Triptolide targets JUN to reverse cisplatin resistance of ovarian cancer: insights from single-cell transcriptome analysis and machine learning validation
This study developed a comprehensive strategy integrating computational predictions with in vitro experimental validations that reverses CDDP resistance in PROC by downregulating JUN, which dismantles intracellular pro-survival networks.
Abstract
Background Platinum-resistant ovarian cancer (PROC) is a major clinical challenge driven by profound intratumoral heterogeneity. Triptolide (TP) exhibits promising anti-tumor potential, yet its precise mechanisms within PROC remain elusive due to the limitations of traditional target-screening strategies. Methods This study developed a comprehensive strategy integrating computational predictions with in vitro experimental validations. First, scRNA-seq data were processed to evaluate TP target genes predicted by SwissTargetPrediction, alongside pseudotime trajectory and CellChat intercellular communication analyses. Subsequently, an ensemble of six machine learning (ML) algorithms (LASSO, Random Forest, Boruta, Decision Tree, XGBoost, and GBM) was utilized to pinpoint the core therapeutic target. To verify direct molecular engagement, molecular docking, molecular dynamics (MD) simulations, and Surface Plasmon Resonance (SPR) assays were performed. Finally, the functional mechanism of the identified target in CDDP resistance was validated in vitro using parental SKOV3 and resistant SKOV3/CDDP cell lines. Results scRNA-seq analysis revealed TP target genes are preferentially enriched in highly genomically unstable malignant epithelial cells. This subpopulation showed an aggressive intercellular communication profile, profound dependence on extracellular matrix (ECM) signals, and dominant secretion of the chemoresistance-related cytokine osteopontin (SPP1). Furthermore, the ML pipeline consistently pinpointed the proto-oncogene JUN as the core therapeutic target. Experiments confirmed TP efficiently suppressed aberrant c-Jun overexpression. Targeted JUN knockdown restored CDDP sensitivity, while its overexpression antagonized the synergistic cytotoxic and apoptotic effects of TP and CDDP. Ultimately, TP reverses CDDP resistance in PROC by downregulating JUN, dismantling intracellular pro-survival networks, and disrupting pro-tumorigenic crosstalk. Conclusion In conclusion, TP reverses CDDP resistance in PROC by downregulating JUN, which dismantles intracellular pro-survival networks. Integrating scRNA-seq and ML provides an accurate paradigm for deciphering botanical pharmacology, laying a strong foundation for the future development of TP-based therapies tailored for PROC.
Background Platinum-based chemotherapy remains the cornerstone of ovarian cancer treatment, yet acquired resistance severely limits efficacy. Because platinum agents can also influence immunogenic cell death and tumor microenvironment (TME) remodeling, clarifying cellular pharmacological mechanisms of sensitivity and resistance within the ovarian cancer tumor ecosystem is important for understanding therapeutic failure. Methods We integrated single-cell RNA-seq from treatment-naïve and post-neoadjuvant chemotherapy ovarian tumors with bulk multi-omics cohorts to map epithelial tumor heterogeneity, transcriptional reprogramming, pathway activation, immune infiltration, and inferred cell–cell communication networks. DUSP5 was identified as a candidate regulator linked to stress-adaptive programs. Functional validation included qPCR, proliferation, migration, colony-formation, carboplatin dose-response, and xenograft assays following stable DUSP5 knockdown. Results Single-cell analysis revealed chemotherapy-associated epithelial states with enhanced stress, EMT, hypoxia, and inflammatory signatures. Elevated DUSP5 expression correlated with MAPK/JAK-STAT/TGF-β signaling, myeloid/stromal infiltration, and clinical outcome differences in independent cohorts. DUSP5 depletion suppressed proliferation and migration, amplified carboplatin-induced MAPK transcriptional responses and pro-apoptotic signaling (BAX/PUMA upregulation, BCL2 downregulation), reduced IC50 values in both OVCAR8 and SKOV3 cells, and significantly inhibited xenograft tumor growth. Conclusion DUSP5 may contribute to platinum response and resistance by linking tumor-intrinsic adaptive programs with TME-associated features in ovarian cancer. These findings support DUSP5 as a candidate biomarker and therapeutic target that warrants further mechanistic and clinical validation.
