Heterogeneous expression pattern of Jak−STAT pathway in breast cancer.

(A) UMAP plot illustrating the major cell populations derived from the single-cell dataset GSE176078. (B) Core marker gene expressions of major cell populations. (C) UMAP plot depicting the minor cell subpopulations. (D-E) Distribution of JAK-STAT pathway scores across major and minor cell types. (F) Ro/e index for JAK-STAT pathway score. (G) JAK-STAT pathway scores in paracancerous epithelial cells versus cancerous epithelial cells. (H) Pseudotime trajectory analysis of epithelial cell states using Monocle2. (I-J) Pseudotime scores computed by Monocle2 and CytoTRACE2-derived cytotrace scores (in which higher scores indicate lower levels of differentiation). (K) Differentiation trajectories of epithelial cell subtypes. (L) Pseudotime dynamics of the JAK-STAT pathway scores. (M) Activity of the JAK-STAT pathway within epithelial cells.

The JAK-STAT pathway promotes tumor malignant phenotypes and immunosuppression.

(A) Ro/e index analysis demonstrates the preferences of JSP-high versus JSP-low cells in tumor cells. (B) Correlation of JSP scores with 14 cancer-related phenotype features. (C) Expression of cancer phenotype signatures in JSP-high and JSP-low tumor cells. (D) Differentially expressed genes in JSP-high versus JSP-low tumor cells. (E) KEGG enrichment analysis of differentially expressed genes. (F) CellChat analysis illustrating the strength of cellular communication in JSP-high and JSP-low samples. (G) Bar chart of CellChat signaling flows. (H) Cell-to-cell communication between cancerous epithelial cells and T cells.

Jak−STAT pathway is essential for normal breast epithelial cell differentiation.

(A) UMAP plot of annotated normal epithelial cells. (B) Core markers of mammary epithelial cells. (C) Expression levels of JSP score across mammary epithelial cell subgroups. (D) Pseudotime trajectory analysis using Monocle2. (E) Relationship between JSP expression and pseudotime scores. (F) Progeny pathway activity, with epithelial cells categorized into high and low groups based on median JSP expression. (G) Volcano plot of differentially expressed genes. (H-I) GO and KEGG enrichment analyses of differentially expressed genes. Significance: *, p < 0.05; ** p < 0.01; ***, p < 0.001.

Jak−STAT pathway is associated with enhanced T cell function.

(A) JSP scores across major T cell subgroups in GSE176078. (B) JSP scores across minor T cell subgroups. (C) Proportions of T cell subgroups. T cells were classified into high and low groups based on the average JSP score. (D) T cell state scores of major T cell subgroups. (E) T cell state scores based on JSP grouping. (F) Related to (E), exhaustion and cytotoxicity scores in T cells. (G-H) Heatmaps of representative functional genes in major T cell subgroups and JSP subgroups. (I) Monocle2 pseudotime analysis UMAP plots; the top panel shows cell types, the bottom panel displays pseudotime scores. (J) Line plot showing the relationship between JSP score and pseudotime score. (K) Differential genes between T cell JSP subgroups. (L) GO enrichment analysis of differential genes between JSP-high and JSP-low T cells. Significance: *, p < 0.05; ** p < 0.01; ***, p < 0.001.

Jak−STAT pathway enhances immunotherapy sensitivity and predicts immunotherapy response.

