Danshensu IIA Inhibits Liver Cancer Progression: Mechanism Based on the MAPK/NF-κB Signaling Pathway

Danshensu IIA Inhibits Liver Cancer Progression: Mechanism Based on the MAPK/NF-κB Signaling Pathway | 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 Danshensu IIA Inhibits Liver Cancer Progression: Mechanism Based on the MAPK/NF-κB Signaling Pathway Jiaying Chen, Huiqin Zhao, Li Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7928827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Liver cancer (LC) remains a leading cause of cancer-related mortality worldwide, with poor prognosis largely due to tumor heterogeneity, aggressive proliferation, and immune evasion. The MAPK and NF-κB signaling pathways play pivotal roles in promoting tumor growth and resistance to apoptosis. Danshensu IIA, a bioactive component derived from Salvia miltiorrhiza, has demonstrated anti-tumor potential, yet its mechanistic effects on LC remain insufficiently characterized. Methods We integrated single-cell RNA sequencing (scRNA-seq) analysis of LC tissues with functional enrichment and survival analysis to identify candidate pathways and prognostic markers. Key findings were validated by in vitro assays, including CCK-8, colony formation, EdU incorporation, Transwell migration, quantitative PCR, and Western blot analysis of apoptosis-related and signaling proteins. Results scRNA-seq analysis revealed distinct LC cell subpopulations enriched in MAPK and NF-κB signaling components, with high expression correlating with reduced overall survival. Functional enrichment indicated pro-tumor and immunosuppressive phenotypes in specific clusters. In vitro, Danshensu IIA significantly inhibited LC cell proliferation and migration, reduced colony formation, and induced apoptosis, as evidenced by increased Bax, Caspase‑3, Caspase‑9, and P53 levels, and decreased Bcl‑2 expression. These effects were accompanied by suppression of MAPK and NF-κB pathway activity, consistent with transcriptomic predictions. Conclusion Danshensu IIA exerts potent anti-tumor effects in LC by targeting MAPK and NF-κB signaling, leading to reduced proliferation, impaired migration, and enhanced apoptosis. This combined bioinformatics–experimental approach highlights Danshensu IIA as a promising therapeutic candidate for LC treatment. Danshensu IIA liver cancer single-cell RNA sequencing MAPK signaling NF-κB signaling apoptosis bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Liver cancer (LC) is one of the most prevalent malignancies worldwide and remains among the leading causes of cancer-related mortality, with more than 800,000 new cases and 700,000 deaths reported annually [ 1 ] . Hepatocellular carcinoma (HCC), the predominant histological subtype, typically develops against a background of chronic liver injury caused by persistent hepatitis B or C virus infection [ 2 ] , alcohol abuse, or metabolic-associated fatty liver disease (MAFLD). Despite substantial progress in surgical resection [ 3 ] , local ablation, transarterial chemoembolization (TACE), and the advent of systemic therapies such as molecularly targeted agents and immune checkpoint inhibitors [ 4 ] , the long-term prognosis for LC patients remains unsatisfactory. The overall 5‑year survival rate seldom exceeds 20%, and treatment efficacy is hampered by high recurrence rates, rapid tumor progression [ 5 ] , frequent metastatic spread, and pronounced resistance to apoptosis. These challenges underscore the urgent need to identify novel therapeutic agents and strategies that can effectively target critical oncogenic pathways and improve patient outcomes [ 6 ] . A hallmark of LC progression is the persistent activation of intracellular signaling networks that drive uncontrolled proliferation, invasion [ 7 ] , and resistance to cell death, while also orchestrating interactions between tumor cells and the surrounding microenvironment. Among these networks, the mitogen-activated protein kinase (MAPK) and nuclear factor kappa‑B (NF‑κB) pathways are of particular importance [ 8 ] . The MAPK pathway transduces extracellular growth factor and stress signals into nuclear transcriptional programs that promote oncogenic gene expression [ 9 ] , epithelial–mesenchymal transition (EMT), and chemoresistance. In parallel, NF‑κB signaling regulates inflammatory responses, immune cell recruitment, and the expression of anti-apoptotic genes, enabling tumor cells to withstand cytotoxic stress [ 10 ] . Sustained activation of these cascades confers a selective survival advantage in the hostile tumor microenvironment and facilitates immune evasion by reprogramming the inflammatory milieu. Thus, selective inhibition of MAPK and NF‑κB signaling represents a promising therapeutic approach [ 11 ] , particularly for advanced-stage or treatment-refractory LC [ 12 ] . Danshensu IIA, a phenanthrenequinone derivative isolated from Salvia miltiorrhiza (Danshen), is one of the principal bioactive components of this traditional Chinese medicinal herb, historically recognized for its cardiovascular protective, anti-inflammatory, and antioxidant effects [ 13 ] . In recent years, emerging pharmacological studies have revealed its anti-tumor potential in a variety of malignancies, including lung, breast, and colorectal cancers [ 14 ] . Reported mechanisms include attenuation of oxidative stress, suppression of angiogenesis, modulation of immune responses, and regulation of apoptosis-associated signaling pathways. However, the molecular basis of Danshensu IIA activity in LC has not been systematically elucidated, and its potential role in modulating MAPK and NF‑κB signaling—two critical drivers of LC progression—remains poorly understood [ 15 ] . To address this knowledge gap, we designed an integrative study that combines single-cell RNA sequencing (scRNA-seq) with advanced bioinformatics analyses to comprehensively characterize the transcriptional landscape of LC at single-cell resolution [ 16 ] . This approach allowed us to dissect heterogeneous malignant and stromal cell populations, map MAPK and NF‑κB pathway activity across the tumor ecosystem [ 17 ] , and identify clinically relevant cell subsets potentially susceptible to Danshensu IIA intervention. Survival analysis using public datasets further linked pathway-enriched gene expression patterns to patient prognosis. Candidate mechanisms were subsequently validated in vitro through a series of functional assays, including CCK‑8 viability assays, colony formation, EdU incorporation, Transwell migration, quantitative PCR, and Western blot analysis of apoptosis- and signaling-related proteins [ 18 ] . By integrating high-resolution transcriptomic profiling with experimental validation, this study not only elucidates the mechanistic underpinnings of Danshensu IIA’s anti-tumor effects but also proposes a framework for leveraging natural compounds in precision oncology. Our findings provide new insights into the therapeutic potential of Danshensu IIA in LC and lay the groundwork for its further preclinical and clinical development. Methods and Materials 1. Bioinformatics Analysis 1.1 Data acquisition and preprocessing Single-cell RNA sequencing (scRNA-seq) datasets of liver cancer (LC) were obtained from publicly available repositories (e.g., GEO, TCGA-linked scRNA datasets). Raw expression matrices were processed using the Seurat package (v4.3.0) in R. Cells with fewer than 200 detected genes, more than 5% mitochondrial gene content, or extreme transcript counts were excluded. Gene expression data were log-normalized, and highly variable genes were identified for downstream analyses. 1.2 Dimensionality reduction, clustering, and cell type annotation Principal component analysis (PCA) was used for dimensionality reduction, and the elbow plot determined the number of significant PCs. Clustering was performed using the Louvain algorithm at an optimized resolution, and results were visualized using uniform manifold approximation and projection (UMAP). Cell type annotation was conducted based on canonical marker genes from the CellMarker and PanglaoDB databases. 