Yingchun Zhou

Background: Intratumor heterogeneity in lung adenocarcinoma (LUAD) profoundly influences tumor progression and immune evasion. However, current prognostic models largely fail to capture the complex cellular dynamics of the tumor microenvironment (TME) at single-cell resolution. Methods: We integrated single-cell RNA sequencing with bulk transcriptomics to derive cell-type-specific signatures. A systematic evaluation of 101 machine learning algorithm combinations was performed across multiple independent cohorts to construct a robust prognostic model. Immune landscape, mutational features, intercellular communication, and immunotherapy responsiveness were comprehensively evaluated. The biological functions of the identified hub gene, transmembrane 4 L six family member 1 (TM4SF1), were rigorously validated in vitro . Results: The optimal StepCox[forward]-Ridge model showed strong prognostic stratification in five cohorts. Low-risk tumors exhibited an immune-inflamed phenotype, suggesting greater predicted good responses to immune checkpoint blockade. In contrast, high-risk tumors had an immunosuppressive TME with overactive cell communication driven by fibroblasts and myeloid cells. TM4SF1 was a key driver in this high-risk signature. TM4SF1 silencing significantly inhibited LUAD cell proliferation, migration, and invasion, and strongly induced apoptosis. TM4SF1 depletion reversed tumor immune evasion by reducing PD-L1, increasing MHC-I, and lowering the secretion of CCL5. Conclusions: This single-cell-guided machine-learning framework provides strong prognostic stratification in LUAD and captures immune phenotypes associated with potential immunotherapy responsiveness. TM4SF1 emerged as a candidate mediator associated with malignant progression and immune-related phenotypes in LUAD, warranting further mechanistic and in vivo investigation. This suggests it could be a useful therapeutic target.

How to screen beneficiary populations has always been a clinical challenge in the treatment of non-small-cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs). Routine blood tests, due to their advantages of being minimally invasive, convenient, and capable of reflecting tumor dynamic changes, have potential value in predicting the efficacy of ICIs treatment. However, there are few models based on routine blood tests to predict the efficacy and prognosis of immunotherapy. Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. For efficacy prediction, we assessed receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, clinical impact curve (CIC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) compared with the Nomogram model. For prognostic evaluation, we utilized the C-index and time-dependent C-index compared with the Nomogram model, Lung Immune Prognostic Index (LIPI) and Systemic Inflammatory Score (SIS). Patients were classified into high-risk and low-risk groups based on RF model, then the Kaplan-Meier (K-M) curve was used to analyze the differences in progression-free survival (PFS) and overall survival (OS) of patients between the two groups. The RF model incorporated RDW-SD, MCV, PDW, CD3⁺CD8⁺, APTT, P-LCR, Ca, MPV, CD4⁺/CD8⁺ ratio, and AST. In the training and validation cohorts, the RF model exhibited an AUC of 1.000 and 0.864, and sensitivity/specificity of (100.0%, 100.0%) and (70.3%, 93.5%), respectively, which had superior performance compared to the Nomogram model (training cohort: AUC = 0.531, validation cohort: AUC = 0.552). The C-index of the RF model was 0.803 in the training cohort and 0.712 in the validation cohort, which was significantly higher than Nomogram model, LIPI and SIS. K-M survival curves revealed that patients in the high-risk group had significantly shorter PFS/OS than those in the low-risk group. In this study, we developed a novel model (RF model) to predict the response to immunotherapy and prognosis in NSCLC patients. The RF model demonstrated better predictive performance for immunotherapy responses than the Nomogram model. Moreover, when predicting the prognosis of immunotherapy, it outperformed the Nomogram model, LIPI, and SIS.

Horseradish peroxidase (HRP) combined with its fluorescence substrates is attracting increasing attention for biochemical analysis. Amplex red is the most widely used fluorescence substrate to HRP; however, it suffers from some drawbacks, such as nonspecific responsiveness toward carboxylesterases. Discovering a new small molecular fluorescence substrate with improved sensitivity and selectivity for HRP is thus desired. Herein, three dihydrofluorescein derivatives (DCFHs) are presented to serve as HRP substrates through fluorescence turn-on methods. The most promising one, 2,7-dichloro-9-(2-(hydroxymethyl)phenyl)-9H-xanthene-3,6-diol (DCFH-1), exhibited excellent sensitivity in the detection of HRP. Moreover, DCFH-1 does not respond to carboxylesterase, thus holding advantages over Amplex red. In the further study, the detection reagent in the commercial ELISA kits was replaced with DCFH-1 to establish a new fluorescence ELISA, which works very well in the quantification of inflammatory cytokine biomarkers from in vitro models.

Pasteurella multocida, one of the major zoonotic pathogens, may cause localized cellulitis, bacteremia, septic shock, and other symptoms in people. Accidental bites or scratches in close contact between humans and cats are one of the causes of P. multocida infection in humans. The prevalence of diseases due to P. multocida in humans may be rising as a result of the popularity of cat cafes in China. However, the epidemiology of P. multocida in cat-themed cafes in South China is yet to be reported. P. multocida in 92 healthy cats from four cafes with pet-cat themes was examined in their tonsils and oral cavities. The antibiotic susceptibility of each isolate was evaluated by using the disk-susceptibility testing method. The 19 P. multocida were selected for whole-genome sequencing (WGS), together with the genome data of eight previously described clinical strains isolated from humans, and the analysis of antimicrobial resistance (AMR) determinants, capsular genotyping, MLST genotyping, and virulence gene profiles was carried out. The results showed that 48.91% of cats (45/90) were positive for P. multocida. All isolated P. multocida stains were highly resistant to erythromycin (15 μg) (100%) and nonsensitive to fluoroquinolones (5 μg) (37.38%), while they were all susceptible to penicillin (10 untis), tetracycline (30 μg), doxycycline (30 μg), and chloramphenicol (30 μg). According to WGS analysis, those with the gyrA resistance gene were all insensitive to fluoroquinolone antibiotics. Virulence gene profiles showed that the genes pfhA, nanH, and plpB were practically all present in cats compared to humans. To conclude, increased antibiotic resistance, along with virulence factors, complicates the treatment of P. multocida infection. Thus, clinical treatment for P. multocida infection should be performed more cautiously.

Background: Gelsolin (GSN) is the most widely expressed actin-severing protein in humans, which could regulate cell morphology, differentiation, movement and apoptosis. This study aims to explore the GSN as a prognostic biomarker of stomach adenocarcinoma (STAD). Methods: In this study, we used several online databases to comprehensively analyze the role of GSN in STAD. Oncomine and HPA databases were used to explore the GSN expression in various cancer, especially in gastric cancer. Then, UALCAN database was used to evaluate the relationship between GSN expression and promoter methylation in clinical characteristics. Finally, we used TIMER to analyze the correlation between GSN expression and immune infiltrates in gastric cancer. Results: GSN was down-regulated in gastric cancer, and decreased expression of GSN was related to worse survival. The GSN expression was significantly related to tumor purity in STAD and significantly correlated with infiltrating level of various immune cells, especially the dendritic cells. Conclusion: Our study proposes that GSN can be served as the biomarker of disease and neoantigen for STAD treatment, which can improve the deficiency of disease-specific targeted therapies currently exist.