3.1 Single-cell transcriptomic atlas of LUAD

After the removal of low-quality cells, a total of 11,485 cells were classified into six major cell types (Fig. 1A). Among them, the epithelial cells originating from tumor tissues are malignant tumor cells, while epithelial cells from normal tissues could be epithelial cells from different tissue origins, such as tracheal epithelial cells. Endothelial cells originating from tumor tissues could be vascular endothelial cells within the tumor, whereas endothelial cells from normal tissues are normal vascular endothelial cells. The remaining four cell types are all immune cells, with macrophages being the most prominent, followed by T cells, B cells, and plasma cells, respectively. All cells are colored according to their tissue origin. It can be observed that cells from tumor tissue and normal tissue exhibit significant heterogeneity, indicating that in the tumor microenvironment, not only have the tumor cells undergone changes, but there is also an alteration in the biological function of other cells (Fig. 1B). Figure 1C shows the marker genes used for the annotation of cell types. We observed a significant difference in the proportion of macrophages between tumor tissue and normal lung tissue, with more macrophages present in the tumor tissue (Fig. 1D). This indicates the important role of macrophages in the tumor immune microenvironment.

Fig. 1

Lung adenocarcinoma single-cell panoramic map. A All cells are colored according to cell type, where dots of different colors represent different types of cells. B All cells are colored based on tissue origin, with dots of different colors representing cells from different tissues. C marker genes of the cells, red indicates high gene expression levels, blue indicates low gene expression levels, and the size of the bubbles represents the percentage of gene expression. D The bars of different colors represent the proportions of different types of cells in tumor tissue or normal lung tissue

3.2 Macrophage single-cell panoramic

A total of 1928 macrophages were screened for further analysis. These macrophages were divided into 11 subgroups (Fig. 2A). Based on the source of the samples, the cells can be divided into those originating from tumor tissue and those from normal lung tissue. After coloring the macrophages according to tissue origin, it can be seen that there is considerable heterogeneity between macrophages originating from normal tissues and those from tumor tissues (Fig. 2B). These macrophages were divided into 11 subsets based on the markers they express (Fig. 2C). Each of these 11 subgroups of macrophages had different functions. We further elucidated the specific functions undertaken by the macrophages in each subgroup using GSVA analysis (Fig. 2D). We further observed that the proportions of different macrophage subpopulations vary between tumor tissues and normal lung tissues (Fig. 2E). This indicates that macrophages exhibit heterogeneity in different microenvironments. Macrophages derived from tumor tissues are generally referred to as TAMs. Previous studies have considered TAMs to have anti-tumor effects, but there is also evidence that TAMs maintain the tumor immune microenvironment, promote tumor immune escape, and facilitate tumor progression [27, 28]. It is generally believed that M1-type macrophages play an anti-tumor role, whereas M2-type macrophages promote tumor development. We used an inflammation score to rate each macrophage and found that macrophages from normal tissues had higher inflammation scores than those from tumor tissues (Fig. 2F). This indicates a large presence of M2-type macrophages in tumor tissues. Subgroup 3 macrophages had higher inflammation scores than the other ten subgroups, suggesting that subgroup 3 is primarily composed of M1-type macrophages (Fig. 2G). Inflammation scores can indirectly indicate the polarity of macrophages; M1 macrophages exhibit higher inflammation scores, while M2 macrophages show lower inflammation scores.

Fig. 2

Macrophages’ single-cell panoramas. A All macrophages are colored according to subgroups, with dots of different colors representing macrophages from different subgroups. B All macrophages are colored according to the tissue of origin, with dots of different colors representing macrophages from different tissues. C Different colored bars represent different macrophage subgroups, and each stripe in the heatmap represents a cell. Yellow indicates increased expression, while purple indicates decreased expression. D Red indicates that the pathway is activated, while blue indicates that the pathway is not activated. E The bars of different colors represent the proportions of different macrophage subgroups in tumor tissue or normal lung tissue. F The inflammasome scores of macrophages originating from tumors and normal tissues. G The inflammasome scores of macrophages from different subgroups

3.3 Network pharmacology and survival analysis

A search of the TCM database revealed that there are 17 main components of Astragalus membranaceus, and the target genes corresponding to the main components total 130 (Table S1). The intersection of target genes corresponding to all main drug components and the characteristic genes of macrophages yielded a total of 8 intersecting genes (Fig. 3A). There are 3 main components corresponding to these 8 intersecting genes, which are quercetin, isorhamnetin, and kaempferol (Fig. 3B). Survival analysis showed the difference in overall survival rates between high and low expression groups of these 8 genes in patients with LUAD. Among them, AHSA1, CYP1B1, SPP1, and STAT1 showed statistical differences (Fig. 3C, E, I, and J), while CTSD, OLR1, PARP1, and PSMD3 did not show significant statistical differences (Fig. 3D, F, G, and H). This suggests that AHSA1, CYP1B1, SPP1, and STAT1 may play an important role in the development and progression of LUAD.

