OBJECTIVES:We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG PET/CT images to investigate the prognostic value of PET/CT-derived parameters for overall survival (OS) among lung cancer patients with malignant pleural effusion (MPE). METHODS:A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation. RESULTS:The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over the LENT score for 12-month survival prediction (AUC: 0.849, 95% CI, 0.795-0.903 vs AUC: 0.732, 95% CI, 0.660-0.796). The internal bootstrap validation had an AUC of 0.840, (95% CI, 0.671-0.922) and demonstrated a well-fitting calibration curve. CONCLUSIONS:The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellently in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment. ADVANCES IN KNOWLEDGE:AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.

BACKGROUND:Programmed death-ligand 1 (PD-L1) expression and CD8-positive (CD8+) T-cell infiltration in tumor tissue are associated with prognosis in non-small cell lung cancer (NSCLC). However, the prognostic value of combined PD-L1/CD8 immune phenotyping in surgically treated NSCLC and the feasibility of predicting high-risk immune phenotypes using routine clinical indicators remain unclear. This study aimed to evaluate the prognostic significance of PD-L1/CD8 status and to develop machine learning models for predicting PD-L1-positive/CD8-negative (PD-L1+ CD8-) status. METHODS:This study included 844 patients with NSCLC who underwent surgical resection at Wuhan Union Hospital and Renmin Hospital of Wuhan University between March 2012 and November 2022. PD-L1 expression and CD8+ T-cell infiltration were assessed by immunohistochemistry, and preoperative clinical data were collected. Kaplan-Meier analysis, stratified survival analysis, univariable and multivariable Cox proportional hazards regression models were used to evaluate the association between PD-L1/CD8 status and overall survival (OS). Patients from Wuhan Union Hospital were divided into training, test, and internal validation sets, whereas patients from Renmin Hospital of Wuhan University were used as an external validation set. Multiple machine learning models were developed to predict PD-L1+ CD8- status. Feature importance and and SHapley Additive exPlanations (SHAP) analysis were used to interpret model predictions. RESULTS:Based on PD‑L1/CD8 status, tumors from the 844 patients were classified into four immune phenotypes. Kaplan-Meier analyses revealed significant differences in OS among the four PD-L1/CD8 subgroups, with the PD-L1+ CD8- subgroup displaying the poorest survival. This pattern was also observed in several stratified analyses. Multivariable Cox regression analysis further demonstrated that PD-L1+ CD8- status was independently associated with worse OS compared with PD-L1+ CD8+ status (hazard ratio = 3.261, 95% confidence interval: 1.310-8.116, P = 0.011). On this basis, machine learning models were developed to predict PD-L1+ CD8- status. Among the evaluated models, the stacking model showed the best overall performance, with area under the curve values of 0.998, 0.700, 0.853, and 0.783 in the training, test, internal validation, and external validation sets, respectively. Feature importance and SHAP analyses suggested that PD-L1+ CD8- status was associated with a composite pattern involving hematologic, biochemical, inflammatory, and coagulation-related indicators. CONCLUSIONS:PD-L1/CD8-based phenotyping identified prognostically distinct subgroups in surgically treated NSCLC, with PD-L1+ CD8- status defining a high-risk phenotype associated with poor survival. A machine learning model based on clinical indicators may enable efficient, noninvasive identification of PD-L1+ CD8- status and help guide treatment decisions.

Artificial intelligence agents are emerging as powerful applications of large language models (LLMs), automating complex tasks and enabling scientific data exploration. However, their use in biomedical data analysis remains limited by the difficulty of handling specialized tools and multistep reasoning. Here we introduce BioMedAgent, a self-evolving LLM multi-agent framework, which learns to use diverse bioinformatics tools and chain them into executable workflows through interactive exploration and memory retrieval algorithms. It allows biomedical users to initiate tasks using natural language, without requiring computational expertise. Evaluated on our newly released BioMed-AQA benchmark comprising 327 biomedical data tasks, BioMedAgent achieved a 77% success rate, surpassing other LLM agents, and generalized robustly to the external BixBench dataset. Beyond benchmarks, it autonomously performs cross-omics analysis, machine-learning modelling and pathology image segmentation, highlighting its potential to advance biomedical research and extend to other scientific domains requiring complex tool integration and multistep reasoning.

