Fig 1: RFS of patients with triple-negative breast cancer. (A) RFS of galectin-9-positive (green) or -negative (blue) patients. (B) RFS of TIM-3-positive (green) or -negative (blue) patients. (C) RFS of galectin-9(+)/TIM-3(+) (double positive) (orange), galectin-9(-)/TIM-3(+) (green), galectin-9(+)/TIM-3(-) (red) and galectin-9(-)/TIM-3(-) (double negative) (blue) patients. RFS, relapse-free survival; TIM-3, T-cell immunoglobulin mucin-3.
Fig 2: Immunohistochemical staining for galectin-9. Positive staining is seen for the neoplastic cells (magnification, ×400).
Fig 3: Immunofluorescence staining results showing the co-expression and co-localization between hepcidin and selected immune checkpoints in formalin-fixed paraffin-embedded kidney renal clear cell carcinoma (KIRC) tissue. The verified checkpoint molecules were as follows: CD28, CD48, LGALS9, CTLA4, and PDCD1. These checkpoint molecules were confirmed as the common associated immune checkpoint molecules in male genitourinary system tumors.
Fig 4: The distinct composition and function of cell types in the tumor microenvironment of cholangiocarcinoma. (A) Overview of the study design. We first analyzed distinct cell composition and function of the normal and cancer tissue in cholangiocarcinoma (CCA) single-cell RNAseq dataset (GSE138709). Next, we developed a method integrating empirical Bayes and Markov random field models (eLBP) which could simultaneously calculate transcription factors, interaction genes, and associated signaling pathways involved in cell-cell communication. We found that TFs IRF1/SPI1 in VEGFA-positive macrophages could enhance the expression of LGALS9 and induce the exhaustion of CD8 T cells via LGALS9/HAVCR2 axis by eLBP method. Meanwhile, we obtained 54 genes involved in cell communication. The 54-gene panel could also group the CCA patients into two subtypes, in which patients in S2 showed high expression level of immune related genes and had better prognosis. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model which was designated as tumor microenvironment risk score (TMRS). The TMRS panel was revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA. (B) Uniform manifold approximation and projection (UMAP) plot showing the cell composition in normal. GSEA enrichment plot showing the function of macrophage subtypes CD68_S100A9 and CD8 T cell subtype CD8_GZMA. NES: Normalized enrichment score. (C) UAMP plot showing the cell composition in cancer. GSEA enrichment plot showing the function of macrophage subtypes VEGFA_MACRO and CD8 T cell subtype TIGIT_CD8. NES: Normalized enrichment scores.
Fig 5: Validation of co-expression of LGALS9 and HAVCR2 and function of transcription factors IRF1/SPI1. (A) Immunohistochemical plot showing the co-expression of LGALS9 and HAVCR2 in two CCA patients. Scale bars represent 50 µm. Bar plot showing that TFs IRF1 (B) and SPI1 (C) could regulate the expression level of LGALS9 (***p < 0.001; **p < 0.01, t-test).
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