Unique Extracellular Matrix Remodeling in Hepatocellular Carcinoma: TP53 GOF- SP1-ADAM9 Axis as a Prognostic and Therapeutic Target

Unique Extracellular Matrix Remodeling in Hepatocellular Carcinoma: TP53 GOF- SP1-ADAM9 Axis as a Prognostic and Therapeutic Target | 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 Unique Extracellular Matrix Remodeling in Hepatocellular Carcinoma: TP53 GOF- SP1-ADAM9 Axis as a Prognostic and Therapeutic Target Ke xin Jiang, Haoqi Pan, Yiyin Zhang, Xuqiu Shen, Yihan Chai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8749587/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) is the predominant form of liver cancer, necessitating improved prognostic models and therapeutic strategies. While TP53 mutations are established as markers of poor prognosis in HCC, their specific role in extracellular matrix (ECM) remodeling within the tumor microenvironment remains unclear, despite ECM dynamics being critical for tumor progression and metastasis. This study aims to elucidate the relationship between TP53 mutations and ECM remodeling to identify novel prognostic and therapeutic targets. Methods Comprehensive bioinformatics analyses were performed using The Cancer Genome Atlas (TCGA) dataset. Single-sample gene set enrichment analysis (ssGSEA) was used to assess ECM content, while LASSO regression and Cox proportional hazards modeling constructed an ECM-related prognostic signature. Functional validation included RT-qPCR, Western blotting, and in vitro gain/loss-of-function experiments to explore the TP53-SP1-ADAM9 axis. Results ECM content was reduced in HCC tissues compared to normal livers, with 559 ECM-related genes differentially expressed. An 18-gene ECM prognostic signature was established, showing significant association with overall survival. TP53 mutations, particularly gain-of-function (GOF) variants, were linked to upregulated ADAM9 expression. Mechanistically, TP53 regulated ADAM9 via transcriptional factor SP1, with GOF mutations enhancing SP1 binding to the ADAM9 promoter. Conclusions This study identifies a TP53-SP1-ADAM9 axis driving ECM remodeling in HCC. The ECM prognostic signature improves survival prediction, while targeting ADAM9 or SP1 represents a promising therapeutic strategy for TP53-mutant HCC. These findings highlight ECM remodeling as a critical node for prognostic and therapeutic intervention. Hepatocellular carcinoma extracellular matrix ADAM9 TP53 GOF mutations SP1 transcription factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Liver cancer is one of the leading causes of cancer-related deaths worldwide, ranking fifth in 2020 ( 1 ). Hepatocellular carcinoma (HCC) accounts for approximately 90% of all liver cancer cases ( 2 ). While radical surgical resection remains the primary curative treatment for early-stage HCC, fewer than 30% of patients are candidates for surgery. As a result, alternative treatments, including targeted therapies, transarterial chemoembolization, and ablative therapies, have become standard options. Despite the continuous emergence of novel treatment modalities represented by molecular targeted drugs such as tyrosine kinase inhibitors or immune checkpoint inhibitors like anti-programmed cell death ligand 1 (PD1/PDL1) immunotherapy ( 3 , 4 ), the prognosis of patients with advanced HCC remains dismal, with a 5-year survival rate of less than 10% ( 5 ). This underscores the urgent need to explore novel therapeutic strategies to improve treatment outcomes for HCC. Given the tumor's well-recognized heterogeneity, precise prognostic systems that account for both traditional tumor evaluation and intratumoral variability are essential for more accurate clinical predictions. Currently, the prognostic assessment of HCC is mainly based on several commonly used HCC staging systems (such as TNM staging, Cancer of the Liver Italian Program score (CLIP), and Barcelona Clinic Liver Cancer staging (BCLC)) ( 6 ). However, each of these models has its specific limitations. Most staging parameters remain some controversial, are not comprehensive enough, and have limitations in their applicability to certain populations, failing to fully capture the complexity of intratumoral heterogeneity. Thus, there is a pressing need for novel prognostic models that incorporate tumor microenvironment factors to better predict clinical outcomes in HCC. The tumor microenvironment (TME) plays a crucial role in cancer progression and metastasis. It consists of the extracellular matrix (ECM), stromal cells, and biologically active molecules secreted by both tumor and stromal cells, along with vascular and lymphatic systems ( 7 , 8 ). The ECM, a dynamic network of proteins surrounding both normal and cancer cells, can be reprogrammed during tumorigenesis, influencing processes such as tumor cell proliferation, angiogenesis, adhesion, migration, invasion, and metastasis ( 9 ). Tumor cells initially adhere to ECM components and engage with surrounding cells, such as tumor-associated neutrophils, cancer-associated fibroblasts (CAFs), and bone marrow-derived suppressor cells, promoting tumor growth and metastasis ( 10 , 11 ). Recent studies have shown that ECM proteins secreted by tumor cells rather than stromal cells contribute to poor prognosis in cancers like pancreatic ductal adenocarcinoma ( 12 ), highlighting ECM-targeted therapies as a promising avenue for treatment. ADAM9 (A Disintegrin and Metalloproteinase 9) plays a pivotal role in extracellular matrix (ECM) remodeling within the tumor microenvironment. As a key regulator of ECM reconstruction, ADAM9 directly degrades ECM components (such as collagens and laminins) through its proteolytic activity, while indirectly promoting ECM reorganization via activation of matrix metalloproteinases (MMPs). This ECM remodeling not only creates favorable conditions for tumor cell migration and invasion, but may also further drive tumor progression by releasing ECM-sequestered growth factors (e.g., TGF-β and VEGF). Notably, ADAM9-mediated ECM alterations are closely associated with tumor cell phenotypic transitions (such as EMT), forming a vicious cycle that promotes HCC metastasis. Given the central role of ADAM9 in ECM homeostasis, targeting the ADAM9-ECM axis may represent a novel therapeutic strategy for HCC. Approximately 13–48% of HCC cases harbor TP53 mutations, which impair the tumor-suppressive functions of the p53 protein by preventing cell apoptosis and growth arrest ( 13 ). These mutations are frequently associated with high-grade tumors and worse clinical outcomes in HCC ( 14 ). TP53 mutations also affect the tumor microenvironment by regulating the expression of angiogenic factors, which can influence treatment responses. For instance, patients with TP53 mutations undergoing bevacizumab therapy show improved progression-free survival ( 15 , 16 ). TP53 mutation types include loss of function (LOF), dominant negative (DN), and gain-of-function (GOF). Among these, GOF mutations often occur at TP53 gene hotspots, damaging the transcriptional activity of wild-type (WT) p53 to promote the proliferation, metastasis, and drug resistance of cancer cells. Given the impact of TP53 on both tumor biology and the ECM, understanding the interaction between TP53 GOF mutations and ECM components is critical for improving prognosis and therapy in HCC. This study employs multi-omics bioinformatics to construct an ECM-based prognostic signature for HCC while investigating TP53 mutation-mediated ECM remodeling mechanisms. Through a comprehensive analysis of matrisome components and mutational profiles, we identify distinct ECM reorganization patterns associated with TP53 mutation subtypes, particularly gain-of-function variants. Functional network analysis reveals critical pathways linking p53 dysfunction to matrix dysregulation, providing both a clinically applicable prognostic model and mechanistic insights into microenvironmental reprogramming in HCC progression. These findings highlight potential therapeutic targets for TP53-mutant HCC through ECM modulation. 2. Materials and Methods 2.1 Cell Culture The human liver cancer cell lines HepG2, PLC/PRF/5 (PLC), Hep3B, and Huh7 were obtained from the American Type Culture Collection (ATCC, USA). These cell lines are commonly used in liver cancer research. Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 1% antibiotics (penicillin, streptomycin, amphotericin B; Thermo Fisher Scientific) and 10% fetal bovine serum (FBS; BasalMedia). Cells were incubated at 37°C in a humidified atmosphere containing 5% CO₂. All cell lines were regularly tested for mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit (Lonza). 2.2 Plasmid Construction The siRNA targeting GAPDH was designed with the following sequences: ● Forward: GGAGCGAGATCCCTCCAAAAT ● Reverse: GGCTGTTGTCATACTTCTCATGG HepG2 cells were seeded into 6-well plates at a density of 3 × 10⁴ cells per well and incubated at 37°C for 24 hours to allow primary cell adhesion. The lentiviral vector pLKO.1 TRC (Addgene, USA) was used for cloning the siRNA oligos according to the manufacturer's standard protocol. A scrambled-siRNA plasmid was used as a negative control. 2.3 RNA Isolation and Quantitative Real-Time PCR Total RNA was extracted using the SteadyPure Quick RNA Extraction Kit (AG, China) according to the manufacturer’s instructions. RNA was reverse transcribed into cDNA using 1 µL of RNA in a 10 µL reaction volume. The reaction conditions were 37°C for 15 minutes, followed by 85°C for 5 minutes. Reverse transcription was performed using a Bioer GeneTouch PCR instrument (Bioer, China). Real-time PCR was carried out on an Applied Biosystems instrument (Applied Biosystems, USA), and the expression levels of candidate genes were normalized to GAPDH. Primer sequences used in this study are provided in Supplementary Table S1 . 2.4 Western Blotting Western blot analysis was performed as previously described (citation/reference). Briefly, cells were lysed using RIPA buffer (Thermo Fisher) and protein concentrations were quantified using the BCA Protein Assay Kit (Thermo Fisher). Equal amounts of protein (20 µg) were loaded onto 10% SDS-PAGE gels, and proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Millipore). Membranes were blocked with 5% non-fat milk in PBS containing 0.1% Tween-20 (PBST) for 1 hour at room temperature. Primary antibodies were incubated overnight at 4°C, including anti-GAPDH (1:50,000; ProteinTech, 10494-1-AP). The membranes were then incubated with HRP-conjugated secondary antibodies (1:5,000) and detected using enhanced chemiluminescence (ECL) substrate (Bio-Rad). 