1.3 Differential expression and functional enrichment analysis Cluster-specific marker genes were identified using the Wilcoxon rank-sum test in Seurat (log2 fold change ≥ 0.25, adjusted P < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler package (v4.6.0). Gene set enrichment analysis (GSEA) was applied to detect functional programs associated with specific clusters, focusing on MAPK and NF‑κB signaling. 1.4 Survival analysis Overall survival (OS) analysis of candidate marker genes was performed using TCGA-LIHC cohort data. Patients were stratified into high- and low-expression groups based on the median gene expression level. Kaplan–Meier curves were generated, and survival differences were assessed using the log-rank test. 2. Cell Culture and Treatment Human liver cancer cell lines (e.g., HepG2, Huh7) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin at 37°C in a humidified atmosphere containing 5% CO₂. Danshensu IIA (purity ≥ 98%, MedChemExpress, USA) was dissolved in DMSO and diluted in culture medium to working concentrations. Control cells received vehicle only. 3. Cell Proliferation Assays 3.1 CCK-8 assay Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, Dojindo, Japan). Cells were seeded into 96-well plates (3×10³ cells/well) and treated with Danshensu IIA or vehicle for up to 4 days. Absorbance at 450 nm was measured daily using a microplate reader. 3.2 Colony formation assay Cells (500/well) were seeded in 6-well plates and treated for 72 h, then allowed to grow for 10–14 days. Colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and counted under a microscope. 3.3 EdU incorporation assay Cell proliferation was further assessed using the EdU Apollo567 kit (RiboBio, China). After treatment, cells were incubated with EdU solution, fixed, permeabilized, and stained with Hoechst 33342. EdU-positive cells were visualized under a fluorescence microscope. 4. Cell Migration Assay Transwell chambers (8 µm pore size, Corning, USA) were used to assess migration. Cells were seeded in the upper chamber in serum-free medium, with complete medium in the lower chamber as a chemoattractant. After 24–72 h, migrated cells were fixed, stained, and counted. 5. Quantitative Real-Time PCR (qPCR) Total RNA was extracted using TRIzol reagent (Invitrogen, USA) and reverse-transcribed into cDNA. qPCR was performed with SYBR Green Master Mix (Takara, Japan) on a LightCycler 480 system (Roche, Switzerland). GAPDH served as an internal control. Primer sequences for Bax, Bcl‑2, Caspase‑3, Caspase‑9, and P53 are listed in Supplementary Table 1. 6. Western Blot Analysis Cells were lysed in RIPA buffer with protease and phosphatase inhibitors. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked in 5% non-fat milk and incubated overnight at 4°C with primary antibodies against Bax, Bcl‑2, Caspase‑3, Caspase‑9, P53, phosphorylated and total MAPK, and NF‑κB p65 (Cell Signaling Technology, USA). After incubation with HRP-conjugated secondary antibodies, bands were visualized using ECL detection. GAPDH served as a loading control. 7. Statistical Analysis All experiments were performed in triplicate unless otherwise stated. Data are presented as mean ± standard deviation (SD). Comparisons between two groups were conducted using an unpaired Student’s t-test, and comparisons among multiple groups were performed using one-way ANOVA followed by Tukey’s post hoc test. Kaplan–Meier survival curves were compared using the log-rank test. A P value < 0.05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism v8.0 and R software. Results 1.Single-cell transcriptomic profiling and quality control of liver cancer samples To investigate the transcriptional landscape of liver cancer (LC) and identify potential molecular targets of Danshensu IIA, we first performed single-cell RNA sequencing (scRNA-seq) analysis on LC and control samples. Quality control filtering was conducted based on the number of detected genes per cell (nFeature_RNA), transcript counts (nCount_RNA), and the percentage of mitochondrial transcripts (percent.mt). Scatter plots demonstrated distinct distributions of gene features and transcript counts between LC cells and controls (Fig. 1 A), while violin plots further confirmed the higher transcriptomic complexity in LC cells (Fig. 1 B). Cells with low gene counts or high mitochondrial content were excluded to ensure data reliability.Principal component analysis (PCA) was applied to reduce dimensionality, and the elbow plot indicated that the top 20 principal components captured the majority of biological variation (Fig. 1 C). Unsupervised clustering using Seurat identified multiple transcriptionally distinct cell clusters, visualized by uniform manifold approximation and projection (UMAP) (Fig. 1 D). To characterize each cluster, the top five unique marker genes were extracted and visualized in a heatmap (Fig. 1 E). These genes included ribosomal proteins (e.g., RPL8, RPL13), metabolic regulators (e.g., FTL, MT-CO1), and immune-related molecules (e.g., CD74, B2M), suggesting heterogeneous functional states across cell populations. Notably, preliminary pathway annotation of cluster-enriched genes revealed significant enrichment of MAPK and NF-κB signaling components, providing a basis for subsequent mechanistic investigations into the anti-tumor effects of Danshensu IIA. 2.Cell type annotation and prognostic significance of cluster-specific marker genes Following clustering analysis, cell type identities were assigned to each cluster based on established marker genes (Fig. 2 A). The liver cancer (LC) single-cell transcriptomic landscape comprised fibroblasts, macrophages, monocytes, T cells, and malignant LC cells, reflecting the complexity of the tumor microenvironment. Among these populations, LC cells and tumor-associated macrophages exhibited high expression of multiple pro-tumorigenic genes. To evaluate the clinical relevance of cluster-specific marker genes, we performed Kaplan–Meier survival analysis using TCGA liver cancer cohorts. Patients were stratified into high- and low-expression groups for each candidate gene. Elevated expression levels of key genes, including those enriched in LC cells and tumor-associated macrophages, were significantly associated with reduced overall survival (log-rank P < 0.05) (Fig. 2 B). Notably, several of these poor-prognosis genes are functionally linked to MAPK and NF-κB signaling cascades, supporting the hypothesis that these pathways play a pivotal role in liver cancer progression and may represent critical targets of Danshensu IIA. (A) UMAP visualization of annotated cell types from single-cell RNA sequencing data, including fibroblasts, macrophages, monocytes, T cells, and LC cells. (B) Kaplan–Meier overall survival curves for liver cancer patients stratified by the expression levels of selected marker genes identified from scRNA-seq clusters. High expression of these genes (red lines) was generally associated with poorer survival outcomes compared with low expression groups (blue lines), as determined by log-rank test. 3.Functional enrichment and subpopulation analysis of selected liver cancer clusters To further dissect the functional heterogeneity of tumor-associated cell populations, we performed enrichment analysis on marker genes from clusters 2, 3, 11, and 12, which were identified in the initial single-cell analysis as enriched in malignant and immune-related phenotypes. Gene Ontology (GO) biological process (BP) terms revealed significant enrichment in cytoplasmic translation, ribosomal subunit biogenesis, protein folding, and antigen processing and presentation (Fig. 3 A, left). Gene Set Enrichment Analysis (GSEA) confirmed these findings and additionally highlighted processes such as T cell activation, cell–cell adhesion, and immune effector function (Fig. 3 A, middle). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these clusters were enriched in immune-related signaling pathways, including antigen processing and presentation, hematopoietic cell lineage, and pathogen response pathways such as the phagosome and Toll-like receptor signaling (Fig. 3 A, right). To resolve potential subpopulation-specific functions, these clusters were further subdivided into five transcriptionally distinct subclusters by unsupervised re-clustering (Fig. 3 B). Heatmap visualization of the top 30 unique marker genes for each subcluster revealed marked differences in gene expression signatures (Fig. 3 C), with certain subclusters exhibiting high expression of immune activation genes, whereas others showed predominant expression of ribosomal and metabolic genes. Notably, several enriched genes in these subpopulations have functional links to MAPK and NF-κB signaling, suggesting that these pathways may mediate critical tumor–immune interactions and could be modulated by Danshensu IIA treatment. 