Integration of mult-omics and nucleotide metabolism reprogramming signature analysis reveals gastric cancer immunological and prognostic features

Abstract

Abstract Background Gastric cancer is a frequent and lethal solid tumor that has a poor prognosis and treatment result. Reprogramming of nucleotide metabolism is a characteristic of cancer development and progression. Methods We used a variety of machine learning techniques to create a novel nucleotide metabolism-related index (NMRI) using gastric cancer sample data obtained from the TCGA and GEO databases. This index is based on genes associated to nucleotide metabolism. Gastric cancer patients were categorized into high and low NMRI groups based on NMRI results. The clinical features, tumor immune microenvironment, response to chemotherapy, and response to immunotherapy were then thoroughly examined. In vitro experiments were then used to confirm the biological role of SERPINE1 in gastric cancer. Results The four nucleotide metabolism-related genes that make up NMRI (GAMT, ORC1, CNGB3, and SERPINE1) were verified in an external dataset and are a valid predictor of prognosis for patients with gastric cancer. The high NMRI group was more responsive to immunotherapy and had greater levels of immune cell infiltration than the low NMRI group. The proliferation and migration of stomach cancer was shown to be decreased by SERPINE1 knockdown in vitro. Conclusions This study's NMRI can reliably predict a patient's prognosis for stomach cancer and pinpoint the patient group that will benefit from immunotherapy, offering important new information on the clinical treatment of stomach cancer.

Keywords

• Gastric cancer
• Nucleotide metabolism
• Prognosis
• Immunotherapy efficacy
• SERPINE1