Cancer Cell and Cancer-Associated Fibroblast Communication-Mediated Molecular Subtypes Portray Non-Inflamed Tumor Microenvironment and Guide the Precision Treatment of Bladder Cancer

IF 3.2 3区 生物学 Q3 MATERIALS SCIENCE, BIOMATERIALS
Shenglin Gao, Chuan Liu, Lixin Mao, Yin Chen, Xiaokai Shi, Chuang Yue, Shouchun Li, Xihu Qin
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Abstract

Cancer-associated fibroblasts (CAFs) drive tumor progression through restructuring of the tumor microenvironment. This investigation aim to elucidate the function of molecular subtypes (MS) derived from cancer cells communication with CAFs, depicting the hallmarks of the tumor microenvironment and precise bladder cancer (BLCA) treatment. The BLCA data from TCGA and several external sources are utilized to generate a novel ligand, receptor, and transcription factor (LRT) associated molecular subtype and their corresponding score (LRT score). The LRT-mediated molecular subtype is identified via unsupervised clustering. LRT score is measured by principal component analysis. Then, the association of LRT clusters to established MS, immunophenotypes, and medical endpoints, together with BLCA treatment strategies is investigated. Two LRT clusters (A and B) are identified. LRT cluster (LRT score) can precisely propose immunophenotypes, classical MS, clinical outcomes, and BLCA therapeutic strategies. Cluster B (Low LRT score) represent a basal subtype and inflamed phenotype specified by high immunity against tumors and unfavorable clinical outcomes. Furthermore, it is highly sensitive to cancer immunotherapy; however, it has low sensitivity to antiangiogenic and targeted therapies. The novel LRT clusters with a strong association with biological characteristics and precise BLCA treatment strategies are derived from the communication between cancer cells and cancer-associated fibroblasts. The LRT may be a useful clinician tool for developing individualized treatment strategies.

Abstract Image

癌细胞和癌症相关成纤维细胞通讯介导的分子亚型描绘非炎症肿瘤微环境并指导膀胱癌的精确治疗。
癌症相关成纤维细胞(CAFs)通过重组肿瘤微环境驱动肿瘤进展。本研究旨在阐明肿瘤细胞与CAFs通讯产生的分子亚型(MS)的功能,描述肿瘤微环境的特征和精确的膀胱癌(BLCA)治疗。利用TCGA和多个外部来源的BLCA数据生成新的配体、受体和转录因子(LRT)相关的分子亚型及其相应的评分(LRT评分)。lrt介导的分子亚型是通过无监督聚类鉴定的。LRT评分采用主成分分析法。然后,研究LRT集群与已建立的MS、免疫表型和医学终点的关系,以及BLCA治疗策略。确定了两个轻轨集群(A和B)。LRT聚类(LRT评分)可以精确地预测免疫表型、典型MS、临床结局和BLCA治疗策略。B类(低LRT评分)代表基础亚型和炎症表型,由对肿瘤的高免疫力和不利的临床结果所指定。此外,它对癌症免疫治疗高度敏感;然而,它对抗血管生成和靶向治疗的敏感性较低。与生物学特性和精确的BLCA治疗策略密切相关的新型LRT簇源于癌细胞和癌症相关成纤维细胞之间的交流。LRT可能是制定个体化治疗策略的有用临床工具。
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来源期刊
Advanced biology
Advanced biology Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
6.60
自引率
0.00%
发文量
130
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