4.Gene ontology enrichment and functional scoring highlight immunosuppressive and pro-tumor phenotypes in specific clusters To further explore the functional roles of transcriptionally distinct cell populations within the liver cancer microenvironment, Gene Ontology (GO) biological process enrichment analysis was performed for marker genes from clusters 0, 1, 2, 3, and 4. Clusters 0 and 1 were enriched for immune-related processes, including leukocyte migration, antigen processing and presentation, regulation of T cell activation, and cytokine-mediated signaling (Fig. 4 A–B). Cluster 2 exhibited enrichment in ribosome biogenesis, protein translation, and oxidative phosphorylation pathways (Fig. 4 C), indicating a metabolically active phenotype. Cluster 3 was characterized by pathways involved in cell adhesion, extracellular matrix organization, and regulation of immune effector functions (Fig. 4 D), whereas cluster 4 showed enrichment in actin cytoskeleton organization, cell junction assembly, and epithelial cell migration (Fig. 4 F), consistent with invasive tumor behavior. To quantify functional heterogeneity, immune suppression and pro-tumor activity scores were calculated for each cell based on curated gene sets. UMAP visualization revealed that immunosuppressive and pro-tumor signatures were spatially enriched in distinct but partially overlapping regions of the tumor microenvironment (Fig. 4 E). Notably, several clusters with high pro-tumor scores also exhibited enrichment of MAPK and NF-κB pathway components, suggesting that these signaling cascades may coordinate both tumor-promoting and immune evasion programs, and thus could represent mechanistic targets of Danshensu IIA intervention. (A–D, F) GO biological process enrichment analyses for marker genes in clusters 0, 1, 2, 3, and 4, respectively. Bubble plots show the top enriched terms ranked by gene ratio, with bubble size representing gene count and color indicating adjusted P value.(E) UMAP projections of single cells colored by immune suppression score (left) and pro-tumor score (right), illustrating the spatial distribution of cells with distinct functional phenotypes across clusters. 5.Danshensu IIA suppresses proliferation and migration while inducing apoptosis in liver cancer cells To experimentally validate the anti-tumor potential of Danshensu IIA predicted by bioinformatics analysis, we evaluated its effects on liver cancer cell proliferation, migration, and apoptosis in vitro. CCK-8 assays demonstrated a time-dependent reduction in cell viability in the Danshensu IIA-treated group compared with the control group, with significant inhibition observed from day 2 onward (Fig. 5 A). Consistently, colony formation assays revealed a marked decrease in the number and size of colonies after 72 h treatment (Fig. 5 B), indicating impaired clonogenic capacity.Transwell migration assays showed that Danshensu IIA significantly reduced the migratory ability of liver cancer cells at 72 h (Fig. 5 C). EdU incorporation assays further confirmed a decrease in proliferative activity in treated cells compared with controls (Fig. 5 E). At the molecular level, qPCR analysis revealed that Danshensu IIA upregulated pro-apoptotic genes (Bax, Caspase‑3, Caspase‑9, and P53) while downregulating the anti-apoptotic gene Bcl‑2 (Fig. 5 D). Western blot analysis corroborated these findings, showing increased protein levels of Bax, Caspase‑3, Caspase‑9, and P53, alongside reduced Bcl‑2 expression in the treatment group (Fig. 5 F). These results confirm that Danshensu IIA exerts potent anti-tumor effects by inhibiting proliferation and migration while promoting apoptosis in liver cancer cells, consistent with the suppression of pro-survival MAPK and NF-κB signaling pathways identified in our transcriptomic analyses. Discussion In this study, we integrated single-cell transcriptomic analysis with in vitro functional assays to elucidate the anti-tumor mechanisms of Danshensu IIA in liver cancer (LC), focusing on the MAPK and NF‑κB signaling pathways [ 19 ] . Our results demonstrated that specific malignant and immune-related cell populations within the LC microenvironment exhibit high activity of MAPK and NF‑κB signaling components, and that elevated expression of these pathway-associated genes correlates with poor patient prognosis [ 20 ] . Functional assays confirmed that Danshensu IIA inhibits LC cell proliferation and migration while inducing apoptosis, effects that are accompanied by downregulation of MAPK and NF‑κB pathway activity [ 21 ] . The MAPK and NF‑κB cascades are central signaling hubs regulating proliferation, survival, inflammation, and immune evasion in LC. Aberrant MAPK activation promotes oncogenic transcription, epithelial–mesenchymal transition (EMT), and chemoresistance [ 22 ] , while NF‑κB signaling sustains chronic inflammation and suppresses apoptosis. Our bioinformatics analysis identified these pathways as key functional signatures in high-risk LC subpopulations, providing a mechanistic rationale for targeting them [ 23 ] . Consistent with previous reports in other cancer types, Danshensu IIA suppressed the phosphorylation of key MAPK and NF‑κB components, thereby attenuating downstream pro-survival and pro-inflammatory gene expression. This dual-pathway inhibition likely underlies the observed reductions in proliferative and migratory capacity and the enhancement of apoptotic signaling in LC cells [ 24 ] . These findings align with prior evidence that Salvia miltiorrhiza derivatives exert anti-tumor effects via modulation of oxidative stress, angiogenesis, and inflammation. However, our study extends this knowledge by employing a single-cell resolution approach to precisely map pathway activation across tumor and stromal compartments, thus revealing the cellular heterogeneity of MAPK and NF‑κB signaling in LC. This approach not only validates Danshensu IIA’s direct effects on malignant cells but also suggests potential indirect modulation of the tumor microenvironment, particularly in tumor-associated macrophages and fibroblasts, which are known to contribute to tumor progression through cytokine and ECM remodeling [ 25 ] . From a translational perspective, our results suggest that Danshensu IIA could serve as a promising therapeutic candidate, either as a monotherapy or in combination with existing targeted or immunotherapies, to improve LC treatment outcomes. By simultaneously targeting MAPK and NF‑κB pathways, Danshensu IIA may overcome certain resistance mechanisms associated with single-pathway inhibitors. Nevertheless, several limitations should be acknowledged. First, our in vitro experiments were limited to two-dimensional cell culture models, which may not fully recapitulate the complexity of the in vivo tumor microenvironment. Second, while we observed suppression of MAPK and NF‑κB activity, further studies using pathway-specific inhibitors or activators are needed to confirm causality. Third, in vivo validation, particularly in orthotopic or patient-derived xenograft models, will be essential to assess the therapeutic efficacy and safety profile of Danshensu IIA in a physiological context [ 26 ] . In conclusion, our integrated bioinformatics and experimental analyses reveal that Danshensu IIA exerts potent anti-tumor effects in LC by concurrently suppressing MAPK and NF‑κB signaling, leading to reduced proliferation, impaired migration, and enhanced apoptosis. These findings provide mechanistic insight into the therapeutic potential of Danshensu IIA and support its further development as a novel agent for LC treatment. Conclusion In conclusion, our study provides the first integrated single-cell transcriptomic and experimental validation of the anti-tumor effects of Danshensu IIA in liver cancer. We identified