Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.

BACKGROUND

Gastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.

METHODS

Based on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.

RESULTS

Monocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.

CONCLUSION

In conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.