This study involved the selection of 282 GC patient samples from Shanghai OUTDO Biotech Co. (https://superchip.com.cn/), and the process was approved by the ethics committee of Shanghai OUTDO Biotech Co (SHYJS-CP-1910016). The staging process followed the guidelines outlined in the 7th edition of the TNM staging guidelines by the American Joint Committee on Cancer (AJCC). None of the patients had undergone chemotherapy or radiotherapy prior to tumor resection. 30 cases of GC and corresponding normal gastric tissues were obtained from radical gastrectomy specimens from the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Zhengzhou University. Additionally, 10 GC patients who underwent neoadjuvant immunotherapy were identified from the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Zhengzhou University ( Supplementary Table S1 ). Fresh GC tumors and adjacent normal liver tissues were collected for subsequent research and analysis. The adjacent normal tissue was required to be at least 2 cm away from the corresponding tumor tissue. The Institutional Review Board of the First Affiliated Hospital of Zhengzhou University approved using the tumor specimens in this study.

The patient samples should be fixed in 4% paraformaldehyde in 0.1 M phosphate-buffered saline, followed by embedding in paraffin and sectioning into 4 μm slices. Immunostaining should be conducted in accordance with the following specific protocol: incubate the sections overnight at 4°C with the monoclonal antibody Pygo2 (1:5000; Abcam, #ab316318), PD-1 (1:100; CST, #86163), TIGIT (1:200; Abcam, #ab243903), and TIM-3 (1:100, Abcam, #ab242080). A second incubation was then performed for one hour at room temperature with an HRP-conjugated secondary antibody before using the DAB Detection Kit (Polymer) (Gene Tech, #GK600510) to perform the peroxidase reaction. Finally, the sections should be examined using a microscope (Olympus Japan CX33-LV2000), and the results should be recorded. Two pathologists blinded to clinical data separately assessed Pygo2 expression by tumor cells and T cells. Positive cell staining density was determined based on the stained cells observed per field of view (cells/mm²) with the Aipathwell software by Servicebio (China).

According to the manufacturer’s instructions, multiplex staining was performed using the PDOne six colors kit (PANOVUE, #10234100050). First, the GC tissue samples are incubated with the primary antibody at 37°C for 1 h. Then, the sections are slowly rinsed three times in TBST buffer, with each rinse lasting 5 min. Then, the sections are slowly rinsed three times in TBST buffer, with each rinse lasting 5 min. Next, the sections are incubated with HRP labeled secondary antibodies at 37°C for 30 min. Finally, they are incubated with PANO TSA staining buffer at room temperature for 15 min. The steps of incubating with the primary antibody, secondary antibody, and PANO TSA staining are repeated until the marker shows color. The primary antibodies used include Pygo2 (1:5000; Abcam, #ab316318), CD8 (1:500; CST, #85336), CD4 (1:600; Abcam, #ab133616), and Pan-CK (1:500; Abcam, #ab215838). All slides are stained with DAPI at room temperature for 5 min and imaged using a multifunctional spectral imaging system (PerkinElmer). The scanned images are imported into HALO software for analysis. Based on a nuclear segmentation algorithm, the nuclei in all samples are identified and the cell count is determined. Using the CytoNuclear algorithm in HALO, the quantity of various immune cells is detected based on nuclear characteristics.

Freshly isolated GC tissues were cut into small pieces and digested with Collagenase IV and DNase I at 37°C on a shaking bed for 30 minutes to achieve complete tissue digestion. The obtained cells were lysed with Lysis buffer and kept on ice. After erythrocyte lysis, the samples were then incubated with a human BD Fc blocker and stained with the LIVE/DEAD Cell Imaging Kit (Invitrogen) in the dark at room temperature for 30 minutes. A series of antibodies, including CD45, CD4, CD8, and Pygo2, were co-incubated with the cells in the stain buffer for 30 minutes. The stained cells were washed and resuspended in the staining buffer before being separated in a Cytoflex LX flow cytometer and analyzed using FlowJo software (version 10).

We extracted peripheral blood T cells from the same gastric cancer patient using the methods reported in previous studies (11, 21, 22). In brief, we isolated peripheral blood mononuclear cells by density gradient centrifugation (Stemcell Technologies, Vancouver, BC, Canada). Then anti-CD3 (100 ng/ml) antibody and IL-2 (10 ng/ml) were added to stimulate peripheral blood mononuclear cells for 5 to 7 days. The T cell population was then extracted and amplified using the ImmunoCult Human CD3/CD28 T Cell Activator (25 µl/mL, Stemcell Technologies). We constructed a Pygo2-hTLv lentivirus suitable for T cell transfection, and constructed Pygo2-overexpressing T cells according to the instructions of the reagent manufacturer (Shanghai Hanbio Co, Ltd).

