Multi-Omics profiling of hepatocellular carcinoma reveals prognostic biomarkers, HBV-driven oncogenic networks, and therapeutic vulnerabilities in genomic-immune crosstalk

Multi-Omics profiling of hepatocellular carcinoma reveals prognostic biomarkers, HBV-driven oncogenic networks, and therapeutic vulnerabilities in genomic-immune crosstalk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-Omics profiling of hepatocellular carcinoma reveals prognostic biomarkers, HBV-driven oncogenic networks, and therapeutic vulnerabilities in genomic-immune crosstalk Blaise Angoma-Sindani, Tat'y Mwata-Velu, Richard Mavuela Maniansa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7040990/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: Hepatocellular carcinoma (HCC) remains a molecularly heterogeneous malignancy with limited therapeutic biomarkers. While transcriptomic studies have identified dysregulated genes, their prognostic relevance, proteomic concordance, and interactions with hepatitis B virus (HBV) mutations remain underexplored. Methods: We integrated multi-omics analyses of two independent HCC cohorts (GEO datasets), proteomic profiling, survival data, HBV mutation associations, immune cell infiltration, drug sensitivity (GDSC), and genomic alteration patterns to define drivers of HCC progression. Results: We identified 23 genes including AURKA, CDK1, MKI67 linked to poor survival and genomic instability, and three protective genes (PLVAP, GSTA4, GREB1). HBV mutations (PreS, A1762T/G1764A) correlated with elevated expression of proliferative (TOP2A, RRM2) and metabolic (SQLE) genes, particularly in genotype C HCC. Despite minimal pathological stage variation, tumors exhibited robust cell cycle/EMT pathway activation (ASPM, CCNB1), highlighting molecular heterogeneity. Proliferative genes paradoxically associated with regulatory immune subsets (B cells, nTregs) and immunosuppression. Drug sensitivity analysis revealed ASPM and STMN1 as therapeutic vulnerabilities, while SPP1 and PRKAA2 marked resistance. Genomic profiling confirmed frequent mutations/CNAs in poor-prognosis genes (MKI67, CDKN2A) and stability in protective genes. Conclusions: This study establishes a multi-omics framework linking HBV-driven oncogenesis, genomic instability, and immune evasion to HCC progression. Prognostic signatures and pathway activation patterns advocate for molecular subtyping to complement clinical staging. The dual association of proliferative genes with immune suppression and drug sensitivity highlights opportunities for combinatorial therapies targeting oncogenic drivers (CDK1, ASPM) and immune checkpoints. These findings advance precision oncology strategies in HBV-associated HCC. Computational Biology Hepatocellular carcinoma Multi-omics profiling Hepatitis B virus Prognostic biomarkers Therapeutic targets Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Liver cancer remains one of the most prevalent malignancies and ranks as the third leading cause of cancer-related deaths worldwide [1,2]. Hepatocellular carcinoma (HCC), the most common form of primary liver cancer in adults, is linked to various risk factors including hepatitis B and C infections, non-alcoholic steatohepatitis (NASH), genetic predispositions, and lifestyle habits such as smoking, alcohol consumption, and poor nutrition [3,4]. Among these, nutrition stands out as a significant modifiable factor influencing HCC development and progression [5-7]. Epidemiological evidence indicates that specific dietary components or their metabolites can induce liver inflammation, fibrosis, and carcinogenesis [7,8], while also affecting treatment outcomes. Over recent decades, rapid globalization and shifting lifestyles have led to changes in dietary habits, with increased consumption of processed foods and additives [9,10]. One common additive, monosodium glutamate (MSG), though classified as safe by regulatory bodies [11], has been implicated in various health disorders, including metabolic and liver-related diseases. Previous research by our team demonstrated MSG's impact on gene expression in cortical tissues [12], underscoring the need to understand its molecular effects in HCC. Current chemotherapeutic strategies for HCC are often limited by toxicity and drug resistance [13,14]. This has fueled interest in naturally derived compounds with anticancer potential [15,16]. Advancements in herbal medicine [17-22], though reported in other conditions, have not been specifically explored in HBV-driven HCC and could be considered in future studies. Interestingly, while low testosterone a key hormone is associated with diabetes [23], elevated testosterone levels have been reported to contribute to the development of HCC[24], which we are interested in future to address. A comprehensive understanding of HCC and other cancers [25,26] requires dissecting its complex molecular landscape, which is shaped by alterations in gene expression, mutational burdens, and immune microenvironment dynamics. Systems biology approaches, which integrate multi-omics data including transcriptomics, genomics, and immunoprofiling, are increasingly being used to uncover the key regulators and pathways involved in HCC progression [27]. Specific gene mutations and expression patterns are known to drive oncogenic pathways [28], modulate tumor microenvironments [29], and influence immune cell infiltration [30]. TP53 and its regulatory genes [25], along with FABP4, ADK, and NM23 [31-33] which are important in other cancers and diseases such as diabetes [34] also appear to be uniquely relevant in this cancer. However, their roles still need to be further investigated in HBV-driven HCC. These changes not only drive tumorigenesis but also influence patient responses to immunotherapy and targeted treatments. Furthermore, pathway dysregulation, often stemming from cumulative genetic aberrations, can serve as potential biomarkers for prognosis and therapeutic targeting [35]. Advances in computational analysis have also enabled the identification of druggable targets and the prediction of responses to therapeutic compounds, including natural bioactive agents which have shown antitumor effects in HCC models [36]. In this study, we analyzed transcriptomic datasets to identify differentially expressed genes in HCC, performed pathway enrichment and mutation profiling, explored immune landscape alterations, conducted survival analysis, and evaluated potential therapeutic agents to uncover comprehensive molecular insights into HCC biology. Methods Transcriptomic data acquisition and differential expression analysis To identify differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO) under the accessions GSE135631 and GSE184733. Raw RNA-seq count data were analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html) with the DESeq2 package (version 1.38.3) in R version 4.2.2, along with associated packages Biobase 2.58.0, GEOquery 2.66.0, and limma 3.54.0. Gene annotation was performed using the GRCh38.p13 reference genome. Samples were categorized into cancer and control groups using predefined binary encodings. Lowly expressed genes were filtered out to retain those with adequate read coverage across samples. Differential gene expression was evaluated using the Wald test, applying an FDR-adjusted p-value cutoff < 0.01 and a fold change threshold ≥ 1.5. Visualization techniques included p-value histograms, MA (MD) plots, dispersion estimates, normalized boxplots, volcano plots, and UMAP for unsupervised clustering. Venn diagrams were generated to identify overlapping DEGs between both datasets, ensuring robustness of the identified gene signatures. All analyses were carried out using a GEO2R (R) script. Protein expression and survival analysis Protein expression profiles were assessed through UALCAN [37] using the CPATC https://proteomics.cancer.gov/ HCC dataset. For survival analysis, we used the Kaplan–Meier Plotter, stratifying patients based on median expression values. Significance was determined using a p-value cutoff of 0.05. The analysis aimed to evaluate the prognostic relevance of key DEGs identified in HCC transcriptomic datasets. HBV-related gene expression analysis To assess HBV-related gene expression patterns, data were mined from OncoDB [38], which provides curated expression datasets specific to hepatitis B virus-associated cancers. These were compared to expression trends in HCC datasets to evaluate virus-associated alterations in gene regulation. Genomic alteration and pathway activity analysis via GSCA Genomic datasets were analyzed using the Gene Set Cancer Analysis (GSCA) platform [39]. GSCA evaluated gene set variation analysis (GSVA) scores among groups using the Wilcoxon test (when number of stages = 2) and ANOVA (when number of stages > 2). Tumor stages were defined across different staging systems. To assess trends in expression across stages, GSCA applied the Mann–Kendall Trend Test. Given the small number of stages (n=4), trend test p-values were treated as suggestive rather than definitive. For instance, a p-value of 0.09 suggests a consistent increase or decrease in gene expression across stages, although not statistically significant under conventional thresholds. Pathway activity profiling Pathway activity was analyzed using the Expression & Pathway Activity module of GSCA. This module