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Camp Borg posted an update 6 months ago
Endothelin-converting enzyme-1 (ECE1) activates the endothelin-1 peptide, which upregulates pathways that are related to diverse hallmarks of cancer. ECE1 is expressed as four isoforms differing in their N-terminal domains. Protein kinase CK2 phosphorylates the N-terminus of isoform ECE1c, enhancing its stability and promoting invasiveness of colorectal cancer cells. However, the specific residues in ECE1c that are phosphorylated by CK2 and how this phosphorylation promotes invasiveness was unknown. see more Here we demonstrate that Ser-18 and Ser-20 are the bona fide residues phosphorylated by CK2 in ECE1c. Thus, biphospho-mimetic ECE1cDD and biphospho-resistant ECE1cAA mutants were constructed and stably expressed in different colorectal cancer cells through lentiviral transduction. Biphospho-mimetic ECE1cDD displayed the highest stability in cells, even in the presence of the specific CK2 inhibitor silmitasertib. Concordantly, ECE1cDD-expressing cells showed enhanced hallmarks of cancer, such as proliferation, migration, invasiveness, and self-renewal capacities. Conversely, cells expressing the less-stable biphospho-resistant ECE1cAA showed a reduction in these features, but also displayed an important sensitization to 5-fluorouracil, an antineoplastic agent traditionally used as therapy in colorectal cancer patients. Altogether, these findings suggest that phosphorylation of ECE1c at Ser-18 and Ser-20 by CK2 promotes aggressiveness in colorectal cancer cells. Therefore, phospho-ECE1c may constitute a novel biomarker of poor prognosis and CK2 inhibition may be envisioned as a potential therapy for colorectal cancer patients.Objective The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters. Methods In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0-3) and intermediate- to high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furtherion-making for patients with ccRCC.Background N6-methyladenosine (m6A) RNA methylation, associated with cancer initiation and progression, is dynamically regulated by the m6A RNA regulators. However, its role in liver carcinogenesis is poorly understood. Methods Three hundred seventy-one hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas database with sequencing and copy number variations/mutations data were included. Survival analysis was performed using Cox regression model. We performed gene set enrichment analysis to explore the functions associated with different HCC groups. Finally, we used a machine-learning model on selected regulators for developing a risk signature (m6Ascore) The prognostic value of m6Ascore was finally validated in another two GEO datasets. Results We demonstrated that 11 m6A RNA regulators are significantly differentially expressed among 371 HCC patients stratified by clinicopathological features (P less then 0.001). We then identified two distinct HCC clusters by applying consensus clustering to stratification in HCC.The low-density lipoprotein receptor (LDLR) family comprises 14 single-transmembrane receptors sharing structural homology and common repeats. These receptors specifically recognize and internalize various extracellular ligands either alone or complexed with membrane-spanning co-receptors that are then sorted for lysosomal degradation or cell-surface recovery. As multifunctional endocytic receptors, some LDLR members from the core family were first considered as potential tumor suppressors due to their clearance activity against extracellular matrix-degrading enzymes. LDLRs are also involved in pleiotropic functions including growth factor signaling, matricellular proteins, and cell matrix adhesion turnover and chemoattraction, thereby affecting both tumor cells and their surrounding microenvironment. Therefore, their roles could appear controversial and dependent on the malignancy state. In this review, recent advances highlighting the contribution of LDLR members to breast cancer progression are discussed with focus on (1) specific expression patterns of these receptors in primary cancers or distant metastasis and (2) emerging mechanisms and signaling pathways. In addition, potential diagnosis and therapeutic options are proposed.Objectives To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 41 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined modal, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model’s performance.
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