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Identification and functional annotation of metabolism-associated lncRNAs and their related protein-coding genes in gastric cancer.

ABSTRACT: BACKGROUND:Long noncoding RNAs (lncRNAs) play important roles in carcinogenesis. However, the roles of metabolism-associated lncRNAs in cancers are still unclear. METHODS:A microarray of metabolism-associated lncRNAs was used to detect their expression patterns between gastric cancer and paired nontumorous tissues. Its results and gastric cancer differential gene expression data from public databases were used to screen the metabolic pathway-associated lncRNAs. A metabolic network with microRNAs (miRNAs), lncRNAs, and protein-coding genes was further constructed. Finally, the expression of TOPORS antisense RNA 1 (TOPORS-AS1), a screened highly expressed lncRNA and its associated protein-coding gene, NADH: ubiquinone oxidoreductase subunit B6 (NