared with HepG2 cells, QGY7703 cells exhibited enhanced Wnt signalling, which can explain why the mesenchymal properties of QGY7703 cells (Grant et al. 2012; Santhekadur et al. 2014) are more pronounced than those of HepG2 cells, which have a more or less epithelial morphology (Donato et al. 2015), making the former cell line more aggressive in terms of migratory and invasive ability than the latter. It has been observed that nearly 13–44% of HCC patients overexpress Alk (4Liu et al. 2016). Increased cytokine expression (Song et al. 2021), ALK expression (Liu et al. 2016), and IGF1R (Guan et al. 2021) expression are known to fuel cell proliferation and survival. Our pathway interaction analysis identified several downregulated pathways in the HepG2 vs Huh7 comparison as well. These included a reduction in cytokine, ALK, and IGF1R signalling cascades. This can explain why HepG2 cells have a morphology similar to that of differentiated hepatocytes compared with that of Huh7 cells, which have a greater growth rate and shorter doubling time. Many studies have shown that mutations in the TP53 and CTNNB1 genes are independent drivers of HCC suggesting that mutations in any of these two genes are sufficient to initiate tumors (Friemel et al. 2016). However, the mechanisms that contribute to tumor formation due to such mutations are poorly understood, and the complex epigenome and its target interactome are yet to be dissected completely, making it difficult to study the downstream targets, pathway overlaps, and regulators involved. An integrated bioinformatic approach is a powerful tool for understanding and obtaining a larger picture of complex cellular networks involved in diseases such as cancer. We employed an integrated bioinformatic approach to identify the upregulated and downregulated miRNAs in HCC clinical samples with CTNNB1 and TP53 mutations based on the small-RNA sequencing reads available from the TCGA-LIHC public cohort. We then compared our in vitro miRNA expression data with those of a public dataset (HepG2 vs Huh7: CTNNB1 mutant vs TP53 mutant) to identify differentially expressed miRNAs that showed similar trends in expression patterns. At least 9 different miRNAs (miR-885-5p, miR-424-5p, miR-130-5p, miR-296-5p, miR-382-5p, miR-181b5p, miR-181d-5p, miR-520d-3p, and miR-328-5p) were differentially expressed and exhibited a similar trend in their expression patterns based on their mutational profiles in both our in vitro and TCGA samples. Hub-gene prediction revealed MYC, BCL2, CASP3, PTEN, STAT3, ESR1, HIF1A, MTOR, CCND1 , and H3C1 as the top interacting gene partners of these 9 miRNAs. Interestingly, we detected HCC and HCV infection as one of the pathways enriched in these hub genes according to the panel of 9 miRNAs mentioned above. Identifying and predicting drug response is a critical step in cancer management. We performed a drug-gene interaction analysis using hub genes to identify drugs that could serve as possible targets and identified at least 878 drug candidates that were available as targets for nine gene targets (ESR1 , PTEN, HIF1A, MTOR, BCL2, MYC, CASP3, CCND1 , and STAT3) , of which several drugs were for antineoplastic purposes. Finally, a pancancer drug analysis identified at least three drugs, namely, sorafenib (a HIF1A/PTEN target), pembrolizumab, and nivolumab (a PTEN target), that are already FDA-approved for HCC treatment and management. Interestingly, PTEN expression is lost or reduced in most HCCs and it is reported that restoring its expression can improve sorafenib resistance and mitigate sorafenib’s activity such as metabolic reprogramming (Zhoa et al. 2020; Miao et al. 2021) and increased apoptosis in HCC cells (Ruan et al. 2012). Likewise, PTEN-loss is also associated with PD-L1-mediated reduction of INF-γ and CD8 + T cells that can further facilitate tumor progression and metastasis (Zhoa et al. 2020; Vidotto et al. 2020). Tumors exhibiting PTEN loss can be potentially treated by pembrolizumab and nivolumab which can potentially target PD-L1 (Finn et al. 2020; Fessas et al. 2023). Further targeted pre-clinical and clinical studies can confirm the use of these drugs in the clinical management of HCC patients presenting specific mutations in either TP53 or CTNNB1 genes. In the present study, employing a systems biology approach, using human HCC cell lines and clinical datasets of HCC patients with different mutational profiles from public datasets, we identified miRNA expression landscapes and predicted their target genes, interactomes, and associated pathways, thereby providing an overall insight into the epigenetic landscape of different HCC cellular subtypes based on mutational statuses. In addition, our study identified druggable genes that can be repurposed for the management of HCC. Although the identified gene targets are experimentally validated, the inclusion of functional studies can add more power to the study. Abbreviations CTNNB1 Beta-catenin ARID1A AT-rich interaction domain 1A ARID2 AT-rich interaction domain 2 CCND1 Cyclin D1 DC Destruction complex miRNA microRNA RAS Rat sarcoma gene EGFR Epidermal growth factor receptor VEGFA Vascular endothelial growth factor A RUNX3 Runt-related transcription factor 3 NOTCH Neurogenic locus homolog IGF1R Insulin-like growth factor receptor 1 ALK anaplastic lymphoma kinase STAT3 Statin 3 BCL2 B-cell leukemia/lymphoma 2 MTOR mammalian target of rapamycin CASP3 cysteine-aspartic acid protease 3 HIF1A Hypoxia-inducible factor 1A H3C4 H3 Clustered Histone 4 ESR1 Estrogen receptor 1 PD L1 -Programmed Cell Death Ligand 1 Declarations Conflict of interest The authors declare no conflicts of interest. Author Contribution SHK: Conceptualization, Data curation, Data validation, Formal analysis, Investigation, Writing - Original draft, Writing - Review & Editing, Funding acquisition VB: Methodology, Formal AnalysisMM: Data curation, Data validation, Formal analysis, Investigation, Writing - Review & EditingGK: Writing – Review & Editing, Supervision, Project AdministrationSPK: Writing – Review & Editing, SupervisionNGS: Writing – Review & EditingVDS: Writing – Review & EditingDPK: Writing – Review & EditingAP: Supervision & Intellectual input,PMV: Supervision & Intellectual inputPKS: Conceptualization, Writing – Review & Editing, Supervision, Project Administration, Funding acquisition. Acknowledgment This study was conducted with the help of financial support from JSS Academy of Higher Education and Research-Institutional Research Grant (JSSAHER/REG/RES/URG/54/2011-12), provided to PKS, and Junior Research Scholarship and contingency support offered to SHK by Lady Tata Memorial Trust. We acknowledge the infrastructure support provided by the Department of Science and Technology to CEMR Laboratory (SR-FST-LS-1/2018/178) and the Department of Biochemistry (SR/FST/LS-1-539/2012). The authors would also like to acknowledge the Department of Biotechnology-Boost to University Inter-disciplinary Life Sciences Departments for Education and Research (BUILDER) program (BT/INF/22/SP43045/2021). We also thank our collaborators from Theracues Innovations Pvt Ltd, Bangalore, India, for performing miRNA probing assays on the NanoString platform and for conducting bioinformatic analysis in part. 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Future Oncol (London England) 19(7):499–507. https://doi.org/10.2217/fon-2022-0916 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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HepG2 vs Huh7 B. QGY7703 vs Huh7 C. Qgy7703 vs HepG2 and waterfall plots of the top 10 differentially expressed miRNAs in D. HepG2 vs Huh7 E. QGY7703 vs Huh7 and F. 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HepG2 vs Huh7 B. QGY7703 vs Huh7 and C. Qgy7703 vs HepG2\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4347735/v1/4724a39a701de17396cdbc1a.png"},{"id":56089895,"identity":"a1cf0d85-30d1-465a-83c3-5a889780fa15","added_by":"auto","created_at":"2024-05-08 12:04:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1733814,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of miRNA-mRNA networks from TCGA-LIHC dataset with CTNNB1 and TP53 mutations A. Differential expression of miRNAs in CTNNB1 and TP53 mutations B. Hub gene targets of differentially expressed miRNAs C. Chord diagram of enriched pathways associated with the hub genes\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4347735/v1/78f00eb3739942dd467b7953.png"},{"id":56612849,"identity":"decadb5a-693c-4a69-9478-28029da9d500","added_by":"auto","created_at":"2024-05-16 15:37:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16306549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4347735/v1/c643a4b8-032b-4973-9449-974ab3a9875a.pdf"},{"id":56090548,"identity":"8904053e-d6ca-4c53-8136-597d0a7d64d2","added_by":"auto","created_at":"2024-05-08 12:12:25","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32023,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4347735/v1/a37f437ea18b142943133327.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dissecting microRNA-regulated networks in hepatocellular carcinoma cell subtypes with different mutational profiles: Evidence from in vitro and in silico studies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is a type of carcinoma affecting functional hepatocytes of epithelial origin (Feng et al. 2021; Qi et al. 2023). Multiple risk factors, such as unhealthy lifestyle (Schlageter et al. 2014), alcohol consumption (Tanai 2020), and the presence of viral infections (Chen et al. 2019; Jiang et al. 2021; De Ballista et al. 2021), support the causation of HCC. A vast majority of the reported HCCs present specific genetic and epigenetic landscapes associated with them. Genetically, somatic mutations in tumor suppressor genes (Di Benedetto et al. 2006; Long et al. 2019) and the activation of oncogenes reportedly act as drivers of cancer initiation and progression (Shen et al. 2026; Liang et al. 2021). Among these are mutations associated with telomerase reverse transcriptase (TERT) (50\u0026ndash;60%), telomere length missense mutations associated with the tumor suppressor \u003cem\u003eTP53\u003c/em\u003e gene (3\u0026ndash;40%), and the transcriptional regulator CTNNB1 (11\u0026ndash;41%) (Zueman et al. 2015). Several other genes, including \u003cem\u003eAXIN1, ARID1A\u003c/em\u003e, and \u003cem\u003eCCND1\u003c/em\u003e, also form a part of the mutation landscape associated with HCC (Zueman et al. 2015).\u003c/p\u003e \u003cp\u003eTP53 is a major tumor suppressor gene commonly mutated in many cancers (Ruijs et al. 2010; Giacomelli et al. 2018; Marei et al. 2021). Mutation of this gene can result in both loss-of-function (Wang et al. 2024) and gain-of-function effects (Shi et al. 2023; Liu et al. 2023). Studies have reported that mutations in specific codons of TP53 can result in a mutant protein with a dominant negative effect on the wild-type protein, rendering the wild-type protein ineffective in its functions (Monti et al. 2011). Common TP53 mutations in HCC associated with a dominant negative effect include missense mutations at positions R249S (Han et al. 2020), Y220C (Paz et al. 2023), and R248W/Q (Yang et al. 2019). In \u003cem\u003ein vitro\u003c/em\u003e-derived HCC cell line models, Huh7 cells carry a missense mutation, Y220C (Tseng et al. 2022).