2.5 Statistical Analysis Data are presented as mean ± standard deviation (SD). GraphPad Prism 6.0 (GraphPad Software, USA) was used for statistical analysis. Differences between two groups were assessed using Student’s t-test, while one-way analysis of variance (ANOVA) was used for comparisons across multiple groups. A p-value < 0.05 was considered statistically significant. 3. Results 3.1 Dynamic Changes of ECM in the Development of Hepatocellular Carcinoma The ECM plays a critical role in shaping the tumor microenvironment, influencing both tumor progression and treatment outcomes. Understanding the dynamic changes in ECM composition during HCC development is essential for identifying molecular mechanisms and potential biomarkers for prognosis. This section aims to analyze ECM alterations in HCC and evaluate their implications for disease progression. The ECM comprises ECM-associated proteins, ECM regulatory proteins, secreted factors, ECM glycoproteins, collagen, and proteoglycans. Considering that most HCC patients have hepatitis B virus (HBV)-induced cirrhosis, which leads to significant collagen accumulation, we first compared the ECM content of normal livers with cirrhotic livers to minimize biases introduced by cirrhosis. Using single-sample gene set enrichment analysis (ssGSEA) on transcriptome data from 371 HCC patients in the TCGA database, we assessed ECM content differences across normal liver tissue, cirrhotic tissue, tumor tissue, and tumor tissue with cirrhosis. Although normal liver tissues may progress to liver cancer via cirrhosis, our analysis revealed no significant differences in extracellular matrix (ECM) content between normal and cirrhotic liver tissues (Fig. 1 A). However, ECM content was markedly reduced in tumor tissue compared to normal liver tissue. To eliminate the impact of individual differences in patients, we also analyzed the ECM content of normal liver tissue and tumor tissue in 50 patients, and found that the ECM content also shows a decreasing trend (Fig. 1 B). Further research on specific ECM components revealed that the major components underwent dynamic changes, especially proteoglycans, which play a key role in providing intrinsic signals needed to coordinate key events in cancer immune regulation (Fig. 1 C, S1). To better understand the molecular mechanisms underlying ECM changes in HCC, we conducted differential gene expression analysis between normal liver and tumor tissues (Fig. 1 D). Using the limma package in R, we identified significantly differentially expressed genes (p < 0.05). Among these, we focused on ECM-related genes and identified 559 ECM molecules with significant changes in HCC (Fig. 1 D, 1 E). This analysis provided a detailed view of the altered ECM landscape in tumor tissues. Additionally, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify 18 ECM molecules most strongly associated with HCC prognosis (Fig. S3). These 18 molecules displayed distinct patterns as risk factors (red) and protective factors (blue), with gray markers indicating no statistical significance (Fig. 1 F). Based on ECM risk scores derived from RNA transcriptome data, we categorized the 371 HCC patients into normal, low-risk, and high-risk groups (Fig. 1 H, S4). This stratification framework demonstrates the potential of ECM-related alterations as prognostic markers. These findings underscore the significant role of ECM remodeling in the progression of HCC and provide a foundation for examining how ECM changes influence patient outcomes. Given the strong association between ECM genes and tumor progression, we next explore their prognostic implications and validate the robustness of ECM-based predictive models. 3.2 ECM Is Associated with the Prognosis of HCC To elucidate the role of ECM in predicting patient outcomes, we investigated how differential ECM gene expression correlates with HCC prognosis. This section focuses on identifying ECM-related molecules as prognostic markers and evaluating their predictive value for overall survival (OS) and disease-free survival (DFS) in HCC patients. Using LASSO regression analysis, we identified 18 ECM molecules most strongly associated with HCC prognosis. These molecules included MMRN1 , SPP1 , GLDN , THBS3 , GPC1 , ANXA10 , LEC3B , CLEC17A , EPO , S100A9 , GFG9 , CXCL5 , PIK3IP1 , MMP1 , ADAM9 , CST7 , MMP25 , and PZP (Fig. 1 G). Their weight coefficients were used to construct a robust prognostic model, enabling stratification of patients based on ECM-related risk factors. To assess the prognostic value of these ECM molecules, we applied the Cox proportional hazards regression model. Our analysis revealed that the ECM scores of these molecules were significantly associated with both OS and DFS in HCC patients (Fig. 1 H). Patients were categorized into low and high ECM score groups based on individual ECM content, and Kaplan-Meier survival analysis was performed (Fig. S2). The survival curves indicated that the high-risk group, characterized by higher ECM scores, exhibited significantly better OS and DFS compared to the low-risk group (Fig. 1 H, S6). Time-dependent ROC curve analysis further confirmed the robust predictive performance of ECM-based risk stratification (Fig. S5). Collectively, these results suggest that low ECM levels are associated with unfavorable clinical outcomes in HCC, highlighting their prognostic value. To ensure the reliability of these findings, we validated the prognostic value of ECM risk scores across three independent cohorts: GSE14520, ICGC, and SRRSH (Fig. S7, S8, S9, S10). Consistently, the results from these external validation cohorts confirmed the predictive significance of ECM risk scores, demonstrating the robustness and applicability of this prognostic model. These analyses reveal the potential of ECM-related molecules as biomarkers for HCC prognosis and provide a framework for integrating ECM risk scores into clinical practice. Given the established role of ECM remodeling in oncogenic signaling, we investigated associated genetic alterations and identified a potential link between TP53 mutations and ECM dysregulation. Since TP53 is a key driver of malignant progression, we further explored how TP53 mutations influence ECM dynamics to elucidate the underlying molecular mechanisms. 3.3 TP53 Mutation May Affect ECM Remodeling Using exon data from 371 HCC patients in the TCGA database, we identified significant differences in the TP53 signaling pathway between patients with high and low ECM risk scores (Fig. 2 A). Notably, we observed a higher incidence of TP53 mutations in the high ECM risk score group (Fig. 2 D). This finding was further validated in the ICGC database, where a significant association between TP53 mutations and elevated ECM risk scores was confirmed (Fig. 2 B). These results suggest that TP53 mutations may directly or indirectly impact ECM composition in the tumor microenvironment. To further explore this relationship, we utilized the GSE124021 dataset to study the effects of TP53 activation on ECM risk scores. In Huh7 cells, treatment with SLMP53-2, a TP53 activator, resulted in a significant decrease in ECM risk scores (Fig. 2 E). Similarly, in TP53 wild-type cell lines, SK-HEP-1 and HepG2, treatment with RITA, another TP53 activator, produced comparable reductions in ECM risk scores (Fig. 2 F). These findings suggest that functional TP53 activity may counteract ECM remodeling processes associated with poor prognosis. In addition, we examined the relationship between TP53 mutations and the expression of ECM-related molecules. Our analysis revealed a strong correlation between TP53 mutations and elevated ADAM9 expression (Fig. 2 C). This observation indicates a potential mechanistic link through which TP53 mutations influence ECM composition, possibly by modulating the activity of key ECM regulators such as ADAM9. Collectively, these findings highlight the role of TP53 mutations in driving ECM remodeling in HCC, suggesting that TP53 activation or restoration could serve as a therapeutic strategy to mitigate ECM-related tumor progression. We further explored how TP53 mutations may regulate ECM remodeling specifically through ADAM9, providing deeper insights into the interplay between TP53 and ECM dynamics. 3.4 TP53 Affects ADAM9 to Remodel ECM The role of TP53 in tumor progression and its potential impact on ECM remodeling is of significant interest, particularly in light of our previous identification of ADAM9 as a high-risk ECM molecule associated with HCC prognosis. This section aims to explore the correlation between TP53 mutations and ADAM9 expression to elucidate their roles in ECM remodeling within the HCC tumor microenvironment. Analysis of the TCGA database showed significantly higher ADAM9 expression in HCC patients harboring TP53 GOF mutations compared to those with wild-type TP53 or other TP53 mutations (Fig. 3 B). Notably, the R249S mutation, a TP53 GOF mutation, was strongly associated with elevated ADAM9 expression levels (Fig. 3 C). This correlation was further validated in multiple patient cohorts, where ADAM9 expression consistently showed a strong association with TP53 GOF mutations (Fig. 3 D). These findings suggest that TP53 GOF mutations play a direct role in driving ECM remodeling through the upregulation of ADAM9. Our investigation revealed a strong association between TP53 mutations and ADAM9 expression, especially in the context of TP53 gain-of-function (GOF) mutations. TP53 mutations, which include missense, nonsense, insertion, and deletion mutations, are prevalent in HCC. Among them, GOF mutations often occur at hotspots of the TP53 gene, promoting the proliferation, metastasis, and drug resistance of cancer cells by impairing the transcriptional activity of wild-type (WT) p53. GOF mutations in TP53 not only endow the mutant protein with new oncogenic properties but also promote tumor progression, metastasis, drug resistance, and remodeling of the tumor microenvironment to facilitate tumor cell growth and spread. To validate these observations at the cellular level, we used HCC cell lines with varying TP53 statuses. In Hep3B (Fig. 4 A), HepG2 (Fig. 4 C), PLC (Fig. 4 E), and Huh7 (Fig. 4 G) cell lines, overexpression of TP53 wild-type and TP53 R249S mutations revealed that the GOF mutation significantly increased ADAM9 expression. Conversely, knocking down TP53 using siRNA in these cell lines resulted in a notable reduction of ADAM9 expression (Fig. 4 B, D, F, H). These results provide compelling evidence that TP53 GOF mutations influence ECM remodeling by upregulating ADAM9. These findings underscore the pivotal role of ADAM9 in ECM remodeling, driven by TP53 GOF mutations, and highlight its potential as a key mediator of tumor-promoting effects in HCC. These insights lay the groundwork for exploring the upstream regulatory mechanisms of ADAM9, including its transcriptional regulation SP1 as a transcription factor regulated by TP53. 