Myosteatosis predicts postoperative complications and long-term survival in robotic gastrectomy for gastric cancer: A propensity score analysis.

BACKGROUND: Robotic gastrectomy is increasingly utilized for gastric cancer, but high morbidity remains a concern. Myosteatosis or low skeletal muscle density reflecting fatty infiltration, associates with complications after other cancer surgeries but has not been evaluated for robotic gastrectomy. METHODS: This retrospective study analysed 381 patients undergoing robotic gastrectomy for gastric cancer from September 2019 to October 2022. Myosteatosis was quantified on preoperative computed tomography (CT) images at lumbar 3 (L3). Propensity score matching addressed potential confounding between myosteatosis and non-myosteatosis groups. Outcomes were postoperative complications, 30 days mortality, 30 days readmissions and survival. RESULTS: Myosteatosis was present in 33.6% of patients. Myosteatosis associated with increased overall (47.7% vs. 26.5%, p < 0.001) and severe complications (12.4% vs. 4.9%, p < 0.001). After matching, myosteatosis remained associated with increased overall complications, major complications, intensive care unit (ICU) transfer and readmission (all p < 0.05). Myosteatosis independently predicted overall [odds ratio (OR) = 2.86, 95% confidence interval (CI): 1.57-5.20, p = 0.001] and severe complications (OR = 4.81, 95% CI: 1.51-15.27, p = 0.008). Myosteatosis also associated with reduced overall (85.0% vs. 93.2%, p = 0.015) and disease-free survival (80.3% vs. 88.4%, p=0.029). On multivariate analysis, myosteatosis independently predicted poorer survival [hazard ratio (HR) = 2.83, 95% CI: 1.32-6.08, p=0.012] and disease-free survival (HR = 1.83, 95% CI: 1.01-3.30, p=0.032). CONCLUSION: Preoperative CT-defined myosteatosis independently predicts increased postoperative complications and reduced long-term survival after robotic gastrectomy for gastric cancer. Assessing myosteatosis on staging CT could optimize preoperative risk stratification.

Impact of preoperative sarcopenia on postoperative complications and prognosis in patients undergoing robotic gastric cancer surgery: A propensity score matching study.

BACKGROUND: Sarcopenia, defined as decreased muscle mass and function, correlates with postoperative morbidity and mortality in cancer surgery. However, sarcopenia's impact specifically following robotic gastrectomy for gastric cancer has not been clearly defined. This study aimed to determine the influence of sarcopenia on short- and long-term clinical outcomes after robotic gastrectomy for gastric cancer. METHODS: This retrospective study analyzed 381 gastric cancer patients undergoing robotic gastrectomy. Sarcopenia was diagnosed by preoperative computed tomography (CT) body composition analysis. Propensity score matching created 147 pairs of sarcopenia and nonsarcopenia patients for comparison. Outcomes included postoperative complications, survival, inflammatory markers, length of stay, intensive care unit (ICU) transfer, and readmissions. RESULTS: Sarcopenia patients exhibited significantly higher rates of overall (53.7% versus 21.1%, P < 0.001), serious (12.9% versus 4.1%, P = 0.007), and grade III-IV complications compared to nonsarcopenia pairs after matching. Sarcopenia independently predicted reduced 3-years overall (HR = 2.53, 95% CI: 1.19-5.40, P = 0.016) and disease-free survival (HR = 1.99, 95% CI: 1.09-3.66, P = 0.026). Sarcopenia patients also showed heightened postoperative leukocyte, neutrophil, platelet, platelet to lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and monocyte to lymphocyte ratio (MLR) levels alongside suppressed lymphocytes, monocytes, and neutrophil to lymphocyte ratio (NLR). CONCLUSION: Preoperative sarcopenia is correlated with increased postoperative complications and poorer long-term survival in gastric cancer patients undergoing robotic gastrectomy. Sarcopenia assessment can optimize preoperative risk stratification and perioperative management in this population.

