bioRxivCancer Biology
Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma
Abstract
Malignant melanoma (MM) is a highly aggressive, metastatic cancer originating from melanocytes. These tumors have an extremely poor prognosis. MM accounts for 4% of skin cancers with an 80% mortality rate. The median survival of patients with metastatic melanoma is approximately six months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the deadliest cancers. Competitive endogenous RNA (ceRNA) models the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a sponge, competitively attracting small RNAs (micoRNAs; miRNAs) and interfering with miRNA function. This can affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and tumor progression. Bioinformatics analysis may identify potentially prognostic and therapeutically-relevant differentially expressed genes in MM, as well as lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network. This may provide further insight into gene regulation and the prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. We used 17 lncRNAs, six miRNAs, and 11 mRNAs to construct a ceRNA interaction network with a regulatory role in patients with metastatic melanoma. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were obtained, and miR-3662 may be used in the treatment of metastatic melanoma patients. Experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate the metastatic process, predict metastatic potential and determine that miR-3662 may be used in the treatment of melanoma.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: biophysical modeling
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Machine Learning Gene Signature to Metastatic ccRCC based on ceRNA Network
Epitácio Farias, Patrick Terrematte, Beatriz Stransky·International Journal of Molecular SciencesPeer-reviewed·Apr 11, 2024
- Meta-analysis of the melanoma miRNome identifies consensus signatures of progression and prognosis regulating metabolic plasticity and stress resistance
B. Gomez-Cabanes, H. Gomez-Martinez, … F. Garcia-Garcia·bioRxiv·Oct 20, 2023
- Pan-cancer systematic identification of lncRNAs associated with cancer prognosis
M. Ung, E. Schaafsma, … C. Cheng·bioRxiv·Jun 21, 2018
- CRISPR-inhibition screen for lncRNAs linked to melanoma growth and metastasis
Stavroula Petroulia, Kathryn Hockemeyer, … Jochen Imig·Cancer Research CommunicationsPeer-reviewed·Jun 24, 2025
- A microRNA Signature of Metastatic Colorectal Cancer
E. Hoye, B. Fromm, … K. Flatmark·bioRxiv·Jun 1, 2020
- Identification of SYT10 as a potential prognostic biomarker in esophageal cancer by comprehensive analysis of a mRNA-pseudogene/lncRNA-miRNA ceRNA network
Milad Daneshmand-Parsa, Sharareh Mahmoudian-Hamedani, Parvaneh Nikpour·Human GenePeer-reviewed·Oct 2, 2023