3.5 SP1 is a Transcription Factor of ADAM9 Regulated by TP53 To uncover the molecular mechanism by which TP53 gain-of-function (GOF) mutations regulate ADAM9 expression, we employed the JASPAR database to identify potential transcription factors binding to the promoter region of the ADAM9 gene. SP1, a transcription factor implicated in regulating numerous genes related to cellular functions, emerged as a key candidate. SP1 is known for its role in ECM remodeling and has been linked to the regulation of genes involved in tumor progression. Based on these insights, we hypothesized that SP1 might serve as a mediator of TP53-driven regulation of ADAM9. To test this hypothesis, we performed SP1 knockdown experiments in HCC cell lines, including Hep3B, HepG2, PLC, and Huh7. RT-qPCR and WB assays demonstrated a marked reduction in ADAM9 expression following SP1 knockdown in all tested cell lines, particularly in Hep3B cells (Fig. 4 B, 6 B), HepG2 cells (Fig. 4 D, 6 A), PLC cells (Fig. 4 F, 6 D), and Huh7 cells (Fig. 4 H, 6 C). These findings confirm that SP1 directly regulates ADAM9 expression. To explore how TP53 mutations influence the SP1-ADAM9 axis, we overexpressed both wild-type TP53 and the TP53 R249S GOF mutant in the same cell lines. The results revealed that TP53 GOF mutations enhanced SP1 binding to the ADAM9 promoter, thereby upregulating ADAM9 transcription (Fig. 5 ). This supports the notion that TP53 GOF mutations play a pivotal role in ECM remodeling by modulating the SP1-ADAM9 regulatory pathway. These results underscore the role of SP1 as a transcriptional regulator of ADAM9 under the influence of TP53 GOF mutations. They highlight a mechanistic link between TP53 mutations, ECM remodeling, and tumor progression in HCC. These findings pave the way for the discussion, where we explore the broader implications of TP53-driven ECM remodeling and its potential as a therapeutic target in HCC. 4. Discussion The ECM, a crucial component of the tumor stroma and metastatic niche, significantly impacts the prognosis of HCC by influencing the tumor microenvironment's complexity and treatment response ( 17 ). Alterations in ECM components may serve as valuable biomarkers for HCC prognosis. In our study, we constructed an ECM-based prognostic marker, validated both internally and externally, which effectively predicts HCC outcomes. By selecting 18 ECM-related genes and their weight coefficients, we developed a robust prognostic model for HCC. Furthermore, we found that tumor-secreted ECM proteins interact with TP53 mutations, suggesting that TP53 mutations and ECM-related molecules are critical in developing new targeted therapies for HCC. The dynamic role of ECM in regulating tumor biology is increasingly recognized. Beyond serving as a physical scaffold, the ECM actively participates in cellular signaling, angiogenesis, and immune modulation ( 18 ). Specific ECM components, such as thrombospondins, influence both tumor growth and immune cell recruitment ( 19 ). Additionally, interactions between CD44, a widely studied ECM receptor, and hyaluronan, a key ECM component, have been shown to regulate tumor cell adhesion, migration, and metastasis. CD44-mediated signaling modulates ECM organization and enhances cancer cell survival, particularly in HCC, by contributing to immune evasion and promoting the epithelial-to-mesenchymal transition ( 20 ). These findings align with our observations that ECM remodeling is pivotal in HCC progression. TP53 mutations, the most common mutations in cancer, have long been viewed as an ideal therapeutic target. However, the challenge lies in targeting TP53 directly, as wild-type TP53 (wtp53) acts as a tumor suppressor, and pharmacologically, it is easier to inhibit than activate ( 21 ). Recent findings have shown that cancer cells harboring TP53 mutations often rely on a gain-of-function (GOF) of the mutated TP53 protein, which regulates the transcription of specific ECM-related genes ( 22 ). The GOF mutation typically occurs in the DNA-binding domain of p53, endowing the mutant protein with novel oncogenic properties—such as enhanced binding to transcription factors like SP1 and altered chromatin remodeling capacity. This allows GOF mutants to drive the expression of pro-metastatic genes (e.g., ADAM9) while repressing ECM-stabilizing factors, thereby promoting matrix degradation and tumor cell dissemination. This GOF mutation is associated with tumor-promoting functions, prompting a shift in cancer treatment strategies. Instead of targeting TP53 directly, focusing on its downstream effectors, such as ADAM9, may offer a promising alternative. By disrupting the GOF-mediated activation of ECM-remodeling enzymes, therapeutic interventions can potentially restore matrix homeostasis and impede tumor progression without the pharmacological challenges of directly modulating p53 activity. The TP53 R249S mutation is a well-known hotspot in HCC, significantly correlated with poor prognosis. Studies have shown that the R249S mutant p53 protein is overexpressed in HCC ( 20 , 23 ), and its high levels correlate with tumor invasiveness, metastasis, and shorter survival periods. The R249S mutation also upregulates matrix metalloproteinases (MMPs), which degrade ECM components, facilitating tumor cell migration ( 24 , 25 ). GOF mutations not only lose the normal functions of p53 but also acquire new properties that promote cancer development. For example, the R175H mutation has been found to reprogram cell signaling pathways, enhancing the migration and invasion ability of cancer cells and even promoting metastasis ( 26 ). These mutations also interact with hypoxic tumor microenvironments, where low oxygen levels regulate ECM composition. Hypoxia-induced signaling alters ECM stiffness and promotes ECM degradation through upregulation of MMPs and integrin pathways, providing cancer cells with a migratory advantage ( 5 ). This dynamic interaction between hypoxia and ECM remodeling underscores the importance of targeting both factors in HCC. Our study demonstrates that ADAM9, a member of the MMP family, is a key target gene of TP53, with its expression positively correlated with TP53 GOF mutations in HCC. ADAM9 plays an essential role in ECM remodeling, promoting tumor cell migration, invasion, and metastasis ( 27 ). It achieves this by degrading ECM components and modifying the physical properties of the ECM. ADAM9 also regulates the function of stromal cells in the tumor microenvironment, influencing fibroblast activation, endothelial cell migration, angiogenesis, and immune cell function ( 28 , 29 ). Consequently, ADAM9 serves as a potential therapeutic target in HCC. Several therapeutic strategies aimed at inhibiting ADAM9 are in development, including small molecule inhibitors, monoclonal antibodies, and RNA interference-based approaches. These strategies aim to block ADAM9's activity, thereby disrupting its role in tumor progression. Targeting ADAM9 could reduce tumor cell proliferation, migration, and invasion, leading to improved tumor microenvironment conditions and potentially enhancing the response to existing therapies. For instance, recent preclinical work has shown that ADAM9-targeted antibody-drug conjugates (ADCs), such as MGC028, exhibit promising efficacy in a variety of cancer models ( 30 , 31 ). Emerging evidence also highlights the role of autophagy in ECM remodeling. Autophagic degradation of ECM components, mediated by lysosomal pathways, contributes to tumor stroma interactions and promotes tumor progression ( 2 ). ADAM9 has been linked to the regulation of autophagy in cancer cells, suggesting a dual role in ECM turnover and intracellular recycling processes, which may present additional therapeutic opportunities. SP1, a key transcription factor, has been implicated in regulating the expression of ECM-related genes in tumors. In our study, we show that SP1, regulated by TP53, influences the expression of ADAM9 in HCC. Inhibition of SP1 expression led to decreased ADAM9 levels, underscoring its pivotal role in TP53-mediated ECM remodeling. SP1 is overexpressed in various cancers, including HCC, and its expression is associated with malignant tumor behaviors such as cell proliferation, invasion, and resistance to apoptosis ( 32 , 33 ). By upregulating pro-angiogenic factors like vascular endothelial growth factor (VEGF) ( 34 ), SP1 contributes to tumor growth and metastasis. Therapeutic strategies targeting SP1 could disrupt its regulation of ADAM9 and other downstream genes, providing a novel approach to treating tumors, including HCC. In summary, our findings uncover a new mechanism by which TP53 GOF mutations regulate tumor biology in HCC through ECM remodeling, with ADAM9 serving as a key effector. These results not only enhance our understanding of ECM dynamics in HCC progression but also offer a theoretical framework for developing targeted therapies aimed at TP53 GOF mutations and ADAM9. Future research should further investigate the molecular mechanisms linking TP53 mutations to ECM remodeling, focusing on SP1 and ADAM9, and assess how these changes influence HCC treatment outcomes and therapeutic responses. 5. Conclusion Our study identifies a novel mechanism by which TP53 gain-of-function mutations contribute to the progression of HCC through ECM remodeling, with ADAM9 as a key target gene. These findings provide insights into how TP53 mutations influence the tumor microenvironment, offering potential new therapeutic targets. Future studies should focus on further elucidating the molecular pathways linking TP53 mutations, ECM remodeling, and cancer progression, ultimately leading to more effective treatments for HCC and other TP53-mutant cancers. Declarations Ethics approval and consent to participate Experiments using clinical specimens were conducted under the approval from the Medical Ethics Committee of Sir Run Run Shaw Hospital of Zhejiang University (2020-568-02). Informed consents were obtained from all patients. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests No conflict of interest was reported in this study. Funding This work was supported by the National Natural Science Foundation of China (82200735), Zhejiang Provincial Natural Science Foundation (LQ24H160027), Zhejiang Province Medical and public health projects (2022519993), Beijing Natural Science Foundation (7244427), Peking University Medicine Sailing Program for Young Scholars’ Scientific & Technological Innovation (BMU2025YFJHPY019). Author contributions K.X.J, H.Q.P and Y.Y.Z developed the concept and design of this study. X.Q.S and Y.H.C analyzed and interpreted the data. K.X.J and J.G wrote the manuscript. J.N.M designed and illustrated the figures. H.Q.P and S.J.X conducted a critical review of the manuscript and provided constructive feedback and suggestions for revisions. Q.J.M, Y.L.L and J.J.X provided guidance and supervision throughout the writing process. The authors are grateful to Lixing Shiyuan Edu & Tech CO., Ltd (Purpose & Beyond Consulting) for assisting in figure polishing and bioinformatics analysis. 