Total protein was extracted using RIPA lysis buffer (Thermo Fisher Scientific), and the protein concentration was determined using the BCA method. For each well, 20 mg of denatured total protein was loaded onto a 10% SDS-PAGE gel for electrophoresis, and then transferred to a PVDF membrane. The membrane was subsequently blocked with a TBST solution containing 5% skimmed milk for one hour. Primary antibodies were properly diluted and incubated with the membrane overnight at 4°C. Immunodetection was performed applying anti-Pygo2 (Abcam, #ab316318), anti-β-catenin antibody (CST, #8480), anti-TCF7 antibody (CST, #2203), and anti-Myc-antibody (CST, #5605). Following three washes with TBST, the membrane was incubated with corresponding secondary antibodies for one hour at room temperature. All protein blots were captured by iBright imaging system (Invitrogen).

Single-cell sequencing data of human GC from GSE163558 was obtained from the Gene Expression Omnibus (GEO) database. The bulk RNA sequencing data, survival data, and clinicopathologic data from four separate groups of GC patients (TCGA-STAD and GSE27342) were obtained from The Cancer Genome Atlas (TCGA) database, and GEO database, respectively.

Single-cell sequencing data from GSE163558 was conducted a sequence of analysis to identified Pygo2+ cells. The raw gene expression matrix was converted into a Seurat object via Seurat R package (V5.0.1). Cells with >6000 or <200 genes or >20% mitochondrial genes were discarded. Potential doublets were removed using the DoubletFinder R package (V2.0.4). Data were normalized, scaled, and subjected to principal component analysis. Principal components analysis (PCA) was performed using the 2000 highly variable genes identified by the Find Variable Features function in Seurat. Distinct clusters of cells were identified using the first 30 PCA components for graph-based clustering with a resolution of 0.8. The Unified Manifold Approximation and Projection (UMAP) method visualized single-cell clusters. We estimated the differentially expressed genes of each cluster using the Find All Markers module, and genes expressed in more than 25% of the cells were selected. The DEGs expressed only in Pygo2+ T cells were considered the characteristic gene signature of Pygo2+ T cells. The cell-cell interaction network was analyzed and visualized using the CellChat R package (version 1.6.1). Single-sample gene set enrichment analysis (ssGSEA) was implemented to estimate the infiltration of Pygo2+ T cells of each sample in TCGA cohort.

The statistical analysis of RNA-seq data, single-cell sequencing data, and mIHC data was performed using GraphPad Prism version 8.0 and SPSS version 23.0 software and R software. The results are expressed as the mean ± standard deviation (S.D.), and group differences were assessed using the chi-square test. Differences in continuous variables between groups were analyzed using the t-test. The log-rank test was used to compare survival curves drawn using the Kaplan–Meier curves. A P value of less than 0.05 was considered statistically significant, with * representing P < 0.05, ** representing P < 0.01, and *** representing P < 0.001. All reported P values are based on two-tailed test results.

The Pygo family primarily consists of Pygo1 and Pygo2. An analysis of mRNA expression differences in the Pygo family between GC tissues and normal tissues was conducted using data from the TCGA and GSE27342 databases. The results indicated that there was no significant difference in the transcription levels of Pygo1 between the two tissue types ( Figure 1A ). Conversely, the expression of Pygo2 in GC tissues was found to be significantly elevated compared to that in normal tissues ( Figure 1B ). IHC staining of Pygo2 protein was performed on tissue sections from 30 patients with GC and normal tissues. The results demonstrated that the expression of Pygo2 in GC tissues was significantly higher than in normal tissues ( Figures 1C, D ). These results indicate that the expression of Pygo2 in GC tissues is increased, but there are certain differences between different GC patients.

The expression of Pygo2 in GC tissue was examined using single-cell sequencing analysis. The analysis revealed that the tissue can be categorized into eight distinct cell types: T cells, epithelial cells (tumor cells), B cells, endothelial cells, macrophages, natural killer (NK) cells, fibroblasts, and mast cells ( Figure 1E , Supplementary Figure S1 ). Further investigation indicated that Pygo2 was predominantly expressed in T cells and tumor cells ( Figure 1F ). The localization of Pygo2 in GC tissue was corroborated through mIHC staining, which demonstrated that Pygo2 was primarily found in CK cells and T cells, with a notable concentration in the cell nucleus. Pygo2 expression was predominantly observed in CD8+ T cells, whereas CD4+ T cells exhibited only minimal expression of Pygo2 ( Figure 2A ). Meanwhile, flow cytometry was used to analyze the expression of Pygo2 in T cells in fresh tumor tissues of GC patients. It was found that Pygo2 was mainly expressed on some CD8+ T cells but rarely CD4+ T cells ( Figure 2B ). Furthermore, we separately assessed the expression levels of Pygo2 in tumor cells, and T cells in GC and normal tissues ( Figure 2C ). The results showed that the expression of Pygo2 in tumor cells, and T cells were both higher in GC, compared with normal gastric tissues ( Figure 2D ). These findings suggest that Pygo2 is primarily expressed in tumor cells and CD8+ T cells within GC tissue. The nuclear concentration of Pygo2 implies that it may play specific functional roles within the cell nucleus.

We conducted an analysis of Pygo2 expression across various cell types within GC tissue and examined its correlation with clinical pathological parameters. The findings indicated that there were no significant clinical pathological differences in Pygo2 expression between the overall cells and tumor cells ( Table 1 ). However, a correlation was observed between Pygo2 expression and the advanced T stage and N stage in T cells ( Table 1 ). These findings suggest that Pygo2-positive T cells (Pygo2+ T) may serve as a potential pathological diagnostic marker for GC.