Intraoperative frozen section is insufficient to diagnose lung cancer invasion; thus, additional procedures are required to evaluate the pathological tumor invasiveness. We aimed to identify risk factors and establish the validity of a model that serves as a supplementary tool to evaluate the risk of lung cancer invasion beyond its primary site. Overall, 1426 patients diagnosed with stage I lung adenocarcinoma who had performed spirometry within 3 months preoperatively were enrolled. Least absolute shrinkage and selection operator logistic regression was used to select optimal indicators to construct the predictive nomogram model to identify invasiveness. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate the predictive performance of the model. Lung function impairment was detected in 565 patients (39.62

We explored the association between frailty and respiratory infectious diseases (RIDs) through a large cohort of 423,691 participants in the UK Biobank. Participants without baseline RIDs were assessed by physical frailty and frailty index. A total of 16,848 participants had repeated assessments. We divided participants into non-frail, prefrail, and frail groups and categorized frailty changes as alleviation, maintenance, or aggravation. We estimated risk for RIDs, including influenza, pneumonia, and other acute lower respiratory infections. Compared with nonfrailty, prefrailty and frailty increased risk for RIDs 1.32-2.29 times. Each 0.1-point increase in frailty index per year raised risk for RIDs by 47%; each 1-point increase in physical frailty per year increased risk by 26%. Frailty worsening (e.g., aggravation of prefrailty) amplified risk by 2.31-4.16 times. Partial frailty improvement did not fully eliminate risk. Frailty is a modifiable, dynamic risk factor, underscoring the need for early frailty identification and intervention to reduce RIDs in high-risk populations.

The advent of immunotherapy and targeted therapy has revolutionized oncology treatment in recent years, marking a transformative shift toward precision medicine. Artificial intelligence (AI) technology has played a pivotal role in tumor immunotherapy, targeted therapy, and related fields, by enhancing treatment efficacy prediction, identifying novel targets, and enabling personalized therapeutic strategies. In its early stages, AI development relied primarily on unimodal data processing architectures, which were limited to analyzing single data types and thus constrained in functionality. However, recent breakthroughs in data acquisition and integration have ushered in a new era of multimodal data fusion in oncology. Consequently, the integration of AI with multimodal data analytics now supports improved diagnostic accuracy, more precise prognosis prediction, and optimized individualized treatment planning—particularly in tumor immunotherapy and targeted therapy. Concurrently, this rapidly evolving domain faces pressing challenges, including data scarcity and lack of standardization, difficulty in aligning multimodal data, and the opaque, “black-box” nature of complex deep-learning models. Therefore, in this review, we aimed to summarize common AI technologies used in tumor diagnosis and treatment, multimodal data sources and fusion methods, applications of AI combined with multimodal data analysis in tumor immunotherapy and targeted therapy, and the key challenges currently facing this integrated approach.

Background: Following the 2021 first International Consensus on Severe Lung Cancer, global attention to patients with PS 2-4 has grown significantly. Recent advances in novel therapies, interventional techniques, and supportive care, along with emerging real world data, have expanded treatment opportunities for this population. To incorporate these advances, we have updated the consensus. Methods: A multidisciplinary panel comprising experts from oncology, radiation oncology, thoracic surgery, radiology, interventional medicine, respiratory medicine, critical care medicine, and nursing. After being presented with a comprehensive review of the current evidence pertaining to severe lung cancer and thorough discussions, the panel reached a consensus on 11 recommendations, each with over 70% expert agreement. Results: The 11 consensus points focused on definition and causes (n=2), assessment and general strategies (n=4), and specific treatment modalities (n=5) were updated or newly developed. This updated consensus emphasizes dynamic and precise detection, robust life support, flexible application of novel therapies, and MDT guided treatment adjustment based on PS dynamics. Early rehabilitation and comprehensive supportive care are integral to disease management. Conclusions: This consensus updates the definition, diagnostic evaluation, and treatment strategies, providing a practical framework for clinicians based on current evidence and multidisciplinary expert consensus. Prospective trials focusing specifically on patients with severe lung cancer are urgently needed.