\u003c/p\u003e \u003cp\u003eThe CTNNB1 gene is yet another gene that is commonly mutated in HCC (Zueman et al. 2015). This gene encodes beta-catenin, which is a prominent transcription factor involved in the canonical WNT signalling cascade (Xu et al. 2022). Particularly in cancers, upon activation of frizzled receptors by the Wnt family of proteins, the destruction complex (DC) is degraded, and beta-catenin in the cytosol is stabilized and translocated into the nucleus, resulting in the active transcription of genes involved in cell proliferation, migration, and survival (Jung and Park et al. 2020). In HCC, CTNNB1 commonly has missense mutations at the S45P (Kumar et al. 2023), T41A (Oversoe et al. 2021; Tao et al. 2021), and G34E (Suarez et al. 2015) positions. The HepG2 cell line is known to carry a deletion in exons 3 and 4 of the CTNNB1 gene (Crippa et al. 2017).\u003c/p\u003e \u003cp\u003eMicroRNAs are regulatory RNAs critical for the regulation of multiple genes (Saliminejad et al. 2019). Aberrant expression of these small RNAs has been well-studied in different cancers, including HCC (Kabekkodu et al. 2020; Liu et al. 2021). Many of these miRNAs, along with their target genes, comprise complex interactomes that define cellular cascades that define cancer development and progression. In our study, we first attempted to elucidate and characterize the epigenetic landscape in terms of the expression of several miRNAs in three different HCC cell lines, identified their mRNA targets, and performed enrichment analysis to identify the potential pathways regulated by these miRNA‒mRNA networks. We then compared the differential miRNA expression analysis in clinical samples from the TCGA-LIHC dataset with CTNNB1 and TP53 missense mutations to understand the expression patterns of miRNAs and to determine whether there was any overlap with our \u003cem\u003ein vitro\u003c/em\u003e data. We then bioinformatically identified the hub gene partners of these miRNAs and characterized them in the CTNNB1 and TP53 mutational HCC subtypes using \u003cem\u003ein silico\u003c/em\u003e tools.\u003c/p\u003e"},{"header":"2. Methods and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Cell culture and RNA isolation\u003c/h2\u003e \u003cp\u003eThe hepatocellular carcinoma cell lines HepG2, Huh7, and QGY7703 were used in the study. HepG2 cells were maintained in minimal essential medium. Huh7 (TP53 mutant) cells were maintained in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium-F12. QGY7703 cells were maintained in high-glucose Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium. All three cell lines were supplemented with 10% fetal bovine serum, L-glutamine, and 100 U/ml penicillin-streptomycin antibiotic cocktail and maintained at 37\u0026deg;C in 5% CO\u003csub\u003e2\u003c/sub\u003e until they were 80% confluent.\u003c/p\u003e \u003cp\u003eTotal cellular RNA was isolated using TRIzol reagent, and the quality of the isolated RNA was assessed on a 2% denaturing agarose gel. Quantification of total RNA was performed using a Nanodrop spectrophotometer, and quantification of total cellular miRNA was performed using a Qubit RNA HS assay (Invitrogen, Cat #Q32855) kit, and quantitatively analysed on an Agilent 2100 Bioanalyzer Pico chip. All the experiments were conducted in biological duplicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Differential miRNA expression analysis\u003c/h2\u003e \u003cp\u003eWe used the nCounter Human v3 miRNA Expression Assay (NS_H_miR_v3B) and the nCounter Analysis System (NanoString Technologies) for miRNA expression analysis according to the manufacturer\u0026rsquo;s protocol. The miRNA expression assay has unique barcodes for 799 endogenous miRNA targets, housekeeping genes [beta-actin (ACTB), beta-2-microglobulin (B2M), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), ribosomal protein L19 (RPL19), and ribosomal protein lateral stalk subunit P0 (RPLP0)], and spike-in miRNAs from across species. These included \u003cem\u003eArabidopsis thaliana\u003c/em\u003e miR-159, \u003cem\u003eCaenorhabditis elegans\u003c/em\u003e miRs-248 and 254, \u003cem\u003eOryza sativa\u003c/em\u003e miRs-414 and 442, and specific positive and negative controls for monitoring ligation as well as overall assay efficiency.