Integrated analysis of single-cell and bulk RNA sequencing identifies a signature based on macrophage marker genes involved in prostate cancer prognosis and treatment responsiveness

Abstract

BackgroundIn the tumor microenvironment, tumor-associated macrophages (TAMs) interact with cancer cells and contribute to the progression of solid tumors. Nonetheless, the clinical signi cance of TAMs-related biomarkers in prostate cancer (PCa) is largely unexplored. The present study aimed to construct a macrophage-related signature (MRS) for predicting the prognosis of PCa patients based on macrophage marker genes and exploring its potential mechanisms. MethodsSix cohorts containing 1056 PCa patients with RNA-Seq and follow-up data were enrolled in this study.Based on macrophage marker genes identi ed by single-cell RNA-sequencing (scRNA-seq) analysis, univariate analysis, least absolute shrinkage and selection operator (Lasso)-Cox regression, and machine learning procedure were performed to derive a consensus MRS. The receiver operating characteristic curve (ROC), concordance index, and decision curve analyses were used to con rm the predictive capacity. ResultsThe predictive performance of MRS for recurrence-free survival (RFS) is stable and robust, and it outperforms traditional clinical variables. Furthermore, the high MRS patients presented abundant macrophage in ltration and high expression of immune checkpoint genes (CTLA4, HAVCR2, and CD86).The frequency of mutations was relatively high in high MRS group. However, the low MRS patients indicated a better response to immune checkpoint blockade (ICB) and leuprolide-based adjuvant chemotherapy. Notably, the abnormal ATF3 expression may be associated with docetaxel and cabazitaxel-resistant in the PCa cell lines. ConclusionsIn this study, a novel MRS was rst developed and validated to accurately predict patients' RFS, assess immune characteristics, infer therapeutic bene ts, and provide an auxiliary tool for personalized therapies.

A facile single-cell patterning strategy based on harbor-like microwell microfluidics

“…Therefore, analyzing cells at single-cell level helps us to gain a more comprehensive understanding of the intracellular life processes. The development of single-cell analysis methods has also facilitated clinical and biomedical research, including the study of pathomechanisms [6], cancer research [7], drug development [8,9], and stem cell differentiation [10]. The preparation of single-cell arrays is the initial and most important step in the single-cell research process, laying the foundation for subsequent downstream experiments.…”

Microfluidics Coupled Mass Spectrometry for Single Cell Multi‐Omics

“…[1,2] Traditional methods in cell analysis usually overlook cell-to-cell variations, failing to adequately capture the complex pattern of cell populations, and overlooking significant information related to the cell-to-cell heterogeneity, such as drug resistance. The development of single-cell analysis methods has driven remarkable progress in clinical and biomedical research, including pathological mechanism study, [3] cancer research, [4,5] drug development, [6] stem cell differentiation, [7] etc.…”