BACKGROUND:This guideline aims to address key clinical questions of long COVID, and to provide evidence-based recommendations. The target population is adults with long COVID. The primary users of the guideline are clinical physicians, clinical pharmacists, nurses and general practitioners in community healthcare institutions worldwide. METHODS:The guideline was registered at the Practice guideline REgistration for transPAREncy platform (PREPARE-2024CN123) and followed a pre-specified protocol. A multidisciplinary working group was established and comprised 60 members from 10 countries and 10 areas of expertise, with a strong background in long COVID research and clinical practice, and methodology of guideline development. Through a two-step process, we determined eight PICO (Population, Intervention, Comparator, Outcome) questions focusing on prevention and treatment of long COVID. After comprehensively searching literature, conducting systematic reviews and investigating patients' values and preferences, three rounds of Delphi survey were conducted among 24 international experts to reach consensus. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach was applied to rate the certainty of evidence and determine the strength of recommendations. RESULTS:The guideline presents 10 specific recommendations, each supported by existing, updated or newly conducted systematic reviews. The key recommendations are pertinent to the following issues: 1) suggestion of vaccination or use of antiviral agents during the acute phase of COVID-19 to prevent long COVID; 2) suggestions against the use of nirmatrelvir/ritonavir and glucocorticoids (patients with persistent respiratory symptoms and olfactory disorders) for long COVID treatment; 3) suggestions supporting the use of multispecies probiotics, cognitive behavioural therapy (patients with fatigue), and personalised rehabilitation (after ruling out post-exertional malaise) for long COVID treatment. CONCLUSIONS:This guideline provides evidence-based recommendations for the prevention and treatment of long COVID. Given the limited and often low-methodological-quality evidence, all recommendations are supported by very low to moderate certainty. Further high-quality studies are needed to strengthen the evidence base.

Background:Precise preoperative discrimination of invasive lung adenocarcinoma (IA) from preinvasive lesions (adenocarcinoma in situ [AIS]/minimally invasive adenocarcinoma [MIA]) and prediction of high-risk histopathological features are critical for optimizing resection strategies in early-stage lung adenocarcinoma (LUAD).Methods:In this multicenter study, 813 LUAD patients (tumors <= 3 cm) formed the training cohort. A total of 1709 radiomic features were extracted from the PET/CT images. Feature selection was performed using the max-relevance and min-redundancy algorithm and least absolute shrinkage and selection operator. Hybrid machine learning models integrating [18F]FDG PET/CT radiomics and clinical-radiological features were developed using H2O.ai AutoML. Models were validated in a prospective internal cohort (N = 256, 2021-2022) and external multicenter cohort (N = 418). Performance was assessed via area under the curve (AUC), calibration, decision curve analysis (DCA), and survival assessment.Results:The hybrid model achieved AUCs of 0.93 (95% CI: 0.90-0.96) for distinguishing IA from AIS/MIA (internal test) and 0.92 (0.90-0.95) in external testing. For predicting high-risk histopathological features (grade-III, lymphatic/pleural/vascular/nerve invasion, and spread through air spaces), AUCs were 0.82 (0.77-0.88) and 0.85 (0.81-0.89) in internal/external sets. DCA confirmed superior net benefit over CT model. The model stratified progression-free (P = 0.002) and overall survival (P = 0.017) in the TCIA cohort.Conclusion:PET/CT radiomics-based models enable accurate non-invasive prediction of invasiveness and high-risk pathology in early-stage LUAD, guiding optimal surgical resection.