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Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations[J]. Nat Genet, 2025. Oh S, Park Y, Lee HJ, et al. A Disintegrin and Metalloproteinase 9 (ADAM9) in Advanced Hepatocellular Carcinoma and Their Role as a Biomarker During Hepatocellular Carcinoma Immunotherapy. Cancers (Basel) 2020;12. Dong Y, Wu Z, He M, et al. ADAM9 mediates the interleukin-6-induced Epithelial-Mesenchymal transition and metastasis through ROS production in hepatoma cells. Cancer Lett 2018;421:1-14. Dong Y, Wu Z, He M, et al. ADAM9 mediates the interleukin-6-induced Epithelial-Mesenchymal transition and metastasis through ROS production in hepatoma cells[J]. Cancer Lett, 2018,421:1-14. Kim J M, Jeung H, Rha S Y, et al. The effect of disintegrin-metalloproteinase ADAM9 in gastric cancer progression[J]. Mol Cancer Ther, 2014,13(12):3074-3085. Scribner J A, Brown J G, Son T, et al. Abstract 1897: Preclinical development of MGC028, an ADAM9-targeted, glycan-linked, exatecan-based antibody-drug conjugate for the treatment of solid cancers[J]. Cancer ResearchCancer Research, 2024,84(6_Supplement):1897. Song J, Nabeel-Shah S, Pu S, et al. Regulation of alternative polyadenylation by the C2H2-zinc-finger protein Sp1[J]. Mol Cell, 2022,82(17):3135-3150. Zhang G, Xie Z, Jiang J, et al. Mechanical confinement promotes heat resistance of hepatocellular carcinoma via SP1/IL4I1/AHR axis[J]. Cell Rep Med, 2023,4(8):101128. Lu H, Yuan P, Ma X, et al. Angiotensin-converting enzyme inhibitor promotes angiogenesis through Sp1/Sp3-mediated inhibition of notch signaling in male mice[J]. Nat Commun, 2023,14(1):731. Scheme 1 Scheme 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Scheme1.jpg SupplementaryMaterialforReview.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers invited by journal 03 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 31 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8749587","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585404791,"identity":"464d4acf-5564-4daa-bcf7-cd90860b4e74","order_by":0,"name":"Ke xin Jiang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"xin","lastName":"Jiang","suffix":""},{"id":585404792,"identity":"4886a93b-b144-429c-aa26-d222729836e3","order_by":1,"name":"Haoqi Pan","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Haoqi","middleName":"","lastName":"Pan","suffix":""},{"id":585404794,"identity":"2b4eef5a-fab3-4ab1-b92f-207b52ea2f3f","order_by":2,"name":"Yiyin Zhang","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiyin","middleName":"","lastName":"Zhang","suffix":""},{"id":585404795,"identity":"9c921cde-2711-43e2-8388-70aba955b0dc","order_by":3,"name":"Xuqiu Shen","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuqiu","middleName":"","lastName":"Shen","suffix":""},{"id":585404798,"identity":"e5e4dc62-47ba-4e0f-b7e3-17da443ecf94","order_by":4,"name":"Yihan Chai","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yihan","middleName":"","lastName":"Chai","suffix":""},{"id":585404803,"identity":"2b9fe82f-a4b5-4a33-950c-5157405bd2c3","order_by":5,"name":"Jing Guo","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Guo","suffix":""},{"id":585404808,"identity":"33a0ccad-40cf-43f1-8fe4-7501f335ac96","order_by":6,"name":"Shunjie Xia","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shunjie","middleName":"","lastName":"Xia","suffix":""},{"id":585404810,"identity":"12636c07-621e-430f-a282-e2eef4eaf942","order_by":7,"name":"Jingnan Mo","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingnan","middleName":"","lastName":"Mo","suffix":""},{"id":585404812,"identity":"1c219068-ed56-4279-8773-d22bf2b3db90","order_by":8,"name":"Qijiang Mao","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qijiang","middleName":"","lastName":"Mao","suffix":""},{"id":585404814,"identity":"e93d3f1a-a4f3-40f4-ab7e-758a5d418391","order_by":9,"name":"Yuelong Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPmYYA0R8MLCxI6iFDaaFDYgZZxSkJRPWgsxg5vlwiLGBoBZ25mcPv7YdlmNj7z382sbgADMD++GjG/A7jM3cWLbtsDEbz7k06xyDO3wMPGlpNwj4xUxasu1wYptEjplxjsEzZgYJHjMCWti/gbTUg7VYGBxmbCCshcdM8mPb4QQ2iRzjxwxEaimTZjiXbtjGc8aMsccgLZmNkF/4+Y9vk/xRZi3Pz95j/OHHHxs7fvbDx/BqAQFmXkjssEmASULKQYDxxx+I1g/EqB4Fo2AUjIKRBwDC+z+d5oU/mgAAAABJRU5ErkJggg==","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuelong","middleName":"","lastName":"Liang","suffix":""},{"id":585404816,"identity":"fb958843-8910-4825-a544-6acaa5c24748","order_by":10,"name":"Junjie Xu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-01-31 11:53:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8749587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8749587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102069184,"identity":"c37dd31e-dbac-4ec7-a93e-30c31db68e54","added_by":"auto","created_at":"2026-02-06 19:04:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe dynamic changes in ECM components during the development of HCC tissue and their impact on biological prognosis. \u003c/strong\u003e(A) Analysis using the ssGSEA algorithm revealed differences in ECM content among non-cirrhotic liver tissue (N-0), cirrhotic liver tissue (N-1), non-cirrhotic HCC tissue (T-0), and cirrhotic HCC tissue (T-1); (B) Paired analysis of normal liver tissue (N) and HCC tissue (T) found a reduction in ECM content in HCC tissue; (C) ECM components exhibit dynamic changes during the development of cirrhosis and HCC; (D) Volcano plot showing differential gene analysis between HCC tissue and normal tissue, with blue representing differentially expressed genes, yellow representing ECM-related genes, and red representing ECM-related differentially expressed genes; (E) Differential gene analysis identified 559 ECM-related differential genes between HCC tissue and normal tissue; (F) The correlation and relative risk of the 18 ECM molecules with HCC prognosis, with red indicating risk factors, blue indicating protective factors, and gray indicating no statistical difference; (G) The risk coefficient of 18 prognostic genes in the risk score; (H) In the TCGA dataset, 371 HCC patients were clearly divided into normal, low-risk, and high-risk groups based on ECM risk scores at the RNA transcriptome level (ns, P \u0026gt; 0.05; *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001; ****, P \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/3ff2dfe2ef23a1536c0fea1e.jpg"},{"id":102069191,"identity":"399c11e7-ab60-4dcf-857e-9bfe67623a16","added_by":"auto","created_at":"2026-02-06 19:04:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor TP53 mutation is closely related to ECM risk scores, and ECM risk scores decrease with TP53 activation. \u003c/strong\u003e(A) In the TCGA cohort, HCC patients with high ECM risk scores (bottom graph) show a higher proportion of TP53 mutations compared to those with low ECM risk scores (top graph); (B) The ICGC database confirmed that HCC patients with high ECM risk scores have a higher proportion of TP53 mutations; (C) A heatmap analysis revealed a high correlation between TP53 and ECM genes such as ADAM9; (D) Patients with TP53 mutations exhibit higher ECM risk scores compared to those with wild-type TP53; (E) The correlation between different doses of the mutant p53 reactivation agent SLMP53-2 and ECM risk scores was assessed in the GSE124021 database; (F) In TP53 wild-type SK-HEP-1 and HepG2 cell lines, the correlation between the small molecule p53 activator RITA and ECM risk scores was compared (ns, P \u0026gt; 0.05; *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/8f1e7df5667d53e00304dd71.jpg"},{"id":102295837,"identity":"91dac1ee-5715-4bdd-95a0-2d39eb7a83b0","added_by":"auto","created_at":"2026-02-10 10:15:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Expression Level of ADAM9 Is Significantly Correlated with TP53 Gain-of-Function (GOF) Mutations. \u003c/strong\u003e(A) Investigation of ECM-related gene expression in cancer versus normal tissues using data from TCGA, ICGC, GSE140520, and SRRSH databases, validating the expression of ADAM9; (B) Box plot showing the expression of ADAM9 (top graph) and GAPDH (bottom graph) in the TCGA cohort; (C) In the TCGA cohort, expression of ADAM9 is significantly correlated with the TP53 R249H mutation; (D) Correlation between ADAM9 expression values and TP53 wild-type (WT), TP53 mutant (MUT), and TP53 GOF mutations across different patient cohorts. (*ns, P＞0.05; *, P\u0026lt;0.05; **, P\u0026lt;0.01; **, P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/0c0ff87011249de6ec756723.jpg"},{"id":102069189,"identity":"b4c6404f-ad91-4d2d-a9cb-9cb05649ce35","added_by":"auto","created_at":"2026-02-06 19:04:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRT-qPCR Detection of the Effects of Overexpression of Wild-type TP53, TP53 R249S, and Knockdown of SP1 and p53 on Gene Expression in Different Hepatocellular Carcinoma Cell Lines. \u003c/strong\u003eIn Hep3B cells(A), HepG2 cells (C), PLC cells(E), and Huh7 cells(G), TP53 wild-type and TP53 R249S overexpression efficiencies were confirmed by RT-qPCR; the expression levels of TP53, ADAM9, and SP1 were measured. The data shown are mean ± SEM (n = 4), normalized to GAPDH. In Hep3B cells(B), HepG2 cells (D), PLC cells(F), and Huh7 cells(H), SP1 and p53 knockdown efficiencies were determined by RT-qPCR; the expression levels of TP53, ADAM9, and SP1 were detected. The data shown are mean ± SEM (n = 4), normalized to GAPDH.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/65b3b2b362a66591448593fe.jpg"},{"id":102069185,"identity":"81986de4-6dc8-4ec0-817d-7765c49ee24e","added_by":"auto","created_at":"2026-02-06 19:04:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanism Diagram of Extracellular Matrix and TP53 GOF Mutation. \u003c/strong\u003eThis schematic diagram illustrates the mechanism by which TP53 mutations, particularly gain-of-function (GOF) mutations, affect extracellular matrix (ECM) remodeling in hepatocellular carcinoma (HCC). In cancer cells, TP53 mutations increase the activity of the transcription factor SP1, which is essential for regulating the transcription of ADAM9. In the context of TP53 GOF mutations, the binding between TP53 GOF mutants and SP1 is enhanced, leading to increased SP1 activity. This, in turn, activates the transcription of ADAM9, influencing tumor behavior and contributing to ECM reduction in the tumor microenvironment. This cascade results in decreased tumor cell adhesion, promoting tumor progression.