\u003c/p\u003e \u003cp\u003eFor quality control in terms of imaging, the limit of detection, positive control, binding density, and ligation was assessed using the raw output miRNA data in Reporter Code Count (RCC) format using nSolver analysis software (NanoString Technologies) V 4.0. Furthermore, for each raw count, normalization was performed using geometric means of positive controls and the top 100 highly expressed miRNAs. HepG2 vs Huh7, HepG2 vs QGY7703, and Huh7 vs QGY7703 were the comparison groups considered for differential miRNA expression analysis. The differential expression in terms of foldchange between the groups was calculated using the build ratio utility of the nSolver, whose significance was calculated using the Student unpaired t-test. All the differentially expressed miRNAs were considered significantly up/downregulated if the threshold fold changes were \u0026ge;\u0026thinsp;+\u0026thinsp;1.5 (upregulated) or \u0026le; -1.5 (downregulated) with p\u0026thinsp;\u0026le;\u0026thinsp;0.05 and if the geometric mean expression was greater than or equal to the average counts of the negative controls provided in the assay panel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Characterization of differentially expressed miRNAs\u003c/h2\u003e \u003cp\u003eThe differentially upregulated and downregulated miRNAs identified from each of the comparison groups were subjected to overrepresentation analysis (ORA) using the miRNA Enrichment Analysis and Annotation tool (miEAA 2.0: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccb-compute2.cs.uni-saarland.de/mieaa2/\u003c/span\u003e\u003cspan address=\"https://ccb-compute2.cs.uni-saarland.de/mieaa2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The ORAs included an FDR (Benjamini\u0026ndash;Hochberg adjustment), the minimum number of required hits per subcategory was set to 1, and the significance level was set to 0.05. ORA was performed against the REACTOME (mirPathdb) database to identify the possible pathways regulated by differentially expressed miRNAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Target prediction and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eFor the statistically significant miRNA features (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we used the miRTarBase v.9.0 database (Hsu et al. 2011; Huang et al. 2020) to identify the experimentally validated mRNA targets. These targets were then queried for Gene Ontology (GO) enrichment in terms of biological process (BP), cellular component (CC), and molecular function (MF) along with REACTOME pathways using the ClusterProfiler R package (Wu et al. 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Enrichment of miRNA‒mRNA-pathway networks\u003c/h2\u003e \u003cp\u003eThe validated mRNA targets of the differentially expressed miRNAs (up- and downregulated) were then subjected to enrichment using MIERNTURNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://userver.bio.uniroma1.it/apps/mienturnet/\u003c/span\u003e\u003cspan address=\"http://userver.bio.uniroma1.it/apps/mienturnet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to identify the prominent miRNA‒mRNA target interactions. These interactions were visualized as a functional enrichment network against the REACTOME pathway database using the ClueGO v2.5.10 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) plugin in Cytoscape (Bindea et al. 2009). Functionally grouped networks were connected based on kappa scores (\u0026ge;\u0026thinsp;0.4), with the color of the nodes representing the involvement of either the target of upregulated miRNAs (red) or downregulated miRNAs (green). Pathways with p\u0026thinsp;\u0026le;\u0026thinsp;0.05 were visualized within the network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Analysis of miRNA expression in different mutational subtypes of HCC in TCGA\u003c/h2\u003e \u003cp\u003eProminent driver mutations in HCC include mutations in the genes \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e. The cell lines chosen for our study, namely, HepG2 and Huh7, carry \u003cem\u003eCTNNB1\u003c/em\u003e deletion and \u003cem\u003eTP53\u003c/em\u003e\u003csup\u003e\u003cem\u003em\u003c/em\u003e\u003c/sup\u003e missense mutation, respectively. We were interested in understanding the representations of miRNA signatures in clinical samples of HCC patients with these two mutations and whether they exhibited an expression pattern similar to that of \u003cem\u003ein vitro\u003c/em\u003e observations. The small RNA sequencing dataset containing the mature miRNA expression values (log2 reads/kb of transcript/million or RPKM\u0026thinsp;+\u0026thinsp;1) for 420 normal and HCC samples from the TCGA-LIHC dataset was downloaded from the UCSC Xena database (Goldman et al. 2020). The accession details of patients with \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutations were downloaded separately from cBioportal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/public-portal\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org/public-portal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patients exhibiting both mutations were excluded from further analysis. The mature miRNA expression details were then manually sorted using the accession details obtained from the TCGA-LIHC dataset. An unpaired t-test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to determine the significantly differentially expressed miRNAs between i-mutated and \u003cem\u003eTP53\u003c/em\u003e-mutated HCC subtypes using Graph Pad Prism v. 9.5.0.\u003c/p\u003e \u003cp\u003eFurther experimentally validated target genes of these miRNAs were obtained from miRTarBase. A protein-protein interactome was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The top 10 hub genes were identified using the maximum clique centrality algorithm through the Cytoscape-CytoHubba plugin. Over-representation analysis was performed for the hub genes against REACTOME, BIOCARTA, and WIKIPATHWAYS datasets using the Molecular Signatures Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gene signatures and their regulated pathways were visualized as a chord diagram using SRPlot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn/en?keywords=chord\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn/en?keywords=chord\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to understand the association of the hub genes with the molecular pathways. Possible druggable gene targets were then identified using the Drug-gene-interaction database (DGIdb: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dgidb.org/\u003c/span\u003e\u003cspan address=\"https://www.dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Cannon et al. 2024). Pandrug analysis was also carried out to prioritize and characterize the drugs available for HCC management (Jimenez et al. 