Background: Stromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).
Methods: Metabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.
Results: A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients' risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.
Conclusion: Our study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.
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http://dx.doi.org/10.1186/s12876-022-02451-2 | DOI Listing |
J Med Internet Res
January 2025
Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.
Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University.
JMIR Form Res
January 2025
Graduate School of Public Health Policy, City University of New York, New York, NY, United States.
Background: Childhood obesity prevalence remains high, especially in racial and ethnic minority populations with low incomes. This epidemic is attributed to various dietary behaviors, including increased consumption of energy-dense foods and sugary beverages and decreased intake of fruits and vegetables. Interactive, technology-based approaches are emerging as promising tools to support health behavior changes.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, University of Jordan, Amman, Jordan.
Background: Chronic respiratory disorders such as asthma and chronic obstructive pulmonary disease (COPD) may deteriorate into acute exacerbations requiring hospitalization. Assessing the predictors of prolonged hospital stays could help identify potential interventions to reduce the burden on patients and healthcare systems.
Aim: This study aimed to identify the risk factors attributed to prolonged hospital stays among patients admitted with acute exacerbations of chronic respiratory disorders in Jordan.
Neurosurgery
February 2025
Global Neurosciences Institute, Philadelphia , Pennsylvania , USA.
Background And Objectives: Despite growing interest in how patient frailty affects outcomes (eg, in neuro-oncology), its role after transsphenoidal surgery for Cushing disease (CD) remains unclear. We evaluated the effect of frailty on CD outcomes using the Registry of Adenomas of the Pituitary and Related Disorders (RAPID) data set from a collaboration of US academic pituitary centers.
Methods: Data on consecutive surgically treated patients with CD (2011-2023) were compiled using the 11-factor modified frailty index.
JAMA Surg
January 2025
Center for Surgery and Public Health, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Importance: Surgeon stress can influence technical and nontechnical skills, but the consequences for patient outcomes remain unknown.
Objective: To investigate whether surgeon physiological stress, as assessed by sympathovagal balance, is associated with postoperative complications.
Design, Setting, And Participants: This multicenter prospective cohort study included 14 surgical departments involving 7 specialties within 4 university hospitals in Lyon, France.
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