Breast cancer (BRCA) is a multifaceted and extremely diverse condition, with conventional diagnostic and therapeutic methods encountering considerable obstacles. With the increasing amount of medical data and the ongoing development of computer technologies, artificial intelligence (AI) has become widely used in BRCA medication research and clinical decision-making. Specifically, AI helps anticipate therapy responses, makes it easier to choose the best treatment regimens based on the molecular and pathological features of tumors, and allows for more accurate risk assessments for BRCA. AI is also essential to drug development, including the identification and prediction of novel therapeutic targets, the screening and prediction of compound structures, the repurposing of existing drugs, and the creation of combination treatments. From static assessments based on molecular subtypes to dynamic tracking of disease development, AI's role in BRCA diagnosis and therapy has changed throughout time. It has transitioned from experience-based therapeutic approaches to data-driven clinical decision-making. In addition to improving patients' quality of life, this shift is essential to turning cancer therapy into a "prevention-predictionpersonalized" paradigm. This review article thoroughly examines the developments in AI applications for important fields such as molecular subtype identification, metastasis and recurrence prediction, BRCA risk stratification, and the creation of new medications. It delves deeper into current technological constraints and clinical translation pathways, emphasizing the need for advances in clinical applicability and technical standardization through tactics like large-scale multi-center clinical trials, innovative cross-modal data integration, and algorithmic architecture optimization. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

The lung is a frequent site of secondary metastasis for various cancers, not only due to its extensive vasculature and lymphatic network but also because its immune microenvironment is highly susceptible to inflammatory modulation by external insults such as smoking, aging, infection, or chemotherapy. Recent evidence indicates that neutrophils play a pivotal role in this process. Upon activation by chronic inflammation and tumor-derived factors, neutrophils form neutrophil extracellular traps (NETs) and release a wide range of inflammatory mediators, which remodel the extracellular matrix (ECM) and activate signaling pathways, collectively disrupting the dormancy-maintaining niche and triggering disseminated tumor cells (DTCs) proliferation. Moreover, neutrophils cooperate with immunosuppressive stromal and immune components to establish a permissive microenvironment that facilitates DTC immune evasion and reactivation. These insights redefine neutrophils as key “dormancy releasers” rather than passive bystanders in tumor progression. Understanding the molecular and cellular mechanisms underlying neutrophil-induced dormancy escape offers novel therapeutic opportunities for preventing metastatic recurrence in the lung.

Background Tiao-Shen-Zhi-Ai Formula (TSZAF) is a compound prescription of traditional Chinese medicine used clinically for the treatment of ovarian cancer. In this study, we selected three main active ingredients from TSZAF and combined them into a new TSZAF monomer combination (TSZAF mc) to investigate its effects and mechanisms on inhibiting ovarian cancer proliferation and inducing apoptosis. Methods The effects of TSZAF mc on proliferative activity and apoptosis in ovarian cancer HEY and SKOV3.IP1 cells were assessed in vitro using CCK-8 assay, colony formation assay, and apoptosis assay. Micromethods, flow cytometry, and immunofluorescence were employed to evaluate the impact of TSZAF mc on aerobic glycolytic metabolites and mitochondrial membrane potential. The mRNA and protein expression of key glycolytic genes were detected by quantitative real-time PCR (RT-PCR) and Western blot (WB). An ovarian cancer subcutaneous tumor model was established in NOD-SCID mice using SKOV3.IP1 cells. TSZAF mc was administered via continuous intraperitoneal injection, and its antitumor efficacy in vivo was assessed through anatomical observation, hematoxylin and eosin (H&E) staining, and immunohistochemistry (IHC). Further RT-PCR, WB, and IHC were performed to validate the expression of key upstream glycolytic genes at mRNA and protein levels. Drug affinity responsive target stability (DARTS) and cellular thermal shift assay (CETSA) were used to confirm binding targets. Molecular docking was performed to predict the binding interactions between the monomers and AKT. Finally, the regulatory relationships within signaling pathways were elucidated based on functional assays. Results TSZAF mc effectively inhibited ovarian cancer proliferation and induced apoptosis. It reduced lactate and ATP production, downregulated mitochondrial membrane potential, and decreased the mRNA and protein expression of key glycolytic genes, including HK2, PKM2, PFKM, GLUT1, and LDHA. In vivo, TSZAF mc suppressed ovarian cancer growth. Moreover, TSZAF mc downregulated the expression of phosphorylated AKT (p-AKT) and phosphorylated FOXO3A (p-FOXO3A) at both protein and tissue levels. CETSA and DARTS demonstrated that TSZAF mc binds AKT. The AKT activator SC79 reversed the inhibitory effects of TSZAF mc on ovarian cancer proliferation and the downregulation of glycolytic proteins. Conclusion TSZAF mc inhibits ovarian cancer progression by regulating the AKT/ FOXO3A-mediated glycolysis pathway, which may represent one of the mechanisms underlying the clinical efficacy of TSZAF in ovarian cancer treatment.

