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Updated: Jul 27, 2025
Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis
Published on: October 17, 2018
Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte
Yuyao Liu1, Haoxue Zhang2,3,4, Yan Mao5
1Department of Burns, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Insights
A new eight-gene monocyte-related signature (MRS) accurately predicts skin cutaneous melanoma (SKCM) patient outcomes and identifies IFITM3 as a key biomarker for improved prognosis.
Area of Science:
- Immunology
- Oncology
- Bioinformatics
Background:
- Immune cell communication in skin cutaneous melanoma (SKCM) microenvironment is not well understood.
- This study identifies signaling roles of immune cell populations and key signals.
- Explores coordination of immune cells and signal pathways for prognostic biomarker discovery.
Purpose of the Study:
- To understand global immune cell communication patterns in SKCM.
- To establish a prognostic signature based on cellular communication biomarkers.
- To identify key genes and pathways for improved SKCM prognosis.
Main Methods:
- Utilized single-cell RNA sequencing (scRNA-seq) data from GEO database.
- Extracted and re-annotated immune cells, computed communication networks.
- Employed machine learning to develop immune-related prognostic combinations from bulk RNA sequencing data.
Main Results:
- Developed an eight-gene monocyte-related signature (MRS) as an independent risk factor for disease-specific survival (DSS).
- MRS showed high predictive value for progression-free survival (PFS), outperforming traditional variables.
- Identified IFITM3 as a key gene with high protein expression in SKCM, validated by immunohistochemistry.
Conclusions:
- MRS is accurate and specific for evaluating SKCM patient outcomes.
- IFITM3 is a potential prognostic biomarker for SKCM.
- The developed signature and biomarker show promise for improving SKCM patient prognosis.
Background:
Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication.
Methods:
The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations.
Results:
An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM.
Conclusion:
MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.
