Melittin intervention induces lncRNA response, mitochondrial dysfunction, and cell proliferation in murine cervical cancer cells
Zhang, R.; Zhuo, H.; Yang, Y.; Zhang, K.; Wang, M.; Jiang, J.; Li, Y.; Qiu, J.; Chen, D.; Yan, T.; Guo, R.
Show abstract
Melittin exhibits antitumor activity in cervical cancer models, yet the long non-coding RNA (lncRNA) response and associated regulatory networks remain poorly understood. Here, strand-specific RNA-seq data from melittin-treated and untreated U14 murine cervical cancer cells were analyzed to characterize melittin-responsive lncRNAs and explore their potential functional associations. A total of 28,162 lncRNAs were identified, including 27,307 known and 855 novel transcripts. Differential expression analysis revealed 404 differentially expressed lncRNAs (DElncRNAs), comprising 191 upregulated and 213 downregulated lncRNAs, w most of which were predicted to localize to the cytoplasm or nucleus. Cis-target analysis identified 52 neighboring mRNAs as putative targets of 46 DElncRNAs. Functional enrichment highlighted mitochondrial electron transfer and redox-related processes, including the mitochondrial electron transfer flavoprotein complex, electron-transferring-flavoprotein dehydrogenase activity, ubiquinone binding, and quinone binding. In parallel, melittin induced mitochondrial membrane depolarization and increased intracellular reactive oxygen species accumulation in U14 cells. Co-expression analysis further identified 138 lncRNAs co-expressed with 161 mRNAs, which were enriched in chromatin remodeling, DNA replication, and DNA repair. EdU incorporation decreased with increasing melittin concentrations, indicating suppression of DNA synthesis and proliferative activity. RT-qPCR analysis confirmed the expression trends of selected DElncRNAs. Collectively, these findings demonstrate extensive remodeling of the lncRNA landscape in melittin-treated U14 cells and suggest that melittin-responsive lncRNA-mRNA networks are associated with mitochondrial redox disruption and impaired DNA synthesis. This study provides a transcriptomic framework for identifying candidate lncRNA-mRNA regulatory axes underlying the antitumor response to melittin.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Discovery of decreased ferroptosis in male colorectal cancer patients with KRAS mutations 92%
- Nrf1 is an indispensable redox-determining factor for mitochondrial homeostasis by integrating multi-hierarchical regulatory networks 91%
- Low level of antioxidant capacity biomarkers but not target overexpression predicts vulnerability to ROS-inducing drugs 91%
Similar papers in this journal
Similar papers in this journal
- The Role of c-Jun Signaling in Cytidine Analog-Induced Cell Death in Melanoma 93%
- Transcriptomic Signature and PROTAC Strategy Revealed Histone Lysine Demethylase as a Target of Anticancer Activity of Deferiprone. 91%
- COVID-19 ORF3a Viroporin Influenced Common and Unique Cellular Signalling Cascades in Lung, Heart and Brain Choroid Plexus Organoids with Additional Enriched MicroRNA Network Analyses for Lung and Brain Tissues 91%
Similar papers in this journal
- Arsenic hexoxide has differential effects on cell proliferation and genome-wide gene expression in human primary mammary epithelial and MCF7 cells 93%
- Genomic alterations and abnormal expression of APE2 in multiple cancers 93%
- Selective Impact of ALK and MELK Inhibition on ERα Stability and Cell Proliferation in Cell Lines Representing Distinct Molecular Phenotypes of Breast Cancer 91%
Similar papers in this journal
- HOOK2 downregulation compromises the tumorigenic and stemness properties of ovarian cancer cells by increasing endoplasmic reticulum stress 91%
- Endometrial cancer progression driven by PTEN-deficiency requires miR-424(322)~503. 91%
- Targeting replication stress in neuroblastoma by exploiting the synergistic potential of second generation RRM2 and CHK1 inhibitors 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.