The objective of this study was to explore the effect of (-)-guaiol on lung cancer using experimental validation, mRNA sequencing, and network pharmacology. Potential targets of (-)-guaiol and lung cancer were identified through SwissTargetPrediction, TCMSP, PharmMapper, OMIM, GeneCards, and DisGeNET databases. Common targets were analyzed using PPI network, topological screening, and functional enrichment using STRING, Cytoscape, and Metascape. Molecular docking with core targets was performed, along with molecular dynamics. In vitro assays (cell counting kit-8 assay, colony formation, wound healing, Transwell, western blot) and in vivo studies (subcutaneous xenograft modeling in nude mice, immunohistochemistry, mRNA sequencing) were conducted to validate the anti-tumor effects and mechanisms of (-)-guaiol compared with the control group. Through multi-database prediction, 153 (-)-guaiol targets and 91 common lung cancer targets were identified. Protein-protein interaction (PPI) network analysis screened 21 core targets (including ESR1, EGFR, etc.). GO and KEGG enrichment analyses revealed that these targets are involved in the regulation of pathways such as fatty acid metabolism. Molecular docking and molecular dynamics results demonstrated that (-)-guaiol possessed a favorable binding affinity toward the target proteins SRC, PTGS2, GSK3B, PPARG, ESR1, and HSP90AA1. mRNA sequencing indicated that the gene expression levels of both PPARG and CD36 were downregulated in lung cancer tissues of mice treated with (-)-guaiol compared with the control group. Combining the results of molecular docking, molecular dynamics, and mRNA sequencing, we selected the PPARG-related signaling pathway for subsequent experiments. Both in vivo and in vitro experiments validated that (-)-guaiol inhibits lung cancer cell proliferation, invasion, and xenograft tumor growth in mice by downregulating the PPARG pathway. To conclude, our results demonstrated that (-)-guaiol suppresses lung cancer progression through downregulation of the fatty acid oxidation-related pathway mediated by PPARG.

Colorectal cancer (CRC) continues to show rising incidence and mortality worldwide, with liver metastasis representing the leading cause of death among affected patients. Epidemiological data indicate that approximately 49

Dickkopf-1 (DKK1) is a secreted glycoprotein that traditionally acts as an antagonist of canonical Wnt/β-catenin signaling. Although it functions as a tumor suppressor in some specific biological background and disease stages, growing evidence links DKK1 to tumor progression, immune evasion, and therapy resistance in a variety of multiple malignancies. This review provides a comprehensive bench-to-bedside overview of DKK1 in cancer. We first delineate how DKK1 regulates both Wnt-dependent and Wnt-independent pathways. From a clinical perspective, we evaluate the application potential of DKK1 as a diagnostic and prognostic biomarker. We further discuss the progress of DKK1-targeted interventions, ranging from monoclonal antibodies in clinical trials to next-generation therapeutic modalities. Finally, we discuss the challenges in clinical translation and suggest future directions for DKK1-based precision medicine. In summary, by integrating preclinical insights with current clinical data, this review provides a strategic roadmap for advancing DKK1-targeted therapies in cancer.

