Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.
Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.
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DOI：
10.1155/2022/2955359
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发表时间：
2022
影响因子：
--
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Hu J;Jiang Y;Wei Q;Li B;Xu S;Wei G;Li P;Chen W;Lv W;Xiao X;Lu Y;Huang X
癌症相关成纤维细胞（CAF）是肿瘤微环境中数量最多的细胞，在癌症发展中起着至关重要的作用。我们的目标是建立一个癌症相关的成纤维细胞乳腺癌预测模型。 我们从Gene Expression Omnibus（GEO）获得乳腺癌（BC）scRNA-seq数据，并使用“Seurat”进行数据处理，包括质量控制、过滤、主成分分析和t-SNE。之后，使用“singleR”软件注释细胞。Seurat的“FindAllMarkers”程序用于定位特定的CAF标记。使用clusterProfiler分析基因本体（GO）和京都基因和基因组百科全书（KEGG）途径富集。利用癌症基因组图谱（TCGA）数据库提供单变量考克斯回归，使用批量RNA-seq数据的最小绝对收缩算子（LASSO）分析。对于模型开发，使用多变量考克斯回归研究。利用pRRophetic和肿瘤免疫功能障碍和排斥（TIDE）算法，预测化疗敏感性和免疫治疗反应。“rms”软件用于简化建模。 整合scRNA-seq（GSE 176078）数据集产生28个细胞簇。此外，已知的细胞类型有助于识别12种细胞类型。我们发现193个标记基因在CAFs中升高。此外，在训练集中创建了与CAF相关的五基因预测模型。在训练集、验证集和外部验证集中，风险评分越高，预后越差。风险评分较高的个体更容易接受免疫治疗和常规化疗药物。 总之，我们建立了一个强有力的预后模型，包括5个基因与CAF，可能作为一个强有力的预后指标，并帮助临床医生作出更合理的药物选择。
The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model. We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and “Seurat” was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, “singleR” software was used to annotate cells. Seurat's “FindAllMarkers” program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The “rms” software was used to facilitate and simplify modeling. Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines. In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.
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影响因子：
16.2
作者：
Feng, Rui-Mei;Zong, Yi-Nan;Xu, Rui-Hua
通讯作者：
Xu, Rui-Hua
Biglycan通过激活FAK信号通路增强胃癌侵袭能力
影响因子：
--
作者：
Hu L;Duan YT;Li JF;Su LP;Yan M;Zhu ZG;Liu BY;Yang QM
通讯作者：
Yang QM
影响因子：
46.9
作者：
Butler A;Hoffman P;Smibert P;Papalexi E;Satija R
通讯作者：
Satija R
影响因子：
12.3
作者：
Fu, Jingxin;Li, Karen;Liu, X. Shirley
通讯作者：
Liu, X. Shirley
影响因子：
3.7
作者：
Geeleher P;Cox N;Huang RS
通讯作者：
Huang RS