Dataset Information

Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.

ABSTRACT: Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.

SUBMITTER: Huang B

PROVIDER: S-EPMC10487765 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.

Huang Biaojie B Chen Qiurui Q Ye Zhiyun Z Zeng Lin L Huang Cuibing C Xie Yuting Y Zhang Rongxin R Shen Han H

International journal of molecular sciences 20230824 17

Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of ...[more]

PMID: 37685980

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Project description:Traditionally, cancer-associated fibroblasts (CAFs), an essential component of tumor microenvironment, were exert a crucial part in colon cancer progression. In this study, single-cell RNA-sequencing (scRNA-seq) data from 23 and bulk RNA-seq data from 452 colon cancer patients were extracted from the GEO database and TCGA-COAD and GEO databases, respectively. From single-cell analysis, 825 differentially expressed genes (DEGs) in CAFs were identified between each pair of six newly defined CAFs, named enCAF, adCAF, vaCAF, meCAF, erCAF, and cyCAF. Cell communication analysis with the iTALK package showed communication relationship between CAFs, including cell autocrine, cytokine, and growth factor subtypes, such as receptor-ligand pairs of TNFSF14-LTBR, IL6-F3, and IL6-IL6ST. Herein, we demonstrated the presence and prognostic value of adCAF and erCAF in colon cancer based on CIBERSORTx, combining single-cell marker genes and transcriptomics data. The prognostic significance of the enCAF and erCAF has been indirectly proved by both the correlation analysis with macrophages and CAFs, and the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiment based on 20 paired tumor samples. A prognostic model was constructed with 10 DEGs using the LASSO Cox regression method. The model was validated using two testing datasets, indicate a significant survival accuracy (p < 0.0025). Correlation analyses between clinical information, such as age, gender, tumor stage and tumor features (tumor purity and immune score), and risk scores revealed our CAF-related model's robustness and excellent performance. Cell infiltration analysis by xCell revealed that the interaction between CAFs and multiple non-specific immune cells such as macrophages and the dendritic cell was a vital factor affecting immune score and prognosis. Finally, we analyzed how common anti-cancer drugs, including camptothecin, docetaxel and bortezomib, and immunotherapy, such as anti-PD-1 treatment, could be different in low-risk and high-risk patients inferred from our CAF-related model. In conclusion, the study utilized refined colon cancer fibroblast subsets and established the prognostic effects from the interaction with nonspecific immune cell.

Dataset Information

Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.

Publications

Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.

Similar Datasets

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