Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a poor prognosis. The absence of effective targeted therapies and endocrine treatment options leads to limited therapeutic options, which remains one of the major clinical challenges in TNBC management. Drug discovery is typically a lengthy and costly process that could be significantly improved through drug repurposing. However, the biological complexity and insufficient repurposing strategies hinder the reuse. This study aims to develop a deep learning-based framework to accelerate drug discovery for TNBC, identify novel therapeutic candidates, and uncover potential drug targets. We developed a deep neural network framework to predict the anticancer efficacy, toxicity profiles, and structural similarities of compounds. By applying this platform to screen over 6,000 compounds from the Drug Repurposing Hub, we identified promising candidates with potential therapeutic efficacy and safety profiles against TNBC. The top-predicted compounds were subsequently validated through comprehensive in vitro and in vivo functional assays. Furthermore, we employed transcriptomic sequencing and mass spectrometry-based proteomics to elucidate the molecular mechanisms underlying the anti-TNBC activity. We identified emodepside, a structurally unique molecule diverging from conventional anticancer agents that exhibited potent antitumor efficacy across multiple TNBC cell lines. Significantly, emodepside administration (5 mg/kg) inhibited tumor growth in xenograft models. Integrated multiomics analyses (RNA-seq/CETSA-MS) identified NAMPT as the primary target. This study demonstrates the viability of our deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients. Emodepside emerges as a promising TNBC therapeutic candidate, with a possible mechanism of promoting TNBC cell apoptosis via NAMPT inhibition.
The metabolic activities of cancer cells undergo complete transformation because they need to maintain their growth while resisting metabolic challenges and environmental dangers from their tumour surroundings. The metabolic changes that occur in cells depend on specific oncogenes together with tumour suppressor genes and stress-response pathways, which control essential bioenergetic and biosynthetic functions. This review presents the current scientific knowledge about genetic regulators, which include MYC, KRAS, PI3K-AKT-mTOR, EGFR, p53, PTEN, and LKB1-AMPK, that control glucose, amino acid, lipid, nucleotide, and mitochondrial metabolism in different human cancers. The research demonstrates that these pathways connect through common metabolic pathways, which produce metabolic flexibility and create complex metabolic patterns that drive tumour diversity and development and resistance to treatment. We present new systems-level frameworks that exceed pathway-based descriptions to show the intricate nature of cancer metabolism. The review investigates how artificial intelligence (AI) and machine learning methods, combined with multi-omics data and genome-scale metabolic models, enable scientists to enhance metabolic phenotyping and discover specific tumour weaknesses and forecast treatment results and combination methods. The study begins with a discussion of present-day obstacles that impede clinical application of research results, which include data inconsistency and the challenges of understanding and testing models. Then it presents upcoming research paths that will develop AI-powered metabolic assessment into biologically understandable and clinically usable tools. The review creates a comprehensive framework that connects genetic control mechanisms with metabolic network functions and AI-driven precision oncology.
FGFR4 signaling is an essential driver in hepatocellular carcinoma. However, traditional screening is often time-consuming, highlighting a need for efficient strategies to identify covalent chemotypes and accelerate FGFR4 drug discovery. We established an integrated AI-driven virtual screening framework to discover FGFR4 covalent inhibitors. The theoretical predictions were evaluated through a biochemical pipeline, encompassing in vitro FGFR4 kinase assays, immunoblotting of intracellular signaling cascades, and bottom-up LC-MS/MS peptide mapping. Biological validation of the computational predictions identified five distinct chemical scaffolds (hits 1, 2, 5, 7, and 8) exhibiting antiproliferative activity. The two most active candidates, hit 1 and hit 2, were selected for further mechanistic profiling. These compounds demonstrated dose-dependent FGFR4 kinase inhibition with IC50 values of 1.06 μM and 3.57 μM, respectively. Cellular assays revealed that both compounds attenuate FGFR4 autophosphorylation and its downstream FRS2/ERK1/2 signaling cascade without inducing non-specific protein degradation. Furthermore, bottom-up LC-MS/MS peptide mapping provided direct structural evidence that hit 1 and hit 2 engage the target cysteine residue via a Michael addition mechanism. Our AI-guided computational workflow identified multiple covalent FGFR4 inhibitors with measurable biological activity. Hit 1 and hit 2 represent structurally characterized covalent scaffolds. This study provides chemical starting points for targeted HCC therapy and demonstrates the integration of theoretical prediction and experimental validation in covalent drug discovery.
