Introduction: Lung cancer is the most prevalent cancer with high mortality in China, and it is associated with the dysbiosis of the lung microbiome. This study attempted to screen for specific microorganisms as potential biomarkers for distinguishing benign lung disease from lung cancer.Methods: Bronchoalveolar lavage fluid (BALF) sample was selected in the study instead of saliva to avoid contamination with oral microorganisms, and microbial taxonomic and functional differences in BALF samples from patients with lung cancer and those with those from patients with benign lung diseases were performed based on metagenomic next‐generation sequencing, for the first time, so that microorganisms other than bacteria could be included.Results: The results showed that the intrasample diversity of malignant samples was different from benign samples, and the microbial differences among malignant samples were smaller, with lower microbial diversity, significantly changed microbial abundance and metabolic functions. Metabolic function analysis revealed amino acid‐related metabolism was more prevalent in benign samples, whereas carbohydrate‐related metabolism was more prevalent in malignant samples. By LEfSe, Metastat and Random Forest analysis, we identified a series of important differential microorganisms. Importantly, the model combining five key genera plus one tumor marker (neuron‐specific enolase) as indicators presented the optimal disease typing performance.Conclusion: Thus results suggest the value of these differential microorganisms enriched in tumors in mechanism research and may be potential new targets for lung cancer therapy. More importantly, the biomarkers identified in this study can be conducive to improve the clinical diagnosis of lung cancer and have good application prospects.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to inhibit pyroptosis and apoptosis, which play important roles in the development and progression of contrast-induced acute kidney injury (CI-AKI). However, to the best of our knowledge, no studies have investigated the potential effect of PCSK9 inhibitors on the prevalence of CI-AKI after percutaneous coronary intervention (PCI). This study aimed to determine whether PCSK9 inhibitors are associated with the prevalence of CI-AKI. The medical records of 309 (mean age, 63.35 years; 71.84% male) patients with acute myocardial infarction who underwent PCI at our institution were retrospectively analyzed. Overall, 149 and 160 patients were assigned to the evolocumab and control groups, respectively. Serum creatinine levels were examined preoperatively and 24–72 h postoperatively and compared between groups. Data were grouped according to the occurrence of CI-AKI, and a univariate analysis was conducted to exclude suspected influencing factors that led to CI-AKI occurrence. After adjusting for confounding factors, a logistic regression analysis was performed to assess the association between evolocumab administration (independent variable) and CI-AKI occurrence (dependent variable). The prevalence of CI-AKI was significantly lower in the evolocumab group (6.7%) than in the control group (20.0%; ). We further evaluated the correlation between exposure factor and outcome. The relative risk(RR) between the use of evolocumab and the occurrence of CI-AKI was 0.34(95% CI 0.17-0.66,p<0.01).This result indicate a significant association between the use of evolocumab and a reduction in the incidence of CI-AKI.The logistic regression analysis results revealed that evolocumab was significantly associated with CI-AKI. The use of PCSK9 inhibitors, hydration therapy, and statin administration appears promising for preventing CI-AKI in patients with acute myocardial infarction undergoing PCI.

Myocarditis is a potentially fatal disease that can progress to inflammatory dilated cardiomyopathy (DCMi), a condition that is a leading cause of noncongenital heart failure in young adults. However, the mechanisms driving the transition from acute myocarditis to DCMi remain poorly understood. Although IL-17A-mediated inflammation is a well-known driver of this progression, the intracellular regulators that sustain and amplify this response remain unclear. Here, we identify the E3 ubiquitin ligase TRIM21, which is upregulated in the hearts of DCMi patients and experimental autoimmune myocarditis (EAM) mice, as a key amplifier of IL-17A-driven inflammation in cardiac fibroblasts (CFs), promoting disease progression and fibrosis. TRIM21 deficiency significantly attenuates EAM progression, reducing inflammatory cell infiltration and fibrotic remodeling. Mechanistically, TRIM21 is induced by IL-17A in CFs and promotes K27-linked polyubiquitination and lysosomal degradation of TRAF3, a negative regulator of IL-17A signaling, thereby establishing a self-reinforcing IL-17A-TRIM21–TRAF3 positive feedback loop. Finally, specific knockdown of TRIM21 in CFs via the AAV9-Postn strategy effectively alleviates EAM severity. These findings identify TRIM21 as an intracellular mediator of sustained IL-17A signaling in myocarditis and highlight its potential as a therapeutic target for preventing progression to DCMi.

Background: Lung cancer remains a leading cause of cancer-related death globally, highlighting the need for improved prognostic tools and personalized treatment strategies. This study aims to identify proteomic biomarkers and develop an AI-driven machine learning model for lung cancer prognosis, patient stratification, and immunotherapy response prediction. Methods: We analysed TCGA LUAD proteomic/clinical data using LASSO and multivariable Cox to derive an eight‑protein risk score; immune infiltration (CIBERSORT), PCA, TIDE/TCIA, and external validation (K21-12) were performed. Findings: Key proteins—XBP1, ETS1, YB1_pS102, ERALPHA_pS118, LCK, PGM1, TIGAR, and Patched—were identified as significant predictors of overall survival. The resulting prognostic model stratified patients into high- and low-risk groups with significant survival differences (p < 0.001), yielding 1-, 2-, and 3-year survival AUCs of 0.738, 0.755, and 0.661. High-risk patients exhibited increased immunosuppressive cells (e.g., activated NK cells, resting mast cells). Validation via an independent cohort (K21-12) confirmed robustness, while Tumor Immune Dysfunction and Exclusion (TIDE) analysis assessed immunotherapy response. Interpretation: This integrated approach demonstrates strong clinical potential for risk stratification and personalized therapy in lung cancer.