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Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma

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AbstractMalignant 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.
Title: Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma
Description:
AbstractMalignant 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.

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