RESUMEN
Although the molecular studies of single gastrointestinal tumors have been widely reported by media, it is not clear about the function of small nucleolar RNA (snoRNA) in the progression, development and prognostic significance in colon adenocarcinoma, and its certain molecular mechanisms and functions remain to be studied. This study aims to dig out the gene expression data profile of colon adenocarcinoma and construct the prognostic molecular pathology prediction-evaluation, ultimately revealing the clinical prognostic value of snoRNA in colon adenocarcinoma. 932 differentially expressed snoRNAs of the colon adenocarcinoma were obtained by edgeR R package. Only 4 prognostically-significant snoRNAs (SNORD14E, SNORD67, SNORD12C, and SNORD17) (P < 0.05) were discovered after univariate COX regression mode analysis. Moreover, through multivariate COX regression mode analysis, 2 prognostically-significant snoRNAs (SNORD14E and SNORD67) (P < 0.05) were obtained. Using the above 473 COAD samples, a prognostic model of risk score was constructed. The inflection point of the prognostic risk score acted as a boundary to divide the patients into high-risk and low-risk groups. The K-M survival curve of the prognostic model of risk score revealed that high risk group has a lower survival rate (P < 0.05). The research has successfully provided valuable prognostic factors and prognostic models for patients with malignant colon tumor.
Asunto(s)
Adenocarcinoma/genética , Biomarcadores de Tumor/genética , Neoplasias del Colon/genética , ARN Nucleolar Pequeño/genética , Adenocarcinoma/patología , Neoplasias del Colon/patología , Humanos , Pronóstico , Análisis de Secuencia de ARNRESUMEN
OBJECTIVE: miRNA has gained attention as a therapeutic target in various malignancies. The proposal of this study was to investigate the biological functions of key miRNAs and target genes in cancers of the digestive tract which include esophageal carcinoma (ESCA), gastric adenocarcinoma (GAC), colon adenocarcinoma (COAD), and rectal adenocarcinoma (READ). MATERIALS AND METHODS: After screening differentially expressed miRNAs (DEMIs) and differentially expressed mRNAs (DEMs) in four digestive cancers from The Cancer Genome Atlas (TCGA) database, the diagnostic value of above DEMIs was evaluated by receiver-operating characteristic (ROC) curve analysis. Then, corresponding DEMIs' target genes were predicted by miRWalk 2.0. Intersection of predicted target genes and DEMs was taken as the target genes of DEMIs, and miRNA-mRNA regulatory networks between DEMIs and target genes were constructed. Meanwhile, the univariate Cox risk regression model was used to screen miRNAs with distinct prognostic value, and Kaplan-Meier analysis was used to determine their significance of prognosis. Furthermore, we performed bioinformatics methods including protein-protein interaction (PPI) networks, gene ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene group RIDA analysis by Gene-Cloud of Biotechnology Information (GCBI) to explore the function and molecular mechanisms of DEMIs and predicted target genes in tumor development. RESULTS: Eventually, 3 DEMIs (miR-7-3, miR-328, and miR-323a) with significant prognostic value were obtained. In addition, 3 DEMIs (miR-490-3p, miR-133a-3p, and miR-552-3p) and 281 target genes were identified, and the 3 DEMIs showed high diagnostic value in READ and moderate diagnostic value in ESCA, GAC, and COAD. Also, the miRNA-mRNA regulatory network with 3 DEMIs and 281 overlapping genes was successfully established. Functional enrichment analysis showed that 281 overlapping genes were mainly related to regulation of cell proliferation, cell migration, and PI3K-Akt signaling pathway. CONCLUSION: The diagnostic value and prognostic value of significant DEMIs in cancers of the digestive tract were identified, which may provide a novel direction for treatment and prognosis improvement of cancers of the digestive tract.