The discovery of next-generation agrochemicals is limited by analogue-based molecular design and the high cost of experimental screening. Here, we utilized a computational workflow combining generative AI, pharmacophore screening, hierarchical molecular docking, and protein-ligand binding compatibility assessment to discover CYP51-targeting antifungal agents. Applied to CYP51 from Rhizoctonia solani, this workflow prioritized thiazole-triazole hybrids, among which compound F50 showed strong antifungal activity. F50 exhibited potent in vitro and in vivo activity against R. solani and field efficacy comparable to or slightly better than tebuconazole under the tested conditions. Preliminary biosafety evaluation indicated lower acute toxicity of F50 toward zebrafish, earthworms, and honeybees than that of tebuconazole. Molecular docking and dynamics simulations suggested that the thiazole-containing scaffold and p-chloro substituent contributed to stable F50 binding within the CYP51 pocket. These results demonstrate the utility of combining generative molecular design with hierarchical virtual screening to identify promising antifungal lead compounds for sustainable crop protection.
Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski's Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.
Patients with head and neck squamous cell carcinoma (HNSC) face a high risk of developing non-small cell lung cancer (NSCLC). However, the shared molecular drivers linking these two malignancies remain poorly defined. We integrated TCGA and GEO datasets to identify shared differentially expressed genes (DEGs) between HNSC and NSCLC, subsequently evaluating their prognostic value, immune infiltration patterns (ssGSEA), and enriched pathways. Clinical validation of SCG5 protein expression was comprehensively performed using tissue microarrays (TMA) via immunohistochemistry. in vitro assays, including siRNA-mediated knockdown, CCK-8, colony formation, and Western blotting, were conducted in both HNSC (SCC-25) and NSCLC (A549) cell lines to ascertain its oncogenic functions and underlying molecular mechanisms. SCG5 was identified as a consistently upregulated, independent predictor of poor survival in both cancers. IHC confirmed SCG5 protein overexpression, correlating with lymph node metastasis and advanced clinical stages. GSEA revealed that SCG5-associated genes were significantly enriched in focal adhesion and the PI3K/AKT signaling pathway in both HNSC and NSCLC. SCG5 expression correlated positively with tumor-infiltrating macrophages (P < 0.001) and with PDCD1LG2, HAVCR2 and SIGLEC15 (P < 0.05). SCG5 knockdown significantly suppressed cell proliferation and colony formation in both SCC-25 and A549 cells, concurrently attenuating PI3K and AKT phosphorylation. SCG5 is a novel shared oncogenic driver and prognostic biomarker for HNSC and NSCLC. Downregulation of SCG5 suppresses the growth of HNSC and NSCLC cells, potentially through modulating the PI3K/AKT signaling pathway, thereby presenting a potential therapeutic target for both malignancies.
Despite the established epidemiological synergy between Porphyromonas gingivalis-induced periodontitis and oral squamous cell carcinoma (OSCC), a critical gap remains in developing single-agent therapeutics that simultaneously neutralize bacterial virulence and suppress host oncogenic signaling. This study aimed to comprehensively evaluate Curcumin 4'-O-β-D-gentiobioside (COG) as a novel, dual-pathway phytochemical intervention targeting the keystone virulence factor RgpB while concurrently disrupting OSCC progression networks. A multi-modal computational approach was used, including network pharmacology, molecular docking, molecular dynamics simulations, and ADMET profiling. Anti-aging activity was predicted through pharmacological activity modeling. Network pharmacology identified Curcumin 4'-O-β-D-gentiobioside (COG; PubChem CID: 46926100) targeting hub genes (TNF, IL6, EGFR, AKT1), enriching immune regulation, apoptosis, and oxidative stress pathways (FDR < 0.05). Docking revealed strong binding to RgpB (XP GScore: - 9.867 kcal/mol), supported by hydrogen bonds and π-stacking. Molecular dynamics confirmed stability (RMSD: 1.0-1.5 Å; ΔG =  - 14 kcal/mol). COG downregulated pro-inflammatory (CXCL10, MAPK8) and oncogenic (MDM2, BCL2L1) mediators, while enhancing ER stress response (KDELC1, ATF4). ADMET analysis predicted high absorption, negligible neurotoxicity, and no hepatotoxicity/genotoxicity. Predicted anti-aging activities included senolytic, antioxidant, and NF-κB inhibition. These results position COG as a promising dual-agent that concurrently neutralizes RgpB virulence, disrupts chronic inflammation, and suppresses OSCC progression, addressing a critical unmet need in oral disease therapeutics. These findings warrant in vivo validation for translation into oral disease therapeutics.