(A) Expression patterns of JSP in the bulk RNA-seq immunotherapy cohort: GSE194040. Red text indicates 10 core JSP components, while green text shows 8 immune markers. TN, triple-negative breast cancer; Score, JSP score. (B) JSP scores in immunotherapy responders (R) versus non-responders (NR). (C) Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve for predicting immunotherapy response. (D) ROC curve for JSP predicting immunotherapy response extracted from (C). (E) UMAP of the single-cell dataset: breast cancer anti-PD1 immunotherapy cohort (anti-PD1_Nat.Med). (F) JSP score in UMAP, pDC, plasmacytoid dendritic cells. (G) Distribution of JSP scores across clinical subtypes of anti-PD1_Nat.Med cohort. (H) Immune microenvironment characteristics (scaled cell type ratio) of JSP-high versus JSP-low patients. (I) Expression of JSP and core JSP components before and during immunotherapy (Pre vs. On). (J) Correlation between JSP scores and cell proportions. (K) ROC curve AUC values: Prediction of T cell expansion using JSP scores and cell composition proportions. Significance: *, p < 0.05; ** p < 0.01; ***, p < 0.001.

STAT4 promotes tumor cell and normal epithelial proliferation.

(A) Venn diagram displaying the JAK-STAT pathway signature genes specific to tumor cells, epithelial cells, and T cells, which were derived from pseudotime trajectory analysis using Monocle2. (B) Survival analysis of the above cell-type-specific JAK-STAT pathway genes in the TCGA dataset. (C-E) Overexpressing STAT4 in normal breast epithelial MCF10A cells, detected by Western blotting (C) and qPCR (D). (E) MCF10A cells were assigned to OE-NC (control) or OE-STAT4 groups for cell proliferation assay. (F-H) Overexpressing STAT4 in MDA-MB-231 breast cancer cells. (F) Protein levels of STAT4 following overexpression. (G) STAT4 mRNA levels following overexpression. (H) Cell proliferation assessed by CCK-8 assay. (I-K) Xenograft tumor experiments in nude mice: tumor volume (J) and weight (K). (L) Gene Set Enrichment Analysis (GSEA) of RNA-sequencing after STAT4 overexpression. (M) Progeny pathway activity analysis. Significance: *, p < 0.05; ** p < 0.01; ***, p < 0.001.

The Jak-STAT signaling pathway is abnormally expressed and prognosis-related in cancers.

(A) Expression of JSP in adjacent non-tumor and tumor tissues across a pan-cancer cohort. (B) Survival curves based on JSP pathway expression, stratified by median value. (C) Expression of JSP in adjacent non-tumor and tumor tissues within specific cancer types. (D) Univariate COX analysis across different cancer types. (E) Univariate COX analysis of core JSP components.

Progeny pathway activity and Monocle2 profiling.

(A) Progeny pathway activity of epithelial cells. (B) Pseudotime trajectory analysis in epithelial cells: heatmap of JSP gene expression. Red brackets: T cell differentiation-specific genes. (C) Relationship between pseudotime score and gene expression (JAK2, STAT3).

Pseudotime trajectory analysis of the JAK-STAT pathway in normal epithelial cells and T cells.

Genes associated with JSP that exhibited elevated expression during late differentiation were designated as cell differentiation-dependent genes, marked with red brackets.

STAT4 as a representative of the JAK-STAT pathway.

(A) Correlation between STAT4 expression and the JAK-STAT pathway score across the TCGA, METABRIC, and SCAN-B cohorts. (B) Survival analysis of STAT4 within these cohorts, using the median as the threshold. (C) Expression patterns of STAT4 in a breast cancer single-cell dataset (GSE176078), highlighting major subtypes, T-cell minor subtypes, and T-cell subsets. (D) Variation in STAT4 expression between TNBC and non-TNBC samples. (E) Expression of STAT4 in breast cancer cell lines, categorized into TNBC and non-TNBC subgroups.

STAT4 up-regulates SLC47A1 to involve ferroptosis in breast cancer cells.

(A-B) Wound healing (A) and cell migration assays (B) were performed following STAT4 overexpression in MDA-MB-231 cells. (C) KEGG pathway enrichment analysis of differentially expressed genes following STAT4 overexpression. (D) Upregulation of SLC47A1 upon STAT4 overexpression. (E) Expression levels of STAT4 and SLC47A1 in xenograft tumors in nude mice. Significance: *, p < 0.05; ** p < 0.01; ***, p < 0.001.