Evaluation of Immune Infiltration Based on Image Plus Helps Predict the Prognosis of Stage III Gastric Cancer Patients with Significantly Different Outcomes in Northeastern China

Investigation of Molecular Mechanisms of S-1, Docetaxel and Cisplatin in Gastric Cancer with a History of Helicobacter Pylori Infection

Gastric cancer rates and fatality rates have not decreased. Gastric cancer treatment has historically included surgery (both endoscopic and open), chemotherapy, targeted therapy, and immunotherapy. One of the aggravating carriers of this cancer is Helicobacter pylori infection. Various drug combinations are used to treat gastric cancer. However, examining the molecular function of these drugs, depending on whether or not there is a history of Helicobacter pylori infection, can be a better help in the treatment of these patients. This study was designed as bioinformatics. Various datasets such as patients with gastric cancer, with and without a history of H. pylori, and chemotherapy drugs cisplatin, docetaxel, and S-1 were selected. Using Venn diagrams, the similarities between gene expression profiles were assessed and isolated. Then, selected the signal pathways, ontology of candidate genes and proteins. Then, in clinical databases, we confirmed the candidate genes and proteins. The association between gastric cancer patients with and without a history of H. pylori with chemotherapy drugs was investigated. The pathways of cellular aging, apoptosis, MAPK, and TGFβ were clearly seen. After a closer look at the ontology of genes and the relationship between proteins, we nominated important biomolecules. Accordingly, NCOR1, KIT, MITF, ESF1, ARNT2, TCF7L2, and KRR1 proteins showed an important role in these connections. Finally, NCOR1, KIT, KRR1, and ESF1 proteins showed a more prominent role in the molecular mechanisms of S-1, Docetaxel, and Cisplatin in gastric cancer associated with or without H. pylori.

Lymphocyte subset is more suitable than systemic inflammatory response biomarker and immunoglobulin in constructing prognostic nomogram model for advanced gastric cancer

The serum factors of inflammation are known to be useful prognostic indicators of gastric cancer (GC). However, few studies have made comparisons to screen out more suitable biomarkers for the construction of Nomogram models. In this study, 566 patients who underwent radical gastrectomy were randomly selected. We evaluated the prognostic value of markers of systemic inflammation, including WBC, NLR, PLR, circulating total T cells, CD4⁺T cells, CD8⁺T cells and CD19⁺B cells, serum IgA, IgM, IgE and IgG, and compared them with traditional tumor markers (CEA, CA19-9, CA72-4 and CA125). Kaplan‒Meier analysis was used to analyze the correlation between biomarkers and overall survival (OS). We used time-dependent ROC analysis to investigate the prognostic accuracy of each biomarker. The risk of death was evaluated by the Cox regression model, and the Nomogram model was constructed by R software. We found that circulating total T cells, CD8⁺T cells, CEA, and CA125 had statistical significance in predicting advanced GC prognosis. Circulating CD8⁺T cells and CA125 were continuously superior to circulating total T cells and CEA in the prediction of 5-year OS. Cox regression found that CA125, circulating CD8⁺T cells, sex, and lymph node metastasis rate were independent risk factors for advanced GC. Furthermore, we combined all these predictors to construct a nomogram, which can supplement the AJCC 8th system. According to the comparison with commonly used serum immune biomarkers, circulating CD8⁺T cells is more sensitive to advanced GC. The prediction function of the Nomogram will supplement the traditional AJCC system, which contributes to individual survival prediction.

Lymph Node Metastasis-Related Gene ITGA4 Promotes the Proliferation, Migration, and Invasion of Gastric Cancer Cells by Regulating Tumor Immune Microenvironment

The Integrin Subunit Alpha 4 (ITGA4) plays important roles in cancers pathogenesis. However, the expression and association with clinicopathological and survival probability have not been previously assessed in gastric cancer (GC). Protein expression of ITGA4 was assessed in TMA using immunohistochemistry and correlated with clinicopathological factors and survival. The mRNA expression of ITGA4 was also assessed in the HMU-GC cohort. Bioinformatics function analysis was conducted through GSEA. The “CIBERSORT” package was used for immune infiltration analysis. “SvyNom” package is used to construct prognosis model. ITGA4 knock down using shRNA. The evaluation of cell function was performed by CCK-8 and Transwell invasion and migration experiments. ITGA4 was significantly associated with N classification ( $P=0.031$ ), tumor location ( $P=0.033$ ), WHO classification ( $P=0.007$ ), and poor prognosis in mRNA level. GSEA analysis of the validation cohort suggested that ITGA4 was associated with macrophage infiltration. Immunohistochemistry showed that ITGA4 was associated with poor prognosis. Multivariate Cox regression analysis found that ITGA4 ( $P=0.045$ ) and lymph node metastasis rate ( $P=0.026$ ) were independent prognostic factors and could construct a prognosis model. ITGA4 knockdown cell line significantly reduced the ability of proliferation, invasion, and metastasis. ITGA4 is associated with patient survival in GC and may be an important prognostic biomarker.

FSIP1 Is Associated with Poor Prognosis and Can Be Used to Construct a Prognostic Model in Gastric Cancer

Gastric cancer (GC) is one of the most common upper gastrointestinal malignant tumors, and the incidence of the GC shows an increasing trend in the past years. Finding more sensitive markers will help to reveal the mechanism of GC progression and clinic diagnoses. This study first analyzed the mRNA expression level of FSIP1 in TCGA GC samples and the significance in predicting the prognosis. KEGG and GO analyses were used to explore the molecular mechanism of FSIP1 in GC progression. This study further retrospectively analyzed 166 clinical samples of GC from Harbin Medical University Cancer Hospital and evaluated the expression level of FSIP1 by immunohistochemistry. Kaplan-Meier and Cox multivariate analysis was used to investigate the prognostic value of FSIP1 expression in GC patients. We also identified correlations between FSIP1 and clinicopathological characteristics. This study found that the mRNA level of FSIP1 was significantly upregulated in GC compared with nontumor specimens and correlated with poor prognosis. Immunohistochemistry confirmed the results of bioinformatics analysis of the TCGA GC database. FSIP1 was associated with pTNM pathological stage, tumor location, and neural invasion. In addition, multivariate Cox regression analysis showed that FSIP1, T classification, and N classification were independent posterior factors of patients and could be combined with pathological features to construct a nomogram prognostic model. Overall, our results suggest that FSIP1 is expected to be an independent prognostic indicator of GC.