Fig. 3

Network pharmacology and survival analysis. A The intersection between the target genes of the drug and the characteristic genes of macrophages. B A network graph of the intersecting genes and corresponding drug components, with blue representing genes and green representing drugs. C KM (Kaplan–Meier) survival curve of AHSA1. D KM survival curve of CTSD. E KM survival curve of CYP1B1. F KM survival curve of OLR1. G KM survival curve of PARP1. H KM survival curve of PSMD3. I KM survival curve of SPP1. J KM survival curve of STAT1. The dashed line represents the 95% confidence interval of the survival curve

3.4 STAT1 plays a key role in LUAD’s TAMs

In our study, we examined the expression levels of various genes in macrophages and observed notable differences. AHSA1, SPP1, and STAT1 are expressed at higher levels in macrophages, while CYP1B1 shows lower expression in these cells (Fig. 4A). Specifically, TAMs and macrophages derived from normal tissue, only STAT1 is significantly upregulated in TAMs. In contrast, AHSA1, SPP1, and CYP1B1 are upregulated in macrophages from normal tissue (Fig. 4B). To validate these findings externally, we utilized data from GSE198099, which confirmed the increased expression of STAT1 and SPP1 in macrophages (Fig. 4C). Furthermore, we identified elevated levels of STAT1 specifically in TAMs (Fig. 4D), suggesting a pivotal role for STAT1 in TAMs. Kaempferol, a known anti-tumor compound, has been shown to induce apoptosis in tumor cells across various cancers, thus exhibiting potential anti-tumor effects. Given the importance of STAT1 in TAMs, it is conceivable that kaempferol may exert its effects by modulating STAT1 in TAMs, altering the tumor immune microenvironment and enhancing anti-tumor activity. To explore this hypothesis, we classified macrophages into STAT1-positive and STAT1-negative groups based on their STAT1 expression levels and conducted enrichment analysis on differentially expressed genes between these groups. Our analysis revealed that upregulated genes in the STAT1-positive group were primarily enriched in immune-related pathways (Fig. 4E and F), highlighting the strong association between STAT1 and immune function in the tumor microenvironment.

Fig. 4

STAT1 plays an important role in TAMs. A Expression levels of prognostically relevant genes in different cell types in GSE117570. B Expression levels of prognostically relevant genes in macrophages of tumor tissue and normal tissue in GSE117570, with blue indicating increased expression and gray indicating decreased expression. C Expression levels of prognostically relevant genes in different cell types in GSE198099. D Expression levels of prognostically relevant genes in macrophages of tumor tissue and normal tissue in GSE198099, with red indicating increased expression and green indicating decreased expression. E GOBP (Gene Ontology Biological Process) enrichment of upregulated genes in macrophages expressing STAT1. F KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment of upregulated genes in macrophages expressing STAT1

3.5 The pseudo-time analysis and immunofluorescence validation

Pseudo-time analysis reveals the status of all macrophages arranged in virtual time under genetic changes. All macrophages can be divided into three states, starting from state 1 and, after reaching the bifurcation node, differentiate into states 2 and 3 (Fig. 5A). When macrophages are colored according to tissue origin, a transition state is observed between macrophages from normal tissue and TAMs (Fig. 5B). STAT1 has the highest expression in state 1 and is most highly expressed in TAMs (Fig. 5C and D). This indicates the crucial role of STAT1 in TAMs, which may have an impact on macrophage polarization. Moreover, after using tissue microarrays to perform immunofluorescence colocalization, it was discovered that STAT1 expression increases in tumor tissues, and STAT1 is expressed in TAMs (Fig. 5E and F). Although STAT1 may also be expressed in other cells, its expression level in TAMs is significantly higher than in macrophages derived from normal tissue. This highlights the role of STAT1 as a key transcription factor in TAMs.

Fig. 5

Pseudotime analysis of STAT1 and immunofluorescence validation. A All macrophages are colored according to their status and distributed along the pseudotime branches. B All macrophages are colored according to their tissue origin and distributed along the pseudotime branches. C Distribution of STAT1 expression along the pseudotime axis, with cells colored by status. D Distribution of STAT1 expression along the pseudotime axis, with cells colored by tissue origin. E Representative images of STAT1 and CD68 immunofluorescence staining and F statistical results of the immunofluorescence. **** means P < 0.001

3.6 Kaempferol affects the M2 polarization of macrophages

We use THP1 cells to simulate the polarization of macrophages, and the polarization toward M1 or M2 can be induced in THP1 cells by using LPS and IL4. Figure 6A shows the flow cytometry gating procedure for detecting macrophages. We use IL4 to induce the polarization toward M2. Then, we add Kaempferol (50 μM) to observe whether it will affect the M2 polarization of THP1 cells. After the induction with IL4, the expression of CD206 in THP1 cells can be detected by flow cytometry. It is found that the expression of CD206 increases after adding IL4, but this trend can be reversed by the addition of Kaempferol (Fig. 6B and C). This suggests that Kaempferol may reduce macrophage M2 polarization. When we add LPS in THP1 cells to induce macrophage M1 polarization, we find that there is no statistical difference in the expression of CD80 between the two groups after adding Kaempferol (Fig. 6D and E), indicating that Kaempferol does not affect the M1 polarization of macrophages. Delivering Kaempferol to TAMs more precisely is one of the anti-tumor strategies.

Fig. 6

Kaempferol inhibits macrophage M2 polarization. A Flow cytometry strategy for screening M1 and M2 macrophages. B Representative flow cytometry graphs of kaempferol inhibiting macrophage M2 polarization and C flow cytometry statistical results (Each group n = 15). D Representative flow cytometry graphs showing that kaempferol has no significant effect on macrophage M1 polarization and E flow cytometry statistical results (Each group n = 15)