Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS: In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS: We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION: In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.

An efficient multi-path 3D convolutional neural network for false-positive reduction of pulmonary nodule detection.

PURPOSE: Considering that false-positive and true pulmonary nodules are highly similar in shapes and sizes between lung computed tomography scans, we develop and evaluate a false-positive nodules reduction method applied to the computer-aided diagnosis system. METHODS: To improve the pulmonary nodule diagnosis quality, a 3D convolutional neural networks (CNN) model is constructed to effectively extract spatial information of candidate nodule features through the hierarchical architecture. Furthermore, three paths corresponding to three receptive field sizes are adopted and concatenated in the network model, so that the feature information is fully extracted and fused to actively adapting to the changes in shapes, sizes, and contextual information between pulmonary nodules. In this way, the false-positive reduction is well implemented in pulmonary nodule detection. RESULTS: Multi-path 3D CNN is performed on LUNA16 dataset, which achieves an average competitive performance metric score of 0.881, and excellent sensitivity of 0.952 and 0.962 occurs to 4, 8 FP/Scans. CONCLUSION: By constructing a multi-path 3D CNN to fully extract candidate target features, it accurately identifies pulmonary nodules with different sizes, shapes, and background information. In addition, the proposed general framework is also suitable for similar 3D medical image classification tasks.

H1521, a novel derivative of 4-hydroxyquinoline-3-carboxamide, suppresses the development of lupus in mice by inducing Th1 cytokine profile in T cells.

Transferring parental splenocytes into unirradiated F1 mice induces a chronic graft-versus-host disease (GVHD), characterized by the production of Th2 cytokines and immunocomplex-mediated glomerulonephritis resembling systemic lupus erythematosus (SLE). The effects of H1521, a new derivative of 4-hydroxyquinoline-3-carboxamide, were investigated in chronic GVHD lupus model. H1521 was administered to chronic GVHD mice for 10 weeks. Nephritic symptoms were monitored and cytokine expression in the spleen was detected. To clarify the direct effect of H1521 on CD4(+) T cell, CD4(+) T cells were isolated and co-cultured with H1521 under neutral and Th1 or Th2 driving conditions in vitro. H1521 (32 mg/kg) reduced the incidence of proteinuria by 50% in chronic GVHD mice. Ameliorated lupus symptoms and improved renal histopathology damage were also observed. Administration of H1521 had little impact on Th1 cytokine IL-2 and IFN-gamma or Th2 cytokine IL-4 and IL-10 mRNA expression. In contrast, severely deficient IFN-gamma production by concanavalin A-stimulated spleen cells in chronic GVHD mice was completely restored by H1521. In accordance with this, decreased T-bet mRNA expression became normalized with H1521 (32 mg/kg) treatment. In addition, in vitro studies demonstrated that H1521 preferentially favored Th1 differentiation in CD4(+) T cell and promoted IFN-gamma secretion in Th1 differential CD4(+) T cell. However, IL-4 secretion in naive or Th2 differential CD4(+) T cell was unaffected by H1521. In conclusion, H1521 can induce Th1 cytokine profile in CD4(+) T cells and has possible therapeutic value in Th2-predominant immune diseases.