Journal: Advanced Science
Article Title: NAT10 Promotes Gastric Cancer Liver Metastasis by Modulation of M2 Macrophage Polarization and Metastatic Tumor Cell Hepatic Adhesion
doi: 10.1002/advs.202410263
Figure Lengend Snippet: NAT10 recruits and polarizes M2‐like macrophages via ac4C modification of CXCL2 mRNA. A) scRNA‐seq data showing the proportions of each cell type in the normal, primary tumor, and metastatic tumor groups. B) MIF staining showing the differences in the expression of NAT10, the number of macrophages (marked by CD68), and the number of M2‐like macrophages (marked by CD206) between the primary tumors and liver metastases of GC patients. C) MIF staining showing the differences in the expression of NAT10, the number of macrophages (marked by F4/80), and the number of M2‐like macrophages (marked by CD206) between the liver metastases of nude mice injected intrasplenically with BGC823 cells overexpressing NAT10 or the corresponding control cells. D) M2‐like macrophages derived from THP‐1 cells that migrated through uncoated filter membranes after 16 h of culture in medium derived from BGC823 cells with NAT10 overexpression or the corresponding control cells. The cells were stained with crystal violet, visualized via microscopy (left panel, scale bars = 200 µm), and counted (right panel). E) qPCR was conducted to measure the mRNA levels of M2‐like markers (CD163 and CD206) in THP‐1 cells cultured with medium derived from BGC823 cells with NAT10 overexpression or the corresponding control cells. F,G) CXCL2, CXCL3, and CXCL8 were identified as overlapping genes between the scRNA‐seq and RNA‐seq (BGC‐P vs BGC‐M3 cells) data. (F) Box plots showing differences in the expression of well‐known macrophage recruitment‐related chemokines among normal, primary tumor, and metastatic tumor cells, as determined via scRNA‐seq. (G) Heatmap showing differences in well‐known macrophage recruitment‐related chemokines between BGC‐P and BGC‐M3 cells according to RNA‐seq. H) The mRNA levels of CXCL2, CXCL3, and CXCL8 in NAT10‐OE GC cells were measured by qRT‐PCR. I) Schematic overview of the experimental design for acRIP‐seq based on cells with stable overexpression and knockout of NAT10 compared with their corresponding controls. J) The ac4C abundance on CXCL2 mRNA transcripts in BGC823 cells and AGS cells, as determined by acRIP‐seq. K) acRIP–qPCR analysis was employed to demonstrate NAT10‐mediated CXCL2 ac4C modification. ac4C modification of CXCL2 increased upon overexpression of NAT10. L) The levels of CXCL2 expression in NAT10‐OE and the corresponding control GC cells treated with actinomycin D (2 µg mL −1 ) at the indicated time points were measured by qRT‐PCR. M) The protein level of CXCL2 in NAT10‐OE GC cells was measured by western blotting. N) The concentration of CXCL2 in the culture supernatant of NAT10‐OE GC cells was measured by ELISA. O) The protein level of CXCL2 in primary tumor and liver metastatic tumor tissues from GC patients was evaluated by IHC staining (scale bars = 100 µm). P) The distribution of the CXCL2 staining score in primary tumors and the corresponding liver metastatic tumors ( n = 12) was determined. Q) Representative images (left panel) and quantification (right panel) of migrated M2‐like macrophages derived from THP‐1 cells cultured in medium derived from NAT10‐KO AGS cells supplemented with human rCXCL2. The cells were stained with crystal violet and visualized via microscopy (scale bar = 200 µm). GC, gastric cancer; scRNA‐seq, single‐cell RNA sequencing; RNA‐seq, RNA sequencing; IHC, immunohistochemical; MIF, multiplex immunofluorescence; OE, overexpressing; KO, knockout. Data are represented as mean ± SEM of three independent experiments. NS, not significant.
Article Snippet: GC cells were plated in 24‐well plates (Corning) at densities that allowed growth to 100% confluence within 24 h. The medium was then collected and added to the lower compartments of a 24‐well Transwell plate (Corning) with or without recombinant CXCL2 (3.6 nm; Novoprotein, Suzhou, China).