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/95e04b53e9e315a0dd135f5e.jpg"},{"id":102069188,"identity":"a2ea292b-24cf-400b-8fa3-a09f6f0ebc8d","added_by":"auto","created_at":"2026-02-06 19:04:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of ADAM9, TP53, and SP1 Protein Expression in Hepatocellular Carcinoma Cell Lines via siRNA Transfection and TP53 Overexpression. \u003c/strong\u003e(A) Western blot analysis of ADAM9, TP53, and SP1 expression in HepG2 cells after siRNA-mediated knockdown of SP1; (B) Western blot analysis of ADAM9, TP53, and SP1 expression in HepG2 cells following overexpression of wild-type TP53 and the TP53 R249S mutant; (C) Western blot analysis of ADAM9, TP53, and SP1 expression in PLC cells after siRNA-mediated knockdown of SP1; (D) Western blot analysis of ADAM9, TP53, and SP1 expression in PLC cells following overexpression of wild-type TP53 and the TP53 R249S mutant; (E) Western blot analysis of ADAM9, TP53, and SP1 expression in Hep3B cells after siRNA-mediated knockdown of SP1; (F) Western blot analysis of ADAM9, TP53, and SP1 expression in Hep3B cells following overexpression of wild-type TP53 and the TP53 R249S mutant; (G) Western blot analysis of ADAM9, TP53, and SP1 expression in Huh7 cells after siRNA-mediated knockdown of SP1; (H) Western blot analysis of ADAM9, TP53, and SP1 expression in Huh7 cells following overexpression of wild-type TP53 and the TP53 R249S mutant. Note: The data shown are mean ± SEM (n = 4), normalized to GAPDH.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/6fb9e2ebcfb039f3ce74367f.jpg"},{"id":102301194,"identity":"1b6c9ebf-1cf5-4309-8e8d-c0f40e580c83","added_by":"auto","created_at":"2026-02-10 11:20:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/762b272d-9241-4a38-90dc-714d1e345f12.pdf"},{"id":102295943,"identity":"2c6548bc-e72e-4b87-959c-77a2aab41aa3","added_by":"auto","created_at":"2026-02-10 10:16:16","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":183084,"visible":true,"origin":"","legend":"","description":"","filename":"Scheme1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/829fe8dabfa88dea0bccc962.jpg"},{"id":102298670,"identity":"a2995641-1b14-438b-8421-b55c014667d2","added_by":"auto","created_at":"2026-02-10 10:57:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3628089,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialforReview.docx","url":"https://assets-eu.researchsquare.com/files/rs-8749587/v1/47785f082ba4e326a6d4ff67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unique Extracellular Matrix Remodeling in Hepatocellular Carcinoma: TP53 GOF- SP1-ADAM9 Axis as a Prognostic and Therapeutic Target","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver cancer is one of the leading causes of cancer-related deaths worldwide, ranking fifth in 2020 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Hepatocellular carcinoma (HCC) accounts for approximately 90% of all liver cancer cases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While radical surgical resection remains the primary curative treatment for early-stage HCC, fewer than 30% of patients are candidates for surgery. As a result, alternative treatments, including targeted therapies, transarterial chemoembolization, and ablative therapies, have become standard options. Despite the continuous emergence of novel treatment modalities represented by molecular targeted drugs such as tyrosine kinase inhibitors or immune checkpoint inhibitors like anti-programmed cell death ligand 1 (PD1/PDL1) immunotherapy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the prognosis of patients with advanced HCC remains dismal, with a 5-year survival rate of less than 10% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This underscores the urgent need to explore novel therapeutic strategies to improve treatment outcomes for HCC.\u003c/p\u003e \u003cp\u003eGiven the tumor's well-recognized heterogeneity, precise prognostic systems that account for both traditional tumor evaluation and intratumoral variability are essential for more accurate clinical predictions. Currently, the prognostic assessment of HCC is mainly based on several commonly used HCC staging systems (such as TNM staging, Cancer of the Liver Italian Program score (CLIP), and Barcelona Clinic Liver Cancer staging (BCLC)) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, each of these models has its specific limitations. Most staging parameters remain some controversial, are not comprehensive enough, and have limitations in their applicability to certain populations, failing to fully capture the complexity of intratumoral heterogeneity. Thus, there is a pressing need for novel prognostic models that incorporate tumor microenvironment factors to better predict clinical outcomes in HCC.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) plays a crucial role in cancer progression and metastasis. It consists of the extracellular matrix (ECM), stromal cells, and biologically active molecules secreted by both tumor and stromal cells, along with vascular and lymphatic systems (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The ECM, a dynamic network of proteins surrounding both normal and cancer cells, can be reprogrammed during tumorigenesis, influencing processes such as tumor cell proliferation, angiogenesis, adhesion, migration, invasion, and metastasis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Tumor cells initially adhere to ECM components and engage with surrounding cells, such as tumor-associated neutrophils, cancer-associated fibroblasts (CAFs), and bone marrow-derived suppressor cells, promoting tumor growth and metastasis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recent studies have shown that ECM proteins secreted by tumor cells rather than stromal cells contribute to poor prognosis in cancers like pancreatic ductal adenocarcinoma (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), highlighting ECM-targeted therapies as a promising avenue for treatment.\u003c/p\u003e \u003cp\u003eADAM9 (A Disintegrin and Metalloproteinase 9) plays a pivotal role in extracellular matrix (ECM) remodeling within the tumor microenvironment. As a key regulator of ECM reconstruction, ADAM9 directly degrades ECM components (such as collagens and laminins) through its proteolytic activity, while indirectly promoting ECM reorganization via activation of matrix metalloproteinases (MMPs). This ECM remodeling not only creates favorable conditions for tumor cell migration and invasion, but may also further drive tumor progression by releasing ECM-sequestered growth factors (e.g., TGF-β and VEGF). Notably, ADAM9-mediated ECM alterations are closely associated with tumor cell phenotypic transitions (such as EMT), forming a vicious cycle that promotes HCC metastasis. Given the central role of ADAM9 in ECM homeostasis, targeting the ADAM9-ECM axis may represent a novel therapeutic strategy for HCC.\u003c/p\u003e \u003cp\u003eApproximately 13\u0026ndash;48% of HCC cases harbor TP53 mutations, which impair the tumor-suppressive functions of the p53 protein by preventing cell apoptosis and growth arrest (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These mutations are frequently associated with high-grade tumors and worse clinical outcomes in HCC (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). TP53 mutations also affect the tumor microenvironment by regulating the expression of angiogenic factors, which can influence treatment responses. For instance, patients with TP53 mutations undergoing bevacizumab therapy show improved progression-free survival (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). TP53 mutation types include loss of function (LOF), dominant negative (DN), and gain-of-function (GOF). Among these, GOF mutations often occur at TP53 gene hotspots, damaging the transcriptional activity of wild-type (WT) p53 to promote the proliferation, metastasis, and drug resistance of cancer cells. Given the impact of TP53 on both tumor biology and the ECM, understanding the interaction between TP53 GOF mutations and ECM components is critical for improving prognosis and therapy in HCC.\u003c/p\u003e \u003cp\u003eThis study employs multi-omics bioinformatics to construct an ECM-based prognostic signature for HCC while investigating TP53 mutation-mediated ECM remodeling mechanisms. Through a comprehensive analysis of matrisome components and mutational profiles, we identify distinct ECM reorganization patterns associated with TP53 mutation subtypes, particularly gain-of-function variants. Functional network analysis reveals critical pathways linking p53 dysfunction to matrix dysregulation, providing both a clinically applicable prognostic model and mechanistic insights into microenvironmental reprogramming in HCC progression. These findings highlight potential therapeutic targets for TP53-mutant HCC through ECM modulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell Culture\u003c/h2\u003e \u003cp\u003eThe human liver cancer cell lines HepG2, PLC/PRF/5 (PLC), Hep3B, and Huh7 were obtained from the American Type Culture Collection (ATCC, USA). These cell lines are commonly used in liver cancer research. Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 1% antibiotics (penicillin, streptomycin, amphotericin B; Thermo Fisher Scientific) and 10% fetal bovine serum (FBS; BasalMedia). Cells were incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂. All cell lines were regularly tested for mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit (Lonza).