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed by using R (version 4.0.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The results were considered statistically significant if p was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Specific miRNAs are differentially expressed in different HCC cells\u003c/h2\u003e \u003cp\u003eWe compared the differentially expressed miRNAs between three different HCC cell lines. Approximately 799 miRNA targets were quantified. The comparison groups for differential expression analyses included HepG2 vs Huh7 cells, QGY7703 vs HepG2 cells, and QGY7703 vs Huh7 cells. nCounter-miRNA analysis revealed specific miRNA signatures that were differentially upregulated and downregulated in different HCC cell lines.\u003c/p\u003e \u003cp\u003eWe observed 13 miRNAs to be downregulated and 28 miRNAs to be upregulated in HepG2 vs Huh7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA.). Differential miRNA expression analysis between QGY-7703 and HepG2 cells revealed a total of 14 downregulated miRNAs and 12 upregulated miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.). Similarly, the comparison between QGY-7703 and Huh7 cells revealed 10 downregulated miRNAs and 34 upregulated miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC.). The top 10 upregulated and downregulated miRNAs in each comparison are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-F. The expression details of these miRNAs in each of the comparisons and their fold changes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. ORA revealed the top 20 downregulated and upregulated pathways associated with the differentially expressed miRNAs in each of the compared groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.). Interestingly, we observed that RAS signalling, EGFR signalling, and VEGFA signalling were upregulated in the HepG2 vs Huh7 comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA.). Compared with those in Huh7 cells, the expression of the RUNX family and NOTCH genes, CCNE1 and ACdk2-mediated G1/S transition, and IGFR-2 activation were downregulated, and oncogene-induced senescence was the prominent pathway with upregulated expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.). Likewise, the QGY7703 vs HepG2 comparison revealed activation of WNT signalling, reduction of PTEN and NOTCH expression, and reduced apoptosis as enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of predicted miRNA targets in different HCC cell lines\u003c/h2\u003e \u003cp\u003eTo understand the possible functional roles and regulatory functions of the differentially expressed miRNAs in each of the cell lines, we predicted the validated mRNA targets using miRTarBase. The targets identified for each of the differentially expressed miRNAs are listed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Functional enrichment in terms of gene ontology (BP, CC, MF, and pathway enrichment) of upregulated and downregulated targets revealed the top 20 annotations of the target genes in terms of HepG2 vs Huh7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.), QGY7703 vs Huh7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD.), and QGY7703 vs HepG2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Functional interactome analysis reveals signature pathways regulated by gene targets and their interactomes\u003c/h2\u003e \u003cp\u003eThe reduced list of mRNA targets obtained from MIERNTURNET of differentially expressed miRNAs was used for generating functional pathway interactomes and to understand the interactions between different cellular pathways. Compared with those in Huh7 cells, cytokine signalling, ALK signalling, and SHC-related events mediated by IGF1R were significantly downregulated in HepG2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA.). Similarly, MAPK3-activated pathways, provirus integration, sulfide oxidation pathways, RUNX3-regulated WNT signalling, and vitamin B1 metabolism were downregulated in QGY7703 cells compared with Huh7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.). Additionally, it was predicted that, compared with HepG2 cells, QGY7703 cells would exhibit a decrease in the expression of \u003cem\u003eRUNX3\u003c/em\u003e, which is commonly involved in the maintenance of cell morphology and migration, and an increase in WNT signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4. The miRNA‒mRNA landscape in HCC patients with different mutational statuses: details from public datasets\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGenetically, HepG2 and Huh7 cells carry mutations in the CTNNB1 and TP53 genes, respectively. We were interested in understanding the miRNA landscape in tumor samples of the publicly available TCGA-LIHC dataset carrying mutations in the CTNNB1 and TP53 genes. A total of 78 