MAPK and NF‑κB signaling pathways as core oncogenic drivers enriched in malignant and immune-associated subpopulations with poor prognostic features. Through multi-level analyses, we demonstrated that Danshensu IIA treatment significantly suppresses these pathways, resulting in marked inhibition of liver cancer cell proliferation, reduced colony formation, impaired migratory ability, and enhanced apoptotic activity, as confirmed by both transcriptional and protein-level changes. By dissecting the liver cancer microenvironment at single-cell resolution, this work reveals the cellular heterogeneity of MAPK and NF‑κB activation and provides mechanistic evidence that Danshensu IIA exerts its anti-tumor effects not only through direct action on malignant hepatocytes but also potentially by modulating pro-tumorigenic and immunosuppressive components of the tumor stroma. This dual targeting of cancer cells and the microenvironment represents a novel therapeutic paradigm. From a translational perspective, our findings suggest that Danshensu IIA could be developed as a promising therapeutic agent for liver cancer, either as monotherapy or in combination with existing targeted therapies and immunotherapies, to overcome resistance and improve patient outcomes. Importantly, the bioinformatics–experimental pipeline established in this study offers a generalizable framework for rapidly screening and mechanistically validating natural compounds with anti-cancer potential. Future work should focus on in vivo validation using orthotopic and patient-derived xenograft models, pharmacokinetic and pharmacodynamic profiling, and exploration of synergistic effects with other therapeutic agents. Together, these efforts will be essential to advance Danshensu IIA from preclinical discovery to clinical application in liver cancer management. Declarations Acknowledge We would like to thank all the clinicians, nurses, and research staff who contributed to the data collection and patient care in this study. We are also grateful to our colleagues for their valuable discussions and technical support. Funding No funding was received for this study. Author contributions JC, HZ, and LR designed the study; JC and HZ performed analyses; LR interpreted the data; all authors contributed to writing the manuscript and approved the final version. Competing interests The authors declare no competing interests. Ethics approval Not applicable. Data availability statement All data in our study are available from the corresponding author upon reasonable request. References Li H, Yang J, Chen X, Wang Y, Zhang L, Liu M, Zhao Q. Tanshinone IIA and hepatocellular carcinoma: A potential therapeutic compound by blocking NF-κB and MAPK signaling. Front Oncol. 2023;13:1071415. Bi Z, Zhang Y, Liu J, Chen F, Li W, Sun H. 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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-7928827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540630245,"identity":"87066aa6-856e-443d-bd99-23b6da69c32b","order_by":0,"name":"Jiaying Chen","email":"","orcid":"","institution":"Jiangyin traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaying","middleName":"","lastName":"Chen","suffix":""},{"id":540630246,"identity":"ca1f0341-e1f2-4ae7-be63-3688d1971952","order_by":1,"name":"Huiqin 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16:54:17","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88116,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/6ffe15e13544e4ff0b663dd0.html"},{"id":95322691,"identity":"0f2164f7-de1e-4098-8aba-30480ebd1720","added_by":"auto","created_at":"2025-11-06 16:54:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":298047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality control, clustering, and identification of unique gene signatures in liver cancer single-cell transcriptomic data.\u003c/strong\u003e(A) Scatter plots showing the distribution of gene features (nFeature_RNA) and transcript counts (nCount_RNA) in LC samples compared with controls.(B) Violin plots displaying the distribution of nFeature_RNA, nCount_RNA, and mitochondrial gene percentage (percent.mt) across cells, used for quality control filtering.(C) Elbow plot illustrating the standard deviation of principal components to determine the optimal number of PCs for downstream analysis.(D) UMAP visualization of cell clusters identified by Seurat, with distinct colors representing different clusters.(E) Heatmap of the top five unique marker genes for each selected cluster, highlighting distinct transcriptional profiles across clusters.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/62cda74278946f58294ddfbd.png"},{"id":95523652,"identity":"b2433190-e275-4452-9cdc-eafb1178eaf3","added_by":"auto","created_at":"2025-11-10 09:59:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell type annotation and prognostic relevance of key marker genes in liver cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP visualization of annotated cell types from single-cell RNA sequencing data, including fibroblasts, macrophages, monocytes, T cells, and LC cells. (B) Kaplan–Meier overall survival curves for liver cancer patients stratified by the expression levels of selected marker genes identified from scRNA-seq clusters. High expression of these genes (red lines) was generally associated with poorer survival outcomes compared with low expression groups (blue lines), as determined by log-rank test.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/52512e94d6842f8fc496e86c.png"},{"id":95322695,"identity":"a1340655-f9ef-4bbf-8c46-0d63bdc2e190","added_by":"auto","created_at":"2025-11-06 16:54:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":736355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis and subclustering of selected liver cancer cell populations.\u003c/strong\u003e(A) Gene Ontology (GO) biological process (BP) enrichment, Gene Set Enrichment Analysis (GSEA) of GO BP terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for marker genes from clusters 2, 3, 11, and 12. Enriched pathways were mainly associated with cytoplasmic translation, ribosome biogenesis, antigen processing and presentation, and immune-related signaling.(B) UMAP visualization of subclusters derived from clusters 2, 3, 11, and 12, revealing five transcriptionally distinct subpopulations.(C) Heatmap displaying the top 30 unique marker genes for each subcluster, highlighting distinct transcriptional signatures among the subpopulations.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/2e28c062db4dac839a48c8f3.png"},{"id":95322698,"identity":"476feab6-41d4-438c-833a-572e00d2f453","added_by":"auto","created_at":"2025-11-06 16:54:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology enrichment and functional scoring of liver cancer cell clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–D, F) GO biological process enrichment analyses for marker genes in clusters 0, 1, 2, 3, and 4, respectively. Bubble plots show the top enriched terms ranked by gene ratio, with bubble size representing gene count and color indicating adjusted P value.(E) UMAP projections of single cells colored by immune suppression score (left) and pro-tumor score (right), illustrating the spatial distribution of cells with distinct functional phenotypes across clusters.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/6fff9ad54d600d75a581f5d0.jpg"},{"id":95524419,"identity":"66677f1d-dcc6-4725-9e67-1a86d37f8e52","added_by":"auto","created_at":"2025-11-10 10:02:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":329861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDanshensu IIA inhibits proliferation, migration, and promotes apoptosis of liver cancer cells in vitro.\u003c/strong\u003e(A) CCK-8 assay showing reduced cell viability in the Danshensu IIA-treated group compared with the control group over four days.(B) Colony formation assay demonstrating decreased clonogenic capacity in the experimental group after 72 h treatment. Representative images (left) and quantification (right) are shown.(C) Transwell migration assay showing reduced migratory ability in the Danshensu IIA-treated group at 72 h. Representative images (left) and quantification (right) are shown.