Exploration of neutrophil-associated genes in the prognosis of bladder urothelial carcinoma based on a machine learning and multi-omics data integration framework

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BACKGROUND: Bladder urothelial carcinoma (BLCA) is a prevalent malignancy. The poor performance of existing therapeutic approaches in the advanced stages of BLCA underscores the critical need for more sensitive and precise biomarkers to improve patient survival and prognosis. METHODS: This study utilized single-cell RNA sequencing (scRNA-seq) data from BLCA and control groups, employing the high-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) algorithm to identify neutrophil-associated genes. These genes were intersected with differentially expressed genes (DEGs) from RNA-seq data, followed by univariate Cox regression analysis. Subsequently, BLCA subtypes were identified using a framework combining autoencoder (DAE) and joint deep semi-nonnegative matrix factorization algorithms. Various machine learning ensemble algorithms were then used to screen prognostic genes and construct a BLCA risk model. RESULTS: We identified several reliable BLCA subtypes with significant differences in enriched pathways and immune landscapes. Based on the risk model, the high- and low-risk groups showed significant differences in the expression patterns and BLCA-related associations of prognostic genes, as well as in immune cell correlations and drug sensitivity. Furthermore, the prognostic genes in the constructed risk model also demonstrated significant value in pan-cancer analysis. CONCLUSION: This study reveals the critical role of neutrophils in the occurrence and progression of BLCA through multi-omics data and bioinformatics analyses, and constructs a risk model with potential clinical applications. Our research provides new insights for precise stratification and personalized treatment of BLCA, promising to improve the clinical prognosis. The source code for the proposed framework is available at https://gitee.com/guancheng-xiao/blca/tree/master/ .

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