As the most common gynecologic malignancy worldwide, cervical cancer (CC) is a serious hazard to health. Therefore, the present study aimed to identify the key genes in CC progression using integrated bioinformatics analysis and experimental validation. The mRNA microarray GSE63514 and microRNA (miRNA) microarray GSE86100 were obtained from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in the progression of CC were identified. Thereafter, GO and KEGG functional enrichment analysis, protein‑protein interaction (PPI) network and significant subnetworks construction, and miRNA‑target regulatory network construction were performed. Based on the results of integrated bioinformatics analysis, the DEGs structural maintenance of chromosomes 4 (SMC4), ATPase family, AAA domain‑containing 2 (ATAD2) and DNA polymerase θ (POLQ) were identified as hub genes in the PPI network and were involved in the first significant subnetwork. In addition, these DEGs were predicted to be regulated by miR‑106B, miR‑17‑5P, miR‑20A and miR‑20B, which were identified as DEMs. Of note, SMC4 and ATAD2 are tumor‑promotors in CC. In the present study, small interfering (si)RNAs were used to knock down POLQ expression. Cell Counting Kit‑8, Transwell, cell cycle and apoptosis analyses revealed that the downregulation of POLQ restrained cell proliferation, migration and invasion, and promoted apoptosis and the arrest of the cell cycle in the G phase. In conclusion, POLQ, which may have a close interaction with SMC4 and ATAD2, may serve a vital role in the progression of CC.
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http://dx.doi.org/10.3892/mmr.2023.13002 | DOI Listing |
PLoS Comput Biol
January 2025
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States.
Native Mass Spectrometry (nMS) is a versatile technique for elucidating protein structure. Surface-Induced Dissociation (SID) is an activation method in tandem MS predominantly employed for determining protein complex stoichiometry alongside information about interface strengths. SID-nMS data can be collected over a range of acceleration energies, yielding Energy Resolved Mass Spectrometry (ERMS) data.
View Article and Find Full Text PDFSTAR Protoc
January 2025
Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Trento, TN, Italy; Department of Medicine, NYU Langone Medical Center, New York, NY 10016, USA. Electronic address:
Reduced expression of nucleolar genes induces stress and DNA damage. Here, we present a protocol to analyze DNA fragmentation at the single-cell level in Drosophila imaginal discs using an optimized alkaline comet assay. We describe steps for larvae development, tissue disaggregation, and single-cell dissociation.
View Article and Find Full Text PDFCancer Rep (Hoboken)
January 2025
Department of Biology, College of Sciences, Shiraz University, Shiraz, Iran.
Background: The breakthrough discovery of novel biomarkers with prognostic and diagnostic value enables timely medical intervention for the survival of patients diagnosed with gastric cancer (GC). Typically, in studies focused on biomarker analysis, highly connected nodes (hubs) within the protein-protein interaction network (PPIN) are proposed as potential biomarkers. However, this study revealed an unexpected finding following the clustering of network nodes.
View Article and Find Full Text PDFAnn Med
December 2025
Department of Rheumatology and Immunology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong Province, People's Republic of China.
Background: Ankylosing spondylitis (AS) is a chronic autoimmune disease that primarily affects the axial joints. Immune cells play a key role in the pathogenesis of AS. This study integrated bioinformatics methods with experimental validation to explore the role of natural killer (NK) cells in AS.
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