Introduction: Clinical prediction models for response to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with advanced non-small cell lung cancer (NSCLC) with bone metastasis (BoM) and EGFR mutations are lacking. This study evaluated predictors of response to the EGFR-TKI icotinib in patients with NSCLC and BoM. Methods: We retrospectively analysed patients with EGFR-mutated NSCLC and BoM treated with icotinib at Wuhan Union Hospital. Least absolute shrinkage and selection operator and multivariate Cox regression analyses identified independent predictors of progression-free survival (PFS). A prognostic nomogram was developed, and its effectiveness was assessed using receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), and calibration curves. Results: Among 194 patients (106 with and 88 without BoM), median PFS in the BoM group was 9.8 months, with a 35.8% overall response rate, and 66.0% disease control rate. In univariate and multivariate Cox regression analyses, BoM was an independent predictor of PFS in the overall cohort (hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.34-3.07; P < 0.001). In the BoM subgroup, albumin (HR: 0.36; 95% CI: 0.21-0.62; P < 0.001), lactate dehydrogenase (LDH) (HR: 1.99; 95% CI: 1.21-3.28; P = 0.007), and the platelet-lymphocyte ratio (PLR) (HR: 1.85; 95% CI: 1.05-3.25; P = 0.033) were independently associated with PFS. A nomogram predicting 3-, 6-, and 12-month PFS achieved time-dependent areas under the ROC curve of 0.765, 0.743, and 0.724, respectively. Calibration plots and DCA demonstrated acceptable performance and potential clinical relevance. Conclusions: Albumin, LDH, and PLR predict PFS in EGFR-mutated NSCLC with BoM treated with icotinib. The proposed nomogram may assist in preliminary risk stratification in this specific population.

Background:MET exon14 skipping mutations (METex14) is an established actionable driver oncogene of non-small-cell lung cancer (NSCLC). While ensartinib is a known second-generation tyrosine kinase inhibitor with primary activity against ALK translocation, it is also classified as a type Ia MET inhibitor. We have previously shown anti-tumor activity against METex14 positive NSCLC both in vivo and in vitro. The EMBRACE trial aims to evaluate the clinical efficacy and safety of ensartinib for treatment of METex14 positive NSCLC. Methods:This is a multicenter single arm phase II investigator-initiated study that enrolled METex14 positive lung cancer after failing first line chemotherapy and/or immunotherapy. Eligible patients received ensartinib 225 mg orally once daily in a continuous 28-day treatment cycle until disease progression, unacceptable side effect, or death. Primary endpoint was investigator-assessed objective response rate (ORR), and the secondary end point included disease control rate (DCR), progression-free survival (PFS), duration of response (DoR) and safety profiles. The study was registered with the Chinese Clinical Trial Registry (ChiCTR2100048767). Findings:From July 2021 to February 2024, a total of 31 patients were enrolled and received ensartinib. Median follow-up time of the 30 evaluable patients was 9.2 months (95% Confidence Interval [CI], 6.3-not estimable). The ORR was 53.3% (16/30; 95% CI, 35.5-71.2) and DCR was 86.7% (26/30; 95% CI, 74.5-98.8). Median PFS was 6.0 months (95% CI, 3.0-8.8) and median DoR was 7.9 months (95% CI, 4.8-8.7). Adverse events (AEs) were reported in 24 patients (80%), with 7 (23.3%) of grade 3. The most common AEs were rash (14/30, 46.7%), followed by anemia (7/30, 23.3%), increased ALT (7/30, 23.3%), increased AST (7/30, 23.3%), and pruritus (6/30, 20%). No serious adverse events or treatment-related deaths occurred. Importantly, the exploratory ctDNA analysis indicates that clearance of circulating tumor DNA (ctDNA) at four weeks treatment was associated with more favorable treatment outcomes comparing with patients having positive ctDNA. Interpretation:Ensartinib has a promising anti-tumor activity and manageable safety in previously treated patients with METex14 positive lung cancer. Funding:This work was supported by the National Natural Science Foundation of China [82370028, 82422001] and the CSCO-MET Aberrant Solid Tumor Research Grant [Y-2022METAZMS-0066].