Abstract Background: Metastasis remains the leading cause of high mortality in non-small cell lung cancer (NSCLC), but early detection is challenging due to the limited sensitivity and specificity of current imaging methods. Peripheral immune markers offer predictive potential, yet their clinical use is limited by a lack of interpretable models. This study developed and validated an interpretable peripheral immune score (PIS) using machine learning (ML) to aid early diagnosis of NSCLC metastasis. Methods: We conducted a multicenter cross-sectional study of NSCLC patients in China. A derivation cohort of 309 patients from three campuses of Shanghai Hospital of Traditional Chinese Medicine (March 2023-May 2025) was split 8:2 for training and validation. Baseline data and 37 peripheral immune markers were collected. Causal inference screened predictive markers, and eight ML algorithms were applied. Model performance was assessed using AUC, decision curve analysis, and calibration. The best model was interpreted using SHAP and deployed as an online PIS Calculator. Results: The Random Forest (RF) model showed the highest performance. After feature reduction, a final interpretable RF model with 19 features accurately predicted metastasis in the validation set (AUC = 0.942). This model was translated into the Intelligent Peripheral Immunity Score (PIS), identifying high-risk patients even without radiographic evidence. Conclusion: The PIS system integrates peripheral immune markers with ML to provide an accurate, interpretable tool for early detection of NSCLC metastasis, overcoming limitations of conventional imaging and complex models, and offering a clinically actionable solution for improved patient management. Citation Format: Fan Xu, Bin Luo, Jianhui Tian, Zhenyang Cheng, Yuan Yao, Youjun Liu, Xiaoyu Yang, Jiangliang Yao, Wang Yao, Xinyi Lu, Yuchen Bao, Yiyang Zhou, Jianchun Wu, Minghua Li, Wenfei Shi, Yajing Cui, Yanhong Wang, Yunxia Wu, Yun Yang, Yan Li. Identification and validation of an explainable predictive model for early diagnosis of non-small cell lung cancer metastasis: A peripheral immune score based on integrative machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5091.

Triple-negative breast cancer (TNBC) urgently requires promising therapeutic targets. This study identifies sclerostin, an osteocyte-derived secretory protein traditionally linked to bone homeostasis, as an unexpected intracellular oncogenic driver in TNBC. Although genetic ablation of sclerostin markedly suppresses tumor progression and lung metastasis, neither its antibody nor recombinant protein exerts any effects, excluding the role of extracellular sclerostin in TNBC. Genetic and pharmacological approaches (sclerostin aptamer-based proteolysis-targeting chimera with potent intracellular sclerostin-degrading activity, Apc101) show the emerging role of intracellular sclerostin in promoting TNBC progression and metastasis. Notably, in both TNBC cell-derived and patient-derived xenograft models, Apc101 significantly suppresses tumor progression. Mechanistically, intracellular sclerostin interacts with caprin1 to stabilize CDK1 and Cyclin B1 mRNAs. Collectively, this study reveals an oncogenic function of intracellular sclerostin in TNBC and proposes that targeting it represents a promising therapeutic strategy.

Metastasis is the leading cause of mortality in non-small cell lung cancer (NSCLC). Accurate assessment of the anti-metastasis effects is critical for developing anti-NSCLC agents. However, existing preclinical models failed to recapitulate and precisely quantify key events during tumor metastasis, including alternative vascularization, hampering mechanistic studies and the discovery of efficacious anti-metastasis agents for NSCLC therapy. Here, we developed a Vascularized Lung Tumoroid-on-a-Chip (VLTOC) platform that precisely reconstitutes the critical tumor-vasculature interface and dynamically recapitulates mosaic vessel (MV) formation, a key intermediate in NSCLC metastasis. Compared to conventional spheroid models, the genes associated with tumor proliferation, metabolism, and invasion were significant upregulated in VLTOC. Notably, VLTOC dynamically recapitulated MV formation at single-cell resolution, enabling the development of quantitative metrics for anti-NSCLC drug testing based on tumoroid expansion and MV development. Validation with five standard chemotherapeutics revealed high concordance with clinical drug sensitivity data. Furthermore, the VLTOC platform showed high concordance with a xenograft mouse model in assessing the pharmacological and toxicological profile of rocaglamide. Collectively, the VLTOC platform and its associated quantitative metrics provide a powerful tool for the simultaneous assessment of anti-tumor efficacy, thereby facilitating mechanistic studies of NSCLC metastasis and accelerating the discovery of novel therapeutics.

