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

Han Gao; Linyun Ma; Qi Zou; Bang Hu; Keyu Cai; Yi Sun; Li Lu; Donglin Ren

doi:10.1016/j.heliyon.2023.e19224

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

Heliyon. 2023 Aug 18;9(9):e19224. doi: 10.1016/j.heliyon.2023.e19224. eCollection 2023 Sep.

Authors

Han Gao^{1

2

3}, Linyun Ma⁴, Qi Zou^{1

2

3}, Bang Hu^{1

2

3}, Keyu Cai^{1

2

3}, Yi Sun⁵, Li Lu^{1

2

3}, Donglin Ren^{1

2

3}

Affiliations

¹ Department of Coloproctology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
⁴ Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
⁵ Kingmed Pathology Center, Guangzhou, China.

Abstract

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.

Keywords: Anti-tumor therapy; Colorectal cancer; Risk signature; Single-cell RNA-Seq; Tumor-associated macrophage; WGCNA.