Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

The tumor microenvironment (TME) in gastric cancer (GC) has been shown to be important for tumor control but the specific characteristics for GC are not fully appreciated. We generated an atlas of 166,533 cells from 10 GC patients with matched paratumor tissues and blood. Our results show tumor-associated stromal cells (TASCs) have upregulated activity of Wnt signaling and angiogenesis, and are negatively correlated with survival. Tumor-associated macrophages andLAMP3⁺DCs are involved in mediating T cell activity and form intercellular interaction hubs with TASCs. Clonotype and trajectory analysis demonstrates that Tc17 (IL-17⁺CD8⁺T cells) originate from tissue-resident memory T cells and can subsequently differentiate into exhausted T cells, suggesting an alternative pathway for T cell exhaustion. Our results indicate thatIL17⁺cells may promote tumor progression throughIL17,IL22, andIL26signaling, highlighting the possibility of targetingIL17⁺cells and associated signaling pathways as a therapeutic strategy to treat GC.

Recent advances in the epidemiology, pathology, molecular mechanisms, and combined modality therapy (CMT) fields have shown that gastric signet ring cell carcinoma (GSRC) should be considered a distinct cancerous entity. Clinical management of this cancer is challenging, with chemoradioresistance and poor outcomes in advanced stages. Pathological and molecular sets of GSRC demonstrate different features of poor cohesion and differentiation according to the WHO, Japanese Gastric Cancer Association, and Laurén classifications. These features also result in poor response to adjuvant and neoadjuvant chemoradiotherapy. Certain studies of GSRC showed the disputed effectiveness of hyperthermic intraperitoneal chemotherapy and immunotherapy. Our aim was to discuss how an improved understanding of these therapeutic benefits may provide better treatment selection for patients, and therefore improve survival. The challenges in the new understanding of GSRC in routine practice and pathology, and the current limitations of treatment will also be discussed.

Neoadjuvant chemotherapy vs upfront surgery for gastric signet ring cell carcinoma: A retrospective, propensity score-matched study

Elderly patients had more severe postoperative complications after pancreatic resection: A retrospective analysis of 727 patients