BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,ofte...BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.展开更多
BACKGROUNDEndoscopic submucosal dissection (ESD) has been advocated by digestiveendoscopists because of its comparable therapeutic effect to surgery, reducedtrauma, faster recovery, and fewer complications. However, E...BACKGROUNDEndoscopic submucosal dissection (ESD) has been advocated by digestiveendoscopists because of its comparable therapeutic effect to surgery, reducedtrauma, faster recovery, and fewer complications. However, ESD for lesions of theduodenum is more challenging than those occurring at other levels of thegastrointestinal tract due to the thin intestinal wall of the duodenum, narrowintestinal space, rich peripheral blood flow, proximity to vital organs, and highrisks of critical adverse events including intraoperative and delayed bleeding andperforation. Because of the low prevalence of the disease and the high risks ofsevere adverse events, successful ESD for lesions of the duodenum has rarelybeen reported in recent years.AIM To investigate the efficacy and safety of ESD in the treatment of duodenal spaceoccupyinglesions.METHODS Clinical data of 24 cases of duodenal lesions treated by ESD at the DigestiveEndoscopy Center of the Affiliated Hospital of Qingdao University from January2016 to December 2019 were retrospectively analyzed.RESULTS All of the 24 cases from 23 patients underwent ESD treatment for duodenal spaceoccupyinglesions under general anesthesia, including 15 male and 8 femalepatients, with a mean age of 58.5 (32.0-74.0) years. There were 12 lesions (50%) inthe duodenal bulb, 9 (37.5%) in the descending part, and 3 (12.5%) in the ball descending junction. The mean diameter of the lesion was 12.75 (range, 11-22)mm. Thirteen lesions originated from the mucosa, of which 4 were low-gradeintraepithelial neoplasia, 3 were hyperplastic polyps, 2 were chronic mucositis, 2were adenomatous hyperplasia, 1 was high-grade intraepithelial neoplasia, and 1was tubular adenoma. Eleven lesions were in the submucosa, including 5neuroendocrine neoplasms, 2 cases of ectopic pancreas, 1 stromal tumor, 1leiomyoma, 1 submucosal duodenal adenoma, and 1 case of submucosal lymphfollicular hyperplasia. The intraoperative perforation rate was 20.8% (5/24),including 4 submucosal protuberant lesions and 1 depressed lesion. The meanlength of hospital stay was 5.7 (range, 3-10) d, and the average follow-up time was25.8 (range, 3.0–50.0) mo. No residual disease or recurrence was found in allpatients, and no complications, such as infection and stenosis, were found duringthe follow-up period.CONCLUSION ESD is safe and effective in the treatment of duodenal lesions;however, theendoscopists should pay more attention to the preoperative preparation,intraoperative skills, and postoperative treatment.展开更多
BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to dev...BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.展开更多
基金Supported by National Natural Science Foundation of China,No.82100581。
文摘BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.
基金Supported by the National NaturalScience Foundation of China, No.81802777the Shandong HigherEducation Research CenterScientific Research Project, No.YJKT201953+2 种基金the ShandongProvince 2018 Professional DegreePostgraduate Teaching CaseLibrary Project, No. SDYAL18049the Shandong Province 2018Postgraduate Mentoring AbilityImprovement Project, No.SDYY18073and the "ClinicalMedicine + X" project of QingdaoUniversity Hospital.
文摘BACKGROUNDEndoscopic submucosal dissection (ESD) has been advocated by digestiveendoscopists because of its comparable therapeutic effect to surgery, reducedtrauma, faster recovery, and fewer complications. However, ESD for lesions of theduodenum is more challenging than those occurring at other levels of thegastrointestinal tract due to the thin intestinal wall of the duodenum, narrowintestinal space, rich peripheral blood flow, proximity to vital organs, and highrisks of critical adverse events including intraoperative and delayed bleeding andperforation. Because of the low prevalence of the disease and the high risks ofsevere adverse events, successful ESD for lesions of the duodenum has rarelybeen reported in recent years.AIM To investigate the efficacy and safety of ESD in the treatment of duodenal spaceoccupyinglesions.METHODS Clinical data of 24 cases of duodenal lesions treated by ESD at the DigestiveEndoscopy Center of the Affiliated Hospital of Qingdao University from January2016 to December 2019 were retrospectively analyzed.RESULTS All of the 24 cases from 23 patients underwent ESD treatment for duodenal spaceoccupyinglesions under general anesthesia, including 15 male and 8 femalepatients, with a mean age of 58.5 (32.0-74.0) years. There were 12 lesions (50%) inthe duodenal bulb, 9 (37.5%) in the descending part, and 3 (12.5%) in the ball descending junction. The mean diameter of the lesion was 12.75 (range, 11-22)mm. Thirteen lesions originated from the mucosa, of which 4 were low-gradeintraepithelial neoplasia, 3 were hyperplastic polyps, 2 were chronic mucositis, 2were adenomatous hyperplasia, 1 was high-grade intraepithelial neoplasia, and 1was tubular adenoma. Eleven lesions were in the submucosa, including 5neuroendocrine neoplasms, 2 cases of ectopic pancreas, 1 stromal tumor, 1leiomyoma, 1 submucosal duodenal adenoma, and 1 case of submucosal lymphfollicular hyperplasia. The intraoperative perforation rate was 20.8% (5/24),including 4 submucosal protuberant lesions and 1 depressed lesion. The meanlength of hospital stay was 5.7 (range, 3-10) d, and the average follow-up time was25.8 (range, 3.0–50.0) mo. No residual disease or recurrence was found in allpatients, and no complications, such as infection and stenosis, were found duringthe follow-up period.CONCLUSION ESD is safe and effective in the treatment of duodenal lesions;however, theendoscopists should pay more attention to the preoperative preparation,intraoperative skills, and postoperative treatment.
基金Supported by National Natural Science Foundation of China,No.81802777.
文摘BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.
