BACKGROUND: Lung cancer currently ranks first globally in both incidence and mortality. Pemetrexed (PMX) serves as a first-line treatment for lung adenocarcinoma (LUAD), but the patients often develop drug resistance during therapy. Milk exosome (mEXO) have the advantages of low immunogenicity, high tissue affinity, and low cost, and mEXO itself has anti-tumor effects. Hyaluronan (HA) naturally bind to CD44, a receptor which is highly expressed in LUAD tissues. This study aims to construct hyaluronan-modified milk exosome (HA-mEXO) and preliminarily investigate their molecular mechanisms for reversing PMX resistance through cellular experiments. METHODS: Exosomes were extracted from milk using high-speed centrifugation, and HA-mEXO was constructed. PMX-resistant A549 and PC-9 cell lines were treated with mEXO and HA-mEXO, respectively. CCK-8 assays, colony formation assays, Transwell assays, and flow cytometry were performed to evaluate proliferation, colony formation, migration, invasion, and apoptosis phenotypes in the treated resistant cell lines. Finally, transcriptomic sequencing, analysis, and cellular functional recovery experiments were conducted to investigate the mechanism by which HA-mEXO reverses PMX resistance in LUAD cells. RESULTS: The expression of CD44 in A549 and PC-9 LUAD drug-resistant cell lines was significantly higher than that in parental cells, and the uptake rate of HA-mEXO by drug-resistant cell lines was significantly higher than that of mEXO. Compared to the mEXO group, HA-mEXO-treated A549 and PC-9 resistant cells exhibited significantly reduced half maximal inhibitory concentration (IC50) values for PMX, markedly diminished clonogenic, migratory, and invasive capabilities, and a significantly increased proportion of apoptotic cells. Western blot analysis revealed that, compared to parental cells, A549 and PC-9 drug-resistant cells exhibited downregulated ZNF516 expression and upregulated ABCC5 expression. Immunofluorescence analysis revealed that HA-mEXO treatment downregulated ABCC5 expression in A549 and PC-9 drug-resistant cells compared to the PBS group, whereas co-treatment with HA-mEXO and ZNF516 knockdown showed no significant change in ABCC5 expression. CONCLUSIONS HA-mEXO carrying ZNF516 suppress ABCC5 expression, thereby enhancing the sensitivity of A549 and PC-9 LAUD drug-resistant cells to PMX.
Humans ; Hyaluronic Acid/chemistry* ; Drug Resistance, Neoplasm/drug effects* ; Exosomes/chemistry* ; Adenocarcinoma of Lung/genetics* ; Pemetrexed/pharmacology* ; Animals ; Lung Neoplasms/pathology* ; Milk/chemistry* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Cell Line, Tumor ; Hyaluronan Receptors/metabolism*