samples exhibited CTNNB1 mutations, and 51 samples had at least one missense mutation in the TP53 tumor suppressor gene. Differential miRNA expression analysis revealed that at least 9 miRNAs were differentially upregulated or downregulated, which was consistent with our comparison of HepG2 and Huh7 cells. These miRNAs included miR-885-5p, miR-424-5p, miR-130-5p, miR-296-5p, miR-382-5p, miR-181b5p, miR-181d-5p, miR-520d-3p, and miR-328-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.). The top 10 hub genes identified for these miRNAs included \u003cem\u003eMYC, CCND1, STAT3, BCL2, PTEN, MTOR, CASP3, HIF1A, H3C4\u003c/em\u003e, and \u003cem\u003eESR1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB.). ORA revealed HCV infection and HCC as one of the enriched pathways. Other enriched pathways involving these hub genes included the involvement of androgen receptors in prostate cancer, EGFR resistance, IL3 and IL24 signalling, CKAP4 signalling, and gastrin signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDrug-gene interaction analysis revealed a total of 207 drug interactions with \u003cem\u003eESR1\u003c/em\u003e, 145 drug interactions with \u003cem\u003ePTEN\u003c/em\u003e, 144 targets with \u003cem\u003eHIF1A\u003c/em\u003e, 142 drug interactions with \u003cem\u003eMTOR\u003c/em\u003e, 76 drug interactions with \u003cem\u003eBCL2\u003c/em\u003e, 69 drug interactions with \u003cem\u003eMYC\u003c/em\u003e, 38 drug interactions with \u003cem\u003eCASP3\u003c/em\u003e, 28 drug interactions with \u003cem\u003eCCND1\u003c/em\u003e, and 29 drug interactions with \u003cem\u003eSTAT3\u003c/em\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Pancancer drug analysis revealed that HIF1A/PTEN-targeting drugs, such as sorafenib, and PTEN-targeting drugs, such as pembrolizumab and nivolumab, are FDA-approved drugs for HCC management (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is a complex disease characterized by genomic and epigenomic changes. Each tumor represents a specific mutation profile in specific gene families that can determine the composition of the tumor and dictate its nature in terms of the tumor cell\u0026rsquo;s ability to remain undifferentiated, migrate, and metastasize (Waarta et al. 2022). Epi/genome-wide studies have been useful in deciphering the association of drug response with the expression profiles of tumor cells with specific mutations (Chiu et al. 2019; Li et al. 2020). Thus, both genetic and epigenetic tumor profiles not only shape the nature of tumor cells but also predict the response and outcomes of clinical intervention.\u003c/p\u003e \u003cp\u003eIn our study, we used cell line models comprising three HCC cell lines, namely, HepG2, Huh7, and QGY7703, to understand the miRNA landscape present in these three cancer cell lines. With the use of specific probes targeting approximately 800 miRNAs, we quantified mature miRNAs that were differentially upregulated or downregulated in each of the cell lines. The differentially expressed miRNAs in HepG2 vs Huh7 cells were associated with the upregulation of the RAS, EGFR, and VEGFA signalling pathways. Furthermore, pathway enrichment analysis revealed downregulation of genes in the RUNX and NOTCH families and upregulation of kinases involved in G1/S transition in QGY7703 cells compared with Huh7 cells. Interestingly, RUNX and NOTCH are well-known tumor suppressors in HCC (Zhu et al. 2021; Krajinović et al. 2023). Downregulation of these two genes in QGY7703 cells may be critical for determining the rigor and growth of QGY7703 cells, which are faster-growing cell lines than Huh7 cells. Furthermore, our bioinformatic analysis revealed that, compared with HepG2 cells, QGY7703 cells exhibited enhanced Wnt signalling, which can explain why the mesenchymal properties of QGY7703 cells (Grant et al. 2012; Santhekadur et al. 2014) are more pronounced than those of HepG2 cells, which have a more or less epithelial morphology (Donato et al. 2015), making the former cell line more aggressive in terms of migratory and invasive ability than the latter.\u003c/p\u003e \u003cp\u003eIt has been observed that nearly 13\u0026ndash;44% of HCC patients overexpress Alk (4Liu et al. 2016). Increased cytokine expression (Song et al. 2021), ALK expression (Liu et al. 2016), and IGF1R (Guan et al. 2021) expression are known to fuel cell proliferation and survival. Our pathway interaction analysis identified several downregulated pathways in the HepG2 vs Huh7 comparison as well. These included a reduction in cytokine, ALK, and IGF1R signalling cascades. This can explain why HepG2 cells have a morphology similar to that of differentiated hepatocytes compared with that of Huh7 cells, which have a greater growth rate and shorter doubling time.