Breast cancer is one of the prominent reasons of death in women. HER2 is a promising target to counter breast cancer. In the current research, a structure-based pharmacophore model was generated to map and screen CMNPD, a comprehensive database of marine natural products. The two compounds (CMNPD30448 (hit1) and CMNPD7060 (hit2)) displayed better LibDock scores than the reference co-crystallized ligand. These compounds demonstrated stable molecular dynamics results conducted for 500 ns with stable root mean square deviation (RMSD) at 0.3 nm, stable radius of gyration (Rg) and root mean square fluctuation (RMSF). On ChEMBL compounds, different PaDEL descriptors and various machine learning (ML) and neural network (NN) methods were used. The results showed that PubChem fingerprints with random forest classification model displayed an accuracy of 0.91 and a receiver operating characteristic area under the curve (ROC-AUC) of 0.96. This model further predicted the retrieved compounds as 'active'. The explainable random forest with LIME showed that PubChem fingerprint440 [C(-C)(-O)(=O)], PubChem fingerprint452 [C(-O)(=O)], PubChem fingerprint380 [C(~O)(~O)], PubChem fingerprint566 [O-C-C-N] and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit1 and PubChem fingerprint700 [O-C-C-C-C-C-O-C], PubChem fingerprint380 [C(~O)(~O)], and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit2 have contributed towards plausible inhibitory potential. These findings suggest the two compounds CMNPD30448 and CMNPD7060 might serve as HER2 inhibitors. Further in vitro and in vivo analysis are required before using them.
Small molecules that modulate protein complexes have transformed cell biology and oncology, yet few chemical starting points exist to probe protein-protein interactions. To expand this space, we developed molecular COUPLrs, elaborated small molecules flanked by two cysteine‑reactive warheads. Using CONNECT, an integrated chemical proteomic platform that identifies proteins and complexes amenable to coupling, we revealed 171 targetable protein classes, including mutant‑selective complexes and assemblies not traditionally addressed by small molecules. We then optimized a COUPLr against the oncogenic fusion EML4‑ALK. This compound engages EML4‑ALK by binding its EML4 domain, remodeling protein dynamics, disrupting downstream signaling, and inducing proteasome‑mediated degradation of the fusion. Finally, we show that FDA‑approved drugs can be converted into COUPLrs to degrade their targets, indicating that this modality can endow existing therapeutics with new functional properties. Overall, molecular COUPLrs offer an unbiased framework to discover, characterize, and pharmacologically exploit protein complexes.
Rare breast cancers represent a clinically important but underrepresented group of malignancies. In this Perspective, rare breast cancers are considered within the broader rare cancer definition of an annual incidence below 6 cases per 100,000 persons, while also recognizing breast-specific rarity based on uncommon histology, molecular hallmarks, clinical presentation or sex-specific occurrence. These conditions are characterized by limited case numbers, biological heterogeneity, reduced clinical trial inclusion and fragmented evidence. These constraints challenge artificial intelligence (AI) development because many systems depend on large, balanced and externally validated datasets. AI may support diagnosis, histopathology, molecular interpretation, prognostic stratification and precision oncology decision support, but its use in rare breast cancers requires evidence standards adapted to small cohorts. This Perspective proposes an evidence-informed clinical governance framework organized around five domains: intended clinical use, small-cohort validation, synthetic data governance, human oversight and lifecycle monitoring. Its distinctive contribution is to translate general AI reporting and governance principles into rare breast cancer-specific safeguards, including objective data-quality checks, leakage prevention, uncertainty-aware validation, synthetic data plausibility scoring, pan-rare model reporting, patient involvement and post-deployment surveillance. Synthetic data may support development and simulation, but should not replace validation on real clinical cases. By linking small-cohort methodology with clinical oversight, regulatory alignment and lifecycle monitoring, the framework offers a practical roadmap for safe AI-enabled rare breast cancer precision oncology. Rare breast cancers are difficult to study because they occur infrequently and are often missing from large clinical datasets. This creates problems for artificial intelligence because AI usually needs large and diverse data to work reliably. Synthetic data may help with training and simulation, but it can also create misleading results if it is not carefully reviewed and separated from validation data. This article proposes a stepwise governance approach for using AI safely in rare breast cancers, with emphasis on clear clinical purpose, careful validation in small cohorts, responsible synthetic data use, expert human oversight and continuous monitoring after implementation.