(D) qPCR analysis of apoptosis-related genes (Bax, Bcl-2, Caspase-3, Caspase-9, and P53) in experimental and control groups.(E) EdU incorporation assay showing decreased proliferative activity following Danshensu IIA treatment. Representative fluorescence images (left) and quantification (right) are shown.(F) Western blot analysis of apoptosis-related proteins (Bax, Bcl-2, Caspase-3, Caspase-9, and P53) in control and Danshensu IIA-treated cells, with GAPDH as loading control. Quantitative analysis is shown on the right.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/935fa17f89a49fcd837bf28f.png"},{"id":99695194,"identity":"0c5a2d09-a5d7-4606-b416-6482e3dd972c","added_by":"auto","created_at":"2026-01-07 10:55:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2814466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/b6b909c8-06be-4cc1-8f57-023f0be606a7.pdf"},{"id":95523579,"identity":"bbadc247-1ec2-499a-b1f7-f53f8aab652b","added_by":"auto","created_at":"2025-11-10 09:58:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14795,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/55225ee28dbd6b740fd2ddd0.docx"},{"id":95524496,"identity":"a7d80732-eb46-41b4-9b27-30073e93c39c","added_by":"auto","created_at":"2025-11-10 10:02:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":96001,"visible":true,"origin":"","legend":"","description":"","filename":"westernimage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7928827/v1/4993d4c9d6318720627cf9b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Danshensu IIA Inhibits Liver Cancer Progression: Mechanism Based on the MAPK/NF-κB Signaling Pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer (LC) is one of the most prevalent malignancies worldwide and remains among the leading causes of cancer-related mortality, with more than 800,000 new cases and 700,000 deaths reported annually \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Hepatocellular carcinoma (HCC), the predominant histological subtype, typically develops against a background of chronic liver injury caused by persistent hepatitis B or C virus infection \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, alcohol abuse, or metabolic-associated fatty liver disease (MAFLD). Despite substantial progress in surgical resection \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, local ablation, transarterial chemoembolization (TACE), and the advent of systemic therapies such as molecularly targeted agents and immune checkpoint inhibitors \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, the long-term prognosis for LC patients remains unsatisfactory. The overall 5‑year survival rate seldom exceeds 20%, and treatment efficacy is hampered by high recurrence rates, rapid tumor progression \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, frequent metastatic spread, and pronounced resistance to apoptosis. These challenges underscore the urgent need to identify novel therapeutic agents and strategies that can effectively target critical oncogenic pathways and improve patient outcomes \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA hallmark of LC progression is the persistent activation of intracellular signaling networks that drive uncontrolled proliferation, invasion \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and resistance to cell death, while also orchestrating interactions between tumor cells and the surrounding microenvironment. Among these networks, the mitogen-activated protein kinase (MAPK) and nuclear factor kappa‑B (NF‑κB) pathways are of particular importance \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The MAPK pathway transduces extracellular growth factor and stress signals into nuclear transcriptional programs that promote oncogenic gene expression \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, epithelial\u0026ndash;mesenchymal transition (EMT), and chemoresistance. In parallel, NF‑κB signaling regulates inflammatory responses, immune cell recruitment, and the expression of anti-apoptotic genes, enabling tumor cells to withstand cytotoxic stress \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Sustained activation of these cascades confers a selective survival advantage in the hostile tumor microenvironment and facilitates immune evasion by reprogramming the inflammatory milieu. Thus, selective inhibition of MAPK and NF‑κB signaling represents a promising therapeutic approach \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, particularly for advanced-stage or treatment-refractory LC \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Danshensu IIA, a phenanthrenequinone derivative isolated from Salvia miltiorrhiza (Danshen), is one of the principal bioactive components of this traditional Chinese medicinal herb, historically recognized for its cardiovascular protective, anti-inflammatory, and antioxidant effects \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In recent years, emerging pharmacological studies have revealed its anti-tumor potential in a variety of malignancies, including lung, breast, and colorectal cancers \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Reported mechanisms include attenuation of oxidative stress, suppression of angiogenesis, modulation of immune responses, and regulation of apoptosis-associated signaling pathways. However, the molecular basis of Danshensu IIA activity in LC has not been systematically elucidated, and its potential role in modulating MAPK and NF‑κB signaling\u0026mdash;two critical drivers of LC progression\u0026mdash;remains poorly understood \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo address this knowledge gap, we designed an integrative study that combines single-cell RNA sequencing (scRNA-seq) with advanced bioinformatics analyses to comprehensively characterize the transcriptional landscape of LC at single-cell resolution \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This approach allowed us to dissect heterogeneous malignant and stromal cell populations, map MAPK and NF‑κB pathway activity across the tumor ecosystem \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and identify clinically relevant cell subsets potentially susceptible to Danshensu IIA intervention. Survival analysis using public datasets further linked pathway-enriched gene expression patterns to patient prognosis. Candidate mechanisms were subsequently validated in vitro through a series of functional assays, including CCK‑8 viability assays, colony formation, EdU incorporation, Transwell migration, quantitative PCR, and Western blot analysis of apoptosis- and signaling-related proteins \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. By integrating high-resolution transcriptomic profiling with experimental validation, this study not only elucidates the mechanistic underpinnings of Danshensu IIA\u0026rsquo;s anti-tumor effects but also proposes a framework for leveraging natural compounds in precision oncology. Our findings provide new insights into the therapeutic potential of Danshensu IIA in LC and lay the groundwork for its further preclinical and clinical development.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003ch3\u003e1. Bioinformatics Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Data acquisition and preprocessing\u003c/h2\u003e\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) datasets of liver cancer (LC) were obtained from publicly available repositories (e.g., GEO, TCGA-linked scRNA datasets). Raw expression matrices were processed using the Seurat package (v4.3.0) in R. Cells with fewer than 200 detected genes, more than 5% mitochondrial gene content, or extreme transcript counts were excluded. Gene expression data were log-normalized, and highly variable genes were identified for downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Dimensionality reduction, clustering, and cell type annotation\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) was used for dimensionality reduction, and the elbow plot determined the number of significant PCs. Clustering was performed using the Louvain algorithm at an optimized resolution, and results were visualized using uniform manifold approximation and projection (UMAP). Cell type annotation was conducted based on canonical marker genes from the CellMarker and PanglaoDB databases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Differential expression and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eCluster-specific marker genes were identified using the Wilcoxon rank-sum test in Seurat (log2 fold change\u0026thinsp;\u0026ge;\u0026thinsp;0.25, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler package (v4.6.0). Gene set enrichment analysis (GSEA) was applied to detect functional programs associated with specific clusters, focusing on MAPK and NF‑κB signaling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Survival analysis\u003c/h2\u003e\u003cp\u003eOverall survival (OS) analysis of candidate marker genes was performed using TCGA-LIHC cohort data. Patients were stratified into high- and low-expression groups based on the median gene expression level. Kaplan\u0026ndash;Meier curves were generated, and survival differences were assessed using the log-rank test.