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Plasmid Construction\u003c/h2\u003e \u003cp\u003eThe siRNA targeting GAPDH was designed with the following sequences:\u003c/p\u003e \u003cp\u003e● Forward: GGAGCGAGATCCCTCCAAAAT\u003c/p\u003e \u003cp\u003e● Reverse: GGCTGTTGTCATACTTCTCATGG\u003c/p\u003e \u003cp\u003eHepG2 cells were seeded into 6-well plates at a density of 3 \u0026times; 10⁴ cells per well and incubated at 37\u0026deg;C for 24 hours to allow primary cell adhesion. The lentiviral vector pLKO.1 TRC (Addgene, USA) was used for cloning the siRNA oligos according to the manufacturer's standard protocol. A scrambled-siRNA plasmid was used as a negative control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 RNA Isolation and Quantitative Real-Time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using the SteadyPure Quick RNA Extraction Kit (AG, China) according to the manufacturer\u0026rsquo;s instructions. RNA was reverse transcribed into cDNA using 1 \u0026micro;L of RNA in a 10 \u0026micro;L reaction volume. The reaction conditions were 37\u0026deg;C for 15 minutes, followed by 85\u0026deg;C for 5 minutes. Reverse transcription was performed using a Bioer GeneTouch PCR instrument (Bioer, China). Real-time PCR was carried out on an Applied Biosystems instrument (Applied Biosystems, USA), and the expression levels of candidate genes were normalized to GAPDH. Primer sequences used in this study are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Western Blotting\u003c/h2\u003e \u003cp\u003eWestern blot analysis was performed as previously described (citation/reference). Briefly, cells were lysed using RIPA buffer (Thermo Fisher) and protein concentrations were quantified using the BCA Protein Assay Kit (Thermo Fisher). Equal amounts of protein (20 \u0026micro;g) were loaded onto 10% SDS-PAGE gels, and proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Millipore). Membranes were blocked with 5% non-fat milk in PBS containing 0.1% Tween-20 (PBST) for 1 hour at room temperature. Primary antibodies were incubated overnight at 4\u0026deg;C, including anti-GAPDH (1:50,000; ProteinTech, 10494-1-AP). The membranes were then incubated with HRP-conjugated secondary antibodies (1:5,000) and detected using enhanced chemiluminescence (ECL) substrate (Bio-Rad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). GraphPad Prism 6.0 (GraphPad Software, USA) was used for statistical analysis. Differences between two groups were assessed using Student\u0026rsquo;s t-test, while one-way analysis of variance (ANOVA) was used for comparisons across multiple groups. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Dynamic Changes of ECM in the Development of Hepatocellular Carcinoma\u003c/h2\u003e \u003cp\u003eThe ECM plays a critical role in shaping the tumor microenvironment, influencing both tumor progression and treatment outcomes. Understanding the dynamic changes in ECM composition during HCC development is essential for identifying molecular mechanisms and potential biomarkers for prognosis. This section aims to analyze ECM alterations in HCC and evaluate their implications for disease progression.\u003c/p\u003e \u003cp\u003eThe ECM comprises ECM-associated proteins, ECM regulatory proteins, secreted factors, ECM glycoproteins, collagen, and proteoglycans. Considering that most HCC patients have hepatitis B virus (HBV)-induced cirrhosis, which leads to significant collagen accumulation, we first compared the ECM content of normal livers with cirrhotic livers to minimize biases introduced by cirrhosis. Using single-sample gene set enrichment analysis (ssGSEA) on transcriptome data from 371 HCC patients in the TCGA database, we assessed ECM content differences across normal liver tissue, cirrhotic tissue, tumor tissue, and tumor tissue with cirrhosis. Although normal liver tissues may progress to liver cancer via cirrhosis, our analysis revealed no significant differences in extracellular matrix (ECM) content between normal and cirrhotic liver tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, ECM content was markedly reduced in tumor tissue compared to normal liver tissue. To eliminate the impact of individual differences in patients, we also analyzed the ECM content of normal liver tissue and tumor tissue in 50 patients, and found that the ECM content also shows a decreasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Further research on specific ECM components revealed that the major components underwent dynamic changes, especially proteoglycans, which play a key role in providing intrinsic signals needed to coordinate key events in cancer immune regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, S1).\u003c/p\u003e \u003cp\u003eTo better understand the molecular mechanisms underlying ECM changes in HCC, we conducted differential gene expression analysis between normal liver and tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Using the limma package in R, we identified significantly differentially expressed genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, we focused on ECM-related genes and identified 559 ECM molecules with significant changes in HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). This analysis provided a detailed view of the altered ECM landscape in tumor tissues.\u003c/p\u003e \u003cp\u003eAdditionally, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify 18 ECM molecules most strongly associated with HCC prognosis (Fig. S3). These 18 molecules displayed distinct patterns as risk factors (red) and protective factors (blue), with gray markers indicating no statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Based on ECM risk scores derived from RNA transcriptome data, we categorized the 371 HCC patients into normal, low-risk, and high-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, S4). This stratification framework demonstrates the potential of ECM-related alterations as prognostic markers.\u003c/p\u003e \u003cp\u003eThese findings underscore the significant role of ECM remodeling in the progression of HCC and provide a foundation for examining how ECM changes influence patient outcomes. Given the strong association between ECM genes and tumor progression, we next explore their prognostic implications and validate the robustness of ECM-based predictive models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ECM Is Associated with the Prognosis of HCC\u003c/h2\u003e \u003cp\u003eTo elucidate the role of ECM in predicting patient outcomes, we investigated how differential ECM gene expression correlates with HCC prognosis. This section focuses on identifying ECM-related molecules as prognostic markers and evaluating their predictive value for overall survival (OS) and disease-free survival (DFS) in HCC patients.\u003c/p\u003e \u003cp\u003eUsing LASSO regression analysis, we identified 18 ECM molecules most strongly associated with HCC prognosis. These molecules included \u003cem\u003eMMRN1\u003c/em\u003e, \u003cem\u003eSPP1\u003c/em\u003e, \u003cem\u003eGLDN\u003c/em\u003e, \u003cem\u003eTHBS3\u003c/em\u003e, \u003cem\u003eGPC1\u003c/em\u003e, \u003cem\u003eANXA10\u003c/em\u003e, \u003cem\u003eLEC3B\u003c/em\u003e, \u003cem\u003eCLEC17A\u003c/em\u003e, \u003cem\u003eEPO\u003c/em\u003e, \u003cem\u003eS100A9\u003c/em\u003e, \u003cem\u003eGFG9\u003c/em\u003e, \u003cem\u003eCXCL5\u003c/em\u003e, \u003cem\u003ePIK3IP1\u003c/em\u003e, \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eADAM9\u003c/em\u003e, \u003cem\u003eCST7\u003c/em\u003e, \u003cem\u003eMMP25\u003c/em\u003e, and \u003cem\u003ePZP\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Their weight coefficients were used to construct a robust prognostic model, enabling stratification of patients based on ECM-related risk factors.\u003c/p\u003e \u003cp\u003eTo assess the prognostic value of these ECM molecules, we applied the Cox proportional hazards regression model. Our analysis revealed that the ECM scores of these molecules were significantly associated with both OS and DFS in HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Patients were categorized into low and high ECM score groups based on individual ECM content, and Kaplan-Meier survival analysis was performed (Fig. S2). The survival curves indicated that the high-risk group, characterized by higher ECM scores, exhibited significantly better OS and DFS compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, S6). Time-dependent ROC curve analysis further confirmed the robust predictive performance of ECM-based risk stratification (Fig. S5). Collectively, these results suggest that low ECM levels are associated with unfavorable clinical outcomes in HCC, highlighting their prognostic value.\u003c/p\u003e \u003cp\u003eTo ensure the reliability of these findings, we validated the prognostic value of ECM risk scores across three independent cohorts: GSE14520, ICGC, and SRRSH (Fig. S7, S8, S9, S10). Consistently, the results from these external validation cohorts confirmed the predictive significance of ECM risk scores, demonstrating the robustness and applicability of this prognostic model.\u003c/p\u003e \u003cp\u003eThese analyses reveal the potential of ECM-related molecules as biomarkers for HCC prognosis and provide a framework for integrating ECM risk scores into clinical practice. Given the established role of ECM remodeling in oncogenic signaling, we investigated associated genetic alterations and identified a potential link between TP53 mutations and ECM dysregulation. Since TP53 is a key driver of malignant progression, we further explored how TP53 mutations influence ECM dynamics to elucidate the underlying molecular mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 TP53 Mutation May Affect ECM Remodeling\u003c/h2\u003e \u003cp\u003eUsing exon data from 371 HCC patients in the TCGA database, we identified significant differences in the TP53 signaling pathway between patients with high and low ECM risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Notably, we observed a higher incidence of TP53 mutations in the high ECM risk score group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This finding was further validated in the ICGC database, where a significant association between TP53 mutations and elevated ECM risk scores was confirmed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These results suggest that TP53 mutations may directly or indirectly impact ECM composition in the tumor microenvironment.\u003c/p\u003e \u003cp\u003eTo further explore this relationship, we utilized the GSE124021 dataset to study the effects of TP53 activation on ECM risk scores. In Huh7 cells, treatment with SLMP53-2, a TP53 activator, resulted in a significant decrease in ECM risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Similarly, in TP53 wild-type cell lines, SK-HEP-1 and HepG2, treatment with RITA, another TP53 activator, produced comparable reductions in ECM risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings suggest that functional TP53 activity may counteract ECM remodeling processes associated with poor prognosis.\u003c/p\u003e \u003cp\u003eIn addition, we examined the relationship between TP53 mutations and the expression of ECM-related molecules. Our analysis revealed a strong correlation between TP53 mutations and elevated ADAM9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This observation indicates a potential mechanistic link through which TP53 mutations influence ECM composition, possibly by modulating the activity of key ECM regulators such as ADAM9.\u003c/p\u003e \u003cp\u003eCollectively, these findings highlight the role of TP53 mutations in driving ECM remodeling in HCC, suggesting that TP53 activation or restoration could serve as a therapeutic strategy to mitigate ECM-related tumor progression. We further explored how TP53 mutations may regulate ECM remodeling specifically through ADAM9, providing deeper insights into the interplay between TP53 and ECM dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 TP53 Affects ADAM9 to Remodel ECM\u003c/h2\u003e \u003cp\u003eThe role of TP53 in tumor progression and its potential impact on ECM remodeling is of significant interest, particularly in light of our previous identification of ADAM9 as a high-risk ECM molecule associated with HCC prognosis. This section aims to explore the correlation between TP53 mutations and ADAM9 expression to elucidate their roles in ECM remodeling within the HCC tumor microenvironment.