This study focuses on tertiary lymphoid structure (TLS) semantic segmentation in whole slide images (WSIs). Unlike TLS binary segmentation, TLS semantic segmentation identifies boundaries and maturity and requires the integration of contextual information to discover discriminative features. Owing to the extensive scale of WSI (e.g., 100,000 × 100,000 pixels), TLS segmentation is typically performed using a patch-based strategy. However, this prevents the model from accessing information outside the patches, thereby limiting its performance. To address this issue, GCUNet, a graph neural network-based contextual learning network for TLS semantic segmentation, is proposed. Given an image patch (target) to be segmented, GCUNet first progressively aggregates the long-range and fine-grained contexts outside the target. Subsequently, a detail and context fusion block (DCFusion) was designed to integrate the context and details of the target to predict the segmentation mask. This study builds four TLS semantic segmentation datasets: TCGA-COAD, TCGA-LUSC, TCGA-BLCA, and PUMCH-PAAD. The first three, comprising 826 WSIs and 15,276 TLSs, will be made publicly available to promote TLS semantic segmentation. Experiments on these datasets demonstrate that GCUNet consistently improves the mean F1-score (mF1) performance compared with existing state-of-the-art methods, with an observed mF1 improvement of at least 7.41% over the best-performing baseline. These results highlight the potential of GCUNet for accurate TLS assessment and facilitate the development of computational pathology-based immune microenvironment analysis.
Intra-tumoral heterogeneity is a cardinal feature of solid tumors, yet how distinct cancer cell states functionally contribute to malignant and stromal diversity in situ remains poorly understood. Using mouse models to lineage-trace or genetically ablate the two predominant cancer cell states in autochthonous pancreatic ductal adenocarcinoma (PDAC), we discover that basal cancer cells are highly plastic, whereas classical cancer cells exhibit limited plasticity. Strikingly, ablation of the basal, but not the classical, state induced rapid and durable tumor collapse, driven by loss of immunosuppressive cancer-associated fibroblasts, macrophage repolarization, and reprogramming of the tumor cytokine milieu, culminating in tumor destruction by cytotoxic lymphocytes. Knockout of a single cytokine, GM-CSF, specifically in basal cells recapitulated macrophage repolarization and lymphocyte recruitment observed upon basal state ablation and shrank tumors. These results reveal the basal cell state controls an immunosuppressive cell circuit critical for PDAC maintenance, motivating therapeutic targeting of the basal cells.
Oral squamous cell carcinoma (OSCC) continues to pose significant therapeutic challenges due to high recurrence rates and treatment-related toxicity. Drug repurposing offers an accelerated, cost-effective pathway to discover novel therapeutic indications for established medications. This narrative review evaluates the mechanistic rationale and clinical potential of repurposing antifungal agents, such as azoles and allylamines, as adjuvant therapies for OSCC. A comprehensive literature search was performed across PubMed and Scopus (up to November 14, 2025). Studies were selected based on their focus on antifungal-mediated inhibition of OSCC growth and metastasis. Methodological quality was assessed using the Scale for the Evaluation of Narrative Review Articles. Antifungal agents, particularly itraconazole and terbinafine, exhibit potent antitumor activity by targeting key oncogenic pathways, including the Sonic Hedgehog (SHH) and phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin signaling axes. Preclinical evidence indicates that these agents induce apoptosis, inhibit angiogenesis, and sensitize OSCC cells to standard chemotherapy and radiotherapy. Specifically, itraconazole shows promise in treating SHH-high tumors, while terbinafine reduces tumor proliferation through antiangiogenic mechanisms. Antifungal drugs represent a biologically viable and resource-efficient strategy for enhancing OSCC treatment outcomes. To maximize clinical translation, future research must prioritize the development of nanoformulations and liposomal azoles to overcome existing bioavailability limitations.