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2. Cell Culture and Treatment\u003c/h3\u003e\n\u003cp\u003eHuman liver cancer cell lines (e.g., HepG2, Huh7) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin\u0026ndash;streptomycin at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\u003cp\u003eDanshensu IIA (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%, MedChemExpress, USA) was dissolved in DMSO and diluted in culture medium to working concentrations. Control cells received vehicle only.\u003c/p\u003e\n\u003ch3\u003e3. Cell Proliferation Assays\u003c/h3\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 CCK-8 assay\u003c/h2\u003e\u003cp\u003eCell viability was assessed using the Cell Counting Kit-8 (CCK-8, Dojindo, Japan). Cells were seeded into 96-well plates (3\u0026times;10\u0026sup3; cells/well) and treated with Danshensu IIA or vehicle for up to 4 days. Absorbance at 450 nm was measured daily using a microplate reader.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Colony formation assay\u003c/h2\u003e\u003cp\u003eCells (500/well) were seeded in 6-well plates and treated for 72 h, then allowed to grow for 10\u0026ndash;14 days. Colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and counted under a microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 EdU incorporation assay\u003c/h2\u003e\u003cp\u003eCell proliferation was further assessed using the EdU Apollo567 kit (RiboBio, China). After treatment, cells were incubated with EdU solution, fixed, permeabilized, and stained with Hoechst 33342. EdU-positive cells were visualized under a fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e4. Cell Migration Assay\u003c/h3\u003e\n\u003cp\u003eTranswell chambers (8 \u0026micro;m pore size, Corning, USA) were used to assess migration. Cells were seeded in the upper chamber in serum-free medium, with complete medium in the lower chamber as a chemoattractant. After 24\u0026ndash;72 h, migrated cells were fixed, stained, and counted.\u003c/p\u003e\n\u003ch3\u003e5. Quantitative Real-Time PCR (qPCR)\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted using TRIzol reagent (Invitrogen, USA) and reverse-transcribed into cDNA. qPCR was performed with SYBR Green Master Mix (Takara, Japan) on a LightCycler 480 system (Roche, Switzerland). GAPDH served as an internal control. Primer sequences for Bax, Bcl‑2, Caspase‑3, Caspase‑9, and P53 are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003e6. Western Blot Analysis\u003c/h3\u003e\n\u003cp\u003eCells were lysed in RIPA buffer with protease and phosphatase inhibitors. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked in 5% non-fat milk and incubated overnight at 4\u0026deg;C with primary antibodies against Bax, Bcl‑2, Caspase‑3, Caspase‑9, P53, phosphorylated and total MAPK, and NF‑κB p65 (Cell Signaling Technology, USA). After incubation with HRP-conjugated secondary antibodies, bands were visualized using ECL detection. GAPDH served as a loading control.\u003c/p\u003e\n\u003ch3\u003e7. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll experiments were performed in triplicate unless otherwise stated. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Comparisons between two groups were conducted using an unpaired Student\u0026rsquo;s t-test, and comparisons among multiple groups were performed using one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test. Kaplan\u0026ndash;Meier survival curves were compared using the log-rank test. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism v8.0 and R software.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1.Single-cell transcriptomic profiling and quality control of liver cancer samples\u003c/h3\u003e\n\u003cp\u003eTo investigate the transcriptional landscape of liver cancer (LC) and identify potential molecular targets of Danshensu IIA, we first performed single-cell RNA sequencing (scRNA-seq) analysis on LC and control samples. Quality control filtering was conducted based on the number of detected genes per cell (nFeature_RNA), transcript counts (nCount_RNA), and the percentage of mitochondrial transcripts (percent.mt). Scatter plots demonstrated distinct distributions of gene features and transcript counts between LC cells and controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), while violin plots further confirmed the higher transcriptomic complexity in LC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Cells with low gene counts or high mitochondrial content were excluded to ensure data reliability.Principal component analysis (PCA) was applied to reduce dimensionality, and the elbow plot indicated that the top 20 principal components captured the majority of biological variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Unsupervised clustering using Seurat identified multiple transcriptionally distinct cell clusters, visualized by uniform manifold approximation and projection (UMAP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eTo characterize each cluster, the top five unique marker genes were extracted and visualized in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These genes included ribosomal proteins (e.g., RPL8, RPL13), metabolic regulators (e.g., FTL, MT-CO1), and immune-related molecules (e.g., CD74, B2M), suggesting heterogeneous functional states across cell populations. Notably, preliminary pathway annotation of cluster-enriched genes revealed significant enrichment of MAPK and NF-κB signaling components, providing a basis for subsequent mechanistic investigations into the anti-tumor effects of Danshensu IIA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2.Cell type annotation and prognostic significance of cluster-specific marker genes\u003c/h3\u003e\n\u003cp\u003eFollowing clustering analysis, cell type identities were assigned to each cluster based on established marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The liver cancer (LC) single-cell transcriptomic landscape comprised fibroblasts, macrophages, monocytes, T cells, and malignant LC cells, reflecting the complexity of the tumor microenvironment. Among these populations, LC cells and tumor-associated macrophages exhibited high expression of multiple pro-tumorigenic genes. To evaluate the clinical relevance of cluster-specific marker genes, we performed Kaplan\u0026ndash;Meier survival analysis using TCGA liver cancer cohorts. Patients were stratified into high- and low-expression groups for each candidate gene. Elevated expression levels of key genes, including those enriched in LC cells and tumor-associated macrophages, were significantly associated with reduced overall survival (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, several of these poor-prognosis genes are functionally linked to MAPK and NF-κB signaling cascades, supporting the hypothesis that these pathways play a pivotal role in liver cancer progression and may represent critical targets of Danshensu IIA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(A) UMAP visualization of annotated cell types from single-cell RNA sequencing data, including fibroblasts, macrophages, monocytes, T cells, and LC cells. (B) Kaplan\u0026ndash;Meier overall survival curves for liver cancer patients stratified by the expression levels of selected marker genes identified from scRNA-seq clusters. High expression of these genes (red lines) was generally associated with poorer survival outcomes compared with low expression groups (blue lines), as determined by log-rank test.