Cancer immunotherapy has transformed cancer treatment, demonstrating the potential for lasting responses in multiple solid and hematologic malignancies and thus has revolutionized cancer treatment in clinic. However, the intricate tumor microenvironment (TME), characterized by a rigid extracellular matrix (ECM) and robust immunosuppressive environment, presents substantial hurdles to the effectiveness of cancer immunotherapy. Thus, cancer-associated fibroblasts (CAFs), the most abundant stromal cells that mediate ECM remodeling and participate in immune suppression, represent promising therapeutic targets for combination immunotherapy. In this study, by using and analyzing single-cell RNA-sequencing (scRNA-seq) in the public datasets, we have identified the elevated expression of glucose transporter 1 (GLUT1) in activated CAF subgroups within tumor sites compared to normal tissues. Moreover, the recent literature has also demonstrated that CAFs undergoing high metabolic levels have been identified to show a better response to immunotherapy. Furthermore, extracellular vesicles (EVs) secreted by CAFs remain unexplored, and their role in drug transport systems and targeting efficiency towards tumorous cells remains uninvestigated. Herein, we identified the elevated expression of glucose transporter 1 (GLUT1) as a prognostic indicator for cancer associated with poor prognosis and investigated the vulnerability of lung tumor cell lines and CAFs to pharmacological GLUT1 inhibition with BAY-876. Based on the possibility of targeting the intrinsic TME-associated metabolism by GLUT1 inhibition, we have firstly employed CAFs-derived extracellular vesicles (cEVs) as a carrier for targeted delivery of BAY-876 into GLUT1-high CAFs and tumor cells. The cEV-BAY-876 (cEVB6) treatment significantly resulted in glucose-rich, low-lactate TME, reversed the activated CAFs phenotype, enabled stromal reprogramming, decreased ECM stiffness and enhanced the infiltration of CD3 + CD8+ T cells in tumor core, thereby achieving an excellent anti-tumor efficiency. Moreover, cEV-B6 treatment synergized anti-programmed death ligand 1 (antiPD-L1) to reinvigorate the exhausted lymphocytes and exerted strong anticancer effects against mice lung tumors. Our study provides the first evidence that tumor stroma-specific therapies by targeting glucose metabolism present a promising strategy of remodeling the extracellular matrix to reverse CAFs into normal type and potentiate cytotoxic T lymphocytes (CTLs) infiltration thereby improving anticancer immunotherapy.

Dendritic cell (DC)-derived extracellular vesicles (DEVs) are promising candidates for cancer vaccines, but their therapeutic effects still need further optimization. In this study, we utilized neoantigens, lipopolysaccharide and IFN-γ to induce the maturation of DCs, and then isolated DEVs derived from these mature DCs. We showed that the immune checkpoint inhibitor (anti-CTLA-4 antibody, aCTLA-4) can improve the immunostimulatory function of DEVs by directly activating T cells through immune checkpoint signal blockade. The cytokine interleukin-12 (IL-12), as one of the third signals for T cell activation, can also enhance the capability of DEVs to activate T cells directly. Based on these findings, we designed the engineered DEVs conjugated with IL-12 and aCTLA-4 (DEV@IL-12-aCTLA-4) to improve the therapeutic potential of DEVs by providing sufficient immune regulatory signals. Moreover, the carrier property of DEVs also contributes to the delivery of IL-12 and aCTLA-4 to lymph nodes. This indicates that the conjugation of DEVs with IL-12 and aCTLA-4 constitutes a complementary approach, where IL-12 and aCTLA-4 help to enhance the T cell activation effect of DEVs, and DEVs facilitate the delivery of IL-12 and aCTLA-4. Our results showed that DEV@IL-12-aCTLA-4 can enhance the Th1 immune response and reverse exhausted CD8+ T cells in the tumour microenvironment, effectively inducing robust T cell immune responses and inhibiting tumour growth in tumour-bearing mice. Overall, this study expands the theoretical foundation of DEVs and provides a universal strategy for optimizing cancer combination immunotherapy by reprogramming DEVs.