compares gene expression across pathway activity groups (activation vs inhibition), which were defined by median-centered pathway scores derived from Reverse Phase Protein Array (RPPA) data. RPPA data (sourced from the TCPA database) covered 7876 samples from 32 TCGA cancer types, profiling 10 cancer-associated pathways: TSC/mTOR, RTK, RAS/MAPK, PI3K/AKT, Hormone ER, Hormone AR, EMT, DNA Damage Response, Cell Cycle, and Apoptosis. RPPA normalization involved centering each protein component by its median and scaling by its standard deviation across all samples. The Pathway Activity Score (PAS) was calculated as the sum of positive regulatory components minus the sum of negative components for each pathway, as described by Akbani et al [40] Samples were dichotomized into high and low expression groups based on the median expression of each gene. The difference in PAS between groups was assessed by Student’s t-test, with FDR ≤ 0.05 considered significant. Genes for which PAS(high expression) > PAS(low expression) were considered activators of the pathway; otherwise, they were inferred as inhibitors, following the methodology of Ye et al [41]. Mutation and copy number variation (CNV) Analysis Single nucleotide variation (SNV) data for 424 HCC, TCGA samples were analysed through GSCA. Seven deleterious mutation types were included: Missense, Nonsense, Frame_Shift_Ins, Splice_Site, Frame_Shift_Del, In_Frame_Del, and In_Frame_Ins. Non-deleterious variants included Silent, Intron, 3’/5’ UTR, and Flank mutations. Copy number variation (CNV) data for TCGA samples were analyzed via GISTIC2.0, which classifies CNV events based on GISTIC scores: -2: Deep deletion (likely homozygous loss) -1: Shallow deletion (likely heterozygous loss) 0: Diploid 1: Low-level gain 2: High-level amplification CNV alterations were summarized for selected genes across cancer types using this standardized classification. Immune infiltration analysis The association between gene expression and immune cell infiltration was evaluated through GSCA's Immune Infiltration & mRNA Expression module, utilizing Spearman correlation analysis. Immune infiltration estimates for 24 immune cell types were derived using ImmuCellAI, which leverages a gene signature-based method. Overlap between input genes and immune gene signatures was carefully avoided to prevent bias in correlation estimates. Drug sensitivity correlation analysis To explore therapeutic relevance, drug response data (IC50 values of 265 compounds) were retrieved from the Genomics of Drug Sensitivity in Cancer data via GSCA database. These were matched with corresponding mRNA expression profiles across 860 cancer cell lines. Pearson correlation was calculated between gene expression and drug IC50 values. The resulting p-values were corrected for multiple testing using the False Discovery Rate (FDR) method. Results In figure 1 the panel A depicts a clinical case involving a male patient diagnosed with hepatocellular carcinoma (HCC), representing the real-world relevance of transcriptomic analysis in liver cancer research. Panel B presents a Venn diagram illustrating the overlap of differentially expressed genes (DEGs) identified between two separate HCC transcriptome datasets obtained from the Gene Expression Omnibus (GEO). The overlap visually demonstrates the shared molecular signatures between the two cohorts, highlighting robust, consistently dysregulated genes associated with HCC pathogenesis. This overlap provides potential biomarkers or therapeutic targets common across patient populations. Volcano plots showing the distribution of DEGs in each of the two transcriptome datasets. These plots provide a comprehensive overview of transcriptomic alterations characterizing HCC in each cohort. HCC gene signatures: Protein expression and HBV association Next, we investigated the protein expression profiles of genes that were both differentially expressed and highly expressed in tumor tissues compared to adjacent normal tissues. With the exception of two genes, GSTA4 and PRKAA2, which were downregulated, and three genes that were not significantly altered, the majority of these genes exhibited elevated protein expression consistent with their mRNA levels (Fig. 2A). This concordance suggests that most of these genes follow a similar pattern at both the transcriptomic and proteomic levels. We then assessed the prognostic relevance of these genes by analyzing their association with overall survival outcomes. Notably, high expression of the following genes was associated with significantly poorer survival: SPINK1, SPP1, TOP2A, ASPM, CENPF, AURKA, PYCR1, MKI67, SQLE, FAM83D, ANLN, PRKAA2, STMN1, CCNB1, ROBO1, CDKN2A, ASF1B, CDK1, RRM2, RRAGD, SPC24, TK1, and KIFC1. In contrast, elevated expression of GREB1, PLVAP, and GSTA4 correlated with improved patient outcomes (Fig. 2B). Finally, to explore whether these gene expression patterns could be influenced by hepatitis B virus (HBV) infection, we analyzed an HCC dataset comprising both HBV-positive and HBV-negative samples. Most genes showed consistent upregulation in HBV-positive patients, indicating a possible link between HBV infection and the expression of these tumor-associated genes. Interestingly, PLVAP was the only gene that exhibited higher expression in HBV-negative samples (Fig. 2C). Multidimensional pathway and gene activation analysis To explore the relationship between pathological stage and pathway activation, we performed a comparative analysis of gene expression and signaling pathways across samples (Fig. 3). Despite modest differences in pathological tumor stages among the cohort (Fig. 3A), our analysis identified a panel of genes consistently upregulated in association with cell cycle and epithelial–mesenchymal transition (EMT) pathways. As shown in Figure 3B, several genes such as TOP2A, TK1, MKI67, CDK1, CCNB1, AURKA, and ASPM showed uniform activation patterns across CellCycle_A, CellCycle_I, and EMT_A pathways. These findings reflect a robust upregulation of proliferative and migratory signaling cascades, indicative of aggressive tumor biology. Additionally, PLVAP, although less frequently discussed in cancer pathway literature, showed significant pathway inhibition patterns, particularly for CellCycle_I and Apoptosis_I pathways, reinforcing its potential suppressive role in tumor progression (Fig. 3C). Genomic landscape of prognostic genes in HCC reveals distinct mutation and CNA patterns Figure 4 presents the mutation and copy number alteration (CNA) profiles of prognostically significant genes in hepatocellular carcinoma (HCC). In the top panel, genes associated with poor prognosis such as MKI67, ROBO1, CENPF, and CDKN2A exhibit frequent mutations, predominantly missense variants. Bubble plots (B, D) further confirm their significant association with amplification and heterozygous deletion events, indicating CNAs may drive their dysregulation. Conversely, the bottom panel (C) display favorable prognostic genes such as GREB1, PLVAP, and GSTA4, which show lower mutation frequencies. These genes are also associated with distinct CNA patterns, supporting their protective role in HCC. Together, these findings highlight the relevance of genomic alterations in shaping clinical outcomes. Gene expression with immune cell infiltration and drug sensitivity Figure 5 panel A illustrates the correlation between gene expression and immune cell infiltration, as visualized through a heatmap. A core group of genes including STMN1, FAM83D, SPC25, TK1, ANLN, TOP2A, ASPM, CENPF, ASF1B, AURKA, KIFC1, CCNB2, BIRC5, CDK1, RRM2, MKI67, SP2, PRKAA2, PYCARD, COX20A2, RHOJ, and SQLE demonstrates a strong positive correlation with B cells. These same genes, excluding RHOJ, also correlate positively with Tr1 cells, and all except PYCARD correlate positively with nTreg cells, suggesting their broader association with regulatory immune responses. Additionally, MKI67, RRM2, CDK1, BIRC5, TOP2A, and ANLN show a positive association with dendritic cells. In contrast, a majority of these genes including SPINK1 and PRKAG2—are negatively correlated with naïve CD4+ T cells, CD4+ monocytes, and NK cells, implying a potential immunosuppressive or immune-evasive gene expression profile. Panel B complements this immune correlation landscape by showing a matrix of associations between mRNA expression and drug sensitivity derived from GDSC data. Positive correlations, represented by red bubbles, indicate that higher expression of genes such as SPP1, PRKAA2, and ANLN is associated with drug resistance. Conversely, blue bubbles indicate negative correlations, with genes like SPC24, STMN1, and ASPM showing increased sensitivity to multiple drugs, highlighting their potential as biomarkers for therapeutic responsiveness. Discussion The integrative transcriptomic analysis highlights the reproducibility and translational value of identifying DEGs in hepatocellular carcinoma (HCC). Shared DEGs across two independent GEO datasets point to robust molecular perturbations common in HCC, underscoring their potential as universal biomarkers or therapeutic targets. The distinct expression patterns between tumor and non-tumor tissues, visualized through volcano plots, reflect consistent biological divergence. Similar strategies in prior studies have reinforced this approach. One analysis of eight GEO datasets