Chengfeng Liu, Wehua Li, Tingjun Liao et al.· Frontiers in Pharmacology· 0 citations
Background Bladder and kidney cancer burden rises globally, with environmental toxicants driving their progression. Methods We integrated global epidemiological analysis, single-cell transcriptomics, cell-cell communication analysis, epithelial subclustering, pseudotime inference, toxicological target prediction, survival modeling, cross-cohort validation, single-cell virtual knockout, spatial transcriptomic deconvolution, molecular docking/dynamics, CETSA, and in vitro assays to define a shared molecular interface linking triphenyl phosphate (TPP) to bladder and kidney cancer. Results Both malignancies exhibited age- and SDI-associated burden patterns. Single-cell profiling identified conserved epithelial, stromal, and immune ecosystems, with tumor epithelial cells occupying central positions in intercellular communication networks. Epithelial subclustering revealed a reproducible EMT-high subcluster 4 in both cancers, which localized to a terminal-like pseudotime state and was associated with poor survival. Predicted TPP targets intersected with subcluster 4 signatures and converged on extracellular matrix organization, adhesion, and leukocyte transendothelial migration pathways. Integrative survival modeling and multi-cohort validation identified MMP9 as a robust prognostic candidate associated with tumor progression. Importantly, single-cell virtual knockout of MMP9 revealed convergent remodeling of proliferative, inflammatory, hypoxia-related, and stress-response programs across bladder and renal cancer epithelial cells, highlighting conserved regulatory circuitry. Spatial transcriptomics further localized MMP9 to macrophage- and fibroblast-enriched niches in both tumor types. Structural modeling and CETSA supported an interaction between TPP and MMP9. Experimentally, TPP upregulated MMP9 at both mRNA and protein levels in T24 and 786-O cells; higher concentrations reduced viability, whereas lower concentrations enhanced migration and clonogenic growth. Conclusions TPP promotes the malignant phenotypes of bladder and kidney cancer via MMP9, which is validated by virtual knockout and in vitro experiments.
Hao Wang, Hongquan Liu, Qian Li et al.· Clinical and Experimental Me...· 0 citations
Background/Objectives: Colorectal cancer (CRC) remains a leading cause of cancer-associated mortality worldwide. The current therapeutic interventions are heavily constrained by the development of resistance and severe systemic toxicity. To address these challenges, this study integrated a multi-disciplinary framework involving high-throughput in silico screening followed by in vitro experimental validation to identify novel genetic targets of CRC and evaluate the efficacy of FDA-approved drugs. The primary objective was to identify safe and selective therapeutic agents capable of modulating their effect. Methods: The methodology employed a systematic screening of recent large-scale Genome-Wide Association Studies (GWASs) to pinpoint novel targets, followed by in silico pathogenicity prediction, homology modelling and high-throughput virtual screening of over 1615 FDA-approved drugs. The prioritized candidates were validated in vitro using MTT cytotoxicity assays and differential gene expression analysis across CRC cell lines (HCT116 and HT29) and a non-tumorigenic control, Human embryonic kidney cell line HEK293. Results: In silico analysis identified CLUH, CLSTN3 and SLC11A2 as novel potential targets. Based on in silico predicted deleterious mutations and subsequent molecular docking-based virtual screening, Telmisartan, Dutasteride and Venetoclax were prioritized. This prioritization was supported by their high binding affinity and dose-dependent cytotoxicity in MTT assays; thus, suggesting their repurposing potential for CRC treatment. Telmisartan exhibited a superior therapeutic profile not only in terms of the statistically significant cytotoxicity (p < 0.01), but also its selective effect on HCT116 and HT29 when compared to high safety profile in HEK293. This was further validated when Telmisartan selectively downregulated CLUH and SLC11A2 in CRC cell lines, HCT116 and HT29 while maintaining expression levels in the non-cancerous HEK293 cell line remained significantly unaffected. Furthermore, a 100 ns molecular dynamics simulation confirmed the stable binding conformation and structural reliability of the SLC11A2 (Trp179Ser)–Telmisartan complex. Conclucions: Our findings conclude that Telmisartan is a promising candidate for drug repurposing for CRC treatment and capable of modulating selected novel biomarkers CLUH and SLC11A2. However, further multi-omics-based confirmatory studies and pre-clinical validation studies are needed in the future to confirm the long-term efficacy of this repositioning strategy.