Tumour-derived microparticles (TMPs), extracellular vesicles traditionally obtained upon ultraviolet (UV) radiation of tumour cells, hold promise in tumour immunotherapies and vaccines and have demonstrated potential as drug delivery systems for tumour treatment. However, concerns remain regarding the limited efficacy and safety of UV-derived TMPs. Here we introduce a microwave (MW)-assisted method for preparing TMPs, termed MW-TMPs. Brief exposure of tumour cells to short-wavelength MW radiation promotes the release of TMPs showing superior in vivo antitumour activity and safety compared with UV-TMPs. MW-TMPs induce immunogenic cell death and reprogramme suppressive tumour immune microenvironments in different lung tumour models, enabling dual targeting of tumour cells by natural killer and T cells. We show that they can efficiently deliver methotrexate to tumours, synergistically boosting the efficacy of PD-L1 blockade. This MW-TMP development strategy is simpler, more efficient and safer than traditional UV-TMP methods.

Aims:The treatment of patients with locally advanced central non-small cell lung cancer (NSCLC) remains controversial. This study aimed to evaluate the effects of neoadjuvant chemotherapy in patients undergoing pneumonectomy. Methods:A retrospective analysis was conducted on patients who underwent pneumonectomy with or without neoadjuvant chemotherapy for locally advanced central NSCLC between 2014 and 2019. Categorical variables were compared using the Chi-square or Fisher's exact test. Survival analysis was performed using the Kaplan-Meier method, with comparisons made via the log-rank test. Multivariate analysis of independent prognostic factors was conducted using the Cox proportional hazards regression model. A p-value < 0.05 was considered statistically significant. Results:Based on inclusion and exclusion criteria, 104 patients were selected from a total of 6,930, including 69 who received neoadjuvant chemotherapy and 35 who did not. Univariate analysis showed that the neoadjuvant chemotherapy group had significantly improved 5-year overall survival (OS: 29.1% vs. 12.8%, χ 2 = 4.089, p = 0.043) and disease-free survival (DFS: 22.3% vs. 8.8%, χ 2 = 3.941, p = 0.047). The downstaging rate in the neoadjuvant chemotherapy group was 29.0%. Subgroup analysis revealed that patients with downstaging had significantly better 5-year OS and DFS compared to those without downstaging (OS: 56.6% vs. 17.1%, χ 2 = 10.266, p = 0.001; DFS: 54.1% vs. 6.0%, χ 2 = 20.785, p < 0.001). Another subgroup analysis showed that although 5-year DFS was 0% in both groups, patients with stage cN2 disease who received neoadjuvant chemotherapy had better 5-year OS (16.3% vs. 7.8%, χ 2 = 5.603, p = 0.018) and a statistically significant difference in DFS ( χ 2 = 7.328, p = 0.007). Conclusions:Neoadjuvant chemotherapy significantly improves prognosis in patients with locally advanced central NSCLC undergoing pneumonectomy. Multivariate analysis confirms its positive impact on survival. Patients who experience downstaging after neoadjuvant chemotherapy show notably better outcomes. For patients with stage cN2 disease, neoadjuvant chemotherapy is associated with improved survival.