identified 96 common DEGs, while another integrating four datasets uncovered 186 consensus DEGs using standardized normalization and statistical thresholds. These findings support the utility of cross-cohort integration for improving DEG reliability [ 42 , 43 ]. Our results align with these observations, affirming the potential of consistent DEGs to inform HCC pathogenesis and guide further functional and therapeutic investigations. Although numerous basic and clinical studies have attempted to clarify the molecular HCC, progress has been limited by the absence of stable and effective biomarkers [ 44 ]. The concordance observed between mRNA and protein levels for most genes underscores the translational relevance of transcriptomic profiling in HCC. Exceptions such as GSTA4 and PRKAA2, which showed discordant downregulation at the protein level, suggest post-transcriptional regulation or protein degradation mechanisms that warrant further investigation. These findings align with broader oncogenic paradigms where dysregulated genes often maintain consistent expression across molecular layers, though exceptions may highlight context-specific regulatory pathways.The prognostic analysis identified 23 genes associated with significantly poorer survival outcomes (e.g., SPINK1, AURKA, CDK1) and three genes (GREB1, PLVAP, GSTA4) linked to improved survival. Notably, genes such as AURKA and CCNB1, which drive cell cycle progression [ 45 , 46 ], exhibited strong correlations with reduced survival, consistent with prior studies implicating mitotic deregulation in HCC aggressiveness. Conversely, GSTA4’s protective role may reflect its involvement in detoxification pathways, potentially counteracting oxidative stress in tumors. These results corroborate published survival data [ 47 ] (e.g., AURKA HR = 1.77, CCNB2 HR = 1.91) and extend the evidence for genes like F9 and CYP2E1 [ 47 ], whose downregulation was previously tied to better outcomes. A key finding is the association between HBV infection and elevated expression of most tumor-associated genes (Fig. 2 C). This aligns with the published meta-analysis [ 48 ] demonstrating that HBV mutations (e.g., PreS, A1762T/G1764A) significantly increase HCC risk, particularly in genotype C or HBeAg-positive patients. The progressive accumulation of these mutations during chronic HBV infection may drive oncogenic pathways reflected in our dataset, such as proliferation (e.g., TOP2A, MKI67) and metabolic reprogramming (e.g., PYCR1, SQLE). PLVAP’s unique upregulation in HBV-negative samples suggests divergent mechanisms in non-viral HCC, possibly involving angiogenesis or immune microenvironment alterations. The published meta-analysis [ 48 ] further emphasizes the clinical utility of mutation-based biomarkers (e.g., PreS mutations with > 80% specificity for HCC prediction). Our findings complement this by proposing that expression patterns of HBV-associated genes (e.g., ASPM, RRM2) could refine risk stratification when combined with mutational profiling. However, discrepancies such as the lack of prognostic relevance for precore mutations (G1896A/C1858T) in the meta-analysis highlight the complexity of HBV-driven hepatocarcinogenesis, where specific mutations may exert indirect effects through host gene dysregulation rather than direct oncogenic activity. Our investigation into the relationship between pathological tumor stage and pathway activation revealed critical insights into the molecular drivers of HCC progression. Despite limited heterogeneity in pathological staging across the cohort, we observed a striking upregulation of genes associated with cell cycle regulation and epithelial–mesenchymal transition (EMT) pathways, including TOP2A, TK1, MKI67, CDK1, CCNB1, AURKA, and ASPM. These findings suggest that even in tumors with comparable clinical stages, molecular heterogeneity marked by proliferative and migratory pathway activation may underpin aggressive biological behavior [ 49 ]. The consistent activation of these pathways aligns with prior studies implicating cell cycle dysregulation (e.g., AURKA, CCNB1) and EMT as hallmarks of advanced HCC, driving tumor proliferation [ 50 ], invasiveness, and metastasis [ 51 ]. Notably, many of these genes (TOP2A, MKI67, CDK1) were previously linked to poor prognosis in our survival analysis (Fig. 2 B), reinforcing their dual role as prognostic biomarkers and functional mediators of tumor aggressiveness. Intriguingly, PLVAP emerged as a key outlier, showing significant inhibition of cell cycle (CellCycle_I) and apoptosis-related (Apoptosis_I) pathways (Fig. 3 C). This observation corroborates our earlier finding that elevated PLVAP expression correlates with improved survival (Fig. 2 B), suggesting a tumor-suppressive role potentially mediated through restraint of proliferative signaling or promotion of apoptotic sensitivity. While PLVAP is not commonly highlighted in cancer pathway, its consistent association with favorable outcomes across multiple analyses underscores its potential as a novel therapeutic target or biomarker for less aggressive HCC subsets. The dissociation between modest pathological stage variation and robust pathway activation raises important questions about the limitations of traditional staging systems [ 52 ] in capturing molecular heterogeneity [ 53 ]. Our data imply that molecular profiling particularly of cell cycle and EMT pathways could complement clinical staging to improve risk stratification. This is especially relevant in HBV-associated HCC, where viral-driven mutations (e.g., PreS, A1762T/G1764A) identified in the published meta-analysis [ 48 ] may mechanistically contribute to the observed pathway dysregulation. For instance, HBV genotype C’s strong association with PreS mutations and HCC risk could synergize with host genes like ASPM or RRM2 to amplify proliferative signaling, bridging virological and transcriptomic drivers of hepatocarcinogenesis. The genomic instability of poor-prognosis genes (e.g., MKI67, CENPF, CDKN2A), which exhibit frequent mutations and copy number amplifications, suggesting their dysregulation is driven by structural genomic changes. Conversely, favorable prognostic genes (GREB1, PLVAP, GSTA4) show lower mutation rates and distinct copy number profiles, supporting their role as protective factors. These findings align with our earlier survival data, where amplification-driven oncogenes like MKI67 and CDKN2A were linked to aggressive disease, while PLVAP and GSTA4 correlated with improved outcomes. The immune correlation analysis underscores a paradoxical relationship: core proliferative genes (STMN1, TOP2A, MKI67, etc.) show strong positive associations with regulatory immune subsets (B cells, Tr1, nTreg, dendritic cells) but negative correlations with cytotoxic populations (naïve CD4 + T cells, NK cells). This suggests a tumor-permissive microenvironment [ 54 ] where proliferative signaling coexists with immunosuppressive mechanisms [ 55 ], potentially driven by regulatory immune cells that dampen antitumor responses. For example, the association of MKI67 and RRM2 with dendritic cells may reflect tumor-driven dendritic cell dysfunction, impairing antigen presentation. Drug sensitivity data further contextualize these genes as therapeutic targets. Resistance-linked genes (SPP1, PRKAA2, ANLN) may represent barriers to conventional therapies, while sensitivity-associated genes (SPC24, STMN1, ASPM) highlight opportunities for precision therapy [ 56 ]. Notably, STMNI, MKI67, ASF1B and PRAGD linked to both poor prognosis and drug sensitivity suggests its potential as a biomarker for patient stratification. While our multi-omics approach provides valuable insights into HCC biology, several limitations must be acknowledged. First, the cohort’s limited pathological stage diversity and modest sample size may restrict the generalizability of our findings, particularly for rare genomic alterations or HBV-negative HCC subsets. Second, while transcriptomic-proteomic concordance was observed for most genes, the mechanistic basis for exceptions (e.g., GSTA4, PRKAA2) remains speculative without functional validation of post-transcriptional regulation. Third, our immune correlation analysis, though revealing paradoxical associations between proliferative genes and regulatory immune cells, lacks spatial or single-cell resolution to confirm causal interactions within the tumor microenvironment. Fourth, the reliance on bulk transcriptomic data may obscure tumor heterogeneity, masking subclonal genomic events or stromal contributions. Finally, while drug sensitivity correlations highlight therapeutic opportunities, in vitro or preclinical validation is needed to confirm these associations and define actionable thresholds for clinical translation. To translate these findings into clinical impact, validation in larger, multi-etiology cohorts is essential to account for HCC heterogeneity driven by diverse risk factors (e.g., HCV, NAFLD). Functional studies, including CRISPR-based screens, should elucidate causal links between HBV mutations and host gene dysregulation, while spatial transcriptomics and single-cell analyses could resolve microenvironmental complexity, clarifying interactions between tumor cells and immune subsets. Preclinical models must test combinatorial therapies, such as cell cycle inhibitors (CDK1) paired with immune checkpoint blockers, to disrupt both proliferative and immunosuppressive networks. Finally, integrating molecular profiling with mutation-based biomarkers (e.g., PreS variants) may refine HCC subtyping, enabling risk-stratified therapeutic approaches and advancing precision oncology in this heterogeneous disease. Conclusions This multi-omics study identifies key molecular drivers of HCC progression, integrating genomic, proteomic, and clinical data to reveal actionable biomarkers and therapeutic insights. Prognostic genes (AURKA, MKI67, CDK1) linked to poor survival and genomic instability contrast with protective markers (PLVAP, GSTA4), highlighting divergent pathways in HCC aggressiveness. HBV mutations (e.g., PreS, A1762T/G1764A) synergize with host genes (ASPM, RRM2) to drive proliferative and metabolic dysregulation, particularly in genotype C-associated HCC. Despite limited pathological stage variation, robust cell cycle/EMT pathway activation underscores the need for molecular subtyping to predict tumor behavior. Proliferative genes paradoxically correlate with immunosuppressive immune subsets (B cells, nTregs), suggesting dual targeting of oncogenic and immune pathways. Drug sensitivity signatures (SPC24, STMN1, ASPM) and resistance markers (SPP1, PRKAA2) further guide precision therapy. Declarations Data availability The datasets analyzed during the current study are available on Geo datasets with ID numbers GSE135631 and GSE184733. All other data analyzed are properly cited and links are provided in the methodology section. Competing interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding None. Authors’ contributions Blaise Angoma-Sindani performed the critical analysis and review, including editing, recommendations, and suggestions, and was responsible for funding acquisition. Tat'y Mwata-Velu contributed to the critical review and editing of the analysis, provided insightful ideas, and supervised the study. Richard Mavuela Maniansa participated in the critical review and editing of the analysis. Rachel Milomba Velu contributed to the critical review and editing of the analysis. Blaise Tshibangu-Mbuebue was involved in the critical review and editing of the analysis. Ayesha Qasim conducted the analysis and participated in the critical review and editing. Emmanuel M. Migabo contributed to the critical review and editing of the analysis and supervised the study. Acknowledgment None. References Chen, H.-J.; Hu, M.-H.; Xu, F.-G.; Xu, H.-J.; She, J.-J.; Xia, H.-P. Understanding the inflammation-cancer transformation in the development of primary liver cancer. 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Gut 2014 , 63 , 844-855. Wen, J.; Wang, X.; Yang, G.; Zheng, J. Aurka promotes renal cell carcinoma progression via regulation of ccnb1 transcription. Heliyon 2024 , 10 . Chen, X.; Ma, J.; Wang, X.a.; Zi, T.; Qian, D.; Li, C.; Xu, C. Ccnb1 and aurka are critical genes for prostate cancer progression and castration-resistant prostate cancer resistant to vinblastine. Frontiers in Endocrinology 2022 , 13 , 1106175. Tosun, H.; Karadas, H.; Ceylan, H. Bioinformatics‐based identification of hepatocellular carcinoma‐associated hub genes and assessment of the restorative effect of tannic acid in rat liver exposed to monosodium glutamate. Cancer Medicine 2024 , 13 , e7404. Liu, S.; Zhang, H.; Gu, C.; Yin, J.; He, Y.; Xie, J.; Cao, G. Associations between hepatitis b virus mutations and the risk of hepatocellular carcinoma: A meta-analysis. Journal of the National Cancer Institute 2009 , 101 , 1066-1082. Mierke, C.T. Phenotypic heterogeneity, bidirectionality, universal cues, plasticity, mechanics, and the tumor microenvironment drive cancer metastasis. Biomolecules 2024 , 14 , 184. Jarrett, A.M.; Lima, E.A.; Hormuth, D.A.; McKenna, M.T.; Feng, X.; Ekrut, D.A.; Resende, A.C.M.; Brock, A.; Yankeelov, T.E. Mathematical models of tumor cell proliferation: A review of the literature. Expert review of anticancer therapy 2018 , 18 , 1271-1286. Weidner, N.; Semple, J.P.; Welch, W.R.; Folkman, J. Tumor angiogenesis and metastasis—correlation in invasive breast carcinoma. New England Journal of Medicine 1991 , 324 , 1-8. Gress, D.M.; Edge, S.B.; Greene, F.L.; Washington, M.K.; Asare, E.A.; Brierley, J.D.; Byrd, D.R.; Compton, C.C.; Jessup, J.M.; Winchester, D.P. Principles of cancer staging. AJCC cancer staging manual 2017 , 8 , 3-30. Marino, F.Z.; Bianco, R.; Accardo, M.; Ronchi, A.; Cozzolino, I.; Morgillo, F.; Rossi, G.; Franco, R. Molecular heterogeneity in lung cancer: From mechanisms of origin to clinical implications. International journal of medical sciences 2019 , 16 , 981. Sun, D.-Z.; Wei, P.-K.; Yue, X.-Q. Xiaotan sanjie decoction normalizes tumor permissive microenvironment in gastric cancer. Oncology Reports 2023 , 49 , 74. Widen, K.; Mozaffari, F.; Choudhury, A.; Mellstedt, H. Overcoming immunosuppressive mechanisms. Ann Oncol 2008 , 19 , vii241-247. Shin, S.H.; Bode, A.M.; Dong, Z. Precision medicine: The foundation of future cancer therapeutics. Npj precision oncology 2017 , 1 , 12. Additional Declarations The authors declare no competing interests. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7040990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480361524,"identity":"e3abda76-7236-444f-9ab1-75a70ff84637","order_by":0,"name":"Blaise Angoma-Sindani","email":"","orcid":"","institution":"Departments of Electrical Engineering and Computer Science, Institut Supérieur Pédagogique Technique de Kinshasa","correspondingAuthor":false,"prefix":"","firstName":"Blaise","middleName":"","lastName":"Angoma-Sindani","suffix":""},{"id":480361525,"identity":"661e6bdc-e427-4aa0-b862-ea9a672e6065","order_by":1,"name":"Tat'y Mwata-Velu","email":"","orcid":"","institution":"2Centro de Investigación en Computación, Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Tat'y","middleName":"","lastName":"Mwata-Velu","suffix":""},{"id":480361526,"identity":"cedd8d70-0bb9-4279-a66c-a1351980d9c9","order_by":2,"name":"Richard Mavuela Maniansa","email":"","orcid":"","institution":"School of Medicine, Cavendish University Zambia","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"Mavuela","lastName":"Maniansa","suffix":""},{"id":480361527,"identity":"29d07e44-ba9e-4464-9500-f48937428d6c","order_by":3,"name":"Rachel Milomba Velu","email":"","orcid":"","institution":"Instituto Nacional de Astrofísica, Óptica y Electrónica","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"Milomba","lastName":"Velu","suffix":""},{"id":480361528,"identity":"019dc4f4-d894-4022-bab0-28a026932f49","order_by":4,"name":"Blaise Tshibangu-Mbuebue","email":"","orcid":"","institution":"Instituto Nacional de Astrofísica, Óptica y Electrónica","correspondingAuthor":false,"prefix":"","firstName":"Blaise","middleName":"","lastName":"Tshibangu-Mbuebue","suffix":""},{"id":480361529,"identity":"8e6218b7-1339-406c-ba7e-0f7c176a5f40","order_by":5,"name":"Ayesha Qasim","email":"","orcid":"","institution":"University of the Punjab, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Qasim","suffix":""},{"id":480361530,"identity":"43eb1157-9c54-4446-96ca-7ad87bcf6892","order_by":6,"name":"Emmanuel M. Migabo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACZjBiYOAnXYtkGwNjAym6GBgMjhGrRbed/eLngoptcsb3e8wfMNTYMci3H8CvxewwT7H0jDO3jc2O8Rg2MBxLZmDsSSCoJUGat+124jawFrYDQGcS1pL8G6ilfnMbSMu/Awxs/A8IaWE/BrIlwYANqIWx7QADjwRhW9isgX4xnHEsrXBGYl8yj4QEIVvOH398u6Ditjx/8+ENHz58s5OT7ydgCwMDjwGCDVTMQ0g9ELATcMcoGAWjYBSMAgDVWj/iqwHlMQAAAABJRU5ErkJggg==","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Emmanuel","middleName":"M.","lastName":"Migabo","suffix":""}],"badges":[],"createdAt":"2025-07-03 19:18:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7040990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7040990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86127919,"identity":"2f3df692-a1cf-44b7-af69-e34c8519b707","added_by":"auto","created_at":"2025-07-07 05:55:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":369519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical context and differential gene expression overlap in hepatocellular carcinoma (HCC).\u003c/strong\u003e (A) A representative clinical case of a male patient diagnosed with hepatocellular carcinoma (HCC). (B) Venn diagram illustrating the overlap of differentially expressed genes (DEGs) between two independent HCC transcriptomic datasets sourced from the Gene Expression Omnibus (GEO). (C, D) Volcano plots showing the distribution of DEGs in each of the two transcriptome datasets. Each plot visualizes log2 fold change versus statistical significance (–log10 p-value), highlighting upregulated and downregulated genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/d1b408eebc0e76125f805160.png"},{"id":86127921,"identity":"334b7dd1-b6fb-4543-a003-553044bb1c5f","added_by":"auto","created_at":"2025-07-07 05:55:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein expression and clinical relevance of candidate genes in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Protein levels of differentially expressed genes showed general concordance with mRNA expression, except for GSTA4 and PRKAA2 (downregulated) and three unchanged genes. (B) High expression of most genes was linked to poor overall survival, while GREB1, PLVAP, and GSTA4 were associated with better outcomes. (C) Most genes were upregulated in HBV-positive patients, except PLVAP, which was elevated in HBV-negative cases.