Integrated transcriptomic analysis reveals a lncRNA-miRNA-TF-mRNA regulatory network underlying quercetin’s anti-hepatocellular carcinoma effects

Integrated transcriptomic analysis reveals a lncRNA-miRNA-TF-mRNA regulatory network underlying quercetin’s anti-hepatocellular carcinoma effects | 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 Article Integrated transcriptomic analysis reveals a lncRNA-miRNA-TF-mRNA regulatory network underlying quercetin’s anti-hepatocellular carcinoma effects Tong Lin, Ningna Weng, Yifan Chen, Zhengrong Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8329255/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 Background Quercetin (QUR) exhibits multiple pharmacological activities against hepatocellular carcinoma (HCC). Competing endogenous RNA (ceRNA) networks and transcription factors (TFs) play critical roles in oncogenesis and cancer progression, however, the integrated ceRNA- and TF-mediated mechanisms of QUR in HCC treatment remain largely unknown. Methods The anti-proliferative, anti-migratory, and pro-apoptotic effects of QUR on HepG2 cells were evaluated using CCK‑8, colony formation, wound‑healing, and flow cytometry assays. Transcriptome sequencing was performed to identify differentially expressed (DE) long non-coding RNAs (lncRNAs), micro RNAs (miRNAs), and messenger RNAs (mRNAs) after QUR treatment. Functional enrichment, protein–protein interaction (PPI) analysis, and survival analyses were performed to elucidate therapeutic mechanisms and prognostic biomarkers. A lncRNA–miRNA–TF–mRNA regulatory network was constructed by integrating multiple databases, and its clinical relevance was assessed using TCGA and GTEx data. Results QUR dose-dependently inhibited the proliferation, colony formation, migration, and induced apoptosis of HCC cells. Transcriptomic profiling identified 647 DEmRNAs, 304 DElncRNAs, and 17 DEmiRNAs. Down‑regulated DEmRNAs were enriched in nucleosome, chromatin, and telomere metabolism, while up‑regulated DEmRNAs were involved in cytokine activity and immune cell differentiation. Key pathways included metabolic reprogramming, cytokine signaling, PI3K/AKT, and viral carcinogenesis. PPI analysis revealed five functional clusters, and survival analysis identified 12 prognosis‑associated DEmRNAs (e.g., AURKA , CCNB1 , KIF20A , and PLK1 ). The constructed regulatory network comprised three DEmiRNAs, eight TFs, five DEmRNAs, and 20 DElncRNAs, revealing coordinated miRNA-TF cross-talk and lncRNA–meditated ceRNA axes that fine-tuned key mRNAs. Mechanistically, QUR might balance oncogene and tumor suppressor expression, thereby inhibiting metabolism, mitosis, and transcription in HCC cells, while enhancing anti‑cancer immunity and extrinsic apoptosis. Conclusions This study delineated a comprehensive lncRNA–miRNA–TF–mRNA network that elucidated the systematic mechanisms of QUR against HCC, offering potential biomarkers and therapeutic targets for further investigation. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Molecular biology Health sciences/Oncology hepatocellular carcinoma quercetin ceRNA transcription factor miRNA lncRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Primary liver cancer ranks as the sixth most commonly diagnosed cancer and the third leading cause of cancer‑related mortality globally, with hepatocellular carcinoma (HCC) accounting for approximately 85% of cases [ 1 ]. Although substantial progress has been made in comprehensive therapies, delayed diagnosis often precludes curative intervention, while frequent recurrence, metastasis, and therapeutic resistance collectively contribute to poor patient outcomes [ 2 ]. The unmet clinical need underscores the urgency for developing novel agents targeting HCC pathogenesis. Natural products with inherent multi‑target characteristics hold great promise in oncology drug discovery. Quercetin (QUR), a ubiquitous dietary flavonoid, exhibits compelling potential as an HCC chemoadjuvant with favorable safety profiles. Preclinical studies have demonstrated that QUR inhibited cell proliferation, invasion, metastasis, oxidative stress, inflammation, liver fibrosis, whereas inducing cell apoptosis, chemo-sensitization, and modulation of the tumor microenvironment in HCC models [ 3 , 4 ]. Nevertheless, its comprehensive mechanistic landscape remains incompletely elucidated. Gene expression regulation involves a complex interplay among transcription factors (TFs), non-coding RNAs (ncRNAs), and coding genes. MicroRNAs (miRNAs) are small ncRNAs of about 22 nucleotides, which repress messenger RNA (mRNA) translation via binding to them. Long non-coding RNAs (lncRNAs) are ncRNAs with more than 200 nucleotides, can act as competitive endogenous RNAs (ceRNAs) by sequestering miRNAs, thereby alleviating miRNA-mediated repression of mRNAs, this mechanism is known as the ceRNA hypothesis. Given that each miRNA can target multiple mRNAs/lncRNAs and each mRNA/lncRNA can interact with several miRNAs, intricate ceRNA networks emerge [ 5 ]. Aberrant ncRNA expression and ceRNA network alterations are deeply implicated in HCC pathogenesis and hold promise for early diagnosis, prognosis prediction, and therapeutic targeting [ 6 ]. Transcription factors (TFs) regulate gene transcription by binding promoter regions of target genes. TFs can positively or negatively regulate miRNA expression, miRNAs can in turn suppress TF translation, forming feed-forward or feedback loops among TFs, miRNAs and target mRNAs [ 7 ]. Recent studies highlighted the involvement of TF-miRNA interactions and ceRNA crosstalk in cancer progression [ 8 – 10 ]. Although preliminary studies suggested QUR modulated miRNA-mRNA interactions in HCC [ 11 , 12 ], the integrated lncRNA-miRNA-TF-mRNA regulatory network mediated by QUR in HCC has not been explored. In this study, transcriptomic profiling and integrated bioinformatic analyses were employed to elucidate the global regulatory mechanisms and key targets of QUR treating HCC. A lncRNA-miRNA-TF-mRNA regulatory network was constructed to decipher the multi-layered molecular basis of QUR’s anti-cancer activity. Our findings might identify key regulatory hubs with potential as therapeutic targets in HCC. Methods Cell culture Human HCC cell line HepG2 was obtained from American Type Culture Collection (ATCC, Manassas, VA, US). HepG2 cells were maintained in high-glucose Dulbecco’s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37°C in a humidified 5% CO₂ atmosphere (all agents from Gibco, Carlsbad, CA, USA). QUR (97% purity, Macklin, Shanghai, China) was dissolved in DMSO (Sigma-Aldrich, St. Louis, MO, USA) to prepare a 320 mM stock solution, which was diluted in culture medium to working concentrations of 20–320 µM. Cell viability assay Cell viability was assessed using the Cell Counting Kit-8 (CCK-8; Biosharp, Anhui, China). HepG2 cells were seeded in 96-well plates at 3×10 4 cells/mL for 24h, then treated with 20, 40, 80, 160, and 320 µM QUR or 0.1% DMSO for 24, 48, or 72 h. After treatment, cells were washed with sterile phosphate-buffered saline (PBS) and incubated with CCK-8 reagent for 2 h at 37℃. Absorbance was detected at 450 nm using a microplate reader (Bio Tek Instruments, Inc., Winooski, VT, US). Half-maximal inhibitory concentration (IC₅₀) values were derived from non-linear regression curve. Colony formation assay HepG2 cells were seeded in 6-well plate at 800 cells/well overnight, then treated with 160 or 320 µM QUR or 0.1% DMSO for 2 days. Consistently, the medium was replaced with fresh culture medium, and cells were cultured for 12 days. Colonies were washed, fixed using 4% paraformaldehyde, stained with 0.1% crystal violet, and those containing at least 50 cells were counted under a microscope. Wound-healing assay HepG2 cells were seeded in 6-well plates at 4.5×10 5 cells/mL and grown to 100% confluence. Cell monolayers were scratched with 200 µL pipette tips to make wounds, and cells were treated with serum-free medium containing 160 or 320 µM QUR or 0.1% DMSO. Wounds images were recorded at 0, 24, and 48 h in five random fields using an inverted microscope (Olympus Corporation, Tokyo, Japan). Wound areas were quantified using Image J 1.5.3 software (NIH, Bethesda, MD, US). Cell apoptosis Cell apoptosis was evaluated using an annexin V-fluoresceine isothiocyanate (FITC)/propidium iodide (PI) detection kit (Bestbios, Shanghai, China). HepG2 cells were seeded in 6-well plates at 1×10 5 /well, incubated overnight, and treated with 160 or 320 µM QUR or 0.1% DMSO for 24 h. Cells were harvested, washed with cold sterile PBS, resuspended in binding buffer, and stained with Annexin V‑FITC and PI for 15 min at 4°C. Stained cells were analyzed on a CytoFLEX S Flow Cytometer (Beckman Coulter, Brea, CA, US), and data were processed with CytExpert 2.0 software. RNA extraction HepG2 cells were treated with 0.1% DMSO or 320 µM QUR ( n = three per group). Total RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, US) and treated with DNase I (Takara Bio, Shiga, Japan) to remove genomic DNA. RNA purity and concentration were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, US), and integrity was verified using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). Samples with an RNA Integrity Number (RIN) ≥ 8 and 28s/18s ratio ≥ 1 were retained for subsequent analysis. mRNA and lncRNA library construction Total RNA was purified using oligo-dT magnetic beads, and ribosomal RNA was depleted with the MGIEasy rRNA Kit (BGI, Shenzhen, China). Purified RNA was fragmented, and first-strand cDNA was synthesized with reverse transcriptase and random primers. Second-strand cDNA was synthetized using RNase H, DNA polymerase I, and dNTPs. Double-stranded cDNA was end-repaired, 3’-adenylated, and size-selected (150–200 bp) using AMPure XP Kit (Beckman, Beijing, China). Adapters were ligated to cDNA fragments, libraries were amplified by PCR, and PCR products were heat-denatured and circularized into DNA Nanoballs (DNBs). miRNA library construction Small RNAs (18–30 nt) were isolated by polyacrylamide gel electrophoresis. 