Nomogram models for predicting overall and cancer-specific survival in early-onset gastric cancer patients: a population-based cohort study.

To develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of early-onset gastric cancer (EOGC) patients. A total of 1077 EOGC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and an additional 512 EOGC patients were recruited from the Fourth Hospital of Hebei Medical University, serving as an external test set. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Based on these factors, two nomogram models were established, and web-based calculators were developed. These models were validated using receiver operating characteristics (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Multivariate analysis identified gender, histological type, stage, N stage, tumor size, surgery, primary site, and lung metastasis as independent prognostic factors for OS and CSS in EOGC patients. Calibration curves and DCA curves demonstrated that the two constructed nomogram models exhibited good performance. These nomogram models demonstrated superior performance compared to the 7th edition of the AJCC tumor-node-metastasis (TNM) classification (internal validation set: 1-year OS: 0.831 vs 0.793, P = 0.072; 1-year CSS: 0.842 vs 0.816, P = 0.190; 3-year OS: 0.892 vs 0.857, P = 0.039; 3-year CSS: 0.887 vs 0.848, P = 0.018; 5-year OS: 0.906 vs 0.880, P = 0.133; 5-year CSS: 0.900 vs 0.876, P = 0.109). In conclusion, this study developed two nomogram models: one for predicting OS and the other for CSS of EOGC patients, offering valuable assistance to clinicians.

Transcriptomics-based liquid biopsy panel for early non-invasive identification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer.

BACKGROUND: This study aimed to develop a novel six-gene expression biomarker panel to enhance the early detection and risk stratification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer (LAGC). METHODS: We used genome-wide transcriptome profiling and rigorous bioinformatics to identify a six-gene expression biomarker panel. This panel was validated across multiple clinical cohorts using both tissue and liquid biopsy samples to predict peritoneal recurrence and micrometastasis in patients with LAGC. RESULTS: Through genome-wide expression profiling, we identified six mRNAs and developed a risk prediction model using 196 samples from a surgical specimen training cohort. This model, incorporating a 6-mRNA panel with clinical features, demonstrated high predictive accuracy for peritoneal recurrence in gastric cancer patients, with an AUC of 0.966 (95% CI: 0.944-0.988). Transitioning from invasive surgical or endoscopic biopsy to noninvasive liquid biopsy, the model retained its predictive efficacy (AUC = 0.963; 95% CI: 0.926-1.000). Additionally, the 6-mRNA panel effectively differentiated patients with or without peritoneal metastasis in 95 peripheral blood specimens (AUC = 0.970; 95% CI: 0.936-1.000) and identified peritoneal micrometastases with a high efficiency (AUC = 0.941; 95% CI: 0.874-1.000). CONCLUSIONS: Our study provides a novel gene expression biomarker panel that significantly enhances early detection of peritoneal recurrence and micrometastasis in patients with LAGC. The RSA model's predictive capability offers a promising tool for tailored treatment strategies, underscoring the importance of integrating molecular biomarkers with clinical parameters in precision oncology.

N6-methyladenosine modified circPAK2 promotes lymph node metastasis via targeting IGF2BPs/VEGFA signaling in gastric cancer.

Circular RNAs (circRNAs) have emerged as key regulators of cancer occurrence and progression, as well as promising biomarkers for cancer diagnosis and prognosis. However, the potential mechanisms of circRNAs implicated in lymph node (LN) metastasis of gastric cancer remain unclear. Herein, we identify a novel N6-methyladenosine (m6A) modified circRNA, circPAK2, which is significantly upregulated in gastric cancer tissues and metastatic LN tissues. Functionally, circPAK2 enhances the migration, invasion, lymphangiogenesis, angiogenesis, epithelial-mesenchymal transition (EMT), and metastasis of gastric cancer in vitro and in vivo. Mechanistically, circPAK2 is exported by YTH domain-containing protein 1 (YTHDC1) from the nucleus to the cytoplasm in an m6A methylation-dependent manner. Moreover, increased cytoplasmic circPAK2 interacts with Insulin-Like Growth Factor 2 mRNA-Binding Proteins (IGF2BPs) and forms a circPAK2/IGF2BPs/VEGFA complex to stabilize VEGFA mRNA, which contributes to gastric cancer vasculature formation and aggressiveness. Clinically, high circPAK2 expression is positively associated with LN metastasis and poor prognosis in gastric cancer. This study highlights m6A-modified circPAK2 as a key regulator of LN metastasis of gastric cancer, thus supporting circPAK2 as a promising therapeutic target and prognostic biomarker for gastric cancer.