\u003c/p\u003e \u003cp\u003eAnalysis of the TCGA database showed significantly higher ADAM9 expression in HCC patients harboring TP53 GOF mutations compared to those with wild-type TP53 or other TP53 mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, the R249S mutation, a TP53 GOF mutation, was strongly associated with elevated ADAM9 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This correlation was further validated in multiple patient cohorts, where ADAM9 expression consistently showed a strong association with TP53 GOF mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings suggest that TP53 GOF mutations play a direct role in driving ECM remodeling through the upregulation of ADAM9.\u003c/p\u003e \u003cp\u003eOur investigation revealed a strong association between TP53 mutations and ADAM9 expression, especially in the context of TP53 gain-of-function (GOF) mutations. TP53 mutations, which include missense, nonsense, insertion, and deletion mutations, are prevalent in HCC. Among them, GOF mutations often occur at hotspots of the TP53 gene, promoting the proliferation, metastasis, and drug resistance of cancer cells by impairing the transcriptional activity of wild-type (WT) p53. GOF mutations in TP53 not only endow the mutant protein with new oncogenic properties but also promote tumor progression, metastasis, drug resistance, and remodeling of the tumor microenvironment to facilitate tumor cell growth and spread.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate these observations at the cellular level, we used HCC cell lines with varying TP53 statuses. In Hep3B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), HepG2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), PLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), and Huh7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) cell lines, overexpression of TP53 wild-type and TP53 R249S mutations revealed that the GOF mutation significantly increased ADAM9 expression. Conversely, knocking down TP53 using siRNA in these cell lines resulted in a notable reduction of ADAM9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, D, F, H). These results provide compelling evidence that TP53 GOF mutations influence ECM remodeling by upregulating ADAM9.\u003c/p\u003e \u003cp\u003eThese findings underscore the pivotal role of ADAM9 in ECM remodeling, driven by TP53 GOF mutations, and highlight its potential as a key mediator of tumor-promoting effects in HCC. These insights lay the groundwork for exploring the upstream regulatory mechanisms of ADAM9, including its transcriptional regulation SP1 as a transcription factor regulated by TP53.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 SP1 is a Transcription Factor of ADAM9 Regulated by TP53\u003c/h2\u003e \u003cp\u003eTo uncover the molecular mechanism by which TP53 gain-of-function (GOF) mutations regulate ADAM9 expression, we employed the JASPAR database to identify potential transcription factors binding to the promoter region of the ADAM9 gene. SP1, a transcription factor implicated in regulating numerous genes related to cellular functions, emerged as a key candidate. SP1 is known for its role in ECM remodeling and has been linked to the regulation of genes involved in tumor progression. Based on these insights, we hypothesized that SP1 might serve as a mediator of TP53-driven regulation of ADAM9.\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we performed SP1 knockdown experiments in HCC cell lines, including Hep3B, HepG2, PLC, and Huh7. RT-qPCR and WB assays demonstrated a marked reduction in ADAM9 expression following SP1 knockdown in all tested cell lines, particularly in Hep3B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), HepG2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), PLC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and Huh7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These findings confirm that SP1 directly regulates ADAM9 expression.\u003c/p\u003e \u003cp\u003eTo explore how TP53 mutations influence the SP1-ADAM9 axis, we overexpressed both wild-type TP53 and the TP53 R249S GOF mutant in the same cell lines. The results revealed that TP53 GOF mutations enhanced SP1 binding to the ADAM9 promoter, thereby upregulating ADAM9 transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This supports the notion that TP53 GOF mutations play a pivotal role in ECM remodeling by modulating the SP1-ADAM9 regulatory pathway.\u003c/p\u003e \u003cp\u003eThese results underscore the role of SP1 as a transcriptional regulator of ADAM9 under the influence of TP53 GOF mutations. They highlight a mechanistic link between TP53 mutations, ECM remodeling, and tumor progression in HCC. These findings pave the way for the discussion, where we explore the broader implications of TP53-driven ECM remodeling and its potential as a therapeutic target in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe ECM, a crucial component of the tumor stroma and metastatic niche, significantly impacts the prognosis of HCC by influencing the tumor microenvironment's complexity and treatment response (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Alterations in ECM components may serve as valuable biomarkers for HCC prognosis. In our study, we constructed an ECM-based prognostic marker, validated both internally and externally, which effectively predicts HCC outcomes. By selecting 18 ECM-related genes and their weight coefficients, we developed a robust prognostic model for HCC. Furthermore, we found that tumor-secreted ECM proteins interact with TP53 mutations, suggesting that TP53 mutations and ECM-related molecules are critical in developing new targeted therapies for HCC.\u003c/p\u003e \u003cp\u003eThe dynamic role of ECM in regulating tumor biology is increasingly recognized. Beyond serving as a physical scaffold, the ECM actively participates in cellular signaling, angiogenesis, and immune modulation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specific ECM components, such as thrombospondins, influence both tumor growth and immune cell recruitment (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, interactions between CD44, a widely studied ECM receptor, and hyaluronan, a key ECM component, have been shown to regulate tumor cell adhesion, migration, and metastasis. CD44-mediated signaling modulates ECM organization and enhances cancer cell survival, particularly in HCC, by contributing to immune evasion and promoting the epithelial-to-mesenchymal transition (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These findings align with our observations that ECM remodeling is pivotal in HCC progression.\u003c/p\u003e \u003cp\u003eTP53 mutations, the most common mutations in cancer, have long been viewed as an ideal therapeutic target. However, the challenge lies in targeting TP53 directly, as wild-type TP53 (wtp53) acts as a tumor suppressor, and pharmacologically, it is easier to inhibit than activate (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Recent findings have shown that cancer cells harboring TP53 mutations often rely on a gain-of-function (GOF) of the mutated TP53 protein, which regulates the transcription of specific ECM-related genes (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The GOF mutation typically occurs in the DNA-binding domain of p53, endowing the mutant protein with novel oncogenic properties\u0026mdash;such as enhanced binding to transcription factors like SP1 and altered chromatin remodeling capacity. This allows GOF mutants to drive the expression of pro-metastatic genes (e.g., ADAM9) while repressing ECM-stabilizing factors, thereby promoting matrix degradation and tumor cell dissemination. This GOF mutation is associated with tumor-promoting functions, prompting a shift in cancer treatment strategies. Instead of targeting TP53 directly, focusing on its downstream effectors, such as ADAM9, may offer a promising alternative. By disrupting the GOF-mediated activation of ECM-remodeling enzymes, therapeutic interventions can potentially restore matrix homeostasis and impede tumor progression without the pharmacological challenges of directly modulating p53 activity.\u003c/p\u003e \u003cp\u003eThe TP53 R249S mutation is a well-known hotspot in HCC, significantly correlated with poor prognosis. Studies have shown that the R249S mutant p53 protein is overexpressed in HCC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and its high levels correlate with tumor invasiveness, metastasis, and shorter survival periods. The R249S mutation also upregulates matrix metalloproteinases (MMPs), which degrade ECM components, facilitating tumor cell migration (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). GOF mutations not only lose the normal functions of p53 but also acquire new properties that promote cancer development. For example, the R175H mutation has been found to reprogram cell signaling pathways, enhancing the migration and invasion ability of cancer cells and even promoting metastasis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These mutations also interact with hypoxic tumor microenvironments, where low oxygen levels regulate ECM composition. Hypoxia-induced signaling alters ECM stiffness and promotes ECM degradation through upregulation of MMPs and integrin pathways, providing cancer cells with a migratory advantage (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This dynamic interaction between hypoxia and ECM remodeling underscores the importance of targeting both factors in HCC.\u003c/p\u003e \u003cp\u003eOur study demonstrates that ADAM9, a member of the MMP family, is a key target gene of TP53, with its expression positively correlated with TP53 GOF mutations in HCC. ADAM9 plays an essential role in ECM remodeling, promoting tumor cell migration, invasion, and metastasis (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). It achieves this by degrading ECM components and modifying the physical properties of the ECM. ADAM9 also regulates the function of stromal cells in the tumor microenvironment, influencing fibroblast activation, endothelial cell migration, angiogenesis, and immune cell function (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Consequently, ADAM9 serves as a potential therapeutic target in HCC.