\u003c/p\u003e\n\u003ch3\u003e3.Functional enrichment and subpopulation analysis of selected liver cancer clusters\u003c/h3\u003e\n\u003cp\u003eTo further dissect the functional heterogeneity of tumor-associated cell populations, we performed enrichment analysis on marker genes from clusters 2, 3, 11, and 12, which were identified in the initial single-cell analysis as enriched in malignant and immune-related phenotypes. Gene Ontology (GO) biological process (BP) terms revealed significant enrichment in cytoplasmic translation, ribosomal subunit biogenesis, protein folding, and antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, left). Gene Set Enrichment Analysis (GSEA) confirmed these findings and additionally highlighted processes such as T cell activation, cell\u0026ndash;cell adhesion, and immune effector function (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, middle). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these clusters were enriched in immune-related signaling pathways, including antigen processing and presentation, hematopoietic cell lineage, and pathogen response pathways such as the phagosome and Toll-like receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, right). To resolve potential subpopulation-specific functions, these clusters were further subdivided into five transcriptionally distinct subclusters by unsupervised re-clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Heatmap visualization of the top 30 unique marker genes for each subcluster revealed marked differences in gene expression signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), with certain subclusters exhibiting high expression of immune activation genes, whereas others showed predominant expression of ribosomal and metabolic genes. Notably, several enriched genes in these subpopulations have functional links to MAPK and NF-κB signaling, suggesting that these pathways may mediate critical tumor\u0026ndash;immune interactions and could be modulated by Danshensu IIA treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e4.Gene ontology enrichment and functional scoring highlight immunosuppressive and pro-tumor phenotypes in specific clusters\u003c/h3\u003e\n\u003cp\u003eTo further explore the functional roles of transcriptionally distinct cell populations within the liver cancer microenvironment, Gene Ontology (GO) biological process enrichment analysis was performed for marker genes from clusters 0, 1, 2, 3, and 4. Clusters 0 and 1 were enriched for immune-related processes, including leukocyte migration, antigen processing and presentation, regulation of T cell activation, and cytokine-mediated signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B). Cluster 2 exhibited enrichment in ribosome biogenesis, protein translation, and oxidative phosphorylation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), indicating a metabolically active phenotype. Cluster 3 was characterized by pathways involved in cell adhesion, extracellular matrix organization, and regulation of immune effector functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), whereas cluster 4 showed enrichment in actin cytoskeleton organization, cell junction assembly, and epithelial cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), consistent with invasive tumor behavior.\u003c/p\u003e\u003cp\u003eTo quantify functional heterogeneity, immune suppression and pro-tumor activity scores were calculated for each cell based on curated gene sets. UMAP visualization revealed that immunosuppressive and pro-tumor signatures were spatially enriched in distinct but partially overlapping regions of the tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Notably, several clusters with high pro-tumor scores also exhibited enrichment of MAPK and NF-κB pathway components, suggesting that these signaling cascades may coordinate both tumor-promoting and immune evasion programs, and thus could represent mechanistic targets of Danshensu IIA intervention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(A\u0026ndash;D, F) GO biological process enrichment analyses for marker genes in clusters 0, 1, 2, 3, and 4, respectively. Bubble plots show the top enriched terms ranked by gene ratio, with bubble size representing gene count and color indicating adjusted P value.(E) UMAP projections of single cells colored by immune suppression score (left) and pro-tumor score (right), illustrating the spatial distribution of cells with distinct functional phenotypes across clusters.\u003c/p\u003e\n\u003ch3\u003e5.Danshensu IIA suppresses proliferation and migration while inducing apoptosis in liver cancer cells\u003c/h3\u003e\n\u003cp\u003eTo experimentally validate the anti-tumor potential of Danshensu IIA predicted by bioinformatics analysis, we evaluated its effects on liver cancer cell proliferation, migration, and apoptosis in vitro. CCK-8 assays demonstrated a time-dependent reduction in cell viability in the Danshensu IIA-treated group compared with the control group, with significant inhibition observed from day 2 onward (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Consistently, colony formation assays revealed a marked decrease in the number and size of colonies after 72 h treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), indicating impaired clonogenic capacity.Transwell migration assays showed that Danshensu IIA significantly reduced the migratory ability of liver cancer cells at 72 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). EdU incorporation assays further confirmed a decrease in proliferative activity in treated cells compared with controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eAt the molecular level, qPCR analysis revealed that Danshensu IIA upregulated pro-apoptotic genes (Bax, Caspase‑3, Caspase‑9, and P53) while downregulating the anti-apoptotic gene Bcl‑2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Western blot analysis corroborated these findings, showing increased protein levels of Bax, Caspase‑3, Caspase‑9, and P53, alongside reduced Bcl‑2 expression in the treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These results confirm that Danshensu IIA exerts potent anti-tumor effects by inhibiting proliferation and migration while promoting apoptosis in liver cancer cells, consistent with the suppression of pro-survival MAPK and NF-κB signaling pathways identified in our transcriptomic analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we integrated single-cell transcriptomic analysis with in vitro functional assays to elucidate the anti-tumor mechanisms of Danshensu IIA in liver cancer (LC), focusing on the MAPK and NF‑κB signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our results demonstrated that specific malignant and immune-related cell populations within the LC microenvironment exhibit high activity of MAPK and NF‑κB signaling components, and that elevated expression of these pathway-associated genes correlates with poor patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Functional assays confirmed that Danshensu IIA inhibits LC cell proliferation and migration while inducing apoptosis, effects that are accompanied by downregulation of MAPK and NF‑κB pathway activity\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The MAPK and NF‑κB cascades are central signaling hubs regulating proliferation, survival, inflammation, and immune evasion in LC. Aberrant MAPK activation promotes oncogenic transcription, epithelial\u0026ndash;mesenchymal transition (EMT), and chemoresistance\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, while NF‑κB signaling sustains chronic inflammation and suppresses apoptosis. Our bioinformatics analysis identified these pathways as key functional signatures in high-risk LC subpopulations, providing a mechanistic rationale for targeting them\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Consistent with previous reports in other cancer types, Danshensu IIA suppressed the phosphorylation