5’-adenylated and 3’-blocked adapters were ligated to small RNAs, followed by 5’-adapter ligation with unique molecular identifiers (UMIs). Unligated adapters were removed, ligated RNAs were reverse-transcribed using UMI-containing primers, and cDNA libraries were amplified by PCR. PCR products of 110–130 bp were purified using the QIAquick Gel Extraction Kit (QIAGEN, Valencia, CA) and agarose gel electrophoresis, subsequently denatured and circularized into DNBs. High throughput sequencing and RNA identification The quality of RNA library was assessed using an Agilent 2100 biological analyzer, and sequencing was performed on the BGISEQ-500 platform (BGI, Shenzhen, China). Raw reads were quality-controlled: mRNA and lncRNA data were filtered using SOAPnuke v1.5.2 [ 13 ], and miRNA reads were filtered using FASTX-Toolkit v0.0.13. Clean reads of mRNA and lncRNA were aligned to the GRCh38 genome using HISAT2 v2.0.4 [ 14 ] and to coding gene sets using Bowtie2 v2.2.5 [ 15 ]. Transcript expression quantification was quantified with StringTie v2.2.0 [ 16 ]. miRNA reads were aligned to coding gene sets using Bowtie2, and miRNA annotation was performed using Rfam 14.0 [ 17 ] and ASRA [ 18 ] databases. Differential expression analysis Transcript abundance was quantified using RSEM and expressed as FPKM. Differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between control and QUR groups were identified using the R package “Deseq2” [ 19 ] with thresholds of |log 2 fold change (FC)|>2 and Benjamini-Hochber adjusted P -value ( q -value) < 0.1. Results were visualized as volcano plots (ggplot2 v3.3.5) and heatmaps (pheatmap v1.0.12). Gene set enrichment analysis (GSEA) GSEA was conducted on all DEmRNA genes ranked by log 2 FC values using the R package “clusterProfiler”. Gene sets “c5.go.v7.5.1.symbols.gmt” and “c2.cp.kegg.v7.5.1.symbols.gmt” were retrieved from the MSigDB v 7.5.1. Gene sets with |normalized enrichment score (NES)| > 1, P value < 0.05, and false discovery rate (FDR) < 0.25 were considered significantly enriched. GO and KEGG pathway enrichment Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway [ 20 ] enrichment analyses were performed separately for up- and down-regulated DEmRNA genes using the R package “phyper”. GO terms covered cellular component (CC), biological process (BP), and molecular function (MF). Terms with q -value < 0.05 were considered significantly enriched. Protein-protein interaction (PPI) network PPI analysis of DEmRNA genes was conducted using STRING v11.5 ( https://cn.string-db.org/ ) [ 21 ], with the settings of “homo sapiens” and a confidence score > 0.7. The PPI network was visualized using Cytoscape 3.7.0 software with isolated genes removed, and hub genes were identified using the CytoHubba plugin (top 10 by degree). Molecular complexes were detected using MCODE v1.6.1 [ 22 ] with cut-offs set as: degree = 2, score = 0.2, k-core = 2, and max depth = 100. Genes in the top five clusters were selected as candidates. Survival analysis The Kaplan-Meier Plotter ( http://www.kmplot.com/ ) [ 23 ] was applied to evaluate associations between candidate DEmRNA gene expression and overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease specific survival (DSS) of HCC patients. DEmRNA genes significantly associated with HCC outcomes were designated as key candidates for further research. miRNA-TF-mRNA-lncRNA network construction The regulatory network was built as follows: (1) Putative TFs regulating key DEmRNAs were predicted using the ChEA3 ( https://maayanlab.cloud/chea3 ) [ 24 ] and TRRUST ( http://www.grnpedia.org/trrust/ ) [ 25 ] databases, from which the top 100 TFs prioritized by ChEA3 were eligible. The overlap between the putative TFs and the DEmRNAs was identified as potential TFs. (2) DEmiRNA–key DEmRNA/TF interactions were predicted using Miranda [ 26 ], TargetScan [ 27 ], and RNAhybrid [ 28 ] databases, pairs confirmed by at least two tools were retained. (3) DElncRNA-DEmiRNA interactions were predicted using miRanda and RNAhybrid databases, with mutual confirmation. (4) Pearson correlation analysis required r 0.9 for DEmRNA-DElncRNA pairs [ 29 ]. Finally, validated interactions were integrated and visualized using Cytoscape v3.7.0 to construct a miRNA-TF-mRNA-lncRNA regulatory network. Clinical relevance of network components Expression profiles of the five DEmRNAs in HCC versus normal liver tissues, and their associations with tumor stage/grade were interrogated using RNA-seq data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx, v8) project. Transcripts per million (TPM) values were log 2 (TPM + 1) transformed. TF expression in HCC was assessed similarly, and their associations with the OS of HCC patients were evaluated using Kaplan-Meier Plotter. The expression and prognostic significance of the DEmiRNAs and DElncRNAs were examined using the StarBase v2.0 platform ( https://rnasysu.com/encori/index.php ) [ 30 ] based on TCGA data. Statistical analysis Data from triplicate experiments were presented as mean ± standard deviation (SD). For experimental data, group comparisons were performed using Student’s t -test (two groups) or one-way ANOVA/Welch’s ANOVA (multiple groups) with Bonferroni or Games-Howell post-hoc tests, respectively. RNA-seq data were processed using Student’s t -test or Kruskal-Wallis test for two-group or multi-group comparisons, respectively. Analyses were conducted using SPSS v21.0 and R v4.0.3, with visualizations created in GraphPad Prism v8.0 and R package “ggplot2”. Results with P < 0.05 considered significant. Results QUR inhibited the proliferation and cloning of HepG2 cells CCK-8 and colony formation assays demonstrated that QUR treatment dose‑ and time‑dependently reduced the viability of HepG2 cells ( P <0.05; Fig. 1A), yielding IC 50 values of 2076.02 µM (24 h), 662.25 µM (48 h), and 264.47 µM (72 h), respectively. Besides, QUR dose-dependently suppressed colony formation of HepG2 cells ( P <0.001; Fig. 1B). Figure 1 QUR inhibited the proliferation and clonogenicity of HepG2 cells. A Cell viability was assessed using CCK-8 assay after treatment with indicated concentrations of QUR for different durations. B Colony formation was assessed in HepG2 cells that were cultured for 12 days following the indicated 48-h treatment. Data are expressed as mean ± SD from three independent experiments. * P < 0.05, ** P < 0.01, *** P < 0.001 vs. control group. # P < 0.05, ## P < 0.01, ### P < 0.001 vs. lower concentration within the same treatment duration. ▲ P < 0.05, ▲▲ P < 0.01, ▲▲▲ P < 0.001 vs. shorter exposure time at the same concentration. QUR suppressed the migration and induced apoptosis of HepG2 cells Wound-healing assays showed that QUR dose-dependently inhibited HepG2 cells migration (Fig. 2A). Annexin V-FITC/PI staining revealed that QUR increased total apoptosis of HepG2 cells in a dose-dependent manner ( P < 0.05), with late apoptosis/necrosis accounting for ~ 80% of affected cells (Fig. 2B), suggesting QUR suppressed the proliferation partly through apoptosis induction. Figure 2 QUR inhibited the migration and induced apoptosis of HepG2 Cells. A Scratch wounds were prepared beforehand, and HepG2 cells were treated as indicated. Wound closure was recorded at 0, 24, and 48 h; scale = 500 µm. B Apoptosis was evaluated by annexin V-FITC/PI staining and flow cytometry after 24 h of treatment. Data are shown as mean ± SD of three independent experiments. * P < 0.05, ** P < 0.01, *** P < 0.001 vs. control group. Identification of the DEmRNAs, DEmiRNAs, and DElncRNAs Transcriptome sequencing identified 647 DEmRNAs (412 up-regulated and 235 down-regulated), and 17 DEmiRNAs (six up-regulated and 11 down-regulated), and 510 DElncRNAs (214 up-regulated and 90 down-regulated) between 320 µM QUR-treated versus control HepG2 cells (Fig. 3). The top ten most significantly altered transcripts were listed in Supplementary Table 1. Figure 3 Identification of differentially expressed RNAs in QUR-treated HepG2 cells. A-C Volcano plots of mRNAs, lncRNAs, and miRNAs. Red, blue, and gray dots denote up-regulated, down-regulated, and non-significantly altered RNAs, respectively. D-F Heatmaps of mRNA, lncRNA, and miRNA expression profiles. Functions of the DEmRNAs GSEA revealed that QUR positively enriched gene sets involved in cellular communication processes (e.g., cell activation, protein localization) and cytokine-mediated signaling (e.g., cytokine-cytokine receptor interaction and JAK-STAT). Negatively enriched gene sets related to chromatin organization (e.g., protein-DNA complex organization, chromatin remodeling), epigenetic regulation, and mitotic cell cycle phase transition (Fig. 4 A-D). The up-regulated DEmRNAs were significantly enriched in 13 GO-BP terms (e.g., cytokine-mediated signaling pathway and positive regulation of T cell differentiation), nine GO-CC terms (e.g., extracellular space and plasma membrane), and one GO-MF term (cytokine activity) (Fig. 4 E). 20 KEGG terms indicated their participation in cytokine-cytokine receptor interaction, JAK-STAT, and TNF signaling pathways (Fig. 4 G). The down-regulated DEmRNAs were significantly enriched in 29 GO-BP terms (e.g., nucleosome assembly and chromatin organization), 11 GO-MF terms (e.g., protein heterodimerization and alkaline phosphatase activity), and 10 GO-CC terms (e.g., nucleosome, extracellular exosome) (Fig. 4 F). KEGG analysis revealed their enrichment in necroptosis and viral carcinogenesis (Fig. 4 G). PPI network and functional modules The PPI network consisted of 216 DEmRNA genes connected by 876 interactions (Fig. 5A). MCODE analysis identified five modules. Module One (11 genes) involved in DNA replication-dependent nucleosome assembly. Module Two (7 genes) implicated in interferon response and negative regulation of viral genome replication. Module Three principally governed mitotic cell cycle transition. Module Four involved in cytokine signaling and extrinsic apoptosis. Module Five associated with chromatin organization (Fig. 5B-F). Details of the five modules are described in Supplementary Table 2. These 35 DEmRNAs were selected for following survival analysis. Figure 5 PPI network and functional modules. A PPI network of proteins encoded by DEmRNA. Pink and green nodes denote up- and down-regulated DEmRNAs, respectively. Edges represent protein interactions. B-F Five major functional modules identified from the PPI network, each accompanied by the top 10 significantly enriched GO-BP terms. PPI, protein-protein interaction. Prognostic significance of the candidate genes Survival analysis identified12 prognosis-associated key DEmRNAs. Elevated expression of AURKA , CCNB1 , CDC20 , KIF20A , and PLK1 was correlated with reduced OS, RFS, PFS, and DSS of HCC patients ( P < 0.05). Elevated H2AC14 expression correlated with worse RFS ( P < 0.05), and elevated H2BC3 and H2BC9 levels were associated with diminished DSS ( P < 0.05). In contrast, higher TNF and IFIT1 expression linked with improved RFS and PFS ( P < 0.05), and augmented BST2 and CSF2 levels correlated with better DSS ( P < 0.05). (Fig. 6) Figure 6 Survival analysis of the key DEmRNA genes in HCC patients. Associations between the expression of candidate DEmRNA genes and overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS) in HCC patients. OS, overall survival; RFS, relapse-free survival; PFS, progression-free survival; DSS disease specific survival. LncRNA-miRNA-TF-mRNA regulatory network The integrated lncRNA-miRNA-TF-mRNA regulatory network comprised three DEmiRNAs (hsa-miR-423-5p, hsa-miR-1260b, hsa-miR-449a), eight TFs (ATF3, CREB5, EGR3, HNF4A, JUN, MYB, NR4A3, RELB), five DEmRNAs (TNF, CSF2, IFIT1, KIF20A, CCNB1), and 20 DElncRNAs, encompassing 65 interactions (Fig. 7 ). Hsa-miR-423-5p served as a central repressive hub, concurrently targeting all the TFs, two mRNAs (TNF and IFIT1), and multiple lncRNAs. Hsa-miR-449a targeted CSF2, CCNB1, KIF20A, IFIT1, and several lncRNAs, and was positively regulated by TFs including JUN, ATF3, MYB, HNF4A, and NR4A3. In contrast, hsa-miR-1260b showed no TF connection. As an upstream TF of hsa-miR-449a, MYB also directly regulate CCNB1 and KIF20A, suggesting a synergistic repression. The network also featured TF-mediated activation, such as JUN-induced CSF2 expression, ATF3/RELB-driven IFIT1 expression, and synergistic upregulation of TNF by ATF3, EGR3, JUN, and RELB. Clinical relevance of the network components Expression levels of CSF2 , KIF20A , IFIT1 , and CCNB1 were elevated in HCC tissues compared with normal liver ( P < 0.001), whereas TNF expression showed no significant alteration (Fig. 8 A). KIF20A and CCNB1 expression increased with histological grades ( P < 0.01) and non-significantly with stages (I–III). IFIT1 expression non‑significantly declined with advancing stages and grades. CSF2 expression showed no significant correlation with either parameter (Fig. 8 B, C). Among the TFs, ATF3 and EGR3 were down-regulated in HCC tissues ( P < 0.05), while CREB5 , HNF4A , NR4A3 , and RELA were up-regulated, compared with normal liver ( P < 0.001). JUN and MYB showed no significant differential expression, but elevated MYB indicated poorer OS of HCC patients. Whereas, high NR4A3 expression correlated with favorable prognosis ( P < 0.05; Fig. 9 ). Hsa-miR-423-5p and hsa-miR-449a were overexpressed in HCC compared to normal liver ( P < 0.05), while hsa-miR-1260b was lowly expressed. None of these miRNAs significantly affected OS (Fig. 10A-C). DElncRNAs, BCRP2 , LINC02154 , NEAT1 , NSUN5P1 , NSUN5P2 , and LINC01829 were overexpressed in HCC ( P < 0.001). High expression of BCRP2 , LINC02154 , and NSUN5P1 indicated poorer OS ( P < 0.05; Fig. 10D-G). Data for other lncRNAs were insufficient for analysis. Figure 10 Clinical relevance of the DEmiRNAs and DElncRNAs in HCC. Expression levels of indicated DEmiRNAs and DElncRNAs in HCC vs. normal liver tissues, and their associations with OS in HCC patients (StarBase database). * P < 0.05, ** P < 0.01, *** P < 0.001. Discussions Mechanisms of QUR in HCC treatment Following QUR’s treatment on HCC cells, GSEA and functional enrichment analyses revealed distinct regulatory patterns. Genes governing chromatin organization, nucleosome assembly, mitotic cell cycle progression, and interferon response were down-regulated. In contrast, genes involved in cytokine-mediated signaling pathways and extrinsic apoptosis were up-regulated. These findings suggested that QUR might exert anti-HCC effects by promoting genomic instability and enhancing immune responses, supporting its potential as a combinatory therapeutic agent. Regulation of the lncRNA-miRNA-TF-mRNA network The regulatory network elucidated in this study underscored a multi-layered architecture controlling gene expression in HCC. miRNA-TF-mRNA interactions TFs and miRNAs orchestrate mRNA expression through tightly interwoven direct and indirect regulatory mechanisms, yielding both synergistic and antagonistic outcomes. For instance, a dual-safeguard mechanism fine-tuned the expression of TNF and IFIT1 : TFs such as ATF3 , RELB , and JUN directly promoted their expression; while hsa-miR-423-5p and hsa-miR-449a suppressed their expression, thereby curbing transcriptional overactivation. Conversely, a coherent inhibitory circuit targeted CCNB1 : MYB directly repressed its expression, and concurrently activated hsa-miR-449a, which further downregulated CCNB1 . Such multi-input regulatory modes might reduce noise in critical biological processes and enhance network stability. Beyond fine-tuning, miRNAs also acted as integrators and amplifiers within the network. The broad repressive influence of hsa-miR-423-5p suggested its role as a master regulator. QUR down-regulated hsa-miR-423-5p, leading to the upregulation of several TFs and downstream genes (e.g., TNF and IFIT1 ). In parallel, multiple TFs, including JUN , ATF3 , MYB , HNF4A , and NR4A3 , not only regulate target genes directly but also converge to promote hsa-miR-449a expression, which in turn repressed its own targets (e.g., CCNB1 , KIF20A ), collectively contributing to coordinated transcriptional outcomes. This configuration implied the presence of coherent feed-forward loops that facilitated signal amplification and coordinated transcriptional control. Key TFs in HCC pathogenesis Multiple TFs embedded in this network exert pivotal and context-dependent roles in HCC. Activating transcription factor 3 ( ATF3 ), downregulated in HCC and induced by cellular stress, suppresses proliferation and motility [ 31 ]. Conversely, CREB5 is upregulated and promotes epithelial-mesenchymal transition (EMT) and malignancy via the ERS-activated CREB5/TNC axis [ 32 ]. Early growth response 3 ( EGR3 ) is a zinc finger TF, inhibits growth through FasL upregulation [ 33 ]. Hepatocyte nuclear factor 4A ( HNF4A ) is a master regulator of hepatocyte differentiation and lipid metabolism, which can function as either an oncogene or cancer suppressor depending on AMPK activity [ 34 ]. The AP-1 complex subunit JUN orchestrates genomic instability and chronic inflammation, driving HCC progression through cell cycle dysregulation and oxidative stress amplification [ 35 , 36 ]. c-Myb ( MYB ) overexpression correlates with therapy resistance and poor prognosis [ 37 ]. Nuclear receptor subfamily 4 group A member 3 ( NR4A3 ) functions as a cancer suppressor by inducing CDKN2AIP-dependent cell cycle arrest through DNA damage induction, with its downregulation marking advanced disease stages [ 38 ]. Nevertheless, RELB , a non-canonical NF-κB transcription factor, enhances chemoresistance and metastasis via inflammatory signaling [ 39 ]. miRNA-TF-mRNA regulatory logic Triangular miRNA-TF-mRNA interactions among form a sophisticated regulatory circuitry enriched in feed-forward and feedback loops. These networks enable bidirectional control to buffer noise while maintaining target protein homeostasis, prevent erroneous phenotypic transitions through inverse signal coupling, and support spatiotemporal precision by segregating miRNA-target expression domains [ 7 ]. Consequently, disruption of key miRNAs or TFs can compromise network integrity, driving cancer initiation and progression. LncRNA-miRNA-mRNA axes We identified several representative ceRNA regulatory axes, including BCRP2/hsa-miR-423-5p/TNF, (LINC02154, LINC01829)/hsa-miR-1260b/CSF2, and (NEAT1, NSUN5P1, NSUN5P2, LINC00473)/hsa-miR-449a/(KIF20A, CCNB1, IFIT1). We speculated that QUR might upregulate BCRP2, LINC02154, and LINC01829, which sequester hsa-miR-423-5p and hsa-miR-1260b, thereby elevating TNF and CSF2 expression. Both cytokines are critical immunomodulators: TNF and GM-CSF (encoded by CSF2 ) can enhance the cytotoxicity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, recruit dendritic cells, thus amplifying anticancer immunity [ 40 , 41 ]. Notably, both TNF and CSF2 exhibit dual roles in HCC. Low-dose TNF can activate NF-κB pathway, promoting proliferation, migration, EMT, and angiogenesis, whereas high-dose TNF can trigger apoptosis [ 42 ]. GM-CSF is essential for innate immunity, yet GM-CSF derived by cancer cells may recruit immune-suppressive cells. We found elevated expression of TNF and CSF2 indicated improved survival of HCC patients, likely reflecting immune activation at therapeutic thresholds. Concurrently, QUR might downregulate NEAT1, NSUN5P1/2, and LINC00473, releasing hsa-miR-449a to target KIF20A and CCNB1, thereby inhibiting biosynthesis and mitosis. Kinesin family member 20A ( KIF20A ) facilitates chromosome and spindle movement during mitosis, and its knockdown induces apoptosis and improves treatment sensitivity [ 43 ]. The oncogene CCNB1 drives G2/M transition and is strongly linked to increased postoperative recurrence and poor survival in HCC [ 44 ]. Collectively, QUR appears to suppress HCC by down-regulating oncogenic drivers ( KIF20A and CCNB1 ) and up-regulating protective immune factors ( TNF and CSF2 ) through ceRNA network modulation. Paradoxically, QUR downregulated IFIT1 , a cancer suppressor known to enhance antiviral immunity [ 45 ]. This observation highlighted the complexity of ceRNA-mediated regulation and the dynamic interplay between “unfavorable” and “favorable” factors. QUR might achieve net cancer-suppressive outcomes by orchestrating a balanced regulatory shift, though further functional validation is required. We observed significant overexpression of hsa-miR-423-5p and hsa-miR-449a in HCC. Elevated hsa-miR-423-5p correlates with reduced RFS post-liver transplantation and promotes invasiveness and regulatory T cell (Treg)-mediated immune evasion [ 45 , 46 ]. Contrary to its cancer-suppressive role via targeting NOTCH1 and MET [ 47 , 48 ], hsa-miR-449a displayed a paradoxical, context-dependent function in our study. We also confirmed miR-1260b up-regulation in HCC, which enhances proliferation and invasion via RGS22 suppression [ 49 ]. We further documented the upregulation of BCRP2 , LINC02154 , NEAT1 , LINC01829 , and NSUN5P1/2 in HCC. NEAT1 overexpression correlates with poor prognosis across solid tumors [ 50 ]. Both NSUN5P1 and NSUN5P2 , pseudogenes of rRNA-modifying NOP2/Sun domain family (NSUN5) gene on chromosome 7, were correlated with adverse clinical outcomes of HCC patients [ 51 ]. We also identified a previously uncharacterized lncRNA, breakpoint cluster region pseudogene 2 ( BCRP2 ), linked to poor survival in HCC. Additionally, we validated the adverse prognostic role of LINC02154 , consistent with its reported mechanism via PI3K/AKT/SPC24 signaling [ 52 ]. Similar with our finding, LINC01829 was ever been reported as a risk factor in colon adenocarcinoma but not previously studied in HCC [ 53 ]. Conclusions In summary, QUR inhibited HCC proliferation, clonogenicity, migration, and induced cell apoptosis. It exerted multimodal anti-cancer effects by disrupting genetic metabolism, mitotic progression, and transcriptional activity, while concurrently enhancing cytokine-mediated immunity and extrinsic apoptosis. Our study delineated a highly interconnected regulatory network in which TFs, miRNAs, and lncRNAs engaged in cross-talk to coordinately fine-tune mRNA output. By identifying key regulatory nodes, this study elucidated QUR’s mechanism of action and provide potential biomarkers or therapeutic targets. Future functional studies are warranted to validate these mechanisms and translate these insights into combinatory treatment strategies for HCC. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interest. Clinical trial number Not applicable. Funding This study was supported by Fujian Provincial Health Technology Project (2022GGA031), Joint Funds for the innovation of science and Technology, Fujian Province (2024Y9592), and Fujian University of Traditional Chinese Medicine Clinical Program (XB2023187). Author Contribution L.T. was responsible for the study design, experimental work, data analysis, and manuscript drafting. W.N. contributed to figure preparation and editing. C.Y. performed the literature review. H.Z. provided project supervision and revision comments of the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements Not applicable. Data Availability The data generated or analyzed during this study are included in the article. All data from this study can be obtained from the authors upon request. References Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-Cancer J. Clin. 74 (3), 229–263 (2024). 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Aging (Albany Ny) . 10 (7), 1627–1639 (2018). Yue, H. et al. LINC02154 promotes the proliferation and metastasis of hepatocellular carcinoma by enhancing SPC24 promoter activity and activating the PI3K-AKT signaling pathway. Cell. Oncol. 45 (3), 447–462 (2022). Liu, C. et al. The Interferon Gamma-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Colon Adenocarcinoma. Front. Oncol. 12 , 876660 (2022). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.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. 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Data are expressed as mean ± SD from three independent experiments. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001 \u003cem\u003evs. \u003c/em\u003econtrol group. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e###\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 \u003cem\u003evs.\u003c/em\u003e lower concentration within the same treatment duration. \u003csup\u003e▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e▲▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e▲▲▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 \u003cem\u003evs. \u003c/em\u003eshorter exposure time at the same concentration.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/b50ec5663d06bdf021e1751c.jpeg"},{"id":98797905,"identity":"072480b1-5004-45e0-8ab7-759a67da1afa","added_by":"auto","created_at":"2025-12-22 14:02:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":576177,"visible":true,"origin":"","legend":"\u003cp\u003eQUR inhibited the migration and induced apoptosis of HepG2 Cells. \u003cstrong\u003eA\u003c/strong\u003e Scratch wounds were prepared beforehand, and HepG2 cells were treated as indicated. Wound closure was recorded at 0, 24, and 48 h; scale ＝ 500 μm. \u003cstrong\u003eB\u003c/strong\u003e Apoptosis was evaluated by annexin V-FITC/PI staining and flow cytometry after 24 h of treatment. Data are shown as mean ± SD of three independent experiments. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001 \u003cem\u003evs. \u003c/em\u003econtrol group.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/f2e43a4f50dc15f1d4b2dd99.jpeg"},{"id":98795308,"identity":"f264200c-fd19-48ad-9787-1c7313458457","added_by":"auto","created_at":"2025-12-22 12:53:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":670196,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed RNAs in QUR-treated HepG2 cells. \u003cstrong\u003eA-C \u003c/strong\u003eVolcano plots of mRNAs, lncRNAs, and miRNAs. Red, blue, and gray dots denote up-regulated, down-regulated, and non-significantly altered RNAs, respectively. \u003cstrong\u003eD-F\u003c/strong\u003e Heatmaps of mRNA, lncRNA, and miRNA expression profiles.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/610865866c2acf26a146e82e.jpeg"},{"id":98795280,"identity":"ade17536-940e-44f3-ab38-b895657e1558","added_by":"auto","created_at":"2025-12-22 12:53:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1071349,"visible":true,"origin":"","legend":"\u003cp\u003eFunctions of the\u003cstrong\u003e \u003c/strong\u003eDEmRNA genes.\u003cstrong\u003e A, B \u003c/strong\u003eGSEA plots of enriched GO-BP and KEGG pathway terms between control and QUR-treated groups, respectively. \u003cstrong\u003eC, D\u003c/strong\u003eBar graphs of GSEA results for GO-BP and KEGG categories, respectively. Gray bars denote non-significant terms, with only the top 10 significant GO-BP terms displayed.\u003cstrong\u003e E, F \u003c/strong\u003eTop five significantly enriched GO terms (covering BP, CC and MF aspects) for the up- and down-regulated DEmRNA genes, respectively.\u003cstrong\u003e \u003c/strong\u003eOnly one MF term was significant for\u003cstrong\u003e \u003c/strong\u003ethe up-regulated DEmRNA genes. \u003cstrong\u003eG, H \u003c/strong\u003eTop 10 significantly enriched KEGG pathway terms for the up- and down-regulated DEmRNA genes, respectively. GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; CC, cellular component; BP, biological process; MF, molecular function.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/0217ef9a3ba7718f7b87a4bf.png"},{"id":98795449,"identity":"55686f9d-8966-4d93-a83e-a449656df2a5","added_by":"auto","created_at":"2025-12-22 12:53:36","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":480845,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network and functional modules. \u003cstrong\u003eA\u003c/strong\u003e PPI network of proteins encoded by DEmRNA. Pink and green nodes denote up- and down-regulated DEmRNAs, respectively. Edges represent protein interactions. \u003cstrong\u003eB-F\u003c/strong\u003e Five major functional modules identified from the PPI network, each accompanied by the top 10 significantly enriched GO-BP terms. PPI, protein-protein interaction.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/7a53e7e50ce0fbb53c78aa0b.jpeg"},{"id":98795138,"identity":"4a0bed6b-4bb7-4cbb-be15-657833de3156","added_by":"auto","created_at":"2025-12-22 12:52:49","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1021820,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of the key DEmRNA genes in HCC patients. Associations between the expression of candidate DEmRNA genes and overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS) in HCC patients. OS, overall survival; RFS, relapse-free survival; PFS, progression-free survival; DSS disease specific survival.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/540e08216d975977a9ce89fa.jpeg"},{"id":98795266,"identity":"65a95a20-5110-43e6-bdf3-12651f98ba47","added_by":"auto","created_at":"2025-12-22 12:53:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1191803,"visible":true,"origin":"","legend":"\u003cp\u003eThe lncRNA-miRNA-TF-mRNA regulatory network.\u003cstrong\u003e \u003c/strong\u003eDiamonds, arrow-head, rectangles, and circles represent DElncRNAs, DEmiRNAs, TFs, and DEmRNAs, respectively. Red and blue nodes represent up- and down-regulation, respectively. Turquoise dashed lines with T-ends represent repression, red solid lines with arrow indicate activation.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/3283baedab80ad23b0a64d53.png"},{"id":98795320,"identity":"c7e4241f-5f5e-4177-8035-a8f1fb845acb","added_by":"auto","created_at":"2025-12-22 12:53:18","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":536868,"visible":true,"origin":"","legend":"\u003cp\u003eClinical relevance of the five key DEmRNAs in HCC. \u003cstrong\u003eA \u003c/strong\u003eExpression levels of CSF2, TNF, KIF20A, IFIT1 and CCNB1 in HCC \u003cem\u003evs.\u003c/em\u003e normal liver tissues. \u003cstrong\u003eB, C \u003c/strong\u003eAssociation between their expression and clinical stage or histological grade of HCC patients. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/555e3826c2e040e3707b421f.jpeg"},{"id":98795278,"identity":"6013b903-9ab7-4b7c-8bff-60f1b2615ff8","added_by":"auto","created_at":"2025-12-22 12:53:08","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1171447,"visible":true,"origin":"","legend":"\u003cp\u003eClinical relevance of the seven TFs in HCC. Expression levels of ATF3, CREB5, EGR3, HNF4A, JUN, MYB, NR4A3, RELB in HCC \u003cem\u003evs.\u003c/em\u003e normal liver tissues, and their associations with OS in HCC patients (StarBase database). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/b8ec7599625eb1b1e4608888.jpeg"},{"id":98795267,"identity":"d46e3a3f-cf9b-468e-8df0-0fc3f97593d1","added_by":"auto","created_at":"2025-12-22 12:53:02","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":422080,"visible":true,"origin":"","legend":"\u003cp\u003eClinical relevance of the DEmiRNAs and DElncRNAs in HCC. Expression levels of indicated DEmiRNAs and DElncRNAs in HCC \u003cem\u003evs.\u003c/em\u003e normal liver tissues, and their associations with OS in HCC patients (StarBase database). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/d8084b1eefd078afd333e293.jpeg"},{"id":99314099,"identity":"05067581-41f2-4b38-a6a8-82173f5a3dfc","added_by":"auto","created_at":"2025-12-31 16:20:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8570655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/a7ca35ae-56de-407e-b6aa-44babb55bb84.pdf"},{"id":98795279,"identity":"2fd4c743-484f-4ad3-80e3-9b711b2382a9","added_by":"auto","created_at":"2025-12-22 12:53:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21227,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8329255/v1/f162a331c44da5f32144c182.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated transcriptomic analysis reveals a lncRNA-miRNA-TF-mRNA regulatory network underlying quercetin’s anti-hepatocellular carcinoma effects","fulltext":[{"header":"Background","content":"\u003cp\u003ePrimary liver cancer ranks as the sixth most commonly diagnosed cancer and the third leading cause of cancer‑related mortality globally, with hepatocellular carcinoma (HCC) accounting for approximately 85% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although substantial progress has been made in comprehensive therapies, delayed diagnosis often precludes curative intervention, while frequent recurrence, metastasis, and therapeutic resistance collectively contribute to poor patient outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The unmet clinical need underscores the urgency for developing novel agents targeting HCC pathogenesis.\u003c/p\u003e \u003cp\u003eNatural products with inherent multi‑target characteristics hold great promise in oncology drug discovery. Quercetin (QUR), a ubiquitous dietary flavonoid, exhibits compelling potential as an HCC chemoadjuvant with favorable safety profiles. Preclinical studies have demonstrated that QUR inhibited cell proliferation, invasion, metastasis, oxidative stress, inflammation, liver fibrosis, whereas inducing cell apoptosis, chemo-sensitization, and modulation of the tumor microenvironment in HCC models [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, its comprehensive mechanistic landscape remains incompletely elucidated.\u003c/p\u003e \u003cp\u003eGene expression regulation involves a complex interplay among transcription factors (TFs), non-coding RNAs (ncRNAs), and coding genes. MicroRNAs (miRNAs) are small ncRNAs of about 22 nucleotides, which repress messenger RNA (mRNA) translation via binding to them. Long non-coding RNAs (lncRNAs) are ncRNAs with more than 200 nucleotides, can act as competitive endogenous RNAs (ceRNAs) by sequestering miRNAs, thereby alleviating miRNA-mediated repression of mRNAs, this mechanism is known as the ceRNA hypothesis. Given that each miRNA can target multiple mRNAs/lncRNAs and each mRNA/lncRNA can interact with several miRNAs, intricate ceRNA networks emerge [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Aberrant ncRNA expression and ceRNA network alterations are deeply implicated in HCC pathogenesis and hold promise for early diagnosis, prognosis prediction, and therapeutic targeting [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTranscription factors (TFs) regulate gene transcription by binding promoter regions of target genes. TFs can positively or negatively regulate miRNA expression, miRNAs can in turn suppress TF translation, forming feed-forward or feedback loops among TFs, miRNAs and target mRNAs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies highlighted the involvement of TF-miRNA interactions and ceRNA crosstalk in cancer progression [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although preliminary studies suggested QUR modulated miRNA-mRNA interactions in HCC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the integrated lncRNA-miRNA-TF-mRNA regulatory network mediated by QUR in HCC has not been explored.\u003c/p\u003e \u003cp\u003eIn this study, transcriptomic profiling and integrated bioinformatic analyses were employed to elucidate the global regulatory mechanisms and key targets of QUR treating HCC. A lncRNA-miRNA-TF-mRNA regulatory network was constructed to decipher the multi-layered molecular basis of QUR\u0026rsquo;s anti-cancer activity. Our findings might identify key regulatory hubs with potential as therapeutic targets in HCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eHuman HCC cell line HepG2 was obtained from American Type Culture Collection (ATCC, Manassas, VA, US). HepG2 cells were maintained in high-glucose Dulbecco\u0026rsquo;s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37\u0026deg;C in a humidified 5% CO₂ atmosphere (all agents from Gibco, Carlsbad, CA, USA). QUR (97% purity, Macklin, Shanghai, China) was dissolved in DMSO (Sigma-Aldrich, St. Louis, MO, USA) to prepare a 320 mM stock solution, which was diluted in culture medium to working concentrations of 20\u0026ndash;320 \u0026micro;M.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell viability assay\u003c/h3\u003e\n\u003cp\u003eCell viability was assessed using the Cell Counting Kit-8 (CCK-8; Biosharp, Anhui, China). HepG2 cells were seeded in 96-well plates at 3\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/mL for 24h, then treated with 20, 40, 80, 160, and 320 \u0026micro;M QUR or 0.1% DMSO for 24, 48, or 72 h. After treatment, cells were washed with sterile phosphate-buffered saline (PBS) and incubated with CCK-8 reagent for 2 h at 37℃. Absorbance was detected at 450 nm using a microplate reader (Bio Tek Instruments, Inc., Winooski, VT, US). Half-maximal inhibitory concentration (IC₅₀) values were derived from non-linear regression curve.\u003c/p\u003e\n\u003ch3\u003eColony formation assay\u003c/h3\u003e\n\u003cp\u003eHepG2 cells were seeded in 6-well plate at 800 cells/well overnight, then treated with 160 or 320 \u0026micro;M QUR or 0.1% DMSO for 2 days. Consistently, the medium was replaced with fresh culture medium, and cells were cultured for 12 days. Colonies were washed, fixed using 4% paraformaldehyde, stained with 0.1% crystal violet, and those containing at least 50 cells were counted under a microscope.\u003c/p\u003e\n\u003ch3\u003eWound-healing assay\u003c/h3\u003e\n\u003cp\u003eHepG2 cells were seeded in 6-well plates at 4.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL and grown to 100% confluence. Cell monolayers were scratched with 200 \u0026micro;L pipette tips to make wounds, and cells were treated with serum-free medium containing 160 or 320 \u0026micro;M QUR or 0.1% DMSO. Wounds images were recorded at 0, 24, and 48 h in five random fields using an inverted microscope (Olympus Corporation, Tokyo, Japan). Wound areas were quantified using Image J 1.5.3 software (NIH, Bethesda, MD, US).\u003c/p\u003e\n\u003ch3\u003eCell apoptosis\u003c/h3\u003e\n\u003cp\u003eCell apoptosis was evaluated using an annexin V-fluoresceine isothiocyanate (FITC)/propidium iodide (PI) detection kit (Bestbios, Shanghai, China). HepG2 cells were seeded in 6-well plates at 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e/well, incubated overnight, and treated with 160 or 320 \u0026micro;M QUR or 0.1% DMSO for 24 h. Cells were harvested, washed with cold sterile PBS, resuspended in binding buffer, and stained with Annexin V‑FITC and PI for 15 min at 4\u0026deg;C. Stained cells were analyzed on a CytoFLEX S Flow Cytometer (Beckman Coulter, Brea, CA, US), and data were processed with CytExpert 2.0 software.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction\u003c/h2\u003e \u003cp\u003eHepG2 cells were treated with 0.1% DMSO or 320 \u0026micro;M QUR (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;three per group). Total RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, US) and treated with DNase I (Takara Bio, Shiga, Japan) to remove genomic DNA. RNA purity and concentration were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, US), and integrity was verified using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). Samples with an RNA Integrity Number (RIN)\u0026thinsp;\u0026ge;\u0026thinsp;8 and 28s/18s ratio\u0026thinsp;\u0026ge;\u0026thinsp;1 were retained for subsequent analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003emRNA and lncRNA library construction\u003c/h3\u003e\n\u003cp\u003eTotal RNA was purified using oligo-dT magnetic beads, and ribosomal RNA was depleted with the MGIEasy rRNA Kit (BGI, Shenzhen, China). Purified RNA was fragmented, and first-strand cDNA was synthesized with reverse transcriptase and random primers. Second-strand cDNA was synthetized using RNase H, DNA polymerase I, and dNTPs. Double-stranded cDNA was end-repaired, 3\u0026rsquo;-adenylated, and size-selected (150\u0026ndash;200 bp) using AMPure XP Kit (Beckman, Beijing, China). Adapters were ligated to cDNA fragments, libraries were amplified by PCR, and PCR products were heat-denatured and circularized into DNA Nanoballs (DNBs).\u003c/p\u003e\n\u003ch3\u003emiRNA library construction\u003c/h3\u003e\n\u003cp\u003eSmall RNAs (18\u0026ndash;30 nt) were isolated by polyacrylamide gel electrophoresis. 5\u0026rsquo;-adenylated and 3\u0026rsquo;-blocked adapters were ligated to small RNAs, followed by 5\u0026rsquo;-adapter ligation with unique molecular identifiers (UMIs). Unligated adapters were removed, ligated RNAs were reverse-transcribed using UMI-containing primers, and cDNA libraries were amplified by PCR. PCR products of 110\u0026ndash;130 bp were purified using the QIAquick Gel Extraction Kit (QIAGEN, Valencia, CA) and agarose gel electrophoresis, subsequently denatured and circularized into DNBs.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHigh throughput sequencing and RNA identification\u003c/h2\u003e \u003cp\u003eThe quality of RNA library was assessed using an Agilent 2100 biological analyzer, and sequencing was performed on the BGISEQ-500 platform (BGI, Shenzhen, China). Raw reads were quality-controlled: mRNA and lncRNA data were filtered using SOAPnuke v1.5.2 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and miRNA reads were filtered using FASTX-Toolkit v0.0.13. Clean reads of mRNA and lncRNA were aligned to the GRCh38 genome using HISAT2 v2.0.4 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and to coding gene sets using Bowtie2 v2.2.5 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Transcript expression quantification was quantified with StringTie v2.2.0 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. miRNA reads were aligned to coding gene sets using Bowtie2, and miRNA annotation was performed using Rfam 14.0 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and ASRA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis\u003c/h2\u003e \u003cp\u003eTranscript abundance was quantified using RSEM and expressed as FPKM. Differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between control and QUR groups were identified using the R package \u0026ldquo;Deseq2\u0026rdquo; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] with thresholds of |log\u003csub\u003e2\u003c/sub\u003e fold change (FC)|\u0026gt;2 and \u003cem\u003eBenjamini-Hochber\u003c/em\u003e adjusted \u003cem\u003eP\u003c/em\u003e-value (\u003cem\u003eq\u003c/em\u003e-value)\u0026thinsp;\u0026lt;\u0026thinsp;0.1. Results were visualized as volcano plots (ggplot2 v3.3.5) and heatmaps (pheatmap v1.0.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGSEA was conducted on all DEmRNA genes ranked by log\u003csub\u003e2\u003c/sub\u003eFC values using the R package \u0026ldquo;clusterProfiler\u0026rdquo;. Gene sets \u0026ldquo;c5.go.v7.5.1.symbols.gmt\u0026rdquo; and \u0026ldquo;c2.cp.kegg.v7.5.1.symbols.gmt\u0026rdquo; were retrieved from the MSigDB v 7.5.1. Gene sets with |normalized enrichment score (NES)| \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG pathway enrichment\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] enrichment analyses were performed separately for up- and down-regulated DEmRNA genes using the R package \u0026ldquo;phyper\u0026rdquo;. GO terms covered cellular component (CC), biological process (BP), and molecular function (MF). Terms with \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction (PPI) network\u003c/h2\u003e \u003cp\u003ePPI analysis of DEmRNA genes was conducted using STRING v11.