Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

Objective To investigate the expression of PLCD3 mRNA in the synovium of osteoarthritis(OA)and its relationship with immune cell infiltration.Methods Based on the differentially expressed genes of OA found in the previous study,the expression of phospholipase Cδ3(PLCD3)mRNA was detected by col-lecting synovial samples from OA group and control group.CIBERSORT algorithm was used to analyze the infiltration pattern of immune cells in OA group and control group,and the correlation between PLCD3 and infiltrating immune cells was further analyzed.Results Compared with the control group,the relative expres-sion level of PLCD3 mRNA was significantly increased in synovial samples of OA group(P<0.05).The pro-portions of B cells naive,NK cells activated,M2 macrophages and mast cells activated in synovial tissues of OA group were relatively high(P<0.05).PLCD3 was positively correlated with the proportion of these four immune cells(P<0.05).Conclusion PLCD3 may be a key biomarker for the diagnosis of OA,which may be involved in the pathogenesis of OA by interacting with infiltrating immune cells.

Objective To investigate the relationship between coagulation indicators and early prognosis in patients with acute respiratory distress syndrome (ARDS).Methods The data of ARDS patients receiving the treatment in the intensive care unit (ICU) from 2008-2019 were selected from the Critical Care Medicine Open Database (MIMIC-Ⅳ V2.0 version) jointly published by MIT,Beth Israel Deaconess Medical Center,and Philips Medical,the data were categorized according to the severity of the patients' disease and the causes of lung damage.The coagulation indexes and 28 d mortality (m28d) rates were compared among different ARDS patients.The receiver operating characteristic (ROC) curve was drawn.The area under the curve was calculated to evaluate the predictive values of the related indicators.The univariate and multivariate logistic re-gression was adopted to analyze the risk factors affecting m28d in the patients with ARDS.Results Maximum prothrombin time (PTmax) in the patients with pulmonary origin ARDS was significantly lower than that in the patients without pulmonary origin ARDS,and the difference was statistically significant (P<0.05).PLTmin,PLTmax and Sequential Organ Failure Assessment (SOFA) score had statistical difference among dif-ferent severity degrees of ARDS patients (P<0.05).Minimum international normalized ratio (INRmin),maxi-mum international normalized ratio (INRmax),minimum prothrombin time (PTmin),PTmax,maximum activated partial thromboplastin time (APTTmax) and SOFA score had statistical differences between the survival group and death group (P<0.05).AUC of INRmin,INRmax,PTmin,PTmax and APTTmax were 0.607,0.624,0.610,0.620 and 0.648 respectively.The multivariate logistic regression analysis showed that APTTmax (OR=1.011,95%CI:1.001-1.022,P=0.029) was an independent risk factor for affecting m28d in the ARDS patients.Conclu-sion Plasma PLT levels in different severities of ARDS patients have the difference and APTTmax on the first day in ICU is an independent risk factor for affecting early prognosis in ARDS patients.

Objective:To explore the tumor microenvironment (TME) module associated with pancreatic ductal adenocarcinoma (PDAC) and identify prognostic biomarkers and potential immunotherapeutic targets.Methods:The genetic expression profile data were collected and selected from a dataset of 142 PDAC patients in The Cancer Genome Altas (TCGA) database and 2 microarray datasets (GSE2150, GSE62452) of 168 PDAC patients in Gene Expression Omnibus (GEO) database, and the cell type enrichment analysis of PDAC gene expression data was analyzed by xCell network tool. According to the median cell enrichment score, 142 patients from TCGA were divided into high score group and low score group, and the cell types with prognostic value were determined by univariate survival analysis and validated by GEO datasets. According to the cell type, the differential expression gene analysis and univariate survival analysis were performed to determine the prognosis related differential expression genes (DEGs), and the prognostic DEGs were analyzed by function enrichment analysis and protein-protein interaction (PPI) network analysis. At the same time, GEO dataset was used to verify the prognosis related DEGs of TCGA datasets. Finally, TISIDB database was searched for the common DEGs of TCGA and GEO database, and its correlation with immune system was analyzed.Results:Cell type enrichment analysis showed that Th1 cell and keratinocyte had the same prognostic value in both TCGA and GEO dataset; the overall survival rate of patients with high score was lower than that of those with low score, and the differences were statistically significant (all P values <0.05). 216 prognosis related DEGs were identified, and the results of functional enrichment showed that 9 of the 21 biological process items were closely related to the immune process, and 4 of the 5 KEGG (Kyoto Encyclopedia Of Genes and Genomes) pathways were closely related to the immune process. Through PPI network analysis, CCR7, CD 27, CD 5, CXCL13, ZAP70, MS4A1 and CCL19 were proved to be possibly closely associated with central genes. Through the validation of GEO datasets, there were 15 DEGs with similar prognostic value in GEO and TCGA datasets, which was searched in TISIDB dataset, and the result showed that GIMAP7 was closely related with the immune process of PDAC. Conclusions:A group of 216 TME genes and 7 central genes related to the prognosis of PDAC were identified, and 5 potential targets for immunotherapy of PDAC were provided, including CCR7, CCL19, CD 27, CXCL13 and GIMAP7.