Comprehensive analysis of competitive endogenous RNAs network: Identification and validation of prediction model composed of mRNA signature and miRNA signature in gastric cancer

Abstract

Gastric cancer (GC), one of the most lethal malignant tumors, is highly aggressive with a poor prognosis, while the molecular mechanisms underlying it remain largely unknown. Although advanced imaging techniques and comprehensive treatment facilitate the diagnosis and survival of some GC patients, the precise diagnosis and prognosis are still a challenge. The present study used publicly available gene expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus datasets including mRNA, micro (mi)RNA and circular (circ)RNA of GC to establish a competing endogenous RNA network (ceRNA). Further, the present study performed least absolute shrinkage and selector operator regression analysis on the hub RNAs to establish a prediction model with mRNA and miRNA. The ceRNA network contained 109 edges and 56 nodes and the visible network contains 13 miRNAs, 9 circRNAs and 34 mRNAs. The five mRNA-based signature were CTF1, FKBP5, RNF128, GSTM2 and ADAMTS1. The area under curve (AUC) value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >4.664) was worse compared with that of the low-risk group (RiskScore ≤4.664; P<0.05) in the training cohort. The five miRNA-based signature were miR-145-5p, miR-615-3p, miR-6507-5p, miR-937-3p and miR-99a-3p. The AUC value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >1.621) was worse compared with that of the low-risk group (RiskScore ≤1.621; P<0.05) in the training cohort. The validation cohorts indicated that both five mRNA and five miRNA-based signatures had strong predictive power in diagnosis and prognosis for GC. In conclusion, a ceRNA network was established for GC and a five mRNA-based signature and a five miRNA-based signature was identified that enabled diagnosis and prognosis of GC by assigning patient to a high-risk group or low-risk group.

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

“…Li et.al showed that miR-6512 downregulated in gastric cancer patients and seems to have a correlation with skin manifestations and fibrosis 46 . Moreover, a study by Ding demonstrated that miR-6507 was among top five down expressed miRNAs in GC and may serve as a good predictive biomarker 47 . Another study introduced miR-6510 as a potential prognostic biomarker in GC patients 48 .…”

Construction and validation of a prognostic model for tongue cancer based on three genes signature

“…Recently, an increasing number of molecular biomarkers have been integrated into models for the prognostic assessment of cancer patients. [4–8] However, the prognostic features and nomographs of multiple mRNA in TSCC patients have not been sufficiently explored.…”

“…Recently, an increasing number of molecular biomarkers have been integrated into models for the prognostic assessment of cancer patients. [4][5][6][7][8] However, the prognostic features and nomographs With the development of second-generation sequencing technology, attention has gradually turned to the analysis and alignment of high-throughput sequencing data, leading to the establishment of The Cancer Genome Atlas (TCGA) database, which provides an avenue for tumor-related research through the mining and analysis of genomic data on cancer genes. Initiated and completed by the National Cancer Institute and the National Human Genome Research Institute in the United States, TCGA is a genomic variation map of human tumors obtained through large-scale sequencing of the human tumor genome, containing information on genomics, transcriptomics, epigenetics, proteomics, as well as clinical information.…”