Identification of uridine phosphatase 1 as a potential therapeutic target in gastric cancer by integrated...

Gastric cancer remains a major global health challenge, and its early diagnosis and prognosis prediction pose significant challenges to the current clinical treatment of gastric cancer. Finding gastric cancer biomarkers is essential to comprehending its pathophysiology and creating novel targeted treatments. Following the acquisition and processing of the gastric cancer sample, the single-cell RNA sequencing data, monocyte subpopulation characterization, and cell type identification were performed. Key gene modules linked to gastric-cancer-related monocytes were identified using high-dimensional weighted gene co-expression network analysis. Machine-learning diagnostic models were created utilizing the discovered gastric-cancer-related monocyte-related genes (GCRMORGs). A prognostic model was developed with the uridine phosphatase 1 ( UPP1 )-related risk scores and verified in separate cohorts, and multiple immunological analyses were performed. Finally, using various experimental assays, we thoroughly investigated the function of the UPP1 gene in gastric cancer. Gastric cancer samples showed a distinct immune milieu topography with an abundance of monocytes. Eventually, 32 GCRMORGs were identified. Diagnostic models demonstrated a high degree of efficacy in differentiating between patients with gastric cancer and the control group. The prognostic model showed significant predictive value for gastric cancer patients' survival. At the same time, we have confirmed from experimental perspectives that a poor prognosis for patients is indicated by a high expression of UPP1 in gastric cancer tissue. Important monocyte subpopulations associated with gastric cancer samples were detected in our investigation. The prognosis of patients with gastric cancer can be predicted using a predictive model based on 32 GCRMORGs. In addition, focusing on UPP1 in gastric cancer may yield novel therapeutic targets and approaches.