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/18967d3e6545a0d574598c97.png"},{"id":86127924,"identity":"58cf7baa-c1eb-460a-bb99-802281b40873","added_by":"auto","created_at":"2025-07-07 05:55:37","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":912052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathways and gene activation analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap showing gene expression intensity across samples, stratified by pathological tumor stage, with minimal differential expression observed across stages. (B) Pathway activation matrix showing the proportion of samples in which specific genes activate (red) or inhibit (blue) canonical pathways such as Apoptosis, Cell Cycle (A: Activation, I: Inhibition), EMT, Hormone signaling, and RAS/MAPK. Genes such as TOP2A, MKI67, and CDK1 are prominently activating proliferative and migratory pathways. (C) Stage analysis for downregulated genes and (D) PLVAP-specific pathway modulation showing consistent inhibition (blue) of apoptosis and cell cycle pathways across analyzed samples, indicating a potential tumor-suppressive function.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/d44c488e9fe6e8d820f8463d.jpeg"},{"id":86127922,"identity":"f0881546-216b-4098-9b46-7039adb12bc7","added_by":"auto","created_at":"2025-07-07 05:55:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation and copy number alteration profiles of prognosis-associated genes in HCC.\u003c/strong\u003e (A, C) Mutation frequency and types for genes linked to poor (top) and good (bottom) prognosis in HCC samples. (B, D) Bubble plots showing significant associations of these genes with amplification and heterozygous deletion events across patient cohorts.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/6df26a525a50dc7505a0a8c3.png"},{"id":86127929,"identity":"31f9b09e-0a9e-4a62-b312-8e97a5f38a36","added_by":"auto","created_at":"2025-07-07 05:55:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":770358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of Gene Expression with Immune Cell Infiltration and Drug Sensitivity. \u003c/strong\u003ePanel A: Heatmap showing correlations between gene expression and immune cell infiltration. A core set of genes positively correlates with B cells, Tr1, and nTreg cells, and negatively with naïve CD4+ T cells, monocytes, and NK cells. Panel B: Correlation matrix between gene expression and GDSC drug sensitivity. Red bubbles indicate positive correlations (resistance), blue bubbles indicate negative correlations (sensitivity). SPP1, PRKAA2, and ANLN associate with resistance, while SPC24, STMN1, and ASPM indicate sensitivity.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/4125d2ddb5dc89df2f924a65.png"},{"id":86128795,"identity":"7bf92d8c-4565-4d34-b2aa-3d6e6c1bbefe","added_by":"auto","created_at":"2025-07-07 06:19:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3814640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7040990/v1/36e35536-1dc2-4d78-a9dc-f60329b09119.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMulti-Omics profiling of hepatocellular carcinoma reveals prognostic biomarkers, HBV-driven oncogenic networks, and therapeutic vulnerabilities in genomic-immune crosstalk\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer remains one of the most prevalent malignancies and ranks as the third leading cause of cancer-related deaths worldwide [1,2]. Hepatocellular carcinoma (HCC), the most common form of primary liver cancer in adults, is linked to various risk factors including hepatitis B and C infections, non-alcoholic steatohepatitis (NASH), genetic predispositions, and lifestyle habits such as smoking, alcohol consumption, and poor nutrition [3,4]. Among these, nutrition stands out as a significant modifiable factor influencing HCC development and progression [5-7]. Epidemiological evidence indicates that specific dietary components or their metabolites can induce liver inflammation, fibrosis, and carcinogenesis [7,8], while also affecting treatment outcomes. Over recent decades, rapid globalization and shifting lifestyles have led to changes in dietary habits, with increased consumption of processed foods and additives [9,10]. One common additive, monosodium glutamate (MSG), though classified as safe by regulatory bodies [11], has been implicated in various health disorders, including metabolic and liver-related diseases. Previous research by our team demonstrated MSG\u0026apos;s impact on gene expression in cortical tissues [12], underscoring the need to understand its molecular effects in HCC. Current chemotherapeutic strategies for HCC are often limited by toxicity and drug resistance [13,14]. This has fueled interest in naturally derived compounds with anticancer potential [15,16]. Advancements in herbal medicine [17-22], though reported in other conditions, have not been specifically explored in HBV-driven HCC and could be considered in future studies. Interestingly, while low testosterone a key hormone is associated with diabetes [23], elevated testosterone levels have been reported to contribute to the development of HCC[24], which we are interested in future to address. A comprehensive understanding of HCC and other cancers [25,26] requires dissecting its complex molecular landscape, which is shaped by alterations in gene expression, mutational burdens, and immune microenvironment dynamics. Systems biology approaches, which integrate multi-omics data including transcriptomics, genomics, and immunoprofiling, are increasingly being used to uncover the key regulators and pathways involved in HCC progression [27]. Specific gene mutations and expression patterns are known to drive oncogenic pathways [28], modulate tumor microenvironments [29], and influence immune cell infiltration [30]. TP53 and its regulatory genes [25], along with FABP4, ADK, and NM23 [31-33] which are important in other cancers and diseases such as diabetes [34]\u0026nbsp; also appear to be uniquely relevant in this cancer. However, their roles still need to be further investigated in HBV-driven HCC.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThese changes not only drive tumorigenesis but also influence patient responses to immunotherapy and targeted treatments. Furthermore, pathway dysregulation, often stemming from cumulative genetic aberrations, can serve as potential biomarkers for prognosis and therapeutic targeting [35]. Advances in computational analysis have also enabled the identification of druggable targets and the prediction of responses to therapeutic compounds, including natural bioactive agents which have shown antitumor effects in HCC models [36].\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed transcriptomic datasets to identify differentially expressed genes in HCC, performed pathway enrichment and mutation profiling, explored immune landscape alterations, conducted survival analysis, and evaluated potential therapeutic agents to uncover comprehensive molecular insights into HCC biology.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTranscriptomic data acquisition and differential expression analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO) under the accessions GSE135631 and GSE184733. Raw RNA-seq count data were analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html) with the DESeq2 package (version 1.38.3) in R version 4.2.2, along with associated packages Biobase 2.58.0, GEOquery 2.66.0, and limma 3.54.0. Gene annotation was performed using the GRCh38.p13 reference genome. Samples were categorized into cancer and control groups using predefined binary encodings. Lowly expressed genes were filtered out to retain those with adequate read coverage across samples. Differential gene expression was evaluated using the Wald test, applying an FDR-adjusted p-value cutoff \u0026lt; 0.01 and a fold change threshold \u0026ge; 1.5. Visualization techniques included p-value histograms, MA (MD) plots, dispersion estimates, normalized boxplots, volcano plots, and UMAP for unsupervised clustering. Venn diagrams were generated to identify overlapping DEGs between both datasets, ensuring robustness of the identified gene signatures. All analyses were carried out using a GEO2R (R) script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProtein expression and survival analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein expression profiles were assessed through UALCAN [37] using the CPATC https://proteomics.cancer.gov/ HCC dataset. For survival analysis, we used the Kaplan\u0026ndash;Meier Plotter, stratifying patients based on median expression values. Significance was determined using a p-value cutoff of 0.05. The analysis aimed to evaluate the prognostic relevance of key DEGs identified in HCC transcriptomic datasets.