Gastric cancer (GC) with pulmonary metastasis is one of the deadliest diseases in the world; however, the underlying pathological mechanisms and potential therapeutic targets remain to be elucidated. As exosomes play indispensable roles in the formation of premetastatic niches (PMN) and cancer metastasis. Therefore, investigating the underlying mechanisms of exosome-mediated pulmonary metastasis of GC may shed new light on identifying novel therapeutic targets for GC treatment. GC-derived exosomes were isolated from the conditioned medium of mouse forestomach carcinoma (MFC) cell line. The effects of MFC-derived exosomes on pulmonary macrophage polarization were analyzed by reversetranscription polymerase chain reaction and flow cytometry. Expression of PD-L1 and other proteins was evaluated by Western blot. Exosomal microRNAs (miRNAs) were analyzed by microarray. GC-derived exosomes (GC-exo) accumulated in high numbers in the lungs and were ingested by macrophages. The extracellular-signalregulated kinase (ERK) signaling pathway was activated by GC-exo, inducing macrophage immunosuppressive-phenotype differentiation and increased PD-L1 expression. miRNA-sequencing identified 130 enriched miRNAs in GC-exo. Among the enriched miRNAs, miR-92a-3p plays a major role in activating ERK signaling via inhibition of PTEN expression. In addition, inhibiting ERK signaling with PD98059 significantly reduced the expression of PD-L1 in macrophages and, therefore, reversed the immunosuppressive PMN and inhibited the colonization of GC cells in the lungs. This study identified a novel mechanism of GC-exo mediated PD-L1 expression in lung macrophages that facilitates lung PMN formation and GC pulmonary metastasis, which also provided a potential therapeutic target for GC with pulmonary metastasis treatment.
Functional analysis of immune subtypes in hepatocellular carcinoma has attracted much attention due to its advantages in solving some optimization problems. At present, the research on the immune subtype of hepatocellular carcinoma is still in its infancy, and the high stability of its system still has problems. Based on fuzzy logic and evolutionary algorithms, this paper constructs a Mate analysis of the optimization problem of immune subtypes and dynamic optimization problems of hepatocellular carcinoma. The model conducts in-depth analysis and research on the biological immune subtype system, solving the problems of reliable information processing and body defense. Tested with existing test functions, very competitive results were achieved. The simulation results show that the improved algorithm based on data statistics has global search ability, the solution accuracy reaches 0.931, and the stability reaches 88.1%.
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