ObjectiveTo investigate the expression of Runt-related transcription factor 1 (RUNX1) in gastric cancer and its correlation with clinicopathological features, prognosis and tumor cell invasion ability. Methods① Database analysis: the expression of RUNX1 in gastric cancer and adjacent tissues were analyzed by TCGA and GEO database. Kaplan-Meier Plotter database was used to analyze the correlation between RUNX1 expression level and overall survival (OS) of gastric cancer patients. GO analysis and KEGG pathway enrichment were used to analyze the possible functions and signaling pathways of RUNX1 in gastric cancer, and gene correlation was verified by GEPIA database. ② Clinical case validation: the cancer tissues and adjacent tissues of 62 patients with gastric cancer admitted to the Second Hospital of Lanzhou University from June 2018 to December 2019 were retrospectively collected for immunohistochemical staining, HE staining and Sirius red staining, and the relation between RUNX1 expression and clinicopathological features and prognosis of patients was explored. ③ Cell experiment: we knocked down RUNX1 by using small interfering RNA, and then analyzed the relation between RUNX1 and the invasion ability of gastric cancer cells by Transwell assay. Results① Database analysis: RUNX1 was highly expressed in gastric cancer tissues and negatively correlated with OS (P<0.001). GO analysis and KEGG pathway enrichment analysis showed that RUNX1 was not only involved in the construction of collagen in extracellular matrix (ECM), but also significantly enriched in ECM-receptor interaction pathway. The results of GEPIA gene correlation analysis showed that RUNX1 was positively correlated with gene expression involved in ECM-receptor interaction pathway (P<0.05). ② Clinical case validation: the results of immunohistochemical staining showed that RUNX1 was relatively highly expressed in gastric cancer tissues, and the high expression of RUNX1 was a risk factor affecting the postoperative OS of gastric cancer patients (RR=5.074, P=0.034); the expression of RUNX1 in gastric cancer tissues was positively correlated with red staining area of Sirius red staining (r=0.46, P<0.001). ③ Cell experiment: invasion experiments confirmed that the number of invasive AGS or HGC27 cells in si-001 group and si-002 group decreased after RUNX1 knockdown. ConclusionRUNX1 is highly expressed in gastric cancer and suggests a worse survival prognosis, and it is possible that RUNX1 promotes the development of gastric cancer by activating the ECM-receptor interaction pathway.
ObjectiveTo evaluate prognostic value of change of immune status in locally advanced gastric cancer (LAGC) patients. Methods We retrospective collected 210 LAGC patients who underwent treatment in our department from January 2013 to December 2018, then we collected lymphocyte-to-monocyte ratio (LMR) and cLMR (change of lymphocyte-to-monocyte ratio, cLMR) before operation and after three cycles of adjuvant chemotherapy. We had developed a new immune state change score (ICS) based on preoperative LMR (pLMR) and cLMR, and explored its prognostic value. The definition of ICS in this study was: ICS=1, pLMR≤4.53 and cLMR≤1; ICS=2, pLMR≤4.53 and cLMR>1, or pLMR>4.53 and cLMR≤1; ICS=3, pLMR>4.53 and cLMR>1. Results The results of multivariate Cox proportional hazard regression model showed that ICS was an influencing factor for overall survival [ICS=2, RR=0.397, 95%CI (0.260, 0.608), P<0.001; ICS=3, RR=0.080, 95%CI (0.040, 0.162), P<0.001), patients with ICS scores of 2 and 3 had better overall survival. In addition, the prognostic accuracy of ICS was superior to pLMR and Clmr, and the C-index of ICS [0.806, 95%CI (0.746, 0.865)] was higher than that of pLMR [0.717, 95%CI (0.635, 0.799), P=0.003)] and cLMR [0.723, 95%CI (0.641, 0.806), P=0.005)]. Based on this, a Nomogram model included ICS, CEA, and pTNM staging was constructed to predict the 3-year and 5-year survival rates of patients. The calibration curve and C-index [0.821, 95%CI (0.783, 0.859)] showed high discrimination and accuracy of Nomogram, and decision curve analysis confirmed that the model had good clinical application value. Conclusions The dynamic changes in the patient’s immune status before and after adjuvant therapy are related to the overall survival of LAGC patients. As an evaluating system which combined the cLMR and pLMR, ICS can better predict the prognosis of LAGC patients.