Construction and Verification of a Fibroblast-Related Prognostic Signature Model for Colon Cancer - Zhao, Zhe; Li, Wenqi; Zhu, LiMeng; Xu, Bei; Jiang, Yudong; Ma, Nan; Liu, LiQun; Qiu, Jie; Zhang, Min (Frontiers in Genetics, 2022)
Traditionally, cancer-associated fibroblasts (CAFs), an important component of TME, were thought to play a significant role in colon cancer progression.Understanding the complex relationship between colon cancer and CAFs, as well as the interactions between different CAF subtypes, will be important for more accurate assessment of prognosis and development of new treatment strategies. 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 Gene Expression Omnibus (GEO) database(GSE132465), and The Cancer Genome Atlas (TCGA)-colon adenocarcinoma (COAD) and GEO (GES39582) databases. From single-cell analysis, 825 differentially expressed genes (DEGs) in CAFs were identified between each pair of 6 newly defined CAFs, named enCAF, adCAF, vaCAF, meCAF, erCAF and cyCAF using Seurat package. 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. These cell communication networks have been studied in tumors but not CAFs in previous stuides. Herein, we demonstrated the presence and prognostic value of adCAF and erCAF in colon cancer based on CIBERSORTx, combining single-cell marker genes and transcriptomic data. The prognostic significance of the enCAF and erCAF has been indirectly proved by both the correlation analysis with macrophages and CAFs, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiment based 20 paired tumor sample. A prognostic model was constructed with 10 DEGs using the LASSO Cox regression method. The model was validated using two testing datasets, demonstrating a significant survival prediction 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 the robustness and excellent performance of our CAF-related model. Cell infiltration analysis by xCell revealed that the interaction between CAFs and multiple non-specific immune cells such as macrophages and dendritic cell was a key 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.