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHBV-related gene expression analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess HBV-related gene expression patterns, data were mined from OncoDB [38], which provides curated expression datasets specific to hepatitis B virus-associated cancers. These were compared to expression trends in HCC datasets to evaluate virus-associated alterations in gene regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenomic alteration and pathway activity analysis via GSCA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic datasets were analyzed using the Gene Set Cancer Analysis (GSCA) platform [39]. GSCA evaluated gene set variation analysis (GSVA) scores among groups using the Wilcoxon test (when number of stages = 2) and ANOVA (when number of stages \u0026gt; 2). Tumor stages were defined across different staging systems. To assess trends in expression across stages, GSCA applied the Mann\u0026ndash;Kendall Trend Test. Given the small number of stages (n=4), trend test p-values were treated as suggestive rather than definitive. For instance, a p-value of 0.09 suggests a consistent increase or decrease in gene expression across stages, although not statistically significant under conventional thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePathway activity profiling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway activity was analyzed using the Expression \u0026amp; Pathway Activity module of GSCA. This module compares gene expression across pathway activity groups (activation vs inhibition), which were defined by median-centered pathway scores derived from Reverse Phase Protein Array (RPPA) data. RPPA data (sourced from the TCPA database) covered 7876 samples from 32 TCGA cancer types, profiling 10 cancer-associated pathways: TSC/mTOR, RTK, RAS/MAPK, PI3K/AKT, Hormone ER, Hormone AR, EMT, DNA Damage Response, Cell Cycle, and Apoptosis. RPPA normalization involved centering each protein component by its median and scaling by its standard deviation across all samples. The Pathway Activity Score (PAS) was calculated as the sum of positive regulatory components minus the sum of negative components for each pathway, as described by Akbani et al [40] Samples were dichotomized into high and low expression groups based on the median expression of each gene. The difference in PAS between groups was assessed by Student\u0026rsquo;s t-test, with FDR \u0026le; 0.05 considered significant. Genes for which PAS(high expression) \u0026gt; PAS(low expression) were considered activators of the pathway; otherwise, they were inferred as inhibitors, following the methodology of Ye et al [41].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMutation and copy number variation (CNV) Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle nucleotide variation (SNV) data for 424 HCC, TCGA samples were analysed through GSCA. Seven deleterious mutation types were included: Missense, Nonsense, Frame_Shift_Ins, Splice_Site, Frame_Shift_Del, In_Frame_Del, and In_Frame_Ins. Non-deleterious variants included Silent, Intron, 3\u0026rsquo;/5\u0026rsquo; UTR, and Flank mutations. Copy number variation (CNV) data for TCGA samples were analyzed via GISTIC2.0, which classifies CNV events based on GISTIC scores:\u003c/p\u003e\n\u003cp\u003e-2: Deep deletion (likely homozygous loss)\u003c/p\u003e\n\u003cp\u003e-1: Shallow deletion (likely heterozygous loss)\u003c/p\u003e\n\u003cp\u003e0: Diploid\u003c/p\u003e\n\u003cp\u003e1: Low-level gain\u003c/p\u003e\n\u003cp\u003e2: High-level amplification\u003c/p\u003e\n\u003cp\u003eCNV alterations were summarized for selected genes across cancer types using this standardized classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImmune infiltration analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between gene expression and immune cell infiltration was evaluated through GSCA\u0026apos;s Immune Infiltration \u0026amp; mRNA Expression module, utilizing Spearman correlation analysis. Immune infiltration estimates for 24 immune cell types were derived using ImmuCellAI, which leverages a gene signature-based method. Overlap between input genes and immune gene signatures was carefully avoided to prevent bias in correlation estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDrug sensitivity correlation analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore therapeutic relevance, drug response data (IC50 values of 265 compounds) were retrieved from the Genomics of Drug Sensitivity in Cancer data via GSCA database. These were matched with corresponding mRNA expression profiles across 860 cancer cell lines. Pearson correlation was calculated between gene expression and drug IC50 values. The resulting p-values were corrected for multiple testing using the False Discovery Rate (FDR) method.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn figure 1 the panel A depicts a clinical case involving a male patient diagnosed with hepatocellular carcinoma (HCC), representing the real-world relevance of transcriptomic analysis in liver cancer research. Panel B presents a Venn diagram illustrating the overlap of differentially expressed genes (DEGs) identified between two separate HCC transcriptome datasets obtained from the Gene Expression Omnibus (GEO). The overlap visually demonstrates the shared molecular signatures between the two cohorts, highlighting robust, consistently dysregulated genes associated with HCC pathogenesis. This overlap provides potential biomarkers or therapeutic targets common across patient populations. Volcano plots showing the distribution of DEGs in each of the two transcriptome datasets. These plots provide a comprehensive overview of transcriptomic alterations characterizing HCC in each cohort. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHCC gene signatures: Protein expression and HBV association\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we investigated the protein expression profiles of genes that were both differentially expressed and highly expressed in tumor tissues compared to adjacent normal tissues. With the exception of two genes, GSTA4 and PRKAA2, which were downregulated, and three genes that were not significantly altered, the majority of these genes exhibited elevated protein expression consistent with their mRNA levels (Fig. 2A). This concordance suggests that most of these genes follow a similar pattern at both the transcriptomic and proteomic levels. We then assessed the prognostic relevance of these genes by analyzing their association with overall survival outcomes. Notably, high expression of the following genes was associated with significantly poorer survival: SPINK1, SPP1, TOP2A, ASPM, CENPF, AURKA, PYCR1, MKI67, SQLE, FAM83D, ANLN, PRKAA2, STMN1, CCNB1, ROBO1, CDKN2A, ASF1B, CDK1, RRM2, RRAGD, SPC24, TK1, and KIFC1. In contrast, elevated expression of GREB1, PLVAP, and GSTA4 correlated with improved patient outcomes (Fig. 2B). Finally, to explore whether these gene expression patterns could be influenced by hepatitis B virus (HBV) infection, we analyzed an HCC dataset comprising both HBV-positive and HBV-negative samples. Most genes showed consistent upregulation in HBV-positive patients, indicating a possible link between HBV infection and the expression of these tumor-associated genes. Interestingly, PLVAP was the only gene that exhibited higher expression in HBV-negative samples (Fig. 2C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMultidimensional pathway and gene activation analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between pathological stage and pathway activation, we performed a comparative analysis of gene expression and signaling pathways across samples (Fig. 3). Despite modest differences in pathological tumor stages among the cohort (Fig. 3A), our analysis identified a panel of genes consistently upregulated in association with cell cycle and epithelial\u0026ndash;mesenchymal transition (EMT) pathways. As shown in Figure 3B, several genes such as TOP2A, TK1, MKI67, CDK1, CCNB1, AURKA, and ASPM showed uniform activation patterns across CellCycle_A, CellCycle_I, and EMT_A pathways. These findings reflect a robust upregulation of proliferative and migratory signaling cascades, indicative of aggressive tumor biology. Additionally, PLVAP, although less frequently discussed in cancer pathway literature, showed significant pathway inhibition patterns, particularly for CellCycle_I and Apoptosis_I pathways, reinforcing its potential suppressive role in tumor progression (Fig. 