To investigate the correlation between Pygo2 expression and the prognosis of GC, we analyzed the results from the GSE15459 database. The results indicated that patients with high expression of Pygo2 in GC tissues have a higher survival rate. Conversely, an analysis of the GSE51105 database produced contradictory findings ( Figure 3A ). This discrepancy suggests that the role of Pygo2 in GC prognosis may be complex and potentially influenced by the cellular localization of Pygo2 expression. Further examination demonstrated that the expression of Pygo2 in overall cells and tumor cells did not significant correlation with patient survival ( Figures 3B, C ). However, a negative correlation was observed between Pygo2 expression in T cells and patient survival rate ( Figure 3D ). Meanwhile, we analyzed the relationship between Pygo2 expression and disease-free survival (DFS). The results showed that high Pygo2+ T cell infiltration was associated with poor DFS. However, the expression of PYGO2 in tumor cells had no significant correlation with DFS ( Figures 3E–G ). These results imply that Pygo2+ T cells could serve as a potential prognostic marker for GC.

T cells are important immune cells in tumor microenvironment. To explore the relationship between Pygo2+ T cells and immune microenvironment, we analyzed the functional differences between Pygo2+ CD8+ T cells and Pygo2- CD8+ T cells using the ssGSEA method through single-cell sequencing data. The results showed that Pygo2+ CD8+ T cells exhibited significantly enhanced apoptotic signaling, suggesting potential functional exhaustion in this subset of T cells ( Supplementary Figure S2 ). Then, we investigated the relationship between Pygo2+ T cells and immune checkpoints through IHC staining ( Figure 4A ). The finding indicated that the density of Pygo2+ T cells was positively correlated with the presence of PD-1+ T cells, TIGIT+ T cells, and TIM3+ T cells ( Figure 4B ). We defined the gene set of Pygo2+ T cells using single-cell RNA sequencing analysis. Consistently, Pygo2 T cells gene signature also showed significant positive correlation with PD-1, TIGIT and TIM3 gene expression by analyzing TCGA data ( Supplementary Figure S3 ). To further explore the effect of Pygo2 on immune microenvironment, GSEA results showed that this gene set was not only associated with T cell exhaustion ( Figure 4C ), but also associated with the activation of the WNT signaling pathway ( Figure 4D ). We identified a positive correlation between Pygo2+ T cell gene signature and TCF7 ( Figure 4E ). We isolated and cultured peripheral blood lymphocytes and overexpressed Pygo2 levels. Additionally, immunoimprinting showed that β-Catenin (key protein of WNT signaling), Myc (WNT target protein) and TCF7 (critical transcription factor of T cell exhaustion) increased significantly after overexpression of T cell Pygo2 ( Figure 4F ). Additionally, we analyzed the interactions between Pygo2+ CD8+ T cells and Pygo2- CD8+ T cells with tumor microenvironment cells. The results showed that Pygo2+ CD8+ T cells exhibited significantly stronger interactions with tumor cells and macrophages compared to Pygo2- CD8+ T cells ( Supplementary Figure S4 ). Further analysis revealed that Pygo2+ CD8+ T cells primarily engage with tumor cells and macrophages through immune checkpoint receptor-ligand pairs, such as CD74, CD55, and SPP1 ( Supplementary Figure S5 ). Collectively, these results suggest that Pygo2 is associated with the immunosuppressive microenvironment.

Currently, challenges exist in the immunotherapy of GC, including limited treatment efficacy and the difficulty in effectively identifying appropriate patient populations (21, 22). These issues are closely linked to the absence of specific biomarkers for GC. In light of this, we utilized gastroscopic tissues from GC patients who had not been treated with ICI for mIHC staining, aiming to categorize patients based on varying ratios of Pygo2⁺ CD8⁺ T cells, and then screened out GC patients with different Pygo2⁺ CD8⁺ T cell ratios ( Figure 5A ). Following ICI treatment, the changes in GC tissues of patients were observed by CT images ( Figure 5B ). No correlation was observed between the overall density of CD8⁺ T cells and ICI therapy ( Figure 5C ). Conversely, the findings showed a negative correlation between the ratio of Pygo2⁺ CD8⁺ T cells density to total CD8⁺ T cells density and the efficacy of ICI treatment ( Figure 5D ). To assess the predictive value of Pygo2⁺ T cells in comparison to the CPS score, we constructed a receiver operating characteristic (ROC) curve ( Figure 5E ). The analysis revealed that the area under the curve (AUC) for percentage of Pygo2⁺ CD8⁺ T cells (AUC=0.775, 95%CI 0.584-0.967) was superior to that of the CPS score (AUC=0.683, 95%CI 0.471-0.859), suggesting that the infiltration level of Pygo2⁺ T cells possessed significant predictive power for evaluating the responsiveness of patients to immunotherapy. The above results show that Pygo2⁺ CD8⁺ T cells may serve as a potential biomarker for predicting the effectiveness of ICI treatment in GC patients.