Sarah Hunachagi, H. H. Alqudihi, S. Abdulazeez et al.· Pharmaceutics· 0 citations
Background: Ovarian cancer remains the most lethal gynecologic cancer, with limited improvements in patient survival despite targeted therapies and a high recurrence rate (~80%). Current standard-of-care for frontline treatment involves platinum-based chemotherapy, but the emergence of resistant clones limits long-term efficacy. Existing models often overlook critical interactions between cancer cells and their microenvironment. Therefore, we investigated the ovarian cancer microenvironment to identify cell populations and markers driving treatment resistance. Methods: We employed a multi-modal systems biology approach, integrating multiplex immunohistochemistry, bulk, and single-cell RNA sequencing to characterize the ovarian cancer microenvironment. Cell type composition was quantified using ImageJ (mIF) and computational deconvolution tools (CIBERSORTx, singleR) for benign (n=6–13) and cancer (n=7–20) samples. Platinum-sensitivity was determined by mapping single-cell data to a clinically annotated reference. Differential expression analysis and pathway enrichment were performed to identify key biological processes between benign vs cancer and sensitive vs resistant phenotypes. Additionally, a combinatorial marker identification tool (COMET) was used to determine a resistant signature in the sc-RNAseq dataset, which was validated using pseudotime in sc-RNAseq and the TCGA-OV bulk-RNAseq cohort. Results: Across modalities, results showed an increase in macrophage and T cell marker expression with an upregulation of inflammatory and immune pathways, alongside decreased fibroblast abundance, in cancer compared to benign tissues. Resistant samples also showed high expression of macrophage and fibroblast markers paired with an enrichment of the epithelial-to-mesenchymal transition pathway while sensitive samples showed high expression of T and NK cell markers and the upregulation of immune pathways. COMET identified two distinct resistant programs: an EMT-associated fibroblast signature characterized by INHBA, TIMP3 and NNMT; and a canonical epithelial ovarian cancer signature characterized by SLPI, MMP7, and WFDC2. Resistant signature scoring of bulk data from the TCGA-OV cohort predicted shorter treatment-free intervals for patients with higher signature scores and longer treatment-free intervals for patients with lower scores. Conclusions: These findings highlight the importance of tumor microenvironment components, particularly macrophages and fibroblasts, as key contributors to resistance in ovarian cancer and establish a potential resistant signature for biomarker discovery.
Adriana Del Pino Herrera, Miguel A. Martínez, Monica Kim et al.· Research Square· 0 citations
Breast cancer (BC), a malignant disease responsible for high fatality worldwide, is characterized by EGFR overexpression or mutation, which contributes to tumor cell survival and progression. The present study explored the efficacy of Genistein in modulating EGFR using an integrated computational approach. Common targets of Genistein (SwissTargetPrediction) and BC (GeneCards) were identified, followed by PPI, GO, and KEGG enrichment analyses using STRING. The Genistein–targets–pathways network was constructed using Cytoscape 3.10.0. Docking, MM/GBSA binding free‐energy calculations, molecular dynamics (MD) simulations, and post‐MD analyses (PCA and FEL) were performed for Genistein and the reference inhibitor Afatinib against EGFR. Docking scores of Genistein (−8.96 kcal/mol) and Afatinib (−10.22 kcal/mol) demonstrated comparable binding within the EGFR ATP‐binding pocket, with Genistein retaining interactions with key catalytic residues. The MM/GBSA binding free‐energy of Genistein (−134.96 kcal/mol) and Afatinib (−137.92 kcal/mol) differed only marginally, indicating comparable binding stability within the EGFR domain. MD simulations, together with RMSD, RMSF, radius of gyration, SASA, PCA, and FEL analyses, confirmed the structural stability of the Genistein‐EGFR complex throughout the simulation. Collectively, these findings suggest that Genistein is a promising multitarget EGFR‐modulating compound that warrants further experimental validation through in vitro and in vivo studies.
N. Lonikar, Sameep Sonvane, N. B. Bavage et al.· ChemistrySelect· 0 citations
The emergence of molecular classifications for gastric cancer (GC), The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG), has advanced targeted and immunotherapies, but their clinical translation faces real-world obstacles including high cost, tissue availability, standardization, and intratumoral heterogeneity. The present review critically compares the two classification systems regarding prognostic utility across geographic populations and boundary conflicts, noting that ACRG is more operable in East Asian populations whereas TCGA is better suited for mechanistic exploration. Focusing on acquired resistance as a core bottleneck in precision therapy, mechanisms underlying anti-Human Epidermal Growth Factor Receptor 2 (HER2) resistance and primary/secondary resistance to immune checkpoint inhibitors (ICIs) were systematically dissected, while also addressing immune-related adverse events and pseudo-/hyperprogression. Moreover, non-immune elements of the tumor microenvironment deserve attention: Cancer-associated fibroblasts limit drug penetration and promote epithelial-mesenchymal transition through physical barriers and paracrine signaling; metabolic reprogramming (high glycolysis and glutamine addiction) impairs chemotherapy and ICI efficacy via an acidic microenvironment and metabolic competition. Finally, multi-target combination strategies are envisioned based on pathway redundancy, along with liquid biopsy-driven dynamic adaptive therapy and single-cell/spatial multi-omics integration for precise microenvironment intervention. The present review aims to offer a systematic reference for moving GC precision therapy from static subtyping toward dynamic, multi-dimensional integration.