Techniques: Modification, Staining, Expressing, Injection, Control, Derivative Assay, Over Expression, Microscopy, Cell Culture, RNA Sequencing, Quantitative RT-PCR, Knock-Out, Western Blot, Concentration Assay, Enzyme-linked Immunosorbent Assay, Immunohistochemistry, Immunohistochemical staining, Multiplex Assay, Immunofluorescence
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![High CXCL1 and <t>CXCL2</t> expression is associated with poor prognosis in patients across multiple cancers. (A) Impact of CXCL1 and CXCL2 expression on RFS in 2,178 patients across 30 cancer types. Log-rank P values were calculated to assess significance. The dashed line indicates P = 0.05. (B) Significance of 9 widely used ICB response biomarkers collected from TIDE in relation to OS across 7 datasets from indicated studies. Red cells: Rho > 0, indicating increased risk. Rho values in the bottom panel represent the average for each biomarker across the 7 datasets (Table ). CXCL1 & CXCL2 , average expression level of CXCL1 and CXCL2 ; MSI, microsatellite instability signature; TIDE, Tumor Immune Dysfunction and Exclusion signature; Merck18, T cell-inflamed signature; TMB, tumor mutation burden. (C) KM plots of OS for patients with breast cancer, grouped by high or low expression of CXCL1 (top) and CXCL2 (bottom). P values were calculated using the log-rank test. Data were derived from RNA-seq and obtained from the KM plotter and TIDE [ , , ]. (D) KM plots of post-progression survival (PPS) for 607 breast cancer patients, grouped by high or low expression of CXCL1 (top) and CXCL2 (bottom). P values were calculated using the log-rank test. Best cutoffs were autocalculated and selected by the KM plotter tool . (E) Genomic alterations of CXCL1 and CXCL2 among all TCGA patients across 30 cancer types. The pie chart shows the percentage of patients with breast cancer and other cancer types. (F) Distribution of the frequency of genomic alterations of CXCL1 and CXCL2 in TCGA cancer patients across 30 cancer types. (G and H) KM plots showing the impact of CXCL1 and CXCL2 genomic alterations on RFS in TCGA patients. Patients were grouped by the presence or absence of CXCL1 and CXCL2 genomic alterations across all TCGA cancer types (G; pan-cancer) or all breast cancer types (H; pan-BRCA). P values were determined using the log-rank test.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8528/pmc12508528/pmc12508528__research.0915.fig.001.jpg)
![Combination treatment with ponatinib enhances the therapeutic efficacy of anti-PD-L1 against TNBC. (A) Expression levels of CXCL1 or CXCL2 and therapeutic responses assessed from publicly available scRNA-seq data for 29 breast cancer patients who received ICB therapy . Cells were colored based on ICB response results (top) and gene expression levels of CXCL1 and CXCL2 (bottom). Es, patients with clonotype expansion ( n = 9); NEs, patients with limited or no clonotype expansion ( n = 20). (B) Receiver operating characteristic (ROC) curves depicting the predictive value of CXCL1 and CXCL2 expression levels for anti-PD-1 therapy response in breast cancer patients. (C) CXCL1 and CXCL2 expression in tumor cells, comparing Es with NEs, calculated using the breast cancer scRNA-seq dataset . P values were determined using the Mann–Whitney test. (D) Expression of known MDSC-related genes [ , , , , ] in MDSCs determined in this study, comparing Es with NEs. MDSCs were identified in the breast cancer scRNA-seq dataset (EGAD00001006608) , through learning from another breast cancer scRNA-seq dataset with MDSC well-defined ( GSE139125 ) , using the cell type prediction method of scPred package . P values were determined using the Mann–Whitney test. (E) Expression of known MDSC-related genes [ , , , , ] in MDSCs, comparing pretreatment with on-treatment conditions. Pre, biopsy collected before anti-PD-1 treatment; On, biopsy collected during subsequent surgery. P values were determined using the Mann–Whitney test. (F) Expression of known MDSC-related genes in the breast TME, comparing Es with NEs from the breast cancer scRNA-seq dataset . P values were determined using the Mann–Whitney test. (G) Expression of known MDSC-related genes in tumor cells, comparing Es with NEs, calculated using the breast cancer scRNA-seq dataset . P values were determined using the Mann–Whitney test. (H) Expression of known MDSC-related genes in MDSCs, comparing across 3 different breast cancer types. P values were determined using the Mann–Whitney test. (I) Expression of known MDSC-related genes in the TME, comparing across 3 different breast cancer types, was calculated using the breast cancer scRNA-seq dataset . P values were determined using the Mann–Whitney test. (J) Schematic diagram of the combination treatment protocol for 4T1-bearing mice; ponatinib (30 mg/kg) or vehicle was administered 4 days per week, and PD-L1 monoclonal antibody (200 μg per injection, twice a week), either alone or in combination, starting when the tumor volume reached 100 mm 3 ( n = 12 to 14 mice per group). (K and L) Growth of 4T1 tumors in BALB/c WT mice treated with the indicated conditions from (J). Tumor volume kinetics were monitored by vernier calipers. Data represent mean ± SEM of n mice. Two-way ANOVA determined statistical significance. * *P < 0.01, ** *P < 0.001, and *** *P < 0.0001. (M) Survival curves for the indicated groups. Statistical significance was determined using the log-rank (Mantel–Cox) test. ns, P > 0.05, * *P < 0.01, and ** *P < 0.001.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8528/pmc12508528/pmc12508528__research.0915.fig.006.jpg)
