AI Article Synopsis
- SIK2 is overexpressed in various cancers and has been linked to cancer progression, specifically in ovarian cancer.
- SIK2 enhances ovarian cancer cell motility, migration, and metastasis by phosphorylating myosin light chain kinase (MYLK), which then activates myosin light chain 2 (MYL2).
- The presence of SIK2 and phosphorylated MYLK in ovarian cancer tissues correlates with poorer survival rates, highlighting SIK2's potential as a target for treatment.
Article Abstract
Salt-inducible kinase 2 (SIK2; also known as serine/threonine-protein kinase SIK2) is overexpressed in several cancers and has been implicated in cancer progression. However, the mechanisms by which SIK2 regulates cancer cell motility, migration and metastasis in ovarian cancer have not been fully discovered. Here, we identify that SIK2 promotes ovarian cancer cell motility, migration and metastasis in vitro and in vivo. Mechanistically, SIK2 regulated cancer cell motility and migration by myosin light chain kinase, smooth muscle (MYLK)-meditated phosphorylation of myosin light chain 2 (MYL2). SIK2 directly phosphorylated MYLK at Ser343 and activated its downstream effector MYL2, promoting ovarian cancer cell motility and metastasis. In addition, we found that adipocytes induced SIK2 phosphorylation at Ser358 and MYLK phosphorylation at Ser343, enhancing ovarian cancer cell motility. Moreover, SIK2 protein expression was positively correlated with the expression of MYLK-pS343 in ovarian cancer cell lines and tissues. The co-expression of SIK2 and MYLK-pS343 was associated with reduced median overall survival in human ovarian cancer samples. Taken together, SIK2 positively regulates ovarian cancer motility, migration and metastasis, suggesting that SIK2 is a potential candidate for ovarian cancer treatment.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251837 | PMC |
| http://dx.doi.org/10.1002/1878-0261.13208 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks.
Biometrics
January 2025
Department of Biostatistics, University of Michigan at Ann Arbor, Ann Arbor, MI 48109, United States.
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.
View Article and Find Full Text PDFImpact of comorbidities on the mortality benefits of lung cancer screening: a post-hoc analysis of PLCO and NLST trials.
J Thorac Oncol
January 2025
Department of General Internal Medicine and Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).
Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).
Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.
Med Image Anal
January 2025
Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
View Article and Find Full Text PDFMelittin suppresses ovarian cancer growth by regulating SREBP1-mediated lipid metabolism.
Phytomedicine
January 2025
School of Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. Electronic address:
Background: Melittin, a major peptide component of bee venom, has demonstrated promising anti-cancer activity across various preclinical cell models, making it a potential candidate for cancer therapy. However, its molecular mechanisms, particularly in ovarian cancer, remain largely unexplored. Ovarian cancer is a life-threatening gynecological malignancy with poor clinical outcomes and limited treatment options.
View Article and Find Full Text PDFOvarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group.
Eur Radiol
January 2025
Department of Radiology, Montpellier Research Center Institute, PINKCC Laboratory, Montpellier, France.
Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).
Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!