Research Paper Volume 16, Issue 6 pp 5676—5702

Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing

Xiaoxu Chen1,2, *, , Zheyu Zhang1,2, *, , Zheng Qin1,2, , Xiao Zhu1,2, , Kaibin Wang1,2, , Lijuan Kang1,2, , Changying Li1,2, , Haitao Wang1,2, ,

  • 1 Department of Oncology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
  • 2 Tianjin Key Laboratory of Precision Medicine for Sex Hormones and Diseases (in Preparation), The Second Hospital of Tianjin Medical University, Tianjin, China
* Equal contribution

Received: November 22, 2023       Accepted: February 26, 2024       Published: March 20, 2024
How to Cite

Copyright: © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Macrophages are found in a variety of tumors and play a critical role in shaping the tumor microenvironment, affecting tumor progression, metastasis, and drug resistance. However, the clinical relevance of marker genes associated with macrophage in kidney renal clear cell carcinoma (KIRC) has yet to be documented. In this study, we initiated a thorough examination of single-cell RNA sequencing (scRNA-seq) data for KIRC retrieved from the Gene Expression Omnibus (GEO) database and determined 244 macrophage marker genes (MMGs). Univariate analysis, LASSO regression, and multivariate regression analysis were performed to develop a five-gene prognostic signature in The Cancer Genome Atlas (TCGA) database, which could divide KIRC patients into low-risk (L-R) and high-risk (H-R) groups. Then, a nomogram was constructed to predict the survival rate of KIRC patients at 1, 3, and 5 years, which was well assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA). Functional enrichment analysis showed that immune-related pathways (such as immunoglobulin complex, immunoglobulin receptor binding, and cytokine-cytokine receptor interaction) were mainly enriched in the H-R group. Additionally, in comparison to the L-R cohort, patients belonging to the H-R cohort exhibited increased immune cell infiltration, elevated expression of immune checkpoint genes (ICGs), and a higher tumor immune dysfunction and exclusion (TIDE) score. This means that patients in the H-R group may be less sensitive to immunotherapy than those in the L-R group. Finally, IFI30 was validated to increase the ability of KIRC cells to proliferate, invade and migrate in vitro. In summary, our team has for the first time developed and validated a predictive model based on macrophage marker genes to accurately predict overall survival (OS), immune characteristics, and treatment benefit in KIRC patients.


scRNA-seq: single cell RNA sequencing; GEO: Gene Expression Omnibus; LASSO: least absolute shrinkage and selection operator; MMGs: macrophage marker genes; KIRC: kidney renal clear cell carcinoma; TCGA: The Cancer Genome Atlas; ROC: receiver operating characteristic; DCA: decision curve analyses; ICGs: immune checkpoint genes; TIDE: tumor immune dysfunction and exclusion; OS: overall survival; PFS: progression free survival; TME: tumor microenvironment; TAMs: tumor associated macrophages; HR: hazard ratio; PFS: progression free survival; PCs: principal components; AUC: area under the curve; PCA: principal component analysis.