Bladder cancer (BCa) stands out as a prevalent and highly lethal malignancy worldwide. Chemoresistance significantly contributes to cancer recurrence and progression. Traditional Tumor Node Metastasis (TNM) stage and molecular subtypes often fail to promptly identify treatment preferences based on sensitivity. In this study, we developed a prognostic signature for BCa with uni-Cox + LASSO + multi-Cox survival analysis in multiple independent cohorts. Six machine learning algorithms were adopted to screen out the hub gene, RAC3. IHC staining was used to validate the expression of RAC3 in BCa tumor tissue. RT-qPCR and Western blot were performed to detect and quantify the mRNA and protein levels of RAC3. CCK8, colony formation, wound healing, and flow cytometry analysis of apoptosis were employed to determine cell proliferation, migration, and apoptosis. Molecular docking was used to find small target drugs, PIK-75. 3D cell viability assay was applied to evaluate the ATP viability of bladder cancer organoids before and after PIK-75 treated. The established clinical prognostic model, GIRS, comprises 13 genes associated with gemcitabine resistance and immunology. This model has demonstrated robust predictive capabilities for survival outcomes across various independent public cohorts. Additionally, the GIRS signature shows significant correlations with responses to both immunotherapy and chemotherapy. Leveraging machine learning algorithms, the hub gene, RAC3, was identified, and potential upstream transcription factors were screened through database analysis. IHC results showed that RAC3 was higher expressed in GEM-resistant BCa patients. Employing molecular docking, the small molecule drug PIK-75, as binding to RAC3, was identified. Experiments on cell lines, organoids and animals validated the biological effects of PIK-75 in bladder cancer. The GIRS signature offers a valuable complement to the conventional anatomic TNM staging system and molecular subtype stratification in bladder cancer. The hub gene, RAC3, plays a crucial role in BCa and is significantly associated with resistance to gemcitabine. The small molecular drug, PIK-75 having the potential as a therapeutic agent in the context of gemcitabine-resistant and immune-related pathways.

中文翻译：

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通过吉西他滨耐药和免疫相关基因分析解读膀胱癌的预后特征并鉴定潜在的小分子药物PIK-75

膀胱癌（BCa）是世界范围内一种流行且高度致命的恶性肿瘤。化疗耐药性显着促进癌症复发和进展。传统的肿瘤淋巴结转移 (TNM) 分期和分子亚型往往无法根据敏感性迅速确定治疗偏好。在本研究中，我们在多个独立队列中通过 uni-Cox + LASSO + multi-Cox 生存分析开发了 BCa 预后特征。采用六种机器学习算法筛选出hub基因RAC3。 IHC染色用于验证BCa肿瘤组织中RAC3的表达。采用 RT-qPCR 和 Western blot 检测并定量 RAC3 的 mRNA 和蛋白水平。采用CCK8、集落形成、伤口愈合和细胞凋亡的流式细胞术分析来确定细胞增殖、迁移和细胞凋亡。利用分子对接寻找小靶点药物PIK-75。应用 3D 细胞活力测定来评估 PIK-75 处理前后膀胱癌类器官的 ATP 活力。已建立的临床预后模型 GIRS 包含 13 个与吉西他滨耐药性和免疫学相关的基因。该模型展示了对各种独立公众群体的生存结果的强大预测能力。此外，GIRS 特征显示与免疫疗法和化疗的反应显着相关。利用机器学习算法，识别出中心基因RAC3，并通过数据库分析筛选出潜在的上游转录因子。 IHC 结果显示，RAC3 在 GEM 耐药的 BCa 患者中表达较高。利用分子对接，鉴定出小分子药物 PIK-75 与 RAC3 结合。对细胞系、类器官和动物的实验验证了 PIK-75 在膀胱癌中的生物学效应。 GIRS 特征为膀胱癌的传统解剖 TNM 分期系统和分子亚型分层提供了宝贵的补充。中心基因 RAC3 在 BCa 中起着至关重要的作用，并且与吉西他滨耐药性显着相关。小分子药物 PIK-75 具有作为吉西他滨耐药和免疫相关途径治疗药物的潜力。