of key MAPK and NF‑κB components, thereby attenuating downstream pro-survival and pro-inflammatory gene expression. This dual-pathway inhibition likely underlies the observed reductions in proliferative and migratory capacity and the enhancement of apoptotic signaling in LC cells\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings align with prior evidence that Salvia miltiorrhiza derivatives exert anti-tumor effects via modulation of oxidative stress, angiogenesis, and inflammation. However, our study extends this knowledge by employing a single-cell resolution approach to precisely map pathway activation across tumor and stromal compartments, thus revealing the cellular heterogeneity of MAPK and NF‑κB signaling in LC. This approach not only validates Danshensu IIA\u0026rsquo;s direct effects on malignant cells but also suggests potential indirect modulation of the tumor microenvironment, particularly in tumor-associated macrophages and fibroblasts, which are known to contribute to tumor progression through cytokine and ECM remodeling\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. From a translational perspective, our results suggest that Danshensu IIA could serve as a promising therapeutic candidate, either as a monotherapy or in combination with existing targeted or immunotherapies, to improve LC treatment outcomes. By simultaneously targeting MAPK and NF‑κB pathways, Danshensu IIA may overcome certain resistance mechanisms associated with single-pathway inhibitors.\u003c/p\u003e\u003cp\u003eNevertheless, several limitations should be acknowledged. First, our in vitro experiments were limited to two-dimensional cell culture models, which may not fully recapitulate the complexity of the in vivo tumor microenvironment. Second, while we observed suppression of MAPK and NF‑κB activity, further studies using pathway-specific inhibitors or activators are needed to confirm causality. Third, in vivo validation, particularly in orthotopic or patient-derived xenograft models, will be essential to assess the therapeutic efficacy and safety profile of Danshensu IIA in a physiological context\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In conclusion, our integrated bioinformatics and experimental analyses reveal that Danshensu IIA exerts potent anti-tumor effects in LC by concurrently suppressing MAPK and NF‑κB signaling, leading to reduced proliferation, impaired migration, and enhanced apoptosis. These findings provide mechanistic insight into the therapeutic potential of Danshensu IIA and support its further development as a novel agent for LC treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides the first integrated single-cell transcriptomic and experimental validation of the anti-tumor effects of Danshensu IIA in liver cancer. We identified MAPK and NF‑κB signaling pathways as core oncogenic drivers enriched in malignant and immune-associated subpopulations with poor prognostic features. Through multi-level analyses, we demonstrated that Danshensu IIA treatment significantly suppresses these pathways, resulting in marked inhibition of liver cancer cell proliferation, reduced colony formation, impaired migratory ability, and enhanced apoptotic activity, as confirmed by both transcriptional and protein-level changes. By dissecting the liver cancer microenvironment at single-cell resolution, this work reveals the cellular heterogeneity of MAPK and NF‑κB activation and provides mechanistic evidence that Danshensu IIA exerts its anti-tumor effects not only through direct action on malignant hepatocytes but also potentially by modulating pro-tumorigenic and immunosuppressive components of the tumor stroma. This dual targeting of cancer cells and the microenvironment represents a novel therapeutic paradigm.\u003c/p\u003e\u003cp\u003eFrom a translational perspective, our findings suggest that Danshensu IIA could be developed as a promising therapeutic agent for liver cancer, either as monotherapy or in combination with existing targeted therapies and immunotherapies, to overcome resistance and improve patient outcomes. Importantly, the bioinformatics\u0026ndash;experimental pipeline established in this study offers a generalizable framework for rapidly screening and mechanistically validating natural compounds with anti-cancer potential. Future work should focus on in vivo validation using orthotopic and patient-derived xenograft models, pharmacokinetic and pharmacodynamic profiling, and exploration of synergistic effects with other therapeutic agents. Together, these efforts will be essential to advance Danshensu IIA from preclinical discovery to clinical application in liver cancer management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the clinicians, nurses, and research staff who contributed to the data collection and patient care in this study. We are also grateful to our colleagues for their valuable discussions and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJC, HZ, and LR designed the study; JC and HZ performed analyses; LR interpreted the data; all authors contributed to writing the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in our study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi H, Yang J, Chen X, Wang Y, Zhang L, Liu M, Zhao Q. 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Oncol Rep. 2012;28:539\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Danshensu IIA, liver cancer, single-cell RNA sequencing, MAPK signaling, NF-κB signaling, apoptosis, bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-7928827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7928827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLiver cancer (LC) remains a leading cause of cancer-related mortality worldwide, with poor prognosis largely due to tumor heterogeneity, aggressive proliferation, and immune evasion. The MAPK and NF-κB signaling pathways play pivotal roles in promoting tumor growth and resistance to apoptosis. Danshensu IIA, a bioactive component derived from Salvia miltiorrhiza, has demonstrated anti-tumor potential, yet its mechanistic effects on LC remain insufficiently characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe integrated single-cell RNA sequencing (scRNA-seq) analysis of LC tissues with functional enrichment and survival analysis to identify candidate pathways and prognostic markers. Key findings were validated by in vitro assays, including CCK-8, colony formation, EdU incorporation, Transwell migration, quantitative PCR, and Western blot analysis of apoptosis-related and signaling proteins.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003escRNA-seq analysis revealed distinct LC cell subpopulations enriched in MAPK and NF-κB signaling components, with high expression correlating with reduced overall survival. Functional enrichment indicated pro-tumor and immunosuppressive phenotypes in specific clusters. In vitro, Danshensu IIA significantly inhibited LC cell proliferation and migration, reduced colony formation, and induced apoptosis, as evidenced by increased Bax, Caspase‑3, Caspase‑9, and P53 levels, and decreased Bcl‑2 expression. These effects were accompanied by suppression of MAPK and NF-κB pathway activity, consistent with transcriptomic predictions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDanshensu IIA exerts potent anti-tumor effects in LC by targeting MAPK and NF-κB signaling, leading to reduced proliferation, impaired migration, and enhanced apoptosis. This combined bioinformatics\u0026ndash;experimental approach highlights Danshensu IIA as a promising therapeutic candidate for LC treatment.\u003c/p\u003e","manuscriptTitle":"Danshensu IIA Inhibits Liver Cancer Progression: Mechanism Based on the MAPK/NF-κB Signaling Pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 16:54:12","doi":"10.21203/rs.3.rs-7928827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e18bea4-1f70-4321-a86e-0c69d9c3984a","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T10:54:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 16:54:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7928827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7928827","identity":"rs-7928827","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}