Abstract At the time of diagnosis, approximately 15%‐20% of patients with rectal cancer (RC) presented synchronous liver metastasis (SLM), which is the most common cause of death in patients with RC. Therefore, preoperative, noninvasive, and accurate prediction of SLM is crucial for personalized treatment strategies. Recently, radiomics has been considered as an advanced image analysis method to evaluate the neoplastic heterogeneity with respect to diagnosis of the tumor and prediction of prognosis. In this study, a total of 1409 radiomics features were extracted for each volume of interest (VOI) from high‐resolution T2WI images of the primary RC. Subsequently, five optimal radiomics features were selected based on the training set using the least absolute shrinkage and selection operator (LASSO) method to construct the radiomics signature. In addition, radiomics signature combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19‐9 (CA19‐9) was included in the multifactor logistic regression to construct the nomogram model. It showed an optimal predictive performance in the validation set as compared to that in the radiomics model. The favorable calibration of the radiomics nomogram showed a nonsignificant Hosmer‐Lemeshow test statistic ( P > .05). The decision curve analysis (DCA) showed that the radiomics nomogram is clinically superior to the radiomics model. Therefore, the nomogram amalgamating the radiomics signature and clinical risk factors serve as an effective quantitative approach to predict the SLM of primary RC.
Infection is the most common complication and cause of death after hematopoietic stem cell transplantation (HSCT). Our study aims to investigate the clinical characteristics and risk factors for death of
Abstract Background This study aimed to evaluate the significance of MRI-based radiomics model derived from high-resolution T2-weighted images (T2WIs) in predicting tumor pathological features of rectal cancer. Methods A total of 152 patients with rectal cancer who underwent surgery without any neoadjuvant therapy between March 2017 and September 2018 were included retrospectively. The patients were scanned using a 3-T magnetic resonance imaging, and high-resolution T2WIs were obtained. Lesions were delineated, and 1029 radiomics features were extracted. Least absolute shrinkage and selection operator was used to select features, and multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) were trained using fivefold cross-validation to build a prediction model. The diagnostic performance of the prediction models was assessed using the receiver operating characteristic curves. Results A total of 1029 features were extracted, and 15, 11, and 11 features were selected to predict the degree of differentiation, T stage, and N stage, respectively. The best performance of the radiomics model for the degree of differentiation, T stage, and N stage was obtained by SVM [area under the curve (AUC), 0.862; 95% confidence interval (CI), 0.750–0.967; sensitivity, 83.3%; specificity, 85.0%], MLP (AUC, 0.809; 95% CI, 0.690–0.905; sensitivity, 76.2%; specificity, 74.1%), and RF (AUC, 0.746; 95% CI, 0.622-0.872; sensitivity, 79.3%; specificity, 72.2%). Conclusion This study demonstrated that the high-resolution T2WI–based radiomics model could serve as pretreatment biomarkers in predicting pathological features of rectal cancer.
The heterogeneous nature of acute myeloid leukemia (AML) and its poor prognosis necessitate therapeutic improvement. Current advances in AML research yield important insights regarding both AML genetics and epigenetics. MicroRNAs (miRNAs) play important roles in cell proliferation, differentiation, and survival and may be useful for AML diagnosis and prognosis. In this study, a novel miRNA, hsa-miR-12462, was identified in bone marrow (BM) samples from AML patients at diagnosis by small RNA sequencing. A significant higher level of hsa-miR-12462 was found in patients who achieve complete remission (AML-CR) after induction therapy compared with those who suffer relapse/refractory (AML-RR). FosB was predicted to be the target of hsa-miR-12462 through RNA sequencing, bioinformatics analysis, and protein-protein interaction (PPI) network analysis and then verified by luciferase activity assay. T-5224, the inhibitor of FosB, was administered to AML cell lines, which could inhibit cell proliferation, promote apoptosis, and restore the sensitivity of AML cells to cytarabine (Ara-C). In summary, a higher level of hsa-miR-12462 in AML cells is associated with increased sensitivity to Ara-C via targeting FosB.
Breast cancer is one of the most common malignant cancers affecting females. Estrogen receptor (ER)-positive breast cancer is responsive to endocrine therapy. Although current therapies offer favorable prospects for improving survival, the development of resistance remains a severe problem. In this study, we explored the resistance mechanisms of ER-positive breast cancer to neoadjuvant endocrine therapy. Microarray data of GSE87411 contained 109 pairs of samples from Z1031 trial, including untreated samples and post-treated samples with neoadjuvant aromatase inhibitor (AI) therapy. The differentially expressed genes (DEGs) were obtained from two different comparisons: untreated samples versus post-treated samples with AIs, and post-treated samples sensitive versus resistant to AIs. Multiple bioinformatic methods were applied to evaluate biological function, protein-protein network and potential binding between target protein and aromatase inhibitor. Then, regulation of gene expression, DNA methylation and clinicopathological factors of breast cancer were further analyzed with TCGA data. From GSE87411 dataset, 30 overlapped DEGs were identified. Cell division was found to be the main function of overlapped DEGs by functional enrichment and gene ontology (GO) analysis. RAD51 recombinase (RAD51), a key protein of homologous recombination, was detected to interact with BReast CAncer genes 2 (BRCA2). Moreover, according to the docking simulation, RAD51 might potentially bind to AIs. Overexpressed RAD51 was associated with hypermethylation of BRCA2, resistance to AIs and poor overall survival of patients with ER-positive breast cancer. Furthermore, RAD51 was found to be a better indicator than MKI67 for predicting resistance in neoadjuvant setting. The results indicated that methylation of BRCA2 led to incomplete suppression on RAD51, which caused an increased expression of RAD51, subsequently AI-resistance and poor prognosis in ER-positive breast cancer. RAD51 could be a new candidate used as a predicative marker and therapeutic target in neoadjuvant endocrine treatment.