Triple-negative breast cancer (TNBC) represents a breast cancer subtype lacking three essential biomarkers: estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This biological signature differentiates TNBC from other breast cancer subtypes and acts as an essential diagnostic criterion for clinical identification and constitutes essential diagnostic criteria for clinical identification. Among all subtypes of breast cancer, TNBC exhibits an exceptionally high level of aggressiveness. Its aggressive behavior and paucity of effective treatment options contribute to its notoriously poor prognosis. The immunogenic cell death (ICD) emergence has raised hopes to create fresh treatment approaches to strengthen TNBC patients' immune responses against tumors. However, the correlation between ICD and TNBC prognosis is still unclear. By analyzing transcriptomic data through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we discovered the differentially expressed genes (DEGs) and linked to ICD in TNBC. A prognostic model utilizing ICD was developed through LASSO regression. Then, we conducted multivariate Cox proportional hazards analysis on the identified DEGs. We used receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis to evaluate the model's predictive accuracy. To comprehensively evaluate the clinical relevance of the ICD signature, we investigated its associations with genomic alterations, tumor microenvironment (TME) characteristics, and therapeutic responses to both chemotherapy and immunotherapy. Furthermore, functional validation was performed using MDA-MB-231 and BT-549 through various in vitro assays, including CCK-8 proliferation tests, colony formation assays, and Transwell migration experiments to assess HEYL's biological role. A 4-gene ICD signature (HEYL, CXCL13, GBP2, and IL22RA2) was developed and stratified TNBC patients into two categories, showing major distinctions in overall survival (OS). We found that higher-risk patients had less favorable results. Meanwhile, they usually had different TMEs with less immune cell infiltration. By contrast, the low-risk group appeared to react better to immunotherapy, as evidenced by their increased immune cell infiltration level and more favorable outcomes. Analysis via the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) uncovered that DEGs were predominantly involved in immune-related pathways, for example, the receptors on the plasma membrane. Additionally, a lower immunological phenotype score (IPS) and increased susceptibility to various chemotherapeutic medicines, including A-443,654, BAY 61-3606, and CP466722, were detected in the low-risk category. Additionally, HEYL knockdown markedly suppressed TNBC cell growth and metastatic capability, whereas its overexpression produced the opposite outcome, promoting both traits. The prognosis and responsiveness to treatment for TNBC can be anticipated by the ICD-related gene profile, highlighting the significance of the immune microenvironment. It might provide insight into the design of tailored immunotherapies for TNBC.
In gastrointestinal oncology, serum tumor markers such as CEA and CA19-9 are typically monitored over several weeks to assess therapeutic efficacy. The immediate impact of cytotoxic therapy on these serum tumor markers, however, remains poorly characterized. We analyzed a single-center cohort of patients with advanced gastrointestinal cancers (pancreatic, biliary, colorectal, and esophageal) treated with 5-fluorouracil-based regimens. Paired serum samples were collected immediately before the start of chemotherapy (0 h) and at the removal of the 48-h 5-FU continuous pump (48 h). The primary endpoint was the percentage change of tumor markers during this time window (∆CEA and ∆CA19-9). Secondary endpoints included the association of these acute kinetics with subsequent radiographic response. 78 cycles of 5-FU-based chemotherapy were included (33 patients, median cycles per patient: 2). CA19-9 increased significantly after chemotherapy (median ∆CA19-9 + 4.8% per patient, Wilcoxon p = 0.015; mixed model p = 0.062; cycle-level range - 11% to + 92%), as did LDH (median ∆LDH + 12.8% per patient, both Wilcoxon and mixed model p < 0.001; cycle-level range - 39% to + 72%). CEA levels remained stable (median ∆CEA - 3.6% per patient, Wilcoxon p = 0.09; mixed model p = 0.05; cycle-level range - 20% to + 22%). There was no statistically significant association between early changes in serum tumor markers and radiographic outcome. In this hypothesis-generating study, 5-FU-based therapy leads to a statistically significant increase in CA19-9 and LDH levels, but not in CEA, after 48 h. The magnitude of the increase did not predict radiographic response.