\u003c/p\u003e \u003cp\u003eMany studies have shown that mutations in the \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eCTNNB1\u003c/em\u003e genes are independent drivers of HCC suggesting that mutations in any of these two genes are sufficient to initiate tumors (Friemel et al. 2016). However, the mechanisms that contribute to tumor formation due to such mutations are poorly understood, and the complex epigenome and its target interactome are yet to be dissected completely, making it difficult to study the downstream targets, pathway overlaps, and regulators involved. An integrated bioinformatic approach is a powerful tool for understanding and obtaining a larger picture of complex cellular networks involved in diseases such as cancer. We employed an integrated bioinformatic approach to identify the upregulated and downregulated miRNAs in HCC clinical samples with \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutations based on the small-RNA sequencing reads available from the TCGA-LIHC public cohort. We then compared our \u003cem\u003ein vitro\u003c/em\u003e miRNA expression data with those of a public dataset (HepG2 vs Huh7: CTNNB1 mutant vs TP53 mutant) to identify differentially expressed miRNAs that showed similar trends in expression patterns. At least 9 different miRNAs (miR-885-5p, miR-424-5p, miR-130-5p, miR-296-5p, miR-382-5p, miR-181b5p, miR-181d-5p, miR-520d-3p, and miR-328-5p) were differentially expressed and exhibited a similar trend in their expression patterns based on their mutational profiles in both our \u003cem\u003ein vitro\u003c/em\u003e and TCGA samples. Hub-gene prediction revealed \u003cem\u003eMYC, BCL2, CASP3, PTEN, STAT3, ESR1, HIF1A, MTOR, CCND1\u003c/em\u003e, and \u003cem\u003eH3C1\u003c/em\u003e as the top interacting gene partners of these 9 miRNAs. Interestingly, we detected HCC and HCV infection as one of the pathways enriched in these hub genes according to the panel of 9 miRNAs mentioned above.\u003c/p\u003e \u003cp\u003eIdentifying and predicting drug response is a critical step in cancer management. We performed a drug-gene interaction analysis using hub genes to identify drugs that could serve as possible targets and identified at least 878 drug candidates that were available as targets for nine gene targets \u003cem\u003e(ESR1\u003c/em\u003e, \u003cem\u003ePTEN, HIF1A, MTOR, BCL2, MYC, CASP3, CCND1\u003c/em\u003e, and \u003cem\u003eSTAT3)\u003c/em\u003e, of which several drugs were for antineoplastic purposes. Finally, a pancancer drug analysis identified at least three drugs, namely, sorafenib (a \u003cem\u003eHIF1A/PTEN\u003c/em\u003e target), pembrolizumab, and nivolumab (a \u003cem\u003ePTEN\u003c/em\u003e target), that are already FDA-approved for HCC treatment and management. Interestingly, \u003cem\u003ePTEN\u003c/em\u003e expression is lost or reduced in most HCCs and it is reported that restoring its expression can improve sorafenib resistance and mitigate sorafenib\u0026rsquo;s activity such as metabolic reprogramming (Zhoa et al. 2020; Miao et al. 2021) and increased apoptosis in HCC cells (Ruan et al. 2012). Likewise, PTEN-loss is also associated with PD-L1-mediated reduction of INF-γ and CD8\u0026thinsp;+\u0026thinsp;T cells that can further facilitate tumor progression and metastasis (Zhoa et al. 2020; Vidotto et al. 2020). Tumors exhibiting PTEN loss can be potentially treated by pembrolizumab and nivolumab which can potentially target PD-L1 (Finn et al. 2020; Fessas et al. 2023). Further targeted pre-clinical and clinical studies can confirm the use of these drugs in the clinical management of HCC patients presenting specific mutations in either \u003cem\u003eTP53\u003c/em\u003e or \u003cem\u003eCTNNB1\u003c/em\u003e genes.\u003c/p\u003e \u003cp\u003eIn the present study, employing a systems biology approach, using human HCC cell lines and clinical datasets of HCC patients with different mutational profiles from public datasets, we identified miRNA expression landscapes and predicted their target genes, interactomes, and associated pathways, thereby providing an overall insight into the epigenetic landscape of different HCC cellular subtypes based on mutational statuses. In addition, our study identified druggable genes that can be repurposed for the management of HCC. Although the identified gene targets are experimentally validated, the inclusion of functional studies can add more power to the study.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCTNNB1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeta-catenin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eARID1A\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAT-rich interaction domain 1A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eARID2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAT-rich interaction domain 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCND1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin D1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDestruction complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003emiRNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emicroRNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRat sarcoma gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEGFR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpidermal growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVEGFA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVascular endothelial growth factor A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRUNX3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRunt-related transcription factor 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNOTCH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurogenic locus homolog\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIGF1R\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin-like growth factor receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanaplastic lymphoma kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTAT3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatin 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBCL2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eB-cell