Background: Psychoneurological symptom clusters (PNSCs) are common in patients with ovarian cancer and are associated with reduced quality of life, treatment interruption, and poor prognosis. However, effective interventions for PNSCs remain limited. Traditional Chinese medicine may provide comprehensive benefits for symptom management. Objective: This study aims to evaluate the efficacy and safety of the TiaoShenZhiAi (TSZA) regimen in alleviating PNSCs in patients with ovarian cancer and to assess its effects on quality of life and survival outcomes. Methods: A total of 316 patients with ovarian cancer aged 18 to 70 years with PNSCs will be included and randomly divided into 2 parallel groups. Both groups will receive standard treatment for ovarian cancer as the basic treatment. The intervention group will receive the TSZA regimen, that is, Compound Ciwujia Granules (containing Acanthopanax senticosus and Schisandra chinensis) combined with psychological intervention. The control group will receive a low-dose active control (simulated Compound Ciwujia Granules) combined with psychological intervention. The primary outcome is the remission rate of PNSCs at 3 months. The secondary outcome measures include the Pittsburgh Sleep Quality Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 scale, the revised Piper Fatigue Scale, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30 Quality of Life Scale, the traditional Chinese medicine syndrome scale, sleep quality, sleep diary, and the 1-year survival analysis. In addition, this study also includes a series of exploratory indicators (including functional magnetic resonance imaging, biomarkers of peripheral blood and tumor tissue, proportion of immune cells, cytokine levels, hypothalamic-pituitary-adrenal axis function, and immune gene expression analysis) and safety indicators (including vital signs, liver and kidney function, and electrocardiogram). The study outcomes will be evaluated based on different indicators during the treatment period (baseline and the 1st, 2nd, and 3rd mo of enrollment) and the follow-up period (the 6th, 9th, and 12th mo of enrollment). Data analysis will be conducted using R (version 4.5.3) software. A one-sided P value of <.03 will be considered statistically significant. Results: This study is designed to enroll a total of 316 participants. Participant enrollment is set to commence in October 2025, with no recruitment having occurred as of April 2026. The recruitment period will extend until September 2028 or until the target enrollment is met. Data analysis is scheduled for November 2028, with submission of the trial results to a peer-reviewed journal anticipated by May 2029. Conclusions: This study will evaluate the efficacy of the TSZA regimen in managing PNSCs in patients with ovarian cancer and generate clinical evidence for a new therapeutic option that improves quality of life and alleviates the symptom burden.

ObjectiveTo explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. MethodsA cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. ResultsThe prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. ConclusionDepression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Brain metastases (BM) from lung cancer remain a devastating complication that severely compromises patient survival. Its development is driven by dynamic and complex interactions between tumor cells and the central nervous system microenvironment, involving blood-brain barrier disruption, immunosuppressive niche formation, neural co-optation, metabolic adaptation, and peripheral immune dysregulation. Current strategies face intrinsic resistance and delivery barriers. This review aimed to systematically examine these mechanisms and therapeutic frontiers, including emerging interventions that preserve vascular-neural integrity, intercept neurotransmitter-mediated tumor support, reprogram brain resident cells, exploit metabolic vulnerabilities, and engineer advanced delivery systems, thereby proposing directions to overcome therapeutic challenges in lung cancer BM.

