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Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer: A bioinformatic analysis.

ABSTRACT:

Abstract

Colon cancer patients suffer from high incidence and mortality rates worldwide. More novel molecular biomarkers should be used for the diagnosis and treatment of colon cancer. Long noncoding RNAs (lncRNAs) are found to be involved in colon cancer tumorigenesis and metastasis. This study aimed to identify novel lncRNAs in colon cancer.Two independent datasets (GSE70880 and GSE110715) were downloaded from the Gene Expression Omnibus database and merged with the sva package. R software was used to distinguish differentially expressed lncRNAs and mRNAs in the merged dataset. The competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs and mRNAs with Cytoscape. Differentially expressed RNAs in the ceRNA network were further verified using the Cancer Genome Atlas database. Gene oncology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and survival analysis were also performed to identify hub genes.A total of 99 differentially expressed lncRNAs and 95 differentially expressed mRNAs were identified in the merged database. Ten lncRNAs, 8 miRNAs, and 6 mRNAs were involved in the ceRNA network, in which LINC00114 and UCA1 were highly expressed in colon cancer. They were both associated with early tumor stages and might be used for the early diagnosis of colon cancer. High expression of LINC00114 can lead to poor overall survival of colon cancer patients. Furthermore, new pathways such as LINC00114/miR-107/PCKS5, UCA1/miR-107/PCKS5, and UCA1/miR-129-5p/SEMA6A were identified.Two novel lncRNAs (LINC00114 and UCA1) in colon cancer were identified by bioinformatics analysis. They might contribute to the occurrence and development of colon cancer. In addition, LINC00114 may be involved in the overall survival of colon cancer patients.

SUBMITTER: Xi G

PROVIDER: S-EPMC8036122 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Malignant melanoma (MM) is a malignant tumor originating from melanocytes, with high aggressiveness, high metastasis and extremely poor prognosis. MM accounts for 4% of skin cancers and 80% of mortality, and the median survival of patients with metastatic melanoma is only about 6 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 serious diseases that endanger human health. Competitive endogenous RNA (ceRNA) is the main model of 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), thus interfering with miRNA function, and affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and progression. Bioinformatics analysis can identify potentially prognostic and therapeutically relevant differentially expressed genes in MM, finding lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network, providing further insight into gene regulation and 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. A total of 17 lncRNAs, six miRNAs, and 11 mRNAs were used to construct a ceRNA interaction network that plays a regulatory role in metastatic melanoma patients. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were finally obtained, which miR-3662 might has potential implication for the treatment of metaststic melanoma patients, and futher experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate metastatic process, predict metastatic potential and indicates that the miR-3662 can be used in the treatment of melanoma.

Dataset Information

Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer: A bioinformatic analysis.

Abstract

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