RESUMO
Platelets are a class of pluripotent cells that, in addition to hemostasis and maintaining vascular endothelial integrity, are also involved in tumor growth and distant metastasis. The tumor microenvironment is a complex and comprehensive system composed of tumor cells and their surrounding immune and inflammatory cells, tumor-related fibroblasts, nearby interstitial tissues, microvessels, and various cytokines and chemokines. As an important member of the tumor microenvironment, platelets can promote tumor invasion and metastasis through various mechanisms. Understanding the role of platelets in tumor metastasis is important for diagnosing the risk of metastasis and prolonging survival. In this study, we more fully elucidate the underlying mechanisms by which platelets promote tumor growth and metastasis by modulating processes, such as immune escape, angiogenesis, tumor cell homing, and tumor cell exudation, and further summarize the effects of platelet-tumor cell interactions in the tumor microenvironment and possible tumor treatment strategies based on platelet studies. Our summary will more comprehensively and clearly demonstrate the role of platelets in tumor metastasis, so as to help clinical judgment of the potential risk of metastasis in cancer patients, with a view to improving the prognosis of patients.
RESUMO
Background: Previous studies reported the related role of RNA n6-methyladenosine (m6A) modification in tumorigenesis and development. However, it is not clear whether m6A modification also plays a potential role in the immune regulation of rectal cancer (RC) and the formation of tumor microenvironment. Methods: In this study, we screened 23 m6A regulatory factors from 369 rectal cancer specimens, further determined the modification patterns of m6A in RC, and systematically linked these modification patterns with the characteristics of TME cell infiltration. The principal component analysis (PCA) algorithm was used to evaluate the m6A modification pattern of a single tumor related to immune response. Results: Three different m6A modification patterns were found in the measurement results, which are related to different clinical results and biological pathways. TME identification results show that the identified m6A pattern is closely related to immune characteristics. According to the m6Ascore extracted from m6A-related signature genes, RC patients were divided into high and low score subgroups combined with tumor mutation burden. Patients with high tumor mutation burden and higher m6Ascore have a significant survival advantage and enhanced immune infiltration. Further analysis showed that patients with higher m6Ascore had higher PD-L1 expression levels and showed better immune response and lasting clinical benefits. Conclusions: M6A modification plays a crucial role in the formation of TME diversity and complexity. The evaluation of the m6A modification mode will help us to enhance our understanding of the characteristics of TME infiltration and provide new insights for more effective immunotherapy strategies.
RESUMO
Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer. Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential. Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups. Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.