## 2 Review of Existing Work  
In the context of personalized medicine, the unique biological information provided by a patient's gene expression patterns is particularly crucial in determining which patients are likely to benefit from neoadjuvant therapy [4]. This section presents a review of studies focused on the selection of bladder cancer patients who benefit from neoadjuvant therapy. Initial research concentrated on analyzing the expression levels of specific individuals' genes, as these expressions are directly linked to the tumor's sensitivity or responsiveness to treatment. For instance, a study of BRCA1 mRNA expression levels in 57 patients with advanced bladder cancer revealed that those with lower or moderate expression levels were more likely to benefit from neoadjuvant therapy (\(p\) = 0.01) [15]. This finding highlights the predictive value of BRCA1 expression levels in the efficacy of platinum-based neoadjuvant chemotherapy and could impact the formulation of personalized treatment strategies. The role of BRCA1 in DNA repair mechanisms may explain its association with treatment responsiveness. Additionally, in another study involving 50 patients receiving neoadjuvant therapy, whole-exome sequencing identified that patients with ERCC2 gene mutations were more likely to benefit from treatment (\(p\) < 0.01). ERCC2's critical role in the DNA repair process may be the key link between its mutation and treatment efficacy [16]. An analysis of 178 cancer-related genes in 71 patients revealed that ERBB2 mutations were common among those who benefited from neoadjuvant therapy, although this result was not statistically significant, possibly due to limited sample size or high data variability [17]. Subsequent research shifted towards multi-gene analysis. For example, after conducting a differential gene expression analysis in 18 patients undergoing neoadjuvant therapy, researchers identified 14 genes with significant expression differences and developed a treatment effect prediction scoring system [18]. This system was tested in a validation cohort of 22 patients and successfully predicted the treatment outcomes in 19 patients, confirming the feasibility of using gene expression differences to construct a treatment effect prediction scoring system [19]. Although traditional statistical methods like the t-test and ANOVA have proven effective in multiple fields, they faced numerous challenges when handling large-scale biomarker data with numerous features, high dimensionality, and nonlinear relationships [20]. Genomic data, for instance, often do not conform to the assumption of a normal distribution and may contain outliers and biomarkers beyond detection limits, potentially skewing data distribution. To address these challenges, new ML techniques have been developed to handle large datasets, cope with complex data distributions, and address nonlinear issues. This research based on models utilizing Support Vector Machine (SVM) algorithms has demonstrated the ability to accurately predict the response of individual cancer patients to various standard chemotherapy drugs based on tumor gene expression characteristics (such as RNA sequencing or microarray data), with an accuracy rate exceeding 80% [21]. This indicates the potential value of machine-learning algorithms in clinical applications, particularly in identifying effective second-line treatment options for patients who fail first-line standard treatments. Moreover, machine-learning methods have also been applied to early identification of patients with metastatic or recurrent colorectal cancer who are sensitive to FOLFOX therapy (a chemotherapy regimen combining 5-FU, leucovorin, and oxaliplatin) [22]. Researchers used microarray meta-analysis to identify differentially expressed genes (DEGs). These genes showed significant expression level differences between patients who responded to FOLFOX therapy and those who did not.