Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.

Aim To investigate the role of FRMD4A in autophagy of placental trophoblast cells in preeclampsia(PE).Methods The placental tissues and clinical data of normal pregnancy and PE were obtained,and the histopathological changes were observed by HE staining.An in vitro model of hypoxia-induced HTR-8/SVneo trophoblast cells was established.The expres-sions of LC3B Ⅱ/Ⅰ and p62 in placental tissues and hypoxic cell models were analyzed by Western blot.The expression of FRMD4A was detected by qRT-PCR,Western blot and immunofluorescence,and the correlation between the expression level of FRMD4A and the clinical characteristics of the subjects was ana-lyzed by Pearson correlation analysis.Hypoxia induced trophoblast cells were transfected with si-FRMD4A,and the expression of LC3 B Ⅱ/Ⅰ and p62 was analyzed by Western blot.Results Compared with the normal group,the expression of LC3B Ⅱ/Ⅰ in PE placental tissues and hypoxia-induced trophoblast models was significantly upregulated,while the expression of p62 was significantly downregulated.Meanwhile,the ex-pression of FRMD4A increased significantly.Moreo-ver,its expression was positively correlated with the maternal systolic blood pressure,diastolic blood pres-sure,and platelet count,but negatively correlated with the neonatal weight(P＜0.01).In addition,hypoxia-induced trophoblast cells transfected with si-FRMD4A showed a significant decrease in LC3B Ⅱ/Ⅰ and an increase in p62 expression.Conclusions The expres-sion of FRMD4A is upregulated in PE placenta and hy-poxia-induced trophoblast cell model.Interfering with it can significantly hinder the autophagy process of trophoblast cells,suggesting that it may serve as a po-tential molecular target to participate in the pathologi-cal process of PE.

Objective:Exploring the role and mechanism of CHMP4C in regulating necroptosis during gastric can-cer development and progression.Method:The expression of CHMP4C in pan-cancer was analyzed by bioinformatics methods,and the expression of CHMP4C was detected in human normal gastric epithelial cells and GC cell lines by RT-qPCR and Western blot.Overexpression or knockdown of CHMP4C was performed in GC cell lines,and the effects of CHMP4C on the growth and proliferation of GC cells were detected using CCK-8 and clone formation assays.The CCK-8 experiment and Hoechst/PI double staining experiment were used to detect the changes in GC cell mortality and PI positive cell ratio after treatment with the necroptsis inducer TSZ or inhibitor necrostatin-1(Nec-1).Western blot assay was used to detect the protein and phosphorylation levels of RIPK1,RIPK3,and MLKL in GC cells.Result:CHMP4C was upregulated in GC tissues and cells.The CCK-8 and clone formation experiments showed that overex-pression of CHMP4C significantly improved the proliferation ability and colony formation efficiency of GC cells,while knockdown of CHMP4C significantly weakened GC cells.Moreover,the results of CCK-8 and Hoechst 33342/PI double staining experiments showed that upregulated CHMP4C could inhibit TSZ induced GC cell death;Nec-1 can reverse the decrease in GC cell viability caused by CHMP4C knockdown.Western blot experiment showed that the levels of p-RIPK1,p-RIPK3,and p-MLKL were significantly decreased in overexpressing cells,while they were increased in knockdown cells.After treatment with Nec-1,the expression levels of these three proteins decreased in knockdown cells.Conclusion:CHMP4C may promote GC progression by negatively regulating necroptosis through inhibiting the phosphorylation of the RIPK1/RIPK3/MLKL signaling pathway,suggesting that it is expected to be a potential target for GC therapy.