BACKGROUND: Lung adenocarcinoma (LUAD) remains one of the leading causes of cancer morbidity and mortality worldwide, and its initiation and progression are closely associated with the tumor immune microenvironment. Increasing evidence suggests that environmental exposure is a critical factor influencing lung cancer development. Among these factors, fine particulate matter (PM2.5), a major component of air pollution, has been strongly linked to elevated lung cancer risk and unfavorable prognosis. However, the underlying immunoregulatory mechanisms by which PM2.5 drives LUAD progression remain poorly understood. Tumor-associated macrophages (TAMs), especially those polarized toward the M2 phenotype, are key components of the tumor microenvironment and play crucial roles in tumor growth, angiogenesis, and immune evasion. This study aims to investigate the effects of PM2.5 exposure on TAMs and to identify the key pro-tumorigenic factors mediating this process. METHODS: A mouse orthotopic lung cancer model under PM2.5 exposure was established to assess lung tumor growth and macrophage phenotypic alterations using in vivo imaging and flow cytometry. A subcutaneous tumor model involving co-inoculated macrophages and tumor cells was used to further verify the effects of PM2.5 on the function of TAMs and tumor malignancy. Combining in vitro experiments, flow cytometry, Western blot, reverse transcription quantitative polymerase chain reaction (RT-qPCR), cell counting kit-8 (CCK-8) assay, colony formation assay, and wound healing assay were employed to evaluate the regulatory effects of PM2.5 on the polarization of bone marrow-derived macrophages (BMDMs) as well as tumor cell proliferation, migration, and colony-forming ability. Transcriptome sequencing integrated with TISIDB (Tumor-immune System Interactions Database) and GEPIA (Gene Expression Profiling Interactive Analysis) databases was performed to identify key cytokines for further functional validation. RESULTS: In the mouse orthotopic lung cancer model, PM2.5 exposure significantly promoted tumor growth and increased the proportion of M2-type TAMs (P<0.05). Subcutaneous co-inoculation with PM2.5-treated BMDMs markedly enhanced tumor proliferation and elevated the intratumoral M2-type TAMs. PM2.5-pretreated BMDMs exhibited an immunosuppressive programmed cell death ligand 1 (PD-L1)+/arginase 1 (Arg1)+ phenotype, and their conditioned media significantly promoted proliferation, migration, and colony formation of Lewis lung carcinoma cells (LLC) and B16 melanoma cells (B16) (P<0.05). Transcriptome analysis revealed that PM2.5 substantially altered macrophage gene expression, with IL-1α identified as a key upregulated secreted cytokine enriched in immunosuppressive related signaling pathways. Clinical database analyses further indicated that IL-1α expression was positively correlated with macrophage and regulatory T cells (Treg) infiltration in the LUAD immune microenvironment, and that high IL-1α expression was associated with worse overall survival in LUAD patients (HR=1.5, P=0.0053). Western blot, RT-qPCR, and immunofluorescence confirmed that PM2.5 exposure significantly upregulated IL-1α expression and secretion in TAMs. CONCLUSIONS PM2.5 exposure facilitates LUAD progression by inducing an immunosuppressive phenotype in macrophages and enhancing the malignant behaviors of tumor cells. Mechanistically, IL-1α may serve as a key pro-tumorigenic cytokine secreted by macrophages under PM2.5 exposure. This study provides new insights into the pathogenesis of PM2.5-associated LUAD and suggests that IL-1α could serve as a potential therapeutic target.
Animals ; Mice ; Tumor-Associated Macrophages/immunology* ; Particulate Matter/toxicity* ; Adenocarcinoma of Lung/metabolism* ; Lung Neoplasms/genetics* ; Humans ; Disease Progression ; Tumor Microenvironment/drug effects* ; Cell Proliferation/drug effects* ; Cell Line, Tumor

Lung cancer remains one of the most prevalent and lethal malignancies worldwide. The advancement of its precise diagnosis and therapeutic development urgently requires in vitro models that can highly recapitulate the pathophysiological characteristics of human tissues. Organ-on-a-chip has emerged as a novel technological platform that integrates microfluidic engineering, biomaterials, and other engineering strategies with organoid culture. This platform enables precise control over the cellular microenvironment, thereby closely mimicking the three-dimensional structure and physiological functions of human organs in vitro. Organ-on-a-chip systems demonstrate significant advantages in cancer research, developmental biology, and disease modeling, as they not only preserve the heterogeneity and pathological features of patient samples but also support co-culture of various cell types to reconstruct the tumor microenvironment (TME). However, standardized construction methods and integrated analytical strategies for this technology in lung cancer research remain to be further refined. This review systematically elaborates on the key technical principles of organ-on-a-chip and its recent advances in lung cancer modeling, drug screening, and immunotherapy research. It aims to provide a theoretical foundation and technical perspective for promoting the deeper application of organ-on-a-chip in precision medicine and translational research for lung cancer.
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Humans ; Lung Neoplasms/drug therapy* ; Organoids/drug effects* ; Lab-On-A-Chip Devices ; Animals ; Tumor Microenvironment

Antibody-drug conjugate (ADC), a novel class of antineoplastic agents, combines tumor-specific targeting with potent cytotoxic activity. In recent years, ADC has achieved notable advances in the treatment of non-small cell lung cancer (NSCLC), particularly within therapeutic sequencing after failure of first-line therapy or the emergence of resistance. This paper will systematically review the efficacy and safety evidence of representative ADC in NSCLC, and further to discuss progress and challenges in ADC structural optimization, toxicity management, biomarker identification, and combination strategies, aiming to provide a comprehensive theoretical foundation and practical reference for clinical practice and future research.
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Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Immunoconjugates/chemistry* ; Lung Neoplasms/drug therapy* ; Drug Resistance, Neoplasm/drug effects* ; Antineoplastic Agents/chemistry*