leukemia/lymphoma 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMTOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emammalian target of rapamycin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCASP3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecysteine-aspartic acid protease 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIF1A\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypoxia-inducible factor 1A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eH3C4\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eH3 Clustered Histone 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eESR1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstrogen receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eL1\u003c/b\u003e-Programmed Cell Death Ligand 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSHK: Conceptualization, Data curation, Data validation, Formal analysis, Investigation, Writing - Original draft, Writing - Review \u0026amp; Editing, Funding acquisition VB: Methodology, Formal AnalysisMM: Data curation, Data validation, Formal analysis, Investigation, Writing - Review \u0026amp; EditingGK: Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Project AdministrationSPK: Writing \u0026ndash; Review \u0026amp; Editing, SupervisionNGS: Writing \u0026ndash; Review \u0026amp; EditingVDS: Writing \u0026ndash; Review \u0026amp; EditingDPK: Writing \u0026ndash; Review \u0026amp; EditingAP: Supervision \u0026amp; Intellectual input,PMV: Supervision \u0026amp; Intellectual inputPKS: Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Project Administration, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThis study was conducted with the help of financial support from JSS Academy of Higher Education and Research-Institutional Research Grant (JSSAHER/REG/RES/URG/54/2011-12), provided to PKS, and Junior Research Scholarship and contingency support offered to SHK by Lady Tata Memorial Trust. We acknowledge the infrastructure support provided by the Department of Science and Technology to CEMR Laboratory (SR-FST-LS-1/2018/178) and the Department of Biochemistry (SR/FST/LS-1-539/2012). The authors would also like to acknowledge the Department of Biotechnology-Boost to University Inter-disciplinary Life Sciences Departments for Education and Research (BUILDER) program (BT/INF/22/SP43045/2021). We also thank our collaborators from Theracues Innovations Pvt Ltd, Bangalore, India, for performing miRNA probing assays on the NanoString platform and for conducting bioinformatic analysis in part.\u003c/p\u003e\u003ch2\u003eData and Code availability\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng J, Zhu R, Yin Y, Wang S, Zhou L, Lv F, Zhao D (2021) Re-Recognizing the Cellular Origin of the Primary Epithelial Tumors of the Liver. 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Future Oncol (London England) 19(7):499\u0026ndash;507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2217/fon-2022-0916\u003c/span\u003e\u003cspan address=\"10.2217/fon-2022-0916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, miRNAs, TP53, CTNNB1, Hub genes","lastPublishedDoi":"10.21203/rs.3.rs-4347735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4347735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) is a carcinoma of epithelial origin. While there are several factors, specific genetic and epigenetic landscapes define the initiation and progression of HCC. Genetic mutations, particularly missense mutations, often act as predictors of the onset of cancers, including HCC. Specifically, mutations associated with telomerase, TP53, and beta-catenin (CTNNB1) are among the three most commonly mutated genes in HCC. These genetic mutations define specific subtypes of HCC, exhibiting specific epigenetic expression patterns in terms of miRNA expression and the interactome. In our current study, we performed a differential expression analysis of multiple miRNAs among three different cell lines, HepG2, Huh7, and QGY7703, which exhibit different mutational patterns. This is the first study to characterize HCC cell lines based on miRNA expressions. We also identified the enriched pathways associated with the significantly differentially expressed miRNAs, bioinformatically predicted their targets, and characterized the interactomes. Additionally, we classified the small RNA sequencing data available from the publicly available dataset based on the mutational status of cancer samples and computed the overlaps of miRNAs exhibiting similar expression patterns consistent with the \u003cem\u003ein vitro\u003c/em\u003e data, predicted the top hub genes and their associated pathways, and predicted their drug targets using an integrated bioinformatic approach.\u003c/p\u003e","manuscriptTitle":"Dissecting microRNA-regulated networks in hepatocellular carcinoma cell subtypes with different mutational profiles: Evidence from in vitro and in silico studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 12:04:19","doi":"10.21203/rs.3.rs-4347735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6166cf8-2e7a-4554-bd38-80b0d4dbdc8a","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-16T15:29:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 12:04:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4347735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4347735","identity":"rs-4347735","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}