Background:Ovarian cancer (OC) is a highly fatal gynecologic malignancy with complex management challenges and limited long-term survival for advanced stages. Large language models (LLMs)-including systems such as GPT-4, Claude, Google Gemini, and others-are emerging artificial intelligence (AI) tools capable of performing health care-related tasks such as diagnostic support, treatment planning, report generation, and patient communication. However, their applications in OC care have not yet been comprehensively assessed. Objective:This protocol outlines a systematic review and meta-analysis aimed at evaluating the use, performance, and clinical impact of LLMs in OC management. We will examine how LLMs have been applied across various domains (eg, diagnosis, prognosis, treatment planning, and patient engagement), the metrics used to assess their performance (eg, accuracy, sensitivity, and area under the curve), and their strengths and limitations. Methods:This review will be conducted in accordance with PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. A comprehensive search strategy will be implemented across biomedical, technical, and Chinese-language databases (eg, PubMed, Embase, Web of Science, IEEE Xplore, and China National Knowledge Infrastructure) from inception to December 31, 2025. Eligible studies include clinical evaluations, validation studies, and real-world implementation reports involving LLMs in OC care. Two independent reviewers will perform screening, data extraction, and quality appraisal using validated tools (eg, version 2 of the Cochrane risk-of-bias tool for randomized trials, Risk of Bias in Nonrandomized Studies of Interventions, Quality Assessment of Diagnostic Accuracy Studies 2, and Prediction Model Study Risk of Bias Assessment Tool+AI). Outcomes of interest include model performance metrics, clinical process impacts, safety concerns, and usability. Meta-analyses will be conducted where feasible using random-effects models in R (meta, metafor, and mada packages), including bivariate models for sensitivity and specificity. Results:The review is currently in progress. The PROSPERO registration has been completed, and the literature search and selection process is underway. Study selection, data extraction, and quality assessment are expected to be completed by mid-2026. Final results will include pooled performance metrics (eg, accuracy, F1-score, and area under the curve), qualitative insights into clinical integration, and identification of limitations such as reporting bias or insufficient external validation. Conclusions:This systematic review will provide the first comprehensive synthesis of evidence on the application of LLMs in OC care. It will identify promising use cases, highlight safety and reporting challenges, and inform future research directions. The findings are expected to support evidence-based integration of LLMs into gynecologic oncology workflows while promoting transparency and methodological rigor in AI evaluation.

Cancer remains one of the leading threats to human health today. With the rapid advancement of nanotechnology, the integration of nanomaterials with therapeutic strategies has shown great potential in addressing the limitations of conventional cancer treatments. Covalent organic frameworks (COFs) are novel crystalline porous polymers with well-defined backbones and nanopores, mainly composed of light elements (H, B, C, N, and O) linked by dynamic covalent bonds. Owing to their tunable morphology, adjustable porosity, intelligent responsive release, and good biocompatibility, COFs have been extensively explored for applications in cancer diagnosis and treatment. This review summarizes recent progress in the synthesis of COFs, their distinctions from other traditional nanomaterials, their tumor microenvironment-responsive release capabilities, and highlights the development of multifunctional COF-based nanoplatforms for cancer imaging and treatment. Finally, the prospects and challenges of COF-based nanoplatforms in tumor therapeutics are discussed, aiming to provide new diagnostic and therapeutic strategies for subsequent tumor prevention and treatment.

