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Background Remodeling the tumor microenvironment (TME) to benefit cancer cells is crucial for tumor progression. Although diffuse-type gastric cancer (DGC) preferentially interacts with the TME, the precise mechanism of the complicated network remains unknown. This study aimed to investigate the mutual activation mechanism underlying DGC progression. Methods Mass cytometry analysis of co-cultured macrophages, noncancerous fibroblasts (NFs), and DGC cells was performed. RNA sequencing was applied to examine gene expression in fibroblasts. DGC cells were treated with cytokines to examine their effect on characteristic changes. The TCGA and Kumamoto University cohorts were used to evaluate the clinical relevance of the in vitro findings. Results Cohort analysis revealed that DGC patients had a poor prognosis. The fibroblasts and macrophages interacted with DGC cells to form a cell cluster in the invasive front of DGC tissue. The original 3D triple co-culture system determined the promotional effects of nonmalignant cells on DGC invasive growth. We notably identified a mixed-polarized macrophage cell type with M1/M2 cell surface markers in a triple co-culture system. IL-1β from mixed-polarized macrophages induced the conversion of NFs to cancer-associated fibroblast-like (CAF-like) cells, promoting the malignant phenotype of DGC cells by inducing the secretion of IL-6, IL-24, and leukemia inhibitory factor (LIF). Moreover, IL-6 and colony stimulating factor 2 (GM-CSF) cooperated to maintain the stable state of mixed-polarized macrophages. Finally, we found that mixedpolarized macrophages were frequently detected in DGC tissues. Conclusion These findings demonstrated that mixed-polarized macrophages exist as a novel subtype through the reciprocal interaction between DGC cells and nonmalignant cells.

The arachidonic acid cascade is a major inflammatory pathway that produces prostaglandin E2 (PGE2). Although inhibition of 15‐hydroxyprostaglandin dehydrogenase (15‐PGDH) is reported to lead to PGE2 accumulation, the role of 15‐PGDH expression in the tumor microenvironment remains unclear. We utilized Panc02 murine pancreatic cancer cells for orthotopic transplantation into wild‐type and 15‐pgdh+/− mice and found that 15‐pgdh depletion in the tumor microenvironment leads to enhanced tumorigenesis accompanied by an increase in cancer‐associated fibroblasts (CAFs) and the promotion of fibrosis. The fibrotic tumor microenvironment is widely considered to be hypovascular; however, we found that the angiogenesis level is maintained in 15‐pgdh+/− mice, and these changes were also observed in a genetically engineered PDAC mouse model. Further confirmation revealed that fibroblast growth factor 1 (FGF1) is secreted by pancreatic cancer cells after PGE2 stimulation, consequently promoting CAF proliferation and vascular endothelial growth factor A (VEGFA) expression in the tumor microenvironment. Finally, in 15‐pgdh+/−Acta2‐TK mice, depletion of fibroblasts inhibited angiogenesis and cancer cell viability in orthotopically transplanted tumors. These findings highlighted the role of 15‐pgdh downregulation in enhancing PGE2 accumulation in the pancreatic tumor microenvironment and in subsequently maintaining the angiogenesis level in fibrotic tumors along with CAF expansion.

Medical image analysis is an interdisciplinary field of comprehensive medical imaging and analyzing, whose goal is to recognize disease diagnosis and lesion area through the related computer vision technology. Benefiting from the continuous development of the convolutional neural networks, medical image analysis based on deep learning has become a research hot spot. In this paper, based on in-depth literature research of results and progress in recent years, we mainly analyze the domestic and foreign research status of Medical Imaging in various application fields such as detection, segmentation and registration. We further compare the performance of representative methods on common data sets, and summary the existing challenges in deep learning-based medical image analysis. Finally, we discuss the solutions to these problems and predict the future development of medical image analysis tasks.