Ovarian serous carcinoma with stomach metastasis: a rare case report and literature review.

Ovarian cancer is a common tumor among women. It is often asymptomatic in the early stages, with most cases already at stage III to IVE at the time of diagnosis. Direct spread and lymphatic metastasis are the primary modes of metastasis, whereas hematogenous spread is rare. An initial diagnosis of ovarian cancer that has metastasized to the stomach is also uncommon. Therefore, clear treatment methods and prognostic data for such metastasis are lacking. In our hospital, we encountered a patient with an initial imaging diagnosis of a gastric tumor and a history of an ovarian tumor with endoscopic abdominal metastasis. Based on the characteristics of the case, the two tumors were considered to be the same. After chemotherapy, a partial response was observed in the stomach and pelvic lesions, suggesting the effectiveness of the treatment. Through three treatments of recurrence, gastroscopy confirmed the stomach to be a metastatic site. Therefore, determining the primary source of advanced tumors is crucial in guiding treatment decisions. Clinicians must approach this comprehensively, relying on thorough evaluation and personal experience.

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients.

BACKGROUND: Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear. AIM: To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients. METHODS: In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents. RESULTS: A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity. CONCLUSION: In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.