Hepatocellular carcinoma (HCC) remains a formidable global health challenge, particularly in developing nations, due to its asymptomatic nature in early stages and consequent late diagnosis. This study aims to identify novel biomarkers and therapeutic targets by focusing on Ubiquitin and Macrophage Activation-Related Genes (UMARGs), which play a crucial role in modulating the tumor microenvironment. Utilizing a comprehensive bioinformatics framework, we integrated transcriptomic data from TCGA and GEO databases with gene signatures from GeneCards. Our methodology included differential gene expression analysis, Cox regression modeling, and protein interaction networking to identify key prognostic determinants and their heterogeneity across HCC molecular subtypes. The analysis revealed two distinct HCC subtypes, Cluster A and Cluster B, each with unique gene expression profiles. Immune microenvironment heterogeneity was highlighted through CIBERSORT deconvolution, showing diverse distributions across 12 cellular subsets, notably activated T cells and macrophages.Survival analysis showed that high expression of HPX was associated with better Overall survival, while high expression of MMP9 was associated with worse Overall survival, and these trends were validated in an external GEO cohort. HPX and MMP9 may serve as candidate prognostic biomarkers for Hepatocellular carcinoma, but their Biological function and clinical application value still require further experimental investigation. Despite limitations, including the lack of experimental validation and reliance on retrospective data, this study provides valuable insights into gene expression dynamics and immune characteristics associated with HCC. These findings lay the groundwork for precision oncology paradigms, facilitating the translational trajectory of these targets to optimize clinical prognosis. Future research should focus on experimental validation and the development of targeted therapies based on these molecular insights. This study underscores the potential of UMARGs in advancing HCC treatment strategies and improving patient outcomes.
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide, characterized by late-stage diagnosis, profound chemoresistance, and a five-year survival rate that barely exceeds 12%. The fibrotic stromal barrier surrounding the tumor actively suppresses immune infiltration and blocks drug delivery, rendering conventional treatment options largely ineffective. CRISPR-Cas9-mediated gene knockout represents a promising strategy to overcome this stromal barrier-associated therapeutic resistance by enabling precise disruption of genes that sustain desmoplastic signaling, stromal-immune crosstalk, and drug efflux pathways within the tumor microenvironment. In this context, CRISPR-Cas9-guided gene knockout has opened a new chapter in PDAC research by enabling precise, scalable analysis of the cancer genome. Functional screens using this technology have mapped critical oncogenic dependencies, identifying mutant KRAS, TP53, SMAD4, and CDKN2A as high-value targets, while simultaneously revealing synthetic lethal interactions that were previously inaccessible through pharmacological approaches. These discoveries are now being translated into therapeutic strategies aimed at silencing driver mutations, restoring chemosensitivity, and reprogramming the immunosuppressive tumor microenvironment. Delivery platforms, including lipid nanoparticles, viral vectors, and extracellular vesicles, are being refined to navigate the physical barriers unique to PDAC. Patient-derived organoids and xenograft models are providing the translational framework needed to evaluate these interventions under clinically relevant conditions. This review examines the molecular mechanisms of CRISPR-guided knockout, the genetic vulnerabilities it has uncovered in PDAC, the therapeutic strategies emerging from this work, and the delivery systems supporting clinical translation. The remaining barriers and the steps needed to bring this technology to patients are also discussed.