\u003c/p\u003e \u003cp\u003eSeveral therapeutic strategies aimed at inhibiting ADAM9 are in development, including small molecule inhibitors, monoclonal antibodies, and RNA interference-based approaches. These strategies aim to block ADAM9's activity, thereby disrupting its role in tumor progression. Targeting ADAM9 could reduce tumor cell proliferation, migration, and invasion, leading to improved tumor microenvironment conditions and potentially enhancing the response to existing therapies. For instance, recent preclinical work has shown that ADAM9-targeted antibody-drug conjugates (ADCs), such as MGC028, exhibit promising efficacy in a variety of cancer models (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Emerging evidence also highlights the role of autophagy in ECM remodeling. Autophagic degradation of ECM components, mediated by lysosomal pathways, contributes to tumor stroma interactions and promotes tumor progression (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). ADAM9 has been linked to the regulation of autophagy in cancer cells, suggesting a dual role in ECM turnover and intracellular recycling processes, which may present additional therapeutic opportunities.\u003c/p\u003e \u003cp\u003eSP1, a key transcription factor, has been implicated in regulating the expression of ECM-related genes in tumors. In our study, we show that SP1, regulated by TP53, influences the expression of ADAM9 in HCC. Inhibition of SP1 expression led to decreased ADAM9 levels, underscoring its pivotal role in TP53-mediated ECM remodeling. SP1 is overexpressed in various cancers, including HCC, and its expression is associated with malignant tumor behaviors such as cell proliferation, invasion, and resistance to apoptosis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). By upregulating pro-angiogenic factors like vascular endothelial growth factor (VEGF) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), SP1 contributes to tumor growth and metastasis. Therapeutic strategies targeting SP1 could disrupt its regulation of ADAM9 and other downstream genes, providing a novel approach to treating tumors, including HCC.\u003c/p\u003e \u003cp\u003eIn summary, our findings uncover a new mechanism by which TP53 GOF mutations regulate tumor biology in HCC through ECM remodeling, with ADAM9 serving as a key effector. These results not only enhance our understanding of ECM dynamics in HCC progression but also offer a theoretical framework for developing targeted therapies aimed at TP53 GOF mutations and ADAM9. Future research should further investigate the molecular mechanisms linking TP53 mutations to ECM remodeling, focusing on SP1 and ADAM9, and assess how these changes influence HCC treatment outcomes and therapeutic responses.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study identifies a novel mechanism by which TP53 gain-of-function mutations contribute to the progression of HCC through ECM remodeling, with ADAM9 as a key target gene. These findings provide insights into how TP53 mutations influence the tumor microenvironment, offering potential new therapeutic targets. Future studies should focus on further elucidating the molecular pathways linking TP53 mutations, ECM remodeling, and cancer progression, ultimately leading to more effective treatments for HCC and other TP53-mutant cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperiments using clinical specimens were conducted under the approval from the Medical Ethics Committee of Sir Run Run Shaw Hospital of Zhejiang University (2020-568-02). Informed consents were obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest was reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82200735), Zhejiang Provincial Natural Science Foundation (LQ24H160027), Zhejiang Province Medical and public health projects (2022519993), Beijing Natural Science Foundation (7244427), Peking University Medicine Sailing Program for Young Scholars’ Scientific \u0026amp; Technological Innovation (BMU2025YFJHPY019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.X.J, H.Q.P and Y.Y.Z developed the concept and design of this study. X.Q.S and Y.H.C analyzed and interpreted the data. K.X.J and J.G wrote the manuscript. J.N.M designed and illustrated the figures. H.Q.P and S.J.X conducted a critical review of the manuscript and provided constructive feedback and suggestions for revisions. Q.J.M, Y.L.L and J.J.X provided guidance and supervision throughout the writing process.\u0026nbsp;The authors are grateful to Lixing Shiyuan Edu \u0026amp; Tech CO., Ltd (Purpose \u0026amp; Beyond Consulting) for assisting in figure polishing and bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020;70:7-30.\u003c/li\u003e\n\u003cli\u003eTang F, Gao R, Jeevan-Raj B, et al. LATS1 but not LATS2 represses autophagy by a kinase-independent scaffold function. 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Extracellular matrix assembly: a multiscale deconstruction. Nat Rev Mol Cell Biol 2014;15:771-85.\u003c/li\u003e\n\u003cli\u003eAdams J, Lawler J. Extracellular matrix: the thrombospondin family. Curr Biol 1993;3:188-90.\u003c/li\u003e\n\u003cli\u003eLam Y K, Yu J, Huang H, et al. TP53 R249S mutation in hepatic organoids captures the predisposing cancer risk[J]. Hepatology, 2023,78(3):727-740.\u003c/li\u003e\n\u003cli\u003eHassin O, Oren M. Drugging p53 in cancer: one protein, many targets[J]. Nat Rev Drug Discov, 2023,22(2):127-144.\u003c/li\u003e\n\u003cli\u003eWang Z, Burigotto M, Ghetti S, et al. Loss-of-Function but Not Gain-of-Function Properties of Mutant TP53 Are Critical for the Proliferation, Survival, and Metastasis of a Broad Range of Cancer Cells[J]. Cancer Discov, 2024,14(2):362-379.\u003c/li\u003e\n\u003cli\u003ePeuget S, Zhou X, Selivanova G. Translating p53-based therapies for cancer into the clinic[J]. 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Cancer Lett 2018;421:1-14.\u003c/li\u003e\n\u003cli\u003eDong Y, Wu Z, He M, et al. ADAM9 mediates the interleukin-6-induced Epithelial-Mesenchymal transition and metastasis through ROS production in hepatoma cells[J]. Cancer Lett, 2018,421:1-14.\u003c/li\u003e\n\u003cli\u003eKim J M, Jeung H, Rha S Y, et al. The effect of disintegrin-metalloproteinase ADAM9 in gastric cancer progression[J]. Mol Cancer Ther, 2014,13(12):3074-3085.\u003c/li\u003e\n\u003cli\u003eScribner J A, Brown J G, Son T, et al. Abstract 1897: Preclinical development of MGC028, an ADAM9-targeted, glycan-linked, exatecan-based antibody-drug conjugate for the treatment of solid cancers[J]. Cancer ResearchCancer Research, 2024,84(6_Supplement):1897.\u003c/li\u003e\n\u003cli\u003eSong J, Nabeel-Shah S, Pu S, et al. Regulation of alternative polyadenylation by the C2H2-zinc-finger protein Sp1[J]. Mol Cell, 2022,82(17):3135-3150.\u003c/li\u003e\n\u003cli\u003eZhang G, Xie Z, Jiang J, et al. Mechanical confinement promotes heat resistance of hepatocellular carcinoma via SP1/IL4I1/AHR axis[J]. Cell Rep Med, 2023,4(8):101128.\u003c/li\u003e\n\u003cli\u003eLu H, Yuan P, Ma X, et al. Angiotensin-converting enzyme inhibitor promotes angiogenesis through Sp1/Sp3-mediated inhibition of notch signaling in male mice[J]. Nat Commun, 2023,14(1):731.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Scheme 1","content":"\u003cp\u003eScheme 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, extracellular matrix, ADAM9, TP53 GOF mutations, SP1 transcription factor","lastPublishedDoi":"10.21203/rs.3.rs-8749587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8749587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is the predominant form of liver cancer, necessitating improved prognostic models and therapeutic strategies. While TP53 mutations are established as markers of poor prognosis in HCC, their specific role in extracellular matrix (ECM) remodeling within the tumor microenvironment remains unclear, despite ECM dynamics being critical for tumor progression and metastasis. This study aims to elucidate the relationship between TP53 mutations and ECM remodeling to identify novel prognostic and therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eComprehensive bioinformatics analyses were performed using The Cancer Genome Atlas (TCGA) dataset. Single-sample gene set enrichment analysis (ssGSEA) was used to assess ECM content, while LASSO regression and Cox proportional hazards modeling constructed an ECM-related prognostic signature. Functional validation included RT-qPCR, Western blotting, and in vitro gain/loss-of-function experiments to explore the TP53-SP1-ADAM9 axis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eECM content was reduced in HCC tissues compared to normal livers, with 559 ECM-related genes differentially expressed. An 18-gene ECM prognostic signature was established, showing significant association with overall survival. TP53 mutations, particularly gain-of-function (GOF) variants, were linked to upregulated ADAM9 expression. Mechanistically, TP53 regulated ADAM9 via transcriptional factor SP1, with GOF mutations enhancing SP1 binding to the ADAM9 promoter.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identifies a TP53-SP1-ADAM9 axis driving ECM remodeling in HCC. The ECM prognostic signature improves survival prediction, while targeting ADAM9 or SP1 represents a promising therapeutic strategy for TP53-mutant HCC. These findings highlight ECM remodeling as a critical node for prognostic and therapeutic intervention.\u003c/p\u003e","manuscriptTitle":"Unique Extracellular Matrix Remodeling in Hepatocellular Carcinoma: TP53 GOF- SP1-ADAM9 Axis as a Prognostic and Therapeutic Target","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 19:03:58","doi":"10.21203/rs.3.rs-8749587/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"214004492257815787477566929211707355394","date":"2026-05-16T12:23:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T18:40:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147439925455339291693322814346813303747","date":"2026-03-07T16:57:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T04:09:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T09:04:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T08:58:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2026-01-31T11:41:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5dc431a-afda-457e-8163-6870c89e9fcf","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"214004492257815787477566929211707355394","date":"2026-05-16T12:23:04+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T19:03:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 19:03:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8749587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8749587","identity":"rs-8749587","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}