Gastrin (GAST) is a well-known hormone regulating gastric biofunctions in the secretion of acid and maintaining its structural integrity. Furthermore, the dysregulation of GAST is also involved in the development of various forms of cancer. However, there are some limitations for illustrating the cellular regulation of GAST and its regulatory mechanisms in gastric malignant transformation and the potential epigenetic regulators systematically. We explored the role of GAST expression in gastric cancer (GC) and normal tissues with the clinical features and investigated the potential relationship between GAST and STAT3/MMP11 pathway by gain or loss of function analyses. Besides, based on our microRNA/mRNA expression profiles, miR-30a-3p was the potential epigenetic regulator and additional experiments were performed to identify the hypothesis. Elevated GAST expression was frequently detected in GC and was associated with worse outcomes (p<0.001). And we firstly demonstrated that GAST was negatively regulated by miR-30a-3p. Moreover, GAST induced GC cell proliferation, migration and invasion mediating STAT3/MMP11 pathway in this study. MiR-30a-3p was the promising suppressor gene through negatively regulating the expression of GAST, and dysregulation of GAST was a prognostic signature associated cell proliferation and metastasis through STAT3/MMP11 pathway in GC.
Early detection and multi-modality curative treatment for pancreatic cancer remain unsatisfactory due to the insufficient understanding of the mechanisms underlying tumor progression. Epigenetic events, including aberrant methylation of tumor suppressor gene promoter regions, may contribute to tumorigenesis involving both the exocrine and endocrine pancreas. Methylation changes of specific gene promoter regions were examined in 48 resected neoplasms of the exocrine and endocrine pancreas, which were obtained as paraffin-embedded tissue samples. The pancreatic neoplasms included acinar cell carcinoma (n=12), adenocarcinoma (n=18), and islet cell tumors (n=18). DNA methylation was determined with a nested methylation-specific PCR (MSP) technique incorporating an initial bisulfite modification of tumor DNA for the promoter regions associated with 14 tumor suppressor genes. In decreasing order, the 6 most frequently methylated genes were: APC 50%, BRCA1 46%, p16INK4a 35%, p15INK4b 35%, RARβ 35%, and p73 33%. Overall, 94% of the tumors had methylation of at least one gene, and methylation of two or more genes was present in 69% of pancreatic tumors. Pancreatic adenocarcinomas had patterns of gene methylation that differed from pancreatic endocrine tumors. These differences were most notable for the APC and hMLH1 genes.
With the increase in aging population in China, elderly Crohn's disease (CD) patients need to receive more attention. This study aims to explore the clinical characteristics and disease process of elderly onset CD (EOCD) patients in a single center.
Abstract Background Multidrug-resistant (MDR) Klebsiella pneumoniae infections, from pancreatic infections to bloodstream infections, influence the mortality of patients with acute pancreatitis (AP) on the condition of limited antibiotic choices. The aim of this study was to investigate the predictor of mortality among AP patients complicated with MDR- K. pneumoniae infections. Methods Seventy-one AP patients who occurred MDR- K. pneumoniae infections from August 1st, 2016 to August 1st, 2020 were enrolled. MDR- K. pneumoniae was defined as the K. pneumoniae strain non-susceptible to at least one agent in three or more antimicrobial categories. MDR- K. pneumoniae isolates were confirmed by Vitek-2 system. Antibiotic susceptibility test was carried out using a micro broth dilution method. Clinical characteristics and drug-resistance rates were retrospectively reviewed, and the predictors of mortality were evaluated by univariate and multivariate analyses. Results The mortality rate of AP patients complicated with MDR- K. pneumoniae infections reached 46.5% (33 of 71), and pancreas (n = 53) was the most common site of MDR- K pneumoniae strains. The drug resistance rates of MDR- K. pneumoniae were high to 11 of 12 common antibiotics (more than 50.0%) except of tigecycline (23.9%). The predictor independently associated with mortality was septic shock (hazard ratio 2.959, 95% confidence intervals 1.396 – 6.272, P = 0.005). Conclusions More attention should be paid for pancreatic MDR- K. pneumoniae infections among AP patients The predictor for mortality of AP patients complicated with MDR- K. pneumoniae infection is septic shock. Therefore, further clinical investigations should focus on areas against septic shock.
'/%3e%3c/g%3e%3cuse xlink:href='%23d' x='45.714'/%3e%3cuse xlink:href='%23e' transform='matrix(-1 0 0 1 479.792 0)'/%3e%3cg id='f' fill='%23fff'%3e%3cpath fill='%23039' d='M232.636 202.406v.005c0 2.212.85 4.227 2.212 5.69 1.365 1.466 3.245 2.377 5.302 2.377 2.067 0 3.944-.905 5.303-2.365 1.358-1.459 2.202-3.472 2.202-5.693v-10.768l-14.992-.013-.028 10.766'/%3e%3ccircle cx='236.074' cy='195.735' r='1.486'/%3e%3ccircle cx='244.392' cy='195.742' r='1.486'/%3e%3ccircle cx='240.225' cy='199.735' r='1.486'/%3e%3ccircle cx='236.074' cy='203.916' r='1.486'/%3e%3ccircle cx='244.383' cy='203.905' r='1.486'/%3e%3c/g%3e%3cuse xlink:href='%23f' y='-26.016'/%3e%3cuse xlink:href='%23f' x='-20.799'/%3e%3cuse xlink:href='%23f' x='20.745'/%3e%3cuse xlink:href='%23f' y='25.784'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)