Annals of medicine
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Anesthetic drugs had been reported may impact the bio-behavior of the tumor. Propofol and sevoflurane are common anesthetics in the operation for glioblastoma (GBM). This study aims to establish a co-expression prognostic-related genes signature base on propofol and sevoflurane anesthesia to predict prognosis and immunotherapy response in GBM. ⋯ Propofol and sevoflurane anesthesia associated impact on the gene expression of GBM, included the methylation level of MGMT promoter. Propofol and sevoflurane anesthesia-based risk score prognostic model, which has good prognostic power and is an independent prognostic factor in GBM patients. Therefore, this model can be used as a new biomarker for judging the prognosis of GBM patients.KEY MESSAGESPropofol and sevoflurane anesthesia-based risk score prognostic model has good prognostic power and is an independent prognostic factor in GBM patients.High Propofol and sevoflurane anesthesia-based risk score GBM patients have high T-cell damage scores and are less sensitive to immunotherapy.Serum methylation level of MGMT promoter decrease during propofol and sevoflurane anesthesia in GBM patients.
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Inflammation is an important pathogenic factor of most malignant tumors. It is essential to understand mechanism underlying inflammation and cancer development, so as to formulate and develop anti-cancer treatment strategies. However, inflammatory-related gene characterization as well as risk model construction in prognosis and response chemotherapy or immunotherapy in NSCLC are still remain unclear. ⋯ Inflammatory-related gene risk-score is the potential chemotherapeutic and immunotherapeutic biomarker for NSCLC, and targeting KRT6A sensitive to mitoxantrone and oxaliplatin in NSCLC.HighlightsInflammatory-related genes can lay a certain foundation for distinguishing high-risk NSCLC cases with dismal prognostic outcome.Risk-score base on inflammatory-related genes is positive correlated with CD274, TGFBR1 and TGFB1 expression.Targeting KRT6A sensitive to mitoxantrone and oxaliplatin in H1299 and HCC827 cells.
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Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. ⋯ The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient's clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions.KEY MESSAGESA prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers.The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision.The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied.
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The primary objectives of this study were to 1) investigate the internal consistency 2) and construct validity of the Short Musculoskeletal Function Assessment Questionnaire (SMFA) in older adults commencing physical rehabilitation in an outpatient setting. ⋯ This study demonstrated that the SMFA has adequate internal consistency and construct validity for self-reported health status in older adults, especially when considering components covering physical health status. However, we only observed fair correlations between SMFA and clinical outcome measures, indicating that SMFA does not adequately capture muscle strength and functional capacity.
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Ovarian clear cell carcinoma (OCCC) has distinct clinical and molecular features and heterogeneous prognosis. Insights into the somatic genomic abnormalities of OCCC provide the basis for deeper understanding and potential therapeutic avenues. Herein, we performed extensive genomic profiling in Chinese patients to illustrate the mutation landscape and genetic prognostic biomarkers of OCCC. ⋯ We described somatic genomic alterations in Chinese OCCC patients and observed different genomic alterations between stage I and stage II/III/IV tumours. Genetic factors may supplement clinical factors in nomogram modelling for PFS prediction.Key MessagesWe performed extensive genomic profiling in a well-annotated cohort of 61 Chinese ovarian clear cell carcinoma (OCCC) patients.PIK3CA mutations were associated with worse overall survival (OS) in stage I OCCC, and SWI/SNF gene mutations were associated with improved OS in stage II/III/IV disease.We propose an easy-to-use nomogram using clinical factors (tumour stage and residual disease) and genetic alterations (SWI/SNF complex mutations, ATM mutations and chr8q CNAs) to predict the progress-free survival (PFS) of OCCC.