Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography.

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics.

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

Identification of inflammatory response-related molecular mechanisms based on the ATM/ATR/p53 pathway in tumor cells.

Inflammatory response is a crucial factor that affects prognosis and therapeutic effect in tumor cells. Although some studies have shown that inflammation could make DNA more vulnerable to external attacks, resulting in serious DNA damage, the underlying mechanism remains unknown. Then, using tumor necrosis factor α (TNF-α) and lipopolysaccharide (LPS), this research elevated the level of inflammation in cancer cells, and hydrogen peroxide (H2O2) and ultraviolet (UV) were utilized as common reactive oxygen species (ROS)-induced DNA damage agents. We show that either H2O2 or UV achieved a more substantial antiproliferative effect in the inflammation environment compared with H2O2 or UV treatment alone. The inflammation environment enhanced H2O2- or UV-induced cell apoptosis and ROS production. Although the phenomenon that inflammation itself could trigger ROS-dependent DNA damage was well known, the underlying mechanism for the sensitization of inflammation to trigger intense DNA damage via ROS in cancer cells remains unclear. In this study, the inflammation-related genes and the corresponding expression information were obtained from the TCGA and fetched genes associated with inflammatory factors. Screening of thirteen inflammatory-related, including ATM, and prognostic genes. In addition, KEGG analysis of prognostic genes shows that biological processes such as DNA replication. ATM and ATR, which belong to the PI3/PI4-kinase family, can activate p53. Inflammation promotes the vulnerability of DNA by activating the ATM/ATR/p53 pathway, while not affecting the DNA damage repair pathway. In brief, this research suggested that inflammation made DNA vulnerable due to the amplifying H2O2- or UV-induced ROS production and the motoring ATM/ATR/p53 pathway. In addition, our findings revealed that inflammation's motoring of the ATM/ATR/p53 pathway plays a crucial role in DNA damage. Therefore, exploring the mechanism between inflammation and ROS-dependent DNA damage would be extremely valuable and innovative. This study would somewhat establish a better understanding of inflammation, DNA damage, and cancer.

Correlation between RNA N6-methyladenosine and ferroptosis in cancer: current status and prospects.

N6-methyladenosine (m6A) is the most abundant chemical modification in eukaryotic cells. It is a post-transcriptional modification of mRNA, a dynamic reversible process catalyzed by methyltransferase, demethylase, and binding proteins. Ferroptosis, a unique iron-dependent cell death, is regulated by various cell metabolic events, including many disease-related signaling pathways. And different ferroptosis inducers or inhibitors have been identified that can induce or inhibit the onset of ferroptosis through various targets and mechanisms. They have potential clinical value in the treatment of diverse diseases. Until now, it has been shown that in several cancer diseases m6A can be involved in the regulation of ferroptosis, which can impact subsequent treatment. This paper focuses on the concept, function, and biological role of m6A methylation modification and the interaction between m6A and ferroptosis, to provide new therapeutic strategies for treating malignant diseases and protecting the organism by targeting m6A to regulate ferroptosis.

Solamargine improves the therapeutic efficacy of anti-PD-L1 in lung adenocarcinoma by inhibiting STAT1 activation.

OBJECTIVE: The effect of solamargine on lung adenocarcinoma and its effect on STAT1 signaling pathway mediated immune escape were studied through network pharmacology and in vitro and in vivo experiments. METHODS: The solamargine targets were screened using the TCMSP and the LUAD targets were screened using the GeneCard, OMIM, PharmGkb, TTD and DrugBank databases. PPI network analysis and target prediction were performed using GO and KEGG. Colony formation assay, EDU staining, wound healing, transwell assay, Hoechst and flow cytometry were used to detect the effects of solamargine on the proliferation, migration and apoptosis of LUAD. Western blotting (WB) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) were used to detect P-STAT1 and PD-L1 expression. And immunofluorescence was used to detect P-STAT1 expression. In vivo experiments, C57BL/6 mice were divided into control group, low concentration group, high concentration group, positive control group and combination group. Every other day, following seven consecutive doses, the size of the tumor was assessed. Finally, the expressions of P-STAT1, STAT1, PD-L1 and apoptosis index proteins were detected by WB. RESULTS: The anti-LUAD effect of solamargine was found by wound healing, colony formation assay, transwell assay, hoechst and EdU staining. The results of network pharmacological analysis showed that solamargine could suppress STAT1 expression level. Further enrichment assay of STAT1 showed that STAT1 was associated with immune-related pathways. In addition, molecular signal analysis by WB and RT-qPCR indicated that solamargine could reduce the expression levels of P-STAT1 and PD-L1 in a concentration-dependent manner. According to the results of in vivo assays, combination of solamargine and immune checkpoint inhibitors (ICIs) durvalumab could significantly inhibit the growth of Lewis transplanted tumors in C57BL/6 mice, and no toxic side effect was recoded. CONCLUSION: These results indicated that solamargine could inhibit the proliferation and promote the apoptosis of LUAD. It also could reduce the expression level of P-STAT1 protein and inhibit the expression level of PD-L1. At the same time, the combination with the ICIs can better block the expression of PD-L1 in cells, thereby inhibiting the immune escape pathway of tumor cells and achieving anti-tumor effects. This study proposed a novel combined therapeutic approach, involving the inhibition of STAT1 by solamargine in conjunction with ICIs.