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Background: The metabolism of stearoyl-GPE plays a key role in the liver metastasis of gastric cancer. This investigation delves into the mechanisms underlying the intricate tumor microenvironment (TME) heterogeneity triggered by stearoyl metabolism in gastric cancer with liver metastasis (LMGC), offering novel perspectives for LMGC.

Objective: Utilizing Mendelian randomization, we determined that stearoyl metabolism significantly contributes to the progression of gastric cancer (GC).

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Background: Regulator of G protein signaling (RGS) proteins participate in tumor formation and metastasis by acting on the α-subunit of heterotrimeric G proteins. The specific effect of RGS, particularly , on the progression of gastric cancer (GC) is not yet clear.

Aim: To explore the role and underlying mechanisms of action of in GC development.

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Gastric cancer (GC) is one of the most prevalent malignant tumors in the world and has an extremely poor prognosis. Regulator of calcineurin 1 (RCAN1), a known tumor suppressor in various cancers, has an undefined role in the proliferation and metastasis of GC. Primary tumor and paired normal gastric tissues were collected from 77 patients with GC for evaluating the mRNA levels of 3 RCAN1 transcripts.

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The mechanism of tumor budding (TB) in gastric adenocarcinoma (GAC) and its relationship with biological indicators and prognostic significance, remains unclear. In this study, we conducted a comprehensive analysis using whole-slide imaging to evaluate TB in 75 cases of GAC. Our findings revealed the risk factors associated with TB in GAC and their impact on patient prognosis.

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Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.

Materials And Methods: GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi).

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