Dataset Information

A novel three-microRNA signature for predicting survival in patients with nasopharyngeal carcinoma.

ABSTRACT:

Background/purpose

Nasopharyngeal carcinoma (NPC) is a malignant neoplasm of the head and neck. This study aims to use integrated bioinformatics technologies to develop a predictive miRNA-signature correlated with the prognosis of NPC.

Materials and methods

Initially, the differentially expressed miRNAs (DEMs) in NPC were identified, and then DEMs related to the prognosis of NPC were further screened. Subsequently, the relatively important DEMs identified by random forest algorithm were used to construct a predictive signature by multivariate COX regression analysis. Moreover, PCA, Kaplan-Meier analysis, time-dependent ROC analysis, and univariate and multivariate COX regression analysis were performed to evaluate the ability of the signature in risk identification and prognosis prediction in NPC.

Results

Hsa-miR-29c, hsa-miR-30e and hsa-miR-93 were selected from DEMs to construct a signature, and their abnormal expression was significantly associated with poor prognosis of NPC. The average AUC values of 1- to 5-year OS, DFS and DMFS predicted by the signature were all above 0.7, and showed better clinical independence than other indexes. In addition, 295 differentially expressed mRNAs could be used as potential target genes of the 3 DEMs. Among them, 56 differentially expressed mRNAs were related to PFS. GO and KEGG enrichment analysis indicated that the poor prognosis of NPC was related to the abnormality of chromosomes, cytokines, and chemokines.

Conclusion

We constructed a three-miRNA signature with good independent performance in predicting the prognosis for NPC. This study may lay the foundation for exploring new therapeutic targets and improving survival outcomes in NPC patients.

SUBMITTER: Zhang SQ

PROVIDER: S-EPMC8740092 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of the present study was to identify microRNAs (miRNAs) that predict the prognosis of patients with nasopharyngeal carcinoma by integrated bioinformatics analysis. First, the original microarray dataset GSE32960, including 312 nasopharyngeal carcinomas and 18 normal samples, was downloaded from the Gene Expression Omnibus database. In addition, 46 differentially expressed miRNAs (DEMs) were screened. Then, four miRNAs, including hsa‑miR‑142‑3p, hsa‑miR‑150, hsa‑miR‑29b, and hsa‑miR‑29c, were obtained as prognostic markers by combining univariate Cox regression analysis with weighted gene coexpression network analysis (WGCNA). Subsequently, the risk score of 312 NPC patients from the signature of miRNAs was calculated, and patients were divided into high‑risk or low‑risk groups. Notably, compared with patients with low‑risk scores, high‑risk groups had shorter disease‑free survival (DFS), overall survival (OS), and distant metastasis‑free survival (DMFS). Receiver operating characteristic curve (ROC) analysis indicated that the risk score was a very effective prognostic factor. Moreover, the Search Tool for the Database for Annotation, Visualization, and Integrated Discovery (DAVID), Cytoscape, starBase, and Retrieval of Interacting Genes database (STRING) were used to establish the miRNA‑mRNA correlation network and the protein‑protein interaction (PPI) network. In addition, the shared genes superimposing 888 protein‑coding genes targeted by four hub miRNAs and 1,601 upregulated differentially expressed mRNAs accounted for 127 and were used for subsequent gene functional enrichment analysis. In particular, biological pathway analysis indicated that these genes mainly participate in some vital pathways related to cancer pathogenesis, such as the focal adhesion, PI3K/Akt, p53, and mTOR signalling pathways. In summary, the identification of NPC patients with a four‑miRNA signature may increase the prognostic value and provide reference information for precision medicine.

Project description:Objective:We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING). Results:With cut-off criteria of P < 0.05 and |log2FC|?>?1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan-Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes. Conclusions:Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.