Background: Tumor-associated macrophages (TAMs) have been demonstrated to be associated with tumor progression. However, the different subpopulations of TAMs and their roles in gastric cancer (GC) remain poorly understood. This study aims to assess the effects of Spi-1 proto-oncogene (SPI1)⁺CD68⁺ TAMs in GC. Methods: The distribution of SPI1⁺CD68⁺ TAMs in GC tissue was estimated by immunohistochemistry, immunofluorescence, and flow cytometry. Single-cell transcriptome analysis and multiplex fluorescence immunohistochemistry were applied to explore the role of SPI1⁺CD68⁺ TAMs in an immune contexture. SPI1 overexpression or knockdown cells were constructed to evaluate its role in macrophage polarization and angiogenesis in vitro and in vivo. Chromatin immunoprecipitation was used to verify the mechanism of SPI1 transcriptional function. The effect of combined antiangiogenic and immunotherapy was further validated using mouse peritoneal metastasis models. Results: Single-cell transcriptome analysis and immunohistochemistry demonstrated that SPI1 was expressed in macrophages, with a higher enrichment in metastatic lesions than in primary tumors. Higher SPI1⁺CD68⁺ TAMs infiltration was associated with poor overall survival. Mechanically, SPI1 promoted the M2-type macrophage polarization. SPI1 could bind to the promoter of vascular endothelial growth factor A and facilitate angiogenesis. Moreover, the level of SPI1⁺CD68⁺ TAMs infiltration was closely related to the efficacy of immunotherapy, especially when combined with antiangiogenic therapy. Conclusions: The present study showed that SPI1⁺CD68⁺ TAMs are a promising biomarker for predicting prognosis, antiangiogenic drug sensitivity, and combination target of immunotherapy in patients with GC.

Background: Neoadjuvant chemotherapy is important to improve the prognosis of patients with advanced gastric cancer. However, it may result in postoperative complications (POCs). The aim of this study is to evaluate risk factors and prognostic impact of POCs in patients receiving neoadjuvant chemotherapy. Methods: We retrospectively collected clinical information of patients who underwent curative gastrectomy after receiving neoadjuvant chemotherapy between 2011 and 2018. Overall survival (OS) was analyzed using the Kaplan–Meier method. Logistic regression and Fisher’s exact test were used to evaluate risk factors for complications. Results: A total of 176 patients were included in our study. The 3-year OS rates for the complication group (n = 30) and non-complication group (n = 146) were 36.7% and 52.7%, respectively (p = 0.0294). Age, BMI, multivisceral resection and operation time were independent risk factors for POCs in patients. Patients with multivisceral resection were more likely to suffer from grade III-IV complications (p = 0.026). Inflammation complications might occur in patients with high BMI (p = 0.017). Low preoperative albumin seemed to be a risk factor for leakage complications (p = 0.033). Conclusions: Our study revealed that patients with POCs had a poor prognosis and we identified the risk factors for complications so that POCs can be avoided in time.

Background: It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice. Methods: We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15). Results: A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset. Conclusions: We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.