Asunto(s)
Tracto Gastrointestinal/metabolismo , MicroARNs/genética , Neoplasias/genética , Pronóstico , Biología Computacional , Tracto Gastrointestinal/patología , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica , Redes Reguladoras de Genes/genética , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Estimación de Kaplan-Meier , MicroARNs/clasificación , Anotación de Secuencia Molecular , Neoplasias/diagnóstico , Neoplasias/patología , ARN Mensajero/genética , Transducción de Señal/genéticaRESUMEN
BACKGROUND: The scientific understanding of long non-coding RNAs (lncRNAs) has improved in recent decades. Nevertheless, there has been little research into the role that lncRNAs play in clear cell renal cell carcinoma (ccRCC). More lncRNAs are assumed to influence the progression of ccRCC via their own molecular mechanisms. METHODS: This study investigated the prognostic significance of differentially expressed lncRNAs by mining high-throughput lncRNA-sequencing data from The Cancer Genome Atlas (TCGA) containing 13,198 lncRNAs from 539 patients. Differentially expressed lncRNAs were assessed using the R packages edgeR and DESeq. The prognostic significance of lncRNAs was measured using univariate Cox proportional hazards regression. ccRCC patients were then categorized into high- and low-score cohorts based on the cumulative distribution curve inflection point the of risk score, which was generated by the multivariate Cox regression model. Samples from the TCGA dataset were divided into training and validation subsets to verify the prognostic risk model. Bioinformatics methods, gene set enrichment analysis, and protein-protein interaction networks, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were subsequently used. RESULTS: It was found that the risk score based on 6 novel lncRNAs (CTA-384D8.35, CTD-2263F21.1, LINC01510, RP11-352G9.1, RP11-395B7.2, RP11-426C22.4) exhibited superior prognostic value for ccRCC. Moreover, we categorized the cases into two groups (high-risk and low-risk), and also examined related pathways and genetic differences between them. Kaplan-Meier curves indicated that the median survival time of patients in the high-risk group was 73.5 months, much shorter than that of the low-risk group (112.6 months; P < 0.05). Furthermore, the risk score predicted the 5-year survival of all 539 ccRCC patients (AUC at 5 years, 0.683; concordance index [C-index], 0.853; 95% CI 0.817-0.889). The training set and validation set also showed similar performance (AUC at 5 years, 0.649 and 0.681, respectively; C-index, 0.822 and 0.891; 95% CI 0.774-0.870 and 0.844-0.938). CONCLUSIONS: The results of this study can be applied to analyzing various prognostic factors, leading to new possibilities for clinical diagnosis and prognosis of ccRCC.
Asunto(s)
Carcinoma de Células Renales/genética , Neoplasias Renales/genética , ARN Largo no Codificante/genética , Análisis de Secuencia de ARN , Carcinoma de Células Renales/patología , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Humanos , Estimación de Kaplan-Meier , Neoplasias Renales/patología , Análisis Multivariante , Pronóstico , Modelos de Riesgos Proporcionales , ARN Largo no Codificante/metabolismo , Curva ROC , Reproducibilidad de los Resultados , Factores de RiesgoRESUMEN
BACKGROUND: MicroRNA is endogenous non-coding small RNA that negative regulate and control gene expression, and increasing evidence links microRNA to oncogenesis and the pathogenesis of cancer. The goal of this study was to explore the potential molecular mechanism of miR-375 in various cancers. METHODS: MiR-375 overexpression in different tumor cell lines was probed with microarray data from Gene Expression Omnibus (GEO). The common target genes of miR-375 were obtained by Robust Rank Aggregation (RRA), and identified by miRWalk2.0 software for target gene prediction. Additionally, we directed in silico analysis including Protein-Protein Interactions (PPI) analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotations to provide a summary of the function of miR-375 in various carcinomas. Eventually, data was obtained from The Cancer Genome Atlas (TCGA) were utilized for a validation in 7 cancers. RESULTS: The nine miR-375 related chips were acquired by the GEO data. The 5 down regulated genes came from 9 available microarray datasets, which overlapped with the potential target genes predicted by miRWalk2.0 software. The target genes were intensely enriched in amino acid biosynthetic and metabolic process from biological process (GO) and Cysteine and methionine metabolism (KEGG analysis). In view of these approaches, VASN, MAT2B, HERPUD1, TPAPPC6B and TAT are probably the most important miR-375 targets. In addition, miR-375 was negatively correlated with MAT2B, which was verified in 5 tumors of TCGA. CONCLUSION: In summary, this study based on common target genes provides an innovative perspective for exploring the molecular mechanism of miR-375 in human tumors.