Maimendong decoction (MMDD), a classic traditional Chinese medicine (TCM) formula prescribed for ‘lung atrophy’, has demonstrated efficacy against various pulmonary disorders. Our prior research confirmed that MMDD inhibit lung cancer metastasis by modulating natural killer (NK) cells. Paris polyphylla (P.P), a TCM herb with known anti-tumor properties, is often incorporated into classic formulas to enhance therapeutic outcomes. This study aimed to boost the anti-metastatic efficacy of MMDD against lung cancer by incorporating Paris polyphylla (Chonglou) and to investigate the underlying mechanisms. The Modified Maimendong Decoction (MMDD + P.P) Was Prepared by Adding 9–18 g of Paris polyphylla. Its Effects on the proliferation, migration, and Apoptosis of CTC-TJH-01 and LLC Cells Were Assessed Using CCK-8, Transwell, and Annexin V-FITC/PI Flow Cytometry assays. Apoptosis-related Proteins Were Analyzed by Western blot. A Tail Vein injection-induced Lung Metastasis Model in C57BL/6 Mice Was Established To Evaluate the Effects of MMDD + P.P, both Alone and in Combination with an anti-PD-1 antibody, on Metastasis Flow cytometry was used to profile T cell and NK cell populations in peripheral blood. Histological and molecular analyses of metastatic tissues were performed using H E staining, immunohistochemistry (for Ki-67 and cleaved caspase-3), and immunofluorescence (for CD8+ T cell and NK cell infiltration). Compared to MMDD alone, MMDD + P.P (18 g) significantly inhibited proliferation and migration, and induced apoptosis in both CTC-TJH-01 and LLC cells. These effects were associated with the upregulation of pro-apoptotic proteins (cleaved caspase-3, BAX, cleaved PARP) and downregulation of anti-apoptotic proteins (BCL-2, Survivin). In vivo, while both MMDD and MMDD + P.P reduced the number of lung metastatic nodules, MMDD + P.P (18 g) was uniquely effective in significantly reducing overall tumor burden, which correlated with decreased Ki-67 and increased cleaved caspase-3 in metastatic foci. Furthermore, MMDD + P.P (18 g) significantly increased the proportions and tumor-infiltration of NK cells and CD8+ T cells. It also synergized with anti-PD-1 therapy, enhancing its anti-metastatic effect and boosting the expression of cytotoxic markers (CD107a, perforin, granzyme B) and TNF-α in CD8+ T cells. Incorporating Paris polyphylla into MMDD enhances its anti-metastatic efficacy through a dual mechanism: directly inducing apoptosis in lung cancer cells and amplifying anti-tumor immunity by increasing the abundance and cytotoxic function of CD8+ T cells. The synergy between MMDD + P.P and anti-PD-1 antibody therapy highlights the potential of this modified TCM formula as a promising adjunctive treatment for inhibiting lung cancer metastasis. ∙Modified Maimendong Decoction Plus Paris polyphylla (MMDD + P.P) exhibits enhanced in vitro anti-lung cancer activity compared to the original MMDD, with superior inhibition of proliferation, migration, and induction of apoptosis. ∙MMDD + P.P demonstrates superior in vivo anti-metastatic efficacy against lung cancer relative to the original MMDD. ∙MMDD + P.P exerts immunomodulatory effects on CD8+ T cells, enhancing their anti-tumor activity. ∙MMDD + P.P potentiates the anti-metastatic efficacy of PD-1 monoclonal antibody therapy in lung cancer.

Lung cancer remains the leading cause of cancer-related death globally, with metastasis driven by circulating tumor cells (CTCs)-particularly clusters-being a major treatment challenge. Despite their critical role, the biological differences between single CTCs and CTC clusters remain unclear. Here, we comprehensively compared their behavioral, transcriptomic, and proteomic profiles in lung cancer models. Compared with single cells, CTC clusters present enhanced metastatic potential, greater survival in the bloodstream and increased resistance to microenvironment. Mechanistically, the Src/FN1 pathway is centrally activated in clusters, promoting intercellular cohesion and protecting against immune clearance and stress in circulation. Pharmacological inhibition of Src with the clinical inhibitor KX2-391 disrupted clustering, impaired CTC survival, and reduced metastasis in preclinical models. Our findings identify the Src/FN1 pathway as a key vulnerability in CTC cluster-driven metastasis, suggesting that Src inhibitors are promising therapeutic strategies to disrupt clustering and improve outcomes in patients with metastatic lung cancer.