Sarcopenia, a hallmark of cancer cachexia, is linked to an unfavorable prognosis in several malignancies. This study investigated the role of sarcopenia as an independent prognostic factor in lung cancer patients with malignant pleural effusion (MPE) and explored the potential underlying mechanisms using metabolomics. Clinical data from 393 lung cancer patients with MPE at Wuhan Union Hospital from January 2016 to September 2021 were analyzed retrospectively. Univariate and multivariate analyses were used to evaluate the prognostic significance of skeletal muscle and adipose tissue measurements obtained from computed tomography scans of the fourth thoracic vertebra (T4) and third lumbar vertebra. Additionally, metabolomic profiling of pleural fluid from 84 patients was performed, and the effects of candidate metabolites were validated in vitro and in vivo. The T4 skeletal muscle area (T4-SMA) served as an independent prognostic indicator for overall survival in lung cancer patients with MPE. Based on their T4-SMA, patients were categorized into sarcopenia and non-sarcopenia groups using sex-specific cutoff values. Metabolomic analysis identified 61 differential metabolites between sarcopenic and non-sarcopenic patients, with significant sex-based differences. Pathway analysis suggested disruptions in amino acid and unsaturated fatty acid metabolism. Furthermore, arachidonic acid was found to induce muscle atrophy by activating multiple signaling pathways associated with inflammation and sarcopenia. These findings indicate that sarcopenia is an independent prognostic factor in lung cancer with MPE. Alterations in fatty acid and amino acid metabolism, as well as inflammation, may contribute to the potential mechanisms underlying sarcopenia.

Background:As a populous country in the world, China ranks among the top in terms of new cancer cases and deaths worldwide. This study aims to provide a detailed evaluation of the cancer burden in China, considering the evolving social, economic, and environmental factors that may have influenced cancer incidence and mortality rates.Methods:The cancer incidence, mortality, and the contributions of risk factors were estimated using data from the Global Burden of Disease Study (GBD) 2021. The number of new cases and deaths with their 95% uncertainty intervals (UIs) were analyzed. The trends of cancer age-standardized incidence rates (ASIR) from 1990 to 2021 and age-standardized death rates (ASDR) from 1980 to 2021 were estimated. Besides, risk factor contributions were also assessed.Results:In 2021, the total burden of cancer in China comprised 13.66 million new cases (95% UI: 11.79 to 15.85 million) and 2.82 million deaths (95% UI: 2.35 to 3.36 million). In 2021, ASIR and ASDR of cancers were 790.2 (95% UI: 676.8-926.3) per 100,000 population and 137.5 (95% UI: 115.1-163.4) per 100,000 population, respectively. In 2021, tracheal, bronchus, and lung cancer showed the highest ASIR of 44.0 (95% UI: 35.4-53.3) per 100,000 population among site-specific tumors, followed by non-melanoma skin cancer (37.5 [95% UI: 32.4-42.7] per 100,000 population), colon and rectum cancer (31.4 [95% UI: 25.5-38.0] per 100,000 population), stomach cancer (29.1 [95% UI: 22.4-36.2] per 100,000 population), breast cancer (19.4 [95% UI: 15.0-24.3] per 100,000 population), esophageal cancer (15.0 [95% UI: 12-18.4] per 100,000 population), and liver cancer (9.5 [95% UI: 7.7-11.8] per 100,000 population). Besides, the ASDR of cancers decreased about 29.78% in males and 42.00% in females in the past forty years in China. Tracheal, bronchus, and lung cancer showed the highest ASIR (62.63 per 100,000 population) and ASDR (56.45 per 100,000 population) in males. Of note, 31.73% of all cancer deaths in China were digestive cancers in 2021. For level 1 risks in 2021, behavioral risks were linked to 73.57% of cancer deaths.Conclusions:The disease burden of cancers remains a major public health concern in China. The ASIR increased from 1990 to 2021 and the ASDR decreased from 1980 to 2021 in cancers. Tracheal, bronchus, and lung cancer remain the most common types of cancer in China.