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with the settings of \u0026ldquo;homo sapiens\u0026rdquo; and a confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.7. The PPI network was visualized using Cytoscape 3.7.0 software with isolated genes removed, and hub genes were identified using the CytoHubba plugin (top 10 by degree). Molecular complexes were detected using MCODE v1.6.1 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] with cut-offs set as: degree\u0026thinsp;=\u0026thinsp;2, score\u0026thinsp;=\u0026thinsp;0.2, k-core\u0026thinsp;=\u0026thinsp;2, and max depth\u0026thinsp;=\u0026thinsp;100. Genes in the top five clusters were selected as candidates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier Plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kmplot.com/\u003c/span\u003e\u003cspan address=\"http://www.kmplot.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was applied to evaluate associations between candidate DEmRNA gene expression and overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease specific survival (DSS) of HCC patients. DEmRNA genes significantly associated with HCC outcomes were designated as key candidates for further research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-TF-mRNA-lncRNA network construction\u003c/h2\u003e \u003cp\u003eThe regulatory network was built as follows: (1) Putative TFs regulating key DEmRNAs were predicted using the ChEA3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/chea3\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/chea3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and TRRUST (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.grnpedia.org/trrust/\u003c/span\u003e\u003cspan address=\"http://www.grnpedia.org/trrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] databases, from which the top 100 TFs prioritized by ChEA3 were eligible. The overlap between the putative TFs and the DEmRNAs was identified as potential TFs. (2) DEmiRNA\u0026ndash;key DEmRNA/TF interactions were predicted using Miranda [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], TargetScan [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and RNAhybrid [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] databases, pairs confirmed by at least two tools were retained. (3) DElncRNA-DEmiRNA interactions were predicted using miRanda and RNAhybrid databases, with mutual confirmation. (4) Pearson correlation analysis required \u003cem\u003er\u003c/em\u003e \u0026lt; -0.8 for DEmiRNA-DEmRNA/DElncRNA pairs, and \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.9 for DEmRNA-DElncRNA pairs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Finally, validated interactions were integrated and visualized using Cytoscape v3.7.0 to construct a miRNA-TF-mRNA-lncRNA regulatory network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical relevance of network components\u003c/h2\u003e \u003cp\u003eExpression profiles of the five DEmRNAs in HCC versus normal liver tissues, and their associations with tumor stage/grade were interrogated using RNA-seq data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx, v8) project. Transcripts per million (TPM) values were log\u003csub\u003e2\u003c/sub\u003e(TPM\u0026thinsp;+\u0026thinsp;1) transformed. TF expression in HCC was assessed similarly, and their associations with the OS of HCC patients were evaluated using Kaplan-Meier Plotter. The expression and prognostic significance of the DEmiRNAs and DElncRNAs were examined using the StarBase v2.0 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/index.php\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] based on TCGA data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData from triplicate experiments were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). For experimental data, group comparisons were performed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test (two groups) or one-way ANOVA/Welch\u0026rsquo;s ANOVA (multiple groups) with Bonferroni or Games-Howell post-hoc tests, respectively. RNA-seq data were processed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Kruskal-Wallis test for two-group or multi-group comparisons, respectively. Analyses were conducted using SPSS v21.0 and R v4.0.3, with visualizations created in GraphPad Prism v8.0 and R package \u0026ldquo;ggplot2\u0026rdquo;. Results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eQUR inhibited the proliferation and cloning of HepG2 cells\u003c/h2\u003e \u003cp\u003e CCK-8 and colony formation assays demonstrated that QUR treatment dose‑ and time‑dependently reduced the viability of HepG2 cells (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; Fig.\u0026nbsp;1A), yielding IC\u003csub\u003e50\u003c/sub\u003e values of 2076.02 \u0026micro;M (24 h), 662.25 \u0026micro;M (48 h), and 264.47 \u0026micro;M (72 h), respectively. Besides, QUR dose-dependently suppressed colony formation of HepG2 cells (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; Fig.\u0026nbsp;1B).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e QUR inhibited the proliferation and clonogenicity of HepG2 cells. \u003cb\u003eA\u003c/b\u003e Cell viability was assessed using CCK-8 assay after treatment with indicated concentrations of QUR for different durations. \u003cb\u003eB\u003c/b\u003e Colony formation was assessed in HepG2 cells that were cultured for 12 days following the indicated 48-h treatment. Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD from three independent experiments. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u003cem\u003evs.\u003c/em\u003e control group. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e###\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u003cem\u003evs.\u003c/em\u003e lower concentration within the same treatment duration. \u003csup\u003e▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e▲▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e▲▲▲\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u003cem\u003evs.\u003c/em\u003e shorter exposure time at the same concentration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eQUR suppressed the migration and induced apoptosis of HepG2 cells\u003c/h2\u003e \u003cp\u003e Wound-healing assays showed that QUR dose-dependently inhibited HepG2 cells migration (Fig.\u0026nbsp;2A). Annexin V-FITC/PI staining revealed that QUR increased total apoptosis of HepG2 cells in a dose-dependent manner (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with late apoptosis/necrosis accounting for ~\u0026thinsp;80% of affected cells (Fig.\u0026nbsp;2B), suggesting QUR suppressed the proliferation partly through apoptosis induction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e QUR inhibited the migration and induced apoptosis of HepG2 Cells. \u003cb\u003eA\u003c/b\u003e Scratch wounds were prepared beforehand, and HepG2 cells were treated as indicated. Wound closure was recorded at 0, 24, and 48 h; scale = 500 \u0026micro;m. \u003cb\u003eB\u003c/b\u003e Apoptosis was evaluated by annexin V-FITC/PI staining and flow cytometry after 24 h of treatment. Data are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of three independent experiments. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u003cem\u003evs.\u003c/em\u003e control group.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of the DEmRNAs, DEmiRNAs, and DElncRNAs\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTranscriptome sequencing identified 647 DEmRNAs (412 up-regulated and 235 down-regulated), and 17 DEmiRNAs (six up-regulated and 11 down-regulated), and 510 DElncRNAs (214 up-regulated and 90 down-regulated) between 320 \u0026micro;M QUR-treated versus control HepG2 cells (Fig.\u0026nbsp;3). The top ten most significantly altered transcripts were listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e Identification of differentially expressed RNAs in QUR-treated HepG2 cells. \u003cb\u003eA-C\u003c/b\u003e Volcano plots of mRNAs, lncRNAs, and miRNAs. Red, blue, and gray dots denote up-regulated, down-regulated, and non-significantly altered RNAs, respectively. \u003cb\u003eD-F\u003c/b\u003e Heatmaps of mRNA, lncRNA, and miRNA expression profiles.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eFunctions of the DEmRNAs\u003c/h2\u003e \u003cp\u003eGSEA revealed that QUR positively enriched gene sets involved in cellular communication processes (e.g., cell activation, protein localization) and cytokine-mediated signaling (e.g., cytokine-cytokine receptor interaction and JAK-STAT). Negatively enriched gene sets related to chromatin organization (e.g., protein-DNA complex organization, chromatin remodeling), epigenetic regulation, and mitotic cell cycle phase transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D).\u003c/p\u003e \u003cp\u003eThe up-regulated DEmRNAs were significantly enriched in 13 GO-BP terms (e.g., cytokine-mediated signaling pathway and positive regulation of T cell differentiation), nine GO-CC terms (e.g., extracellular space and plasma membrane), and one GO-MF term (cytokine activity) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). 20 KEGG terms indicated their participation in cytokine-cytokine receptor interaction, JAK-STAT, and TNF signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eThe down-regulated DEmRNAs were significantly enriched in 29 GO-BP terms (e.g., nucleosome assembly and chromatin organization), 11 GO-MF terms (e.g., protein heterodimerization and alkaline phosphatase activity), and 10 GO-CC terms (e.g., nucleosome, extracellular exosome) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). KEGG analysis revealed their enrichment in necroptosis and viral carcinogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePPI network and functional modules\u003c/h2\u003e \u003cp\u003e The PPI network consisted of 216 DEmRNA genes connected by 876 interactions (Fig.\u0026nbsp;5A). MCODE analysis identified five modules. Module One (11 genes) involved in DNA replication-dependent nucleosome assembly. Module Two (7 genes) implicated in interferon response and negative regulation of viral genome replication. Module Three principally governed mitotic cell cycle transition. Module Four involved in cytokine signaling and extrinsic apoptosis. Module Five associated with chromatin organization (Fig.\u0026nbsp;5B-F). Details of the five modules are described in Supplementary Table\u0026nbsp;2. These 35 DEmRNAs were selected for following survival analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e PPI network and functional modules. \u003cb\u003eA\u003c/b\u003e PPI network of proteins encoded by DEmRNA. Pink and green nodes denote up- and down-regulated DEmRNAs, respectively. Edges represent protein interactions. \u003cb\u003eB-F\u003c/b\u003e Five major functional modules identified from the PPI network, each accompanied by the top 10 significantly enriched GO-BP terms. PPI, protein-protein interaction.