Entosis is a non-apoptotic form of cell-in-cell death implicated in tumor progression and immune evasion. However, the prognostic and immunological significance of entosis-related genes in head and neck squamous cell carcinoma (HNSCC) has not been systematically studied. This study aimed to construct and validate an entosis-anchored hybrid prognostic signature for HNSCC and to characterize its association with the tumor immune microenvironment and therapeutic response. Gene expression and clinical data of 565 HNSCC patients from The Cancer Genome Atlas (TCGA) were used as the discovery cohort, randomly split into training (n = 396) and testing (n = 169) sets. A hybrid strategy integrating entosis pathway genes with genome-wide candidates was employed to construct a 14-gene prognostic signature via LASSO-Cox regression. External validation was performed in GSE65858, GSE41613, and CPTAC cohorts, with a fixed-effect meta-analysis synthesizing the results. Immune infiltration, immune checkpoint expression, TIDE-based immune evasion, drug sensitivity (oncoPredict/GDSC2 cell-line data), and a clinical nomogram were also evaluated. The 14-gene entosis-anchored signature (ERS-14) comprised four entosis core genes (MYL2, CDKN2A, LAMP1, SPP1) and ten genome-wide prognostic genes. In the TCGA cohort, ERS-14 achieved a concordance index (C-index) of 0.703 and time-dependent AUC values of 0.737, 0.739, and 0.725 at 1, 3, and 5 years, respectively. Meta-analysis across external cohorts confirmed its prognostic value (HR = 1.551, 95% CI 1.147-2.096, p = 0.004, I² = 0%). Multivariate Cox regression demonstrated that ERS-14 was an independent predictor (HR = 4.362, p < 2 × 10⁻²⁰), and subgroup analysis revealed consistent prognostic performance across all 13 clinical subgroups tested. The high-risk group exhibited a T cell exclusion phenotype characterized by elevated cancer-associated fibroblast (CAF) scores (p = 0.008), downregulated immune checkpoints, and significantly increased resistance to cisplatin (p = 0.0001) and multiple targeted agents. We developed and validated ERS-14, an entosis-anchored hybrid prognostic signature in HNSCC that predicts survival, identifies an immunosuppressive microenvironment driven by T cell exclusion, and reveals therapeutic vulnerabilities that may inform treatment selection.
Glioblastoma is the most lethal primary central nervous system malignancy, with limited therapeutic options and poor prognosis. This PubMed-based bibliometric study systematically analyzed publication trends, collaboration patterns, journal and author distributions, and thematic evolution at the intersection of programmed cell death (PCD) and glioma immunotherapy over the period 2006-2026. A total of 799 eligible records, including 644 original research articles and 155 narrative reviews, were included. Annual publications increased steadily, peaking at 131 in 2025. China and the United States dominated global output, while international collaboration varied across countries and regions. Keyword co-occurrence analysis identified core themes: apoptosis, necroptosis, pyroptosis, ferroptosis, tumor microenvironment (TME), immunotherapy-related strategies, and prognosis, reflecting a gradual shift from basic PCD mechanisms toward TME-targeted immunotherapy and prognostic stratification. This field is widely distributed across immunology, oncology, molecular biology, nanoscience, and multidisciplinary journals. Author and institutional analyses revealed key contributors within the dataset. In summary, this bibliometric landscape provides a structured overview of research hotspots, collaboration networks, and emerging directions in glioma PCD and immunotherapy. The findings offer descriptive insights for identifying research priorities, knowledge gaps, and collaborative opportunities, while they should be interpreted as observational patterns rather than direct evidence of clinical efficacy or translational priority.
Due to the rising worldwide concern of antibiotic resistance, the creation of new antimicrobial agents has become an essential necessity. Novel antimicrobial peptides (AMPs), celebrated for their extensive efficacy and minimal likelihood of resistance development, are considered among the most promising treatment options. This paper introduced the rational design and synthesis of a new antimicrobial peptide, RK7 (RKKYWLL), which targeted the biophysical differences between human and staphylococcal cell membranes to achieve selective disturbance and damage of the membrane. Models of human and staphylococcal cell membranes were first developed, subsequently followed by molecular docking screens to discover peptides with a strong affinity for staphylococcal membranes while demonstrating inertness toward human membranes. Molecular dynamics (MD) simulations showed that RK7 could specifically interact with the bacterial membrane. AI-assisted design suggested that RK7 is nontoxic and has good stability. Following antibacterial and cytotoxicity testing, RK7 was shown to be nontoxic to normal human cells while exhibiting significant staphylococcal inhibition. RK7 demonstrated a 98.14% inhibition rate against Staphylococcus at a dose of 62.5 μg/mL. This research provides a novel strategy for designing antimicrobial peptides based on compositional differences in cell membranes, offering an innovative approach for the targeted design of future antimicrobial agents.