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@cancers-mdpi.bsky.social Sep 14, 2026, 6:37 AM

🎯Editor's Choice
Integrative Single-Cell and Machine Learning Analysis Identifies a #Nucleotide Metabolism-Related Signature Predicting #Prognosis and Immunotherapy Response in LUAD
🧑‍🔬Shuai Zhao, Han Zhang et al.
🏫 Tianjin University, Tianjin Medical University
📎 www.mdpi.com/2072-6694/18...

Figure 1. Single-cell clustering, annotation, marker validation, and functional characterization. (A) UMAP visualization showing unbiased clustering of cells into distinct transcriptional populations. (B) Distribution of cells across individual samples, illustrating the contribution of each dataset to the integrated atlas. (C) Annotation of major cell lineages based on canonical marker genes, including T cells, B cells, NK cells, macrophages, monocytes, epithelial cells, fibroblasts, endothelial cells, mast cells, and proliferating cells. (D) Dot plot displaying representative marker genes for each cell type, with dot size indicating the proportion of expressing cells and dot color reflecting expression intensity. (E) Relative abundance of major cell types across patients. (F) Functional enrichment analysis of each annotated cell population, including GO Biological Process, KEGG pathways, and WikiPathway signatures.

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