Recurrent metastatic patterns and prognosis after radical surgery in patients with alpha-fetoprotein-producing gastric cancer: a retrospective cohort study.

Alpha-fetoprotein-producing gastric cancer (AFPGC) is a rare and aggressive subtype of gastric cancer associated with poor prognosis. This study aimed to investigate the recurrent metastatic patterns and prognostic factors in AFPGC patients undergoing radical surgical resection. Data from 241 AFPGC patients diagnosed between January 2017 and January 2020 who underwent surgical resection were analyzed across multiple centers. Recurrence patterns, metastatic sites, and survival outcomes were evaluated. Univariate and multivariate analyses were performed to identify risk factors for recurrent metastasis, overall survival (OS), and disease-free survival (DFS). There is an annual increase in the proportion of AFPGC cases, rising from 3.45% in 2017 to 7.88% in 2023. Higher serum AFP level was associated with increased likelihood of lymph node metastasis (P=0.006), deeper invasion depth (P=0.000) and greater tumor diameter (P=0.036). Independent predictors of recurrent metastasis included T4 infiltration, lymph node metastasis, tumor diameter >5 cm, poorly differentiated-undifferentiated pathology, preoperative AFP>1000 ng/mL, and postoperative increasing trend in AFP levels. The 5-year OS and DFS rates were 36.5% and 34.2%, respectively, with poorer survival linked to higher preoperative AFP levels and postoperative increasing trend in AFP level. Independent risk factors for poor OS and DFS included T4 infiltration, lymph node metastasis, poorly differentiated-undifferentiated pathology, preoperative AFP>1000 ng/mL, and postoperative increasing trend in AFP. Serum AFP level can serve as a potential predictive and prognostic biomarker. Identifying independent risk factors informs risk stratification and personalized treatment for AFPGC patients.

Development and validation of nomogram models for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: a cohort study based on the SEER database.

OBJECTIVE: To establish nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer liver metastasis (GCLM) patients. METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database for 5,451 GCLM patients diagnosed between 2010 and 2015 were analyzed. The cohort was divided into a training set (3,815 cases) and an internal validation set (1,636 cases). External validation included 193 patients from the Fourth Hospital of Hebei Medical University and 171 patients from the People's Hospital of Shijiazhuang City, spanning 2016-2018. Multivariable Cox regression analysis identified eight independent prognostic factors for OS and CSS in GCLM patients, including age, histological type, grade, tumor size, surgery, chemotherapy, bone metastasis, and lung metastasis. Two nomogram models were developed based on these factors and evaluated using time-dependent receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. RESULTS: Internal validation showed that the nomogram models outperformed the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system in predicting 1-year, 2-year, and 3-year OS and CSS in GCLM patients (1-year OS: 0.801 vs. 0.593, P < 0.001; 1-year CSS: 0.807 vs. 0.598, P < 0.001; 2-year OS: 0.803 vs. 0.630, P < 0.001; 2-year CSS: 0.802 vs. 0.633, P < 0.001; 3-year OS: 0.824 vs. 0.691, P < 0.001; 3-year CSS: 0.839 vs. 0.692, P < 0.001). CONCLUSION: This study developed and validated nomogram models using SEER database data to predict OS and CSS in GCLM patients. These models offer improved prognostic accuracy over traditional staging systems, aiding in clinical decision-making.