Object: Inhibition of interleukin-1β (IL-1β) is recognized as one of the effective strategies for anti-inflammatory therapy. However, current IL-1βinhibitors are protein-based products, which are costly and require stringent storage conditions. Therefore, orally available natural compounds that inhibit IL-1β hold promise as potential therapeutic agents. Method: In our previous work, we utilized molecular screening and dynamic simulation to discover that Moracin D can stably bind to the active pocket of IL-1β. In this study, we employed Microscale Thermophoresis (MST) experiments to validate the binding affinity of Moracin D to IL-1βand further confirmed the binding affinity of IL-1βcomplexed with Moracin D to its receptor. We employed Limited proteolysis-coupled mass spectrometry analysis to identify the peptide segments of IL-1β that interact with Moracin D. In vitro cellular experiments were conducted to verify the action and molecular mechanisms of Moracin D in blocking IL-1β. Finally, an acute lung injury model was utilized to confirm the anti-inflammatory effects and safety of Moracin D.Result:1.Moracin D Binds to IL-1βand Decreases Its Receptor Affinity We found that Moracin D can tightly bind to IL-1βwith an association constant of 0.1uM through MST experiments, (Figure A), and the binding affinity of IL-1β complexed with Moracin D to its receptor is reduced(Figure B). We confirmed the key amino acid residue of IL-1βinvolved in the interaction with Moracin D using Lip-MS technology, which was identified as IL56(Figure C).2.Moracin D Inhibits IL-1β-Mediated Cellular Signaling Pathways Compared to the control group, IL-1β complexed with Moracin D exhibited reduced ability to upregulate IL-6 and IL-8 in vitro(Figure D). Interestingly, we also found that Moracin D can promote M2 polarization of macrophages, further reducing the production of IL-1β(Figure E). RNA sequencing revealed that the promotion of M2 polarization by Moracin D in macrophages is associated with HMGN2 (Figure F).3.Moracin D Alleviates Acute Lung Injury in vivo Oral administration of Moracin D to mice with LPS-induced acute lung injury for 36hours resulted in a decrease in pulmonary inflammatory cytokines (IL-1β, IL-6 and TNF-α)(Figure G). Hematoxylin and eosin (H&E) staining showed a reduction in neutrophils within lung tissue, and a significant alleviation of pulmonary edema and interstitial hemorrhage, without causing damage to liver or kidney function(Figure H).

Cell size is an important component of cell morphological characteristics. It reflects the characteristics of the cell type, nutritional status, growth stage and physiological function. The cell size of cells of the same type tends to be homogeneous and stable. However, in tumour cells, mutations in cell cycle genes and cytoskeletal genes and overexpression of the corresponding signalling pathways often lead to large variations in tumour cell size. Tumour cells regulate cell size and growth and proliferation through multiple signalling pathways, such as PI3K/Akt/mTOR, Myc and Hippo pathways, which work together to regulate cell size and proliferation. This allows tumour cells to adapt to different survival environments. Alterations in cell size also cause tumours to perform different functions, leading to alterations in tumour stemness, invasive migration and anti-tumour immunity by affecting immune cells in the tumour immune microenvironment. In this review, we describe the endogenous and exogenous factors affecting tumour cell size, analyse the mechanisms by which tumour cells regulate cell size and the effects of cell size on tumour malignancy and tumour immunity, summarise the potential therapeutic targets for cell size, and look forward to possible future research directions and clinical applications.

Background:Although advancements in cancer therapies have substantially improved the survival of cancer patients, these treatments may also result in acute or chronic lung injury. Cancer treatment-related lung injury (CTLI) presents with a diverse array of clinical manifestations and can involve multiple sites. Due to the lack of specific diagnostic protocols, CTLI can deteriorate rapidly and may be life-threatening if not promptly addressed. Unfortunately, there is no universally accepted consensus document on the diagnosis and management of CTLI. Methods:A multidisciplinary panel comprising experts from respiratory and critical care medicine, oncology, radiation oncology, thoracic surgery, radiology, pathology, infectious diseases, pharmacy, and rehabilitation medicine participated in this consensus development. Through a systematic literature review and detailed panel discussions, the team formulated nine key recommendations. Results:This consensus document addresses the concept, epidemiology, pathogenesis, risk factors, diagnostic approach, evaluation workflow, management strategies, differential diagnosis, type-specific management and clinical staging of CTLI. Emphasis is placed on raising awareness among clinicians and therapeutic practices through comprehensive guidelines. Conclusions:The consensus provides a detailed diagnostic protocol for CTLI and introduces a structured management framework based on grading, typing, and staging. It highlights the critical role of multidisciplinary team (MDT) collaboration and emphasizes the need for individualized, whole-process patient care strategies to optimize clinical outcomes.