Polycystic ovary syndrome(PCOS)is a common endocrine disorder closely associated with insulin resistance(IR)and hyperandrogenemia(HA).Oxidative stress has been closely linked to chronic metabolic diseases such as IR and diabetes,and is also considered one of the key factors in the pathogenesis and progression of PCOS.Under physiological conditions,insulin-like growth factor 1(IGF-1)plays a crucial role in regulating cellular proliferation,metabolic processes,and alleviating oxidative stress.However,in PCOS patients,an oxidative stress state is frequently observed,which interacts with IR and HA to stimulate elevated IGF-1 levels.This,in turn,exacerbates oxidative stress and inflammatory responses,ultimately worsening the patholog-ical state of PCOS.This review summarizes the molecular mechanisms underlying the roles of IGF-1 and oxidative stress in the onset and progression of PCOS,aiming to provide new insights into its pathophysiology and potential therapeutic strategies.

Polycystic ovary syndrome(PCOS)is a common endocrine disorder closely associated with insulin resistance(IR)and hyperandrogenemia(HA).Oxidative stress has been closely linked to chronic metabolic diseases such as IR and diabetes,and is also considered one of the key factors in the pathogenesis and progression of PCOS.Under physiological conditions,insulin-like growth factor 1(IGF-1)plays a crucial role in regulating cellular proliferation,metabolic processes,and alleviating oxidative stress.However,in PCOS patients,an oxidative stress state is frequently observed,which interacts with IR and HA to stimulate elevated IGF-1 levels.This,in turn,exacerbates oxidative stress and inflammatory responses,ultimately worsening the patholog-ical state of PCOS.This review summarizes the molecular mechanisms underlying the roles of IGF-1 and oxidative stress in the onset and progression of PCOS,aiming to provide new insights into its pathophysiology and potential therapeutic strategies.

Abstract Polycystic ovarian syndrome(PCOS) is a very common endocrine and reproductive disease. Its etiology and pathogenesis are complex and not yet fully clear. At present, the clinical treatment is mainly symptomatic. Studies have revealed that ferroptosis, as a new form of cell death, may play a key regulatory role in the occurrence and development of PCOS. In addition, there is an increase in autophagy/mitochondrial autophagy in PCOS patients, which may be closely related to the occurrence of ferroptosis. This review summarizes the pathogenesis of mitochondrial autophagy and ferroptosis in PCOS, and analyzes the interrelationship between mitochondrial autophagy and ferroptosis in granulosa cells, in order to provide new insights and potential therapeutic targets for the clinical treatment of PCOS.

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

Objective To construct a diagnostic and prognostic model for malignant pleural effusion (MPE) in patients with non-M1b stage (AJCC 7th edition) non-small cell lung cancer (NSCLC) by machine learning. Methods Retrospective analysis was conducted on patients diagnosed with NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2015, excluding those in the M1b stage. Two sets of data were collected: data 1 (patients with non-M1b stage NSCLC, n=47 392) was used to construct the MPE diagnostic model; and data 2 (patients with M1a stage NSCLC and MPE, n=2 422) was used to construct a prognostic model. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen feature variables, with a training set and validation set ratio of 7:3. Models were built using eight machine learning algorithms, with evaluation metrics including accuracy, precision, recall, F1 score, area under the ROC curve (AUC), decision curve, calibration curve, and precision recall curve (PR), with ROC-AUC as the main evaluation metric. Results The incidence of MPE in patients with non-M1b stage NSCLC was 5.12%, and the 1-year survival rate of patients with MPE was 32.5%. LASSO regression identified nine diagnostic-related variables and 12 prognostic-related variables. The AUC values of the models constructed by eight machine learning algorithms all exceeded 0.70. The random forest model performed the best in the diagnostic model (training set AUC=0.908, validation set AUC=0.897), and the XGBoost model showed the best performance in the prognostic model (training set AUC=0.905, validation set AUC=0.875). Other evaluation indicators showed good results and balanced distribution. SHAP feature importance analysis showed that tumor size, lymph node metastasis, and histological type were important influencing factors for the occurrence of MPE, and chemotherapy intervention was the most remarkably prognostic factor. Conclusion The random forest diagnostic model constructed in this study can effectively predict the risk of MPE in patients with non-M1b stage NSCLC, and the XGBoost prognostic model can predict the prognosis of M1a-stage NSCLC patients with concurrent MPE.