Unraveling dynamic interactions between tumor-associated macrophages and consensus molecular subtypes in colorectal cancer: An integrative analysis of single-cell and bulk RNA transcriptome

被引:4
作者
Gao, Han [1 ,2 ,3 ]
Ma, Linyun [4 ]
Zou, Qi [1 ,2 ,3 ]
Hu, Bang [1 ,2 ,3 ]
Cai, Keyu [1 ,2 ,3 ]
Sun, Yi [5 ]
Lu, Li [1 ,2 ,3 ]
Ren, Donglin [1 ,2 ,3 ]
机构
[1] Sun Yat Sen Univ, Affiliated Hosp 6, Dept Coloproctol, Guangzhou, Peoples R China
[2] Sun Yat Sen Univ, Affiliated Hosp 6, Biomed Innovat Ctr, Guangzhou, Peoples R China
[3] Sun Yat Sen Univ, Affiliated Hosp 6, Guangdong Prov Key Lab Colorectal & Pelv Floor Dis, Guangzhou, Peoples R China
[4] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Anesthesiol, Guangzhou, Peoples R China
[5] Kingmed Pathol Ctr, Guangzhou, Peoples R China
关键词
Tumor-associated macrophage; Single-cell RNA-Seq; WGCNA; Risk signature; Anti-tumor therapy; Colorectal cancer; EXPRESSION; POLARIZATION; SURVIVAL;
D O I
10.1016/j.heliyon.2023.e19224
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: Accumulating research substantiated that tumor-associated macrophages (TAMs) have a significant impact on the tumorigenesis, progression, and distant metastasis, representing a novel target for various cancers. However, the underlying dynamic changes and interactions between TAMs and tumor cells remain largely elusive in colorectal cancer (CRC).Methods: We depicted the dynamic changes of macrophages using sing-cell RNA-seq data and extracted TAM differentiation-related genes. Next, we utilized the weighted gene co-expression network analysis (WGCNA) to acquire CMS-related modular genes using bulk RNA-seq data. Finally, we utilized univariate Cox and Lasso Cox regression analyses to identify TAM differentiation-related biomarkers and established a novel risk signature model. We employed quantitative real-time polymerase chain reaction (qRT-PCR) on CRC tissue samples and used immunohistochemistry (IHC) data frome the HPA database to validate the mRNA and protein expression of prognostic genes. The interaction of TAMs and each consensus molecular subtype (CMS) subpopulation was analyzed at the cellular level.Results: A total of 47,285 cells from single-cell dataset and 1197 CRC patients from bulk dataset were obtained. Among those, 6400 myeloid cells were re-clustered and annotated. RNASE1, F13A1, DAPK1, CLEC10A, RPN2, REG4 and RGS19 were identified as prognostic genes and the risk signature model was established based on the above genes. The qRT-PCR analysis indicated that the expression of RNASE1 and DAPK1 were significantly up-regulated in CRC tumor tissues. The cell-cell communication analysis demonstrated complex interactions between TAMs and CMS malignant cell subpopulations.Conclusion: This study presents an in-depth dissection of the dynamic features of TAMs in the tumor microenvironment and provides promising therapeutic targets for CRC.
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页数:16
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