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenomic landscape of prognostic genes in HCC reveals distinct mutation and CNA patterns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 presents the mutation and copy number alteration (CNA) profiles of prognostically significant genes in hepatocellular carcinoma (HCC). In the top panel, genes associated with poor prognosis such as MKI67, ROBO1, CENPF, and CDKN2A exhibit frequent mutations, predominantly missense variants. Bubble plots (B, D) further confirm their significant association with amplification and heterozygous deletion events, indicating CNAs may drive their dysregulation. Conversely, the bottom panel (C) display favorable prognostic genes such as GREB1, PLVAP, and GSTA4, which show lower mutation frequencies. These genes are also associated with distinct CNA patterns, supporting their protective role in HCC. Together, these findings highlight the relevance of genomic alterations in shaping clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene expression with immune cell infiltration and drug sensitivity\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 panel A illustrates the correlation between gene expression and immune cell infiltration, as visualized through a heatmap. A core group of genes including STMN1, FAM83D, SPC25, TK1, ANLN, TOP2A, ASPM, CENPF, ASF1B, AURKA, KIFC1, CCNB2, BIRC5, CDK1, RRM2, MKI67, SP2, PRKAA2, PYCARD, COX20A2, RHOJ, and SQLE demonstrates a strong positive correlation with B cells. These same genes, excluding RHOJ, also correlate positively with Tr1 cells, and all except PYCARD correlate positively with nTreg cells, suggesting their broader association with regulatory immune responses. Additionally, MKI67, RRM2, CDK1, BIRC5, TOP2A, and ANLN show a positive association with dendritic cells. In contrast, a majority of these genes including SPINK1 and PRKAG2\u0026mdash;are negatively correlated with na\u0026iuml;ve CD4+ T cells, CD4+ monocytes, and NK cells, implying a potential immunosuppressive or immune-evasive gene expression profile. Panel B complements this immune correlation landscape by showing a matrix of associations between mRNA expression and drug sensitivity derived from GDSC data. Positive correlations, represented by red bubbles, indicate that higher expression of genes such as SPP1, PRKAA2, and ANLN is associated with drug resistance. Conversely, blue bubbles indicate negative correlations, with genes like SPC24, STMN1, and ASPM showing increased sensitivity to multiple drugs, highlighting their potential as biomarkers for therapeutic responsiveness.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integrative transcriptomic analysis highlights the reproducibility and translational value of identifying DEGs in hepatocellular carcinoma (HCC). Shared DEGs across two independent GEO datasets point to robust molecular perturbations common in HCC, underscoring their potential as universal biomarkers or therapeutic targets. The distinct expression patterns between tumor and non-tumor tissues, visualized through volcano plots, reflect consistent biological divergence. Similar strategies in prior studies have reinforced this approach. One analysis of eight GEO datasets identified 96 common DEGs, while another integrating four datasets uncovered 186 consensus DEGs using standardized normalization and statistical thresholds. These findings support the utility of cross-cohort integration for improving DEG reliability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our results align with these observations, affirming the potential of consistent DEGs to inform HCC pathogenesis and guide further functional and therapeutic investigations. Although numerous basic and clinical studies have attempted to clarify the molecular HCC, progress has been limited by the absence of stable and effective biomarkers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe concordance observed between mRNA and protein levels for most genes underscores the translational relevance of transcriptomic profiling in HCC. Exceptions such as GSTA4 and PRKAA2, which showed discordant downregulation at the protein level, suggest post-transcriptional regulation or protein degradation mechanisms that warrant further investigation. These findings align with broader oncogenic paradigms where dysregulated genes often maintain consistent expression across molecular layers, though exceptions may highlight context-specific regulatory pathways.The prognostic analysis identified 23 genes associated with significantly poorer survival outcomes (e.g., SPINK1, AURKA, CDK1) and three genes (GREB1, PLVAP, GSTA4) linked to improved survival. Notably, genes such as AURKA and CCNB1, which drive cell cycle progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], exhibited strong correlations with reduced survival, consistent with prior studies implicating mitotic deregulation in HCC aggressiveness. Conversely, GSTA4\u0026rsquo;s protective role may reflect its involvement in detoxification pathways, potentially counteracting oxidative stress in tumors. These results corroborate published survival data [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] (e.g., AURKA HR\u0026thinsp;=\u0026thinsp;1.77, CCNB2 HR\u0026thinsp;=\u0026thinsp;1.91) and extend the evidence for genes like F9 and CYP2E1 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], whose downregulation was previously tied to better outcomes.\u003c/p\u003e\u003cp\u003eA key finding is the association between HBV infection and elevated expression of most tumor-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This aligns with the published meta-analysis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] demonstrating that HBV mutations (e.g., PreS, A1762T/G1764A) significantly increase HCC risk, particularly in genotype C or HBeAg-positive patients. The progressive accumulation of these mutations during chronic HBV infection may drive oncogenic pathways reflected in our dataset, such as proliferation (e.g., TOP2A, MKI67) and metabolic reprogramming (e.g., PYCR1, SQLE). PLVAP\u0026rsquo;s unique upregulation in HBV-negative samples suggests divergent mechanisms in non-viral HCC, possibly involving angiogenesis or immune microenvironment alterations. The published meta-analysis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] further emphasizes the clinical utility of mutation-based biomarkers (e.g., PreS mutations with \u0026gt;\u0026thinsp;80% specificity for HCC prediction). Our findings complement this by proposing that expression patterns of HBV-associated genes (e.g., ASPM, RRM2) could refine risk stratification when combined with mutational profiling. However, discrepancies such as the lack of prognostic relevance for precore mutations (G1896A/C1858T) in the meta-analysis highlight the complexity of HBV-driven hepatocarcinogenesis, where specific mutations may exert indirect effects through host gene dysregulation rather than direct oncogenic activity.\u003c/p\u003e\u003cp\u003eOur investigation into the relationship between pathological tumor stage and pathway activation revealed critical insights into the molecular drivers of HCC progression. Despite limited heterogeneity in pathological staging across the cohort, we observed a striking upregulation of genes associated with cell cycle regulation and epithelial\u0026ndash;mesenchymal transition (EMT) pathways, including TOP2A, TK1, MKI67, CDK1, CCNB1, AURKA, and ASPM. These findings suggest that even in tumors with comparable clinical stages, molecular heterogeneity marked by proliferative and migratory pathway activation may underpin aggressive biological behavior [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The consistent activation of these pathways aligns with prior studies implicating cell cycle dysregulation (e.g., AURKA, CCNB1) and EMT as hallmarks of advanced HCC, driving tumor proliferation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], invasiveness, and metastasis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Notably, many of these genes (TOP2A, MKI67, CDK1) were previously linked to poor prognosis in our survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), reinforcing their dual role as prognostic biomarkers and functional mediators of tumor aggressiveness.\u003c/p\u003e\u003cp\u003eIntriguingly, PLVAP emerged as a key outlier, showing significant inhibition of cell cycle (CellCycle_I) and apoptosis-related (Apoptosis_I) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This observation corroborates our earlier finding that elevated PLVAP expression correlates with improved survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting a tumor-suppressive role potentially mediated through restraint of proliferative signaling or promotion of apoptotic sensitivity. While PLVAP is not commonly highlighted in cancer pathway, its consistent association with favorable outcomes across multiple analyses underscores its potential as a novel therapeutic target or biomarker for less aggressive HCC subsets. The dissociation between modest pathological stage variation and robust pathway activation raises important questions about the limitations of traditional staging systems [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] in capturing molecular heterogeneity [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Our data imply that molecular profiling particularly of cell cycle and EMT pathways could complement clinical staging to improve risk stratification. This is especially relevant in HBV-associated HCC, where viral-driven mutations (e.g., PreS, A1762T/G1764A) identified in the published meta-analysis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] may mechanistically contribute to the observed pathway dysregulation. For instance, HBV genotype C\u0026rsquo;s strong association with PreS mutations and HCC risk could synergize with host genes like ASPM or RRM2 to amplify proliferative signaling, bridging virological and transcriptomic drivers of hepatocarcinogenesis.