Tumor metastasis is the leading cause of cancer-related mortality. Disseminated tumor cells (DTCs), serving as the critical 'seeds' in the metastatic cascade, hold the key to determining the success or failure of metastasis. Following dissemination from the primary tumor and colonization of distant organs, DTCs often enter a prolonged state of dormancy. Their subsequent escape from immune surveillance through sophisticated mechanisms enables them to transition from this dormant state to a proliferative one, ultimately culminating in clinically detectable metastatic lesions. A profound understanding of DTCs immune evasion is therefore essential for unraveling the fundamental biology of metastasis and developing effective anti-metastatic strategies. This article systematically reviewed the latest advances in the mechanisms underlying DTCs immune evasion, focusing on three core aspects: defects in antigen presentation, formation of an immunosuppressive microenvironment, and metabolism reprogramming-mediated immunosuppression. Specifically, DTCs achieve 'immune invisibility' by downregulating major histocompatibility complex class-I (MHC-I) molecule expression; they actively construct a local 'protective shield' by recruiting immunosuppressive cells such as regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs); and they impair effector immune cell function at the energetic and metabolic levels by remodeling glucose, amino acid, and lipid metabolism. Building upon this, we innovatively integrate the traditional Chinese medicine (TCM) theories of 'hidden toxicity due to vital qi deficiency' and the 'metastatic state' to elucidate the dynamic pathogenic relationship between the body's systemic 'Zhengqi' (vital energy) status and the dormancy-awakening switch of DTCs, offering a novel holistic perspective for comprehending the metastatic process. Finally, we discussed the prospects of multi-target combination therapeutic strategies against DTCs immune evasion and highlight the potential of emerging technologies, such as single-cell sequencing and spatial transcriptomics, aiming to provide valuable insights for future in-depth research and clinical translation in the field of anti-metastasis therapy.
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Currently, Lenvatinib, a tyrosine kinase inhibitor, is used as a first-line treatment for advanced hepatocellular carcinoma (HCC), but its efficacy remains unsatisfactory. Therefore, we investigated the effect of Lenvatinib in combination with Arsenic trioxide (ATO) on HCC and the underlying mechanisms. The antitumor activity of the combination was evaluated in vitro using MTT and colony formation assays, and in vivo using xenograft models in nude mice. mRNA-seq was performed to explore the potential mechanisms. Reactive oxygen species (ROS) and intracellular Fe2+ were measured to evaluate ferroptosis in HCC cells. Our findings showed that the addition of ATO significantly enhanced the anti-HCC effects of Lenvatinib both in vitro and in vivo. Moreover, RNA sequencing analysis indicated that ATO augmented the anti-HCC effect of Lenvatinib by inducing ferroptosis. Both ATO alone and the combination treatment markedly induced a significant elevation of ROS and ferroptosis, which was effectively blocked by the administration of ferrostatin-1 and deferoxamine mesylate. Furthermore, ATO upregulated the levels of heme oxygenase 1 (HMOX1) and Fe2+ in HCCLM3 and Huh7 cells. Knockdown of HMOX1 attenuated the effect of ATO on HCC cell viability, ROS and Fe2+ levels in HCC cells. Additionally, ATO and the combination of Lenvatinib and ATO also decreased the expression of GPX4 protein both in vitro and in vivo. In conclusion, ATO enhances the antitumor activity of Lenvatinib against HCC by inducing ferroptosis through upregulation of HMOX1 and downregulation GPX4.

TME is a core player in the development of a cancerous lesion, the immune evasive potential of the lesion, and its response to therapy. Sphingolipid metabolism, which governs a number of cellular processes, has been recognised as a player involved in the control of immune heterogeneity within the TME. Sphingolipid metabolism-related genes prevalent in the TME of LUAD and LUSC were identified using transcriptomic analysis and clinical samples from the TCGA and GTEx databases. Lasso regression and survival SVM in the Etra Application were employed as machine learning algorithms to determine patient outcomes and to reveal key immune factors associated with gene expression and chemotherapeutic response. Gene expression in lung cancer cells was explored through scRNA-seq data. Thereafter, mediation impact analysis was further performed to explain the defined relation between the immune cell subsets and sphingolipid metabolites and their risk impact on lung cancers. Genes involved in sphingolipid metabolism were dysregulated in lung cancer, correlating with immune cell infiltration and TME remodelling. Lasso regression identified ASAH1 and SMPD1 as strong prognostic markers. scRNA-seq revealed higher gene expression in T cells, macrophages and fibroblasts. Sphingomyelin partially mediated the link between T lymphocyte abundance and lung cancer risk. High-risk phenotypes exhibited enhanced immune evasion via altered regulatory T cell and macrophage polarisation. This research highlights the contribution of sphingolipid metabolism in shaping the TME and its implications for immunotherapy.