Abstract Background Disulfidptosis is a new type of regulated cell death that involves cytoskeletal collapse, induced by excessive disulfide bond formation. However, understanding of the biological characteristics and clinical significance of disulfidptosis in pan-cancers remains limited. Methods We obtained transcriptome data from TCGA via UCSC Xena. Based on the expression of disulfidptosis-related genes (DRG), we constructed a consensus DRG-related signature (DRGS) using the LASSO Cox regression model. A nomogram incorporating the DRG score was developed as a quantitative tool for predicting prognosis. We utilized the z-score algorithm to integrate gene expression characteristics and activity of specific pathways. Comprehensive analyses were performed to investigate tumor microenvironment and mutation profiles. We evaluated the responses of subgroups to immunotherapy and conducted drug screening. Finally, we utilized immunofluorescence (IF) to evaluate the expression of hub genes in patients with ovarian cancer (OV). Results The DRGS was considered a prognostic factor for various types of cancer, with higher scores indicating more unfavorable outcomes. DRGS can also serve as a predictive indicator for various malignant biological processes. The independent prognostic significance for survival was confirmed using multivariate analysis. The group characterized by high expression levels of inverted formin 2 (INF2) demonstrated an attenuated response to palbociclib treatment and an immunosuppressive phenotype. In OV, INF2 was associated with poor clinical outcomes. Conclusion Our study demonstrated a prognostic DRGS, which holds great promise as a robust tool for uncovering clinical characteristics, predicting survival outcomes, and reflecting the response to targeted therapy across various cancer types.
Novel coronavirus is a serious disease-causing virus which spreads through the air, such a highly contagious virus will cause great harm to the body after disease. After the Novel coronavirus infects someone, viruses hidden in the body will spread rapidly and widely in the population as the carrier moves, that cause catastrophic consequences. Therefore, how to quickly detect the infection of novel coronary pneumonia has become an urgent issue. Analysing the lung image of Computed Tomography (CT) is an important method to accurately detect whether people is infected by novel coronavirus in medical practice. In this paper, firstly, we use the binarized features of the novel coronary pneumonia image, and then use the features of histogram and mask as additional features, finally we design an improved network based on Efficient-Net. Through comparative experiments with other mainstream Convolutional Neural Network(CNN) networks, it is found that the model proposed in this paper reduces the parameters of the model and improves the detection accuracy.
Background: Noninvasive stool-based DNA methylation testing emerges as a new approach for detecting colorectal cancer (CRC).However, its feasibility for early detection of CRC and precancerous lesions in the Chinese population remains inconclusive.Methods: In this study, we establish a possibilities screening method (sDNA-FOBT) for detecting CRC and precancerous lesions (hyperplastic polyps [HP] and adenomas [AD]) and evaluate its detection performance in the Chinese population.This method combined a molecular assay of DNA methylation markers (BMP3, NDRG4, and SDC2) with the human hemoglobin test (FOBT) in stool samples.Results: The sensitivity of sDNA-FOBT was 85.42% for CRC, 85.71% for AD, and 28.21% for HP, respectively, at the specificity of 92%.The diagnostic efficacy of sDNA-FOBT for detecting CRC and precancerous lesions was significantly higher than FOBT alone (sensitivity: 61.70% vs. 51.06%,P<0.01; AUC: 0.78 vs. 0.72, P<0.001), especially for CRC (AUC: 0.91 vs. 0.86, P<0.001) and AD (AUC: 0.91 vs. 0.75, P<0.05).No significant difference was observed between the detection sensitivity of sDNA-FOBT and the clinical variables.Notably, compared with FOBT, sDNA-FOBT was more effective in the detection of CRC and precancerous lesions in the patients aged >50 y (62.34% vs 54.55%, P<0.05). Conclusion:Our results demonstrate that sDNA-FOBT is a promising method for screening CRC and precancerous lesions in the Chinese population.Further studies are required to validate the results in a larger sample capacity.