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrognostic significance of the candidate genes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurvival analysis identified12 prognosis-associated key DEmRNAs. Elevated expression of \u003cem\u003eAURKA\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eCDC20\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, and \u003cem\u003ePLK1\u003c/em\u003e was correlated with reduced OS, RFS, PFS, and DSS of HCC patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Elevated \u003cem\u003eH2AC14\u003c/em\u003e expression correlated with worse RFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and elevated \u003cem\u003eH2BC3\u003c/em\u003e and \u003cem\u003eH2BC9\u003c/em\u003e levels were associated with diminished DSS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, higher \u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eIFIT1\u003c/em\u003e expression linked with improved RFS and PFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and augmented \u003cem\u003eBST2\u003c/em\u003e and \u003cem\u003eCSF2\u003c/em\u003e levels correlated with better DSS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (Fig.\u0026nbsp;6)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e Survival analysis of the key DEmRNA genes in HCC patients. Associations between the expression of candidate DEmRNA genes and overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS) in HCC patients. OS, overall survival; RFS, relapse-free survival; PFS, progression-free survival; DSS disease specific survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLncRNA-miRNA-TF-mRNA regulatory network\u003c/h2\u003e \u003cp\u003eThe integrated lncRNA-miRNA-TF-mRNA regulatory network comprised three DEmiRNAs (hsa-miR-423-5p, hsa-miR-1260b, hsa-miR-449a), eight TFs (ATF3, CREB5, EGR3, HNF4A, JUN, MYB, NR4A3, RELB), five DEmRNAs (TNF, CSF2, IFIT1, KIF20A, CCNB1), and 20 DElncRNAs, encompassing 65 interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHsa-miR-423-5p served as a central repressive hub, concurrently targeting all the TFs, two mRNAs (TNF and IFIT1), and multiple lncRNAs. Hsa-miR-449a targeted CSF2, CCNB1, KIF20A, IFIT1, and several lncRNAs, and was positively regulated by TFs including JUN, ATF3, MYB, HNF4A, and NR4A3. In contrast, hsa-miR-1260b showed no TF connection. As an upstream TF of hsa-miR-449a, MYB also directly regulate CCNB1 and KIF20A, suggesting a synergistic repression. The network also featured TF-mediated activation, such as JUN-induced CSF2 expression, ATF3/RELB-driven IFIT1 expression, and synergistic upregulation of TNF by ATF3, EGR3, JUN, and RELB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eClinical relevance of the network components\u003c/h2\u003e \u003cp\u003eExpression levels of \u003cem\u003eCSF2\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eIFIT1\u003c/em\u003e, and \u003cem\u003eCCNB1\u003c/em\u003e were elevated in HCC tissues compared with normal liver (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas \u003cem\u003eTNF\u003c/em\u003e expression showed no significant alteration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). \u003cem\u003eKIF20A\u003c/em\u003e and \u003cem\u003eCCNB1\u003c/em\u003e expression increased with histological grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and non-significantly with stages (I\u0026ndash;III). \u003cem\u003eIFIT1\u003c/em\u003e expression non‑significantly declined with advancing stages and grades. \u003cem\u003eCSF2\u003c/em\u003e expression showed no significant correlation with either parameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003eAmong the TFs, \u003cem\u003eATF3\u003c/em\u003e and \u003cem\u003eEGR3\u003c/em\u003e were down-regulated in HCC tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while \u003cem\u003eCREB5\u003c/em\u003e, \u003cem\u003eHNF4A\u003c/em\u003e, \u003cem\u003eNR4A3\u003c/em\u003e, and \u003cem\u003eRELA\u003c/em\u003e were up-regulated, compared with normal liver (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eMYB\u003c/em\u003e showed no significant differential expression, but elevated \u003cem\u003eMYB\u003c/em\u003e indicated poorer OS of HCC patients. Whereas, high \u003cem\u003eNR4A3\u003c/em\u003e expression correlated with favorable prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Hsa-miR-423-5p and hsa-miR-449a were overexpressed in HCC compared to normal liver (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while hsa-miR-1260b was lowly expressed. None of these miRNAs significantly affected OS (Fig.\u0026nbsp;10A-C). DElncRNAs, \u003cem\u003eBCRP2\u003c/em\u003e, \u003cem\u003eLINC02154\u003c/em\u003e, \u003cem\u003eNEAT1\u003c/em\u003e, \u003cem\u003eNSUN5P1\u003c/em\u003e, \u003cem\u003eNSUN5P2\u003c/em\u003e, and \u003cem\u003eLINC01829\u003c/em\u003e were overexpressed in HCC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High expression of \u003cem\u003eBCRP2\u003c/em\u003e, \u003cem\u003eLINC02154\u003c/em\u003e, and \u003cem\u003eNSUN5P1\u003c/em\u003e indicated poorer OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;10D-G). Data for other lncRNAs were insufficient for analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;10\u003c/b\u003e Clinical relevance of the DEmiRNAs and DElncRNAs in HCC. Expression levels of indicated DEmiRNAs and DElncRNAs in HCC \u003cem\u003evs.\u003c/em\u003e normal liver tissues, and their associations with OS in HCC patients (StarBase database). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eMechanisms of QUR in HCC treatment\u003c/h2\u003e \u003cp\u003eFollowing QUR\u0026rsquo;s treatment on HCC cells, GSEA and functional enrichment analyses revealed distinct regulatory patterns. Genes governing chromatin organization, nucleosome assembly, mitotic cell cycle progression, and interferon response were down-regulated. In contrast, genes involved in cytokine-mediated signaling pathways and extrinsic apoptosis were up-regulated. These findings suggested that QUR might exert anti-HCC effects by promoting genomic instability and enhancing immune responses, supporting its potential as a combinatory therapeutic agent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegulation of the lncRNA-miRNA-TF-mRNA network\u003c/h3\u003e\n\u003cp\u003eThe regulatory network elucidated in this study underscored a multi-layered architecture controlling gene expression in HCC.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-TF-mRNA interactions\u003c/h2\u003e \u003cp\u003eTFs and miRNAs orchestrate mRNA expression through tightly interwoven direct and indirect regulatory mechanisms, yielding both synergistic and antagonistic outcomes. For instance, a dual-safeguard mechanism fine-tuned the expression of \u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eIFIT1\u003c/em\u003e: TFs such as \u003cem\u003eATF3\u003c/em\u003e, \u003cem\u003eRELB\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e directly promoted their expression; while hsa-miR-423-5p and hsa-miR-449a suppressed their expression, thereby curbing transcriptional overactivation. Conversely, a coherent inhibitory circuit targeted \u003cem\u003eCCNB1\u003c/em\u003e: \u003cem\u003eMYB\u003c/em\u003e directly repressed its expression, and concurrently activated hsa-miR-449a, which further downregulated \u003cem\u003eCCNB1\u003c/em\u003e. Such multi-input regulatory modes might reduce noise in critical biological processes and enhance network stability.\u003c/p\u003e \u003cp\u003eBeyond fine-tuning, miRNAs also acted as integrators and amplifiers within the network. The broad repressive influence of hsa-miR-423-5p suggested its role as a master regulator. QUR down-regulated hsa-miR-423-5p, leading to the upregulation of several TFs and downstream genes (e.g., \u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eIFIT1\u003c/em\u003e). In parallel, multiple TFs, including \u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003eATF3\u003c/em\u003e, \u003cem\u003eMYB\u003c/em\u003e, \u003cem\u003eHNF4A\u003c/em\u003e, and \u003cem\u003eNR4A3\u003c/em\u003e, not only regulate target genes directly but also converge to promote hsa-miR-449a expression, which in turn repressed its own targets (e.g., \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e), collectively contributing to coordinated transcriptional outcomes. This configuration implied the presence of coherent feed-forward loops that facilitated signal amplification and coordinated transcriptional control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eKey TFs in HCC pathogenesis\u003c/h2\u003e \u003cp\u003eMultiple TFs embedded in this network exert pivotal and context-dependent roles in HCC. Activating transcription factor 3 (\u003cem\u003eATF3\u003c/em\u003e), downregulated in HCC and induced by cellular stress, suppresses proliferation and motility [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, \u003cem\u003eCREB5\u003c/em\u003e is upregulated and promotes epithelial-mesenchymal transition (EMT) and malignancy via the ERS-activated CREB5/TNC axis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Early growth response 3 (\u003cem\u003eEGR3\u003c/em\u003e) is a zinc finger TF, inhibits growth through FasL upregulation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Hepatocyte nuclear factor 4A (\u003cem\u003eHNF4A\u003c/em\u003e) is a master regulator of hepatocyte differentiation and lipid metabolism, which can function as either an oncogene or cancer suppressor depending on AMPK activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The AP-1 complex subunit JUN orchestrates genomic instability and chronic inflammation, driving HCC progression through cell cycle dysregulation and oxidative stress amplification [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. c-Myb (\u003cem\u003eMYB\u003c/em\u003e) overexpression correlates with therapy resistance and poor prognosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Nuclear receptor subfamily 4 group A member 3 (\u003cem\u003eNR4A3\u003c/em\u003e) functions as a cancer suppressor by inducing CDKN2AIP-dependent cell cycle arrest through DNA damage induction, with its downregulation marking advanced disease stages [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Nevertheless, \u003cem\u003eRELB\u003c/em\u003e, a non-canonical NF-κB transcription factor, enhances chemoresistance and metastasis via inflammatory signaling [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003emiRNA-TF-mRNA regulatory logic\u003c/h2\u003e \u003cp\u003eTriangular miRNA-TF-mRNA interactions among form a sophisticated regulatory circuitry enriched in