\u003c/p\u003e\u003cp\u003eThe genomic instability of poor-prognosis genes (e.g., MKI67, CENPF, CDKN2A), which exhibit frequent mutations and copy number amplifications, suggesting their dysregulation is driven by structural genomic changes. Conversely, favorable prognostic genes (GREB1, PLVAP, GSTA4) show lower mutation rates and distinct copy number profiles, supporting their role as protective factors. These findings align with our earlier survival data, where amplification-driven oncogenes like MKI67 and CDKN2A were linked to aggressive disease, while PLVAP and GSTA4 correlated with improved outcomes. The immune correlation analysis underscores a paradoxical relationship: core proliferative genes (STMN1, TOP2A, MKI67, etc.) show strong positive associations with regulatory immune subsets (B cells, Tr1, nTreg, dendritic cells) but negative correlations with cytotoxic populations (na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells, NK cells). This suggests a tumor-permissive microenvironment [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] where proliferative signaling coexists with immunosuppressive mechanisms [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], potentially driven by regulatory immune cells that dampen antitumor responses. For example, the association of MKI67 and RRM2 with dendritic cells may reflect tumor-driven dendritic cell dysfunction, impairing antigen presentation. Drug sensitivity data further contextualize these genes as therapeutic targets. Resistance-linked genes (SPP1, PRKAA2, ANLN) may represent barriers to conventional therapies, while sensitivity-associated genes (SPC24, STMN1, ASPM) highlight opportunities for precision therapy [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Notably, STMNI, MKI67, ASF1B and PRAGD linked to both poor prognosis and drug sensitivity suggests its potential as a biomarker for patient stratification.\u003c/p\u003e\u003cp\u003eWhile our multi-omics approach provides valuable insights into HCC biology, several limitations must be acknowledged. First, the cohort\u0026rsquo;s limited pathological stage diversity and modest sample size may restrict the generalizability of our findings, particularly for rare genomic alterations or HBV-negative HCC subsets. Second, while transcriptomic-proteomic concordance was observed for most genes, the mechanistic basis for exceptions (e.g., GSTA4, PRKAA2) remains speculative without functional validation of post-transcriptional regulation. Third, our immune correlation analysis, though revealing paradoxical associations between proliferative genes and regulatory immune cells, lacks spatial or single-cell resolution to confirm causal interactions within the tumor microenvironment. Fourth, the reliance on bulk transcriptomic data may obscure tumor heterogeneity, masking subclonal genomic events or stromal contributions. Finally, while drug sensitivity correlations highlight therapeutic opportunities, in vitro or preclinical validation is needed to confirm these associations and define actionable thresholds for clinical translation. To translate these findings into clinical impact, validation in larger, multi-etiology cohorts is essential to account for HCC heterogeneity driven by diverse risk factors (e.g., HCV, NAFLD). Functional studies, including CRISPR-based screens, should elucidate causal links between HBV mutations and host gene dysregulation, while spatial transcriptomics and single-cell analyses could resolve microenvironmental complexity, clarifying interactions between tumor cells and immune subsets. Preclinical models must test combinatorial therapies, such as cell cycle inhibitors (CDK1) paired with immune checkpoint blockers, to disrupt both proliferative and immunosuppressive networks. Finally, integrating molecular profiling with mutation-based biomarkers (e.g., PreS variants) may refine HCC subtyping, enabling risk-stratified therapeutic approaches and advancing precision oncology in this heterogeneous disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis multi-omics study identifies key molecular drivers of HCC progression, integrating genomic, proteomic, and clinical data to reveal actionable biomarkers and therapeutic insights. Prognostic genes (AURKA, MKI67, CDK1) linked to poor survival and genomic instability contrast with protective markers (PLVAP, GSTA4), highlighting divergent pathways in HCC aggressiveness. HBV mutations (e.g., PreS, A1762T/G1764A) synergize with host genes (ASPM, RRM2) to drive proliferative and metabolic dysregulation, particularly in genotype C-associated HCC. Despite limited pathological stage variation, robust cell cycle/EMT pathway activation underscores the need for molecular subtyping to predict tumor behavior. Proliferative genes paradoxically correlate with immunosuppressive immune subsets (B cells, nTregs), suggesting dual targeting of oncogenic and immune pathways. Drug sensitivity signatures (SPC24, STMN1, ASPM) and resistance markers (SPP1, PRKAA2) further guide precision therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available on Geo datasets with ID numbers GSE135631 and GSE184733. All other data analyzed are properly cited and links are provided in the methodology section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlaise Angoma-Sindani performed the critical analysis and review, including editing, recommendations, and suggestions, and was responsible for funding acquisition. Tat\u0026apos;y Mwata-Velu contributed to the critical review and editing of the analysis, provided insightful ideas, and supervised the study. Richard Mavuela Maniansa participated in the critical review and editing of the analysis. Rachel Milomba Velu contributed to the critical review and editing of the analysis. Blaise Tshibangu-Mbuebue was involved in the critical review and editing of the analysis. Ayesha Qasim conducted the analysis and participated in the critical review and editing. Emmanuel M. Migabo contributed to the critical review and editing of the analysis and supervised the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen, H.-J.; Hu, M.-H.; Xu, F.-G.; Xu, H.-J.; She, J.-J.; Xia, H.-P. Understanding the inflammation-cancer transformation in the development of primary liver cancer. \u003cem\u003eHepatoma Research \u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e4\u003c/em\u003e, N/A-N/A.\u003c/li\u003e\n\u003cli\u003eCeylan, H. 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Precision medicine: The foundation of future cancer therapeutics. \u003cem\u003eNpj precision oncology \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e1\u003c/em\u003e, 12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institut Supérieur Pédagogique Technique de Kinshasa","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, Multi-omics profiling, Hepatitis B virus, Prognostic biomarkers, Therapeutic targets","lastPublishedDoi":"10.21203/rs.3.rs-7040990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7040990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Hepatocellular carcinoma (HCC) remains a molecularly heterogeneous malignancy with limited therapeutic biomarkers. While transcriptomic studies have identified dysregulated genes, their prognostic relevance, proteomic concordance, and interactions with hepatitis B virus (HBV) mutations remain underexplored.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We integrated multi-omics analyses of two independent HCC cohorts (GEO datasets), proteomic profiling, survival data, HBV mutation associations, immune cell infiltration, drug sensitivity (GDSC), and genomic alteration patterns to define drivers of HCC progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified 23 genes including AURKA, CDK1, MKI67 linked to poor survival and genomic instability, and three protective genes (PLVAP, GSTA4, GREB1). HBV mutations (PreS, A1762T/G1764A) correlated with elevated expression of proliferative (TOP2A, RRM2) and metabolic (SQLE) genes, particularly in genotype C HCC. Despite minimal pathological stage variation, tumors exhibited robust cell cycle/EMT pathway activation (ASPM, CCNB1), highlighting molecular heterogeneity. Proliferative genes paradoxically associated with regulatory immune subsets (B cells, nTregs) and immunosuppression. Drug sensitivity analysis revealed ASPM and STMN1 as therapeutic vulnerabilities, while SPP1 and PRKAA2 marked resistance. Genomic profiling confirmed frequent mutations/CNAs in poor-prognosis genes (MKI67, CDKN2A) and stability in protective genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThis study establishes a multi-omics framework linking HBV-driven oncogenesis, genomic instability, and immune evasion to HCC progression. Prognostic signatures and pathway activation patterns advocate for molecular subtyping to complement clinical staging. The dual association of proliferative genes with immune suppression and drug sensitivity highlights opportunities for combinatorial therapies targeting oncogenic drivers (CDK1, ASPM) and immune checkpoints. These findings advance precision oncology strategies in HBV-associated HCC.\u003c/p\u003e","manuscriptTitle":"Multi-Omics profiling of hepatocellular carcinoma reveals prognostic biomarkers, HBV-driven oncogenic networks, and therapeutic vulnerabilities in genomic-immune crosstalk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 05:55:32","doi":"10.21203/rs.3.rs-7040990/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":"8147d00a-4d10-4de6-b4e4-27a844f3a01c","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51014413,"name":"Computational Biology"}],"tags":[],"updatedAt":"2025-07-07T05:55:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-07 05:55:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7040990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7040990","identity":"rs-7040990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}