Background Pittsburgh modified TNM criteria is one of the prognostic models of orthotopic liver transplantation (OLT) for hepatocellular carcinoma (HCC). In this study, we applied this prognostic system in a series of HCC patients receiving OLT to verify its reliability in the clinical prognostic prediction. Methods The clinical record and follow-up data of 102 patients with HCC underwent OLT was collected. The patients were classified by 3 staging systems: the Pittsburgh Modified TNM Criteria, International Union Against Cancer (UICC) pTNM Staging System, and Milan Criteria. Survival rates of the patients were analyzed using the Kaplan-Meier method and the Log-Rank test, and then the prognostic values of the 3 staging systems were compared. Results Among the 3 staging systems, the Pittsburgh Modified TNM Criteria showed the best stratification of patients with different prognosis. The overall survival rates of the patients at the Pittsburgh modified TNM stage I, II, III, and IV were 94.4%, 83.3%, 58.2%, and 36.8% at 1 year, and 79.4%, 62.5%, 26.2%, and 10.5% at 3 years, respectively. For those patients exceeding the Milan Criteria, the patients at Pittsburgh stages I and II had a significant higher survival rate than those at Pittsburgh stages III and IV (P<0.001). Conclusions The Pittsburgh Modified TNM Criteria is a more reliable postoperative staging system than the UICC pTNM staging system for HCC patients receiving OLT. As providing more accurate prognostic classification, it could be reasonable to combine the Milan Criteria for recipient selection.
This study aimed to develop a classifier of prognosis after resection or liver transplantation (LT) for HCC by directly analysing the ubiquitously available histological images using deep learning based neural networks. Nucleus map set was used to train U-net to capture the nuclear architectural information. Train set included the patients with HCC treated by resection and has a distinct outcome. LT set contained patients with HCC treated by LT. Train set and its nuclear architectural information extracted by U-net were used to train MobileNet V2 based classifier (MobileNetV2_HCC_Class), purpose-built for classifying supersized heterogeneous images. The MobileNetV2_HCC_Class maintained relative higher discriminatory power than the other factors after HCC resection or LT in the independent validation set. Pathological review showed that the tumoral areas most predictive of recurrence were characterized by presence of stroma, high degree of cytological atypia, nuclear hyperchomasia, and a lack of immune infiltration. A clinically useful prognostic classifier was developed using deep learning allied to histological slides. The classifier has been extensively evaluated in independent patient populations with different treatment, and gives consistent excellent results across the classical clinical, biological and pathological features. The classifier assists in refining the prognostic prediction of HCC patients and identifying patients who would benefit from more intensive management.
Background: Calreticulin (CALR) exon 9 frameshift mutations have recently been identified in 30–40% of patients with essential thrombocythemia (ET) and primary myelofibrosis (PMF) without JAK2 or MPL mutations. We aimed to develop a qPCR assay to screen type I and II mutations of CALR.Methods: Three different fluorescent-labeled hydrolysis probes and one pair of primers in a closed-tube system were developed to detect CALR type I and II mutations and distinguish them from wild-type. The sensitivity and specificity were validated using TA-cloning plasmids containing CALR wild-type and type I and II mutants, respectively. Fifty-nine ET and PMF specimens were screened by TaqMan qPCR and sequenced by Sanger sequencing. For intra-assay validation, 20 replicates of the assay were performed with each sample. For inter-assay validation, four replications of each sample were carried out and repeated continuously for 5 days.Results: We found that triplex probe-based TaqMan qPCR was reliable in detecting CALR type I and II mutants within DNA that was diluted to 1% of total DNA with the wild-type DNA as background. In 59 patient specimens, six of the observed mutations of CALR were type I and five were type II. Genotyping results obtained from TaqMan qPCR were 100% concordant with Sanger sequencing. The intra- and inter-assay CVs of TaqMan qPCR were less than 3%, respectively.Conclusions: Triplex probe-based TaqMan qPCR is an accurate and sensitive method for screening ET or PMF patients with type I and II mutations in CALR.
Abstract Background Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Method In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan–Meier analysis, receiver operating characteristic curve, and univariate/multivariate Cox analysis were used to evaluate the prognostic model. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model. Results A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model’s predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst. Conclusion The proposed 11-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.
'/%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)