feed-forward and feedback loops. These networks enable bidirectional control to buffer noise while maintaining target protein homeostasis, prevent erroneous phenotypic transitions through inverse signal coupling, and support spatiotemporal precision by segregating miRNA-target expression domains [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, disruption of key miRNAs or TFs can compromise network integrity, driving cancer initiation and progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eLncRNA-miRNA-mRNA axes\u003c/h2\u003e \u003cp\u003eWe identified several representative ceRNA regulatory axes, including BCRP2/hsa-miR-423-5p/TNF, (LINC02154, LINC01829)/hsa-miR-1260b/CSF2, and (NEAT1, NSUN5P1, NSUN5P2, LINC00473)/hsa-miR-449a/(KIF20A, CCNB1, IFIT1). We speculated that QUR might upregulate BCRP2, LINC02154, and LINC01829, which sequester hsa-miR-423-5p and hsa-miR-1260b, thereby elevating \u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eCSF2\u003c/em\u003e expression. Both cytokines are critical immunomodulators: TNF and GM-CSF (encoded by \u003cem\u003eCSF2\u003c/em\u003e) can enhance the cytotoxicity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, recruit dendritic cells, thus amplifying anticancer immunity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Notably, both TNF and CSF2 exhibit dual roles in HCC. Low-dose TNF can activate NF-κB pathway, promoting proliferation, migration, EMT, and angiogenesis, whereas high-dose TNF can trigger apoptosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. GM-CSF is essential for innate immunity, yet GM-CSF derived by cancer cells may recruit immune-suppressive cells. We found elevated expression of \u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eCSF2\u003c/em\u003e indicated improved survival of HCC patients, likely reflecting immune activation at therapeutic thresholds.\u003c/p\u003e \u003cp\u003eConcurrently, QUR might downregulate NEAT1, NSUN5P1/2, and LINC00473, releasing hsa-miR-449a to target KIF20A and CCNB1, thereby inhibiting biosynthesis and mitosis. Kinesin family member 20A (\u003cem\u003eKIF20A\u003c/em\u003e) facilitates chromosome and spindle movement during mitosis, and its knockdown induces apoptosis and improves treatment sensitivity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The oncogene \u003cem\u003eCCNB1\u003c/em\u003e drives G2/M transition and is strongly linked to increased postoperative recurrence and poor survival in HCC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, QUR appears to suppress HCC by down-regulating oncogenic drivers (\u003cem\u003eKIF20A\u003c/em\u003e and \u003cem\u003eCCNB1\u003c/em\u003e) and up-regulating protective immune factors (\u003cem\u003eTNF\u003c/em\u003e and \u003cem\u003eCSF2\u003c/em\u003e) through ceRNA network modulation. Paradoxically, QUR downregulated \u003cem\u003eIFIT1\u003c/em\u003e, a cancer suppressor known to enhance antiviral immunity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This observation highlighted the complexity of ceRNA-mediated regulation and the dynamic interplay between \u0026ldquo;unfavorable\u0026rdquo; and \u0026ldquo;favorable\u0026rdquo; factors. QUR might achieve net cancer-suppressive outcomes by orchestrating a balanced regulatory shift, though further functional validation is required.\u003c/p\u003e \u003cp\u003eWe observed significant overexpression of hsa-miR-423-5p and hsa-miR-449a in HCC. Elevated hsa-miR-423-5p correlates with reduced RFS post-liver transplantation and promotes invasiveness and regulatory T cell (Treg)-mediated immune evasion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Contrary to its cancer-suppressive role via targeting NOTCH1 and MET [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], hsa-miR-449a displayed a paradoxical, context-dependent function in our study. We also confirmed miR-1260b up-regulation in HCC, which enhances proliferation and invasion via RGS22 suppression [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe further documented the upregulation of \u003cem\u003eBCRP2\u003c/em\u003e, \u003cem\u003eLINC02154\u003c/em\u003e, \u003cem\u003eNEAT1\u003c/em\u003e, \u003cem\u003eLINC01829\u003c/em\u003e, and \u003cem\u003eNSUN5P1/2\u003c/em\u003e in HCC. \u003cem\u003eNEAT1\u003c/em\u003e overexpression correlates with poor prognosis across solid tumors [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Both \u003cem\u003eNSUN5P1\u003c/em\u003e and \u003cem\u003eNSUN5P2\u003c/em\u003e, pseudogenes of rRNA-modifying NOP2/Sun domain family (NSUN5) gene on chromosome 7, were correlated with adverse clinical outcomes of HCC patients [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We also identified a previously uncharacterized lncRNA, breakpoint cluster region pseudogene 2 (\u003cem\u003eBCRP2\u003c/em\u003e), linked to poor survival in HCC. Additionally, we validated the adverse prognostic role of \u003cem\u003eLINC02154\u003c/em\u003e, consistent with its reported mechanism via PI3K/AKT/SPC24 signaling [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar with our finding, \u003cem\u003eLINC01829\u003c/em\u003e was ever been reported as a risk factor in colon adenocarcinoma but not previously studied in HCC [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, QUR inhibited HCC proliferation, clonogenicity, migration, and induced cell apoptosis. It exerted multimodal anti-cancer effects by disrupting genetic metabolism, mitotic progression, and transcriptional activity, while concurrently enhancing cytokine-mediated immunity and extrinsic apoptosis. Our study delineated a highly interconnected regulatory network in which TFs, miRNAs, and lncRNAs engaged in cross-talk to coordinately fine-tune mRNA output. By identifying key regulatory nodes, this study elucidated QUR\u0026rsquo;s mechanism of action and provide potential biomarkers or therapeutic targets. Future functional studies are warranted to validate these mechanisms and translate these insights into combinatory treatment strategies for HCC.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Fujian Provincial Health Technology Project (2022GGA031), Joint Funds for the innovation of science and Technology, Fujian Province (2024Y9592), and Fujian University of Traditional Chinese Medicine Clinical Program (XB2023187).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.T. was responsible for the study design, experimental work, data analysis, and manuscript drafting. W.N. contributed to figure preparation and editing. C.Y. performed the literature review. H.Z. provided project supervision and revision comments of the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data generated or analyzed during this study are included in the article. All data from this study can be obtained from the authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCa-Cancer J. 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Oncol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 876660 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, quercetin, ceRNA, transcription factor, miRNA, lncRNA","lastPublishedDoi":"10.21203/rs.3.rs-8329255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8329255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eQuercetin (QUR) exhibits multiple pharmacological activities against hepatocellular carcinoma (HCC). Competing endogenous RNA (ceRNA) networks and transcription factors (TFs) play critical roles in oncogenesis and cancer progression, however, the integrated ceRNA- and TF-mediated mechanisms of QUR in HCC treatment remain largely unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe anti-proliferative, anti-migratory, and pro-apoptotic effects of QUR on HepG2 cells were evaluated using CCK‑8, colony formation, wound‑healing, and flow cytometry assays. Transcriptome sequencing was performed to identify differentially expressed (DE) long non-coding RNAs (lncRNAs), micro RNAs (miRNAs), and messenger RNAs (mRNAs) after QUR treatment. Functional enrichment, protein\u0026ndash;protein interaction (PPI) analysis, and survival analyses were performed to elucidate therapeutic mechanisms and prognostic biomarkers. A lncRNA\u0026ndash;miRNA\u0026ndash;TF\u0026ndash;mRNA regulatory network was constructed by integrating multiple databases, and its clinical relevance was assessed using TCGA and GTEx data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eQUR dose-dependently inhibited the proliferation, colony formation, migration, and induced apoptosis of HCC cells. Transcriptomic profiling identified 647 DEmRNAs, 304 DElncRNAs, and 17 DEmiRNAs. Down‑regulated DEmRNAs were enriched in nucleosome, chromatin, and telomere metabolism, while up‑regulated DEmRNAs were involved in cytokine activity and immune cell differentiation. Key pathways included metabolic reprogramming, cytokine signaling, PI3K/AKT, and viral carcinogenesis. PPI analysis revealed five functional clusters, and survival analysis identified 12 prognosis‑associated DEmRNAs (e.g., \u003cem\u003eAURKA\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, and \u003cem\u003ePLK1\u003c/em\u003e). The constructed regulatory network comprised three DEmiRNAs, eight TFs, five DEmRNAs, and 20 DElncRNAs, revealing coordinated miRNA-TF cross-talk and lncRNA\u0026ndash;meditated ceRNA axes that fine-tuned key mRNAs. Mechanistically, QUR might balance oncogene and tumor suppressor expression, thereby inhibiting metabolism, mitosis, and transcription in HCC cells, while enhancing anti‑cancer immunity and extrinsic apoptosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study delineated a comprehensive lncRNA\u0026ndash;miRNA\u0026ndash;TF\u0026ndash;mRNA network that elucidated the systematic mechanisms of QUR against HCC, offering potential biomarkers and therapeutic targets for further investigation.\u003c/p\u003e","manuscriptTitle":"Integrated transcriptomic analysis reveals a lncRNA-miRNA-TF-mRNA regulatory network underlying quercetin’s anti-hepatocellular carcinoma effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 12:35:18","doi":"10.21203/rs.3.rs-8329255/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":"b7ce1c9a-1dd6-49f0-a35e-05011fe05259","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60023796,"name":"Biological sciences/Cancer"},{"id":60023797,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":60023798,"name":"Biological sciences/Molecular biology"},{"id":60023799,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-26T13:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 12:35:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8329255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8329255","identity":"rs-8329255","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}