Combined small cell lung cancer (CSCLC) is a cancer that mixes small cell lung cancer (SCLC) with non-small cell lung cancer (NSCLC) components according to the World Health Organization's 2015 New Pathologic Classification of Lung Cancer. Composed of a mixture of SCLC and NSCLC components, CSCLC is classified as a subtype of SCLC in neuroendocrine tumors. Currently, research on SCLC mainly focuses on single-component pure SCLC, with relatively few studies on CSCLC, which is clinically rare and has no standardized treatment protocols and lacks a unified perception of the clinicopathological features and prognostic predictive indexes of CSCLC. Further observation of efficacy and prognosis is needed. We report the treatment course of a case of CSCLC and provide a literature review of the current status of research on CSCLC.
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Humans ; Small Cell Lung Carcinoma/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Male ; Middle Aged

OBJECTIVE: To study effective methods for reducing lung V₅, V₁₀, and mean lung dose (MLD) in the design of volumetric modulated arc therapy for central lung cancer by using different arc configurations and dose-limiting blocks designs. METHODS: Five groups of plans were designed for the enrolled patients. Group A used a full-arc field. Group B used a partial-arc field. Groups C, D, and E used full-arc fields with vertical-length, semi-ring, and triangular dose-limiting blocks added respectively. The dosimetric similarities of target areas and the dosimetric differences in lung V₅, V₁₀, V₂₀, and MLD among the groups were compared. RESULTS: Compared with group A, groups B, C, D, and E had decreased homogeneity and conformity of the target area, but significantly lower V₅ and V₁₀ of the whole lung. The MLD of groups C, D, and E was lower than that of group A. CONCLUSION Using a full-arc field combined with dose-limiting blocks can effectively reduce lung V₅, V₁₀, MLD, and monitor units (MU).
Lung Neoplasms/radiotherapy* ; Humans ; Radiotherapy, Intensity-Modulated/methods* ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Lung/radiation effects*

OBJECTIVE: To investigate the impact of different iterative cone beam CT (iCBCT) scanning beam currents from a ring-mounted linac on synthetic CT image quality for lung adaptive radiotherapy under lung scanning protocol. METHODS: The CIRS lung motion phantom was configured to simulate conventional respiratory motion pattern, followed by 4D-CT simulation. After transferring the radiotherapy plan to the ring-mounted Halcyon 3.0 linac, three groups of typical iCBCT scans with different beam currents [ I _low (160 mA), I _middle (282 mA), and I _high (491 mA)] were performed and corresponding image reconstructions were completed. Synthetic CT (sCT) images were subsequently obtained based on the deformable registration algorithm. RESULTS: Compared to the corresponding CBCT images, the sCT images exhibited a significant reduction in artifacts. The fine structure of the planning CT (pCT) image was preserved for sCT images corresponding to different scanning beam currents, with Dice similarity coefficients exceeding 0.90 for all cases. CONCLUSION The image quality of sCT corresponding to different iCBCTs is comparable to that of pCT, and changes in iCBCT beam parameters have a negligible impact on sCT image quality. Taking into account both image quality and imaging dose factors associated with the beam currents, iCBCT with a lower beam current on the ring-mounted Halcyon linac offers greater clinical value in lung adaptive radiotherapy.
Cone-Beam Computed Tomography/methods* ; Phantoms, Imaging ; Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging*

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

OBJECTIVES: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the EGFR and KRAS genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict EGFR and KRAS gene mutation status in NSCLC patients, thereby providing a reference for precision oncology. METHODS: Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting EGFR and KRAS gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation. RESULTS: The models effectively predicted EGFR and KRAS mutations in NSCLC patients, achieving an AUC of 0.95 for EGFR mutations and 0.90 for KRAS. The models also demonstrated high accuracy (EGFR 89.66%; KRAS 87.10%), sensitivity (EGFR 93.33%; KRAS 87.50%), and specificity (EGFR 85.71%; KRAS 86.67%). CONCLUSIONS A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating EGFR and KRAS gene mutation status in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Mutation ; Proto-Oncogene Proteins p21(ras)/genetics* ; ErbB Receptors/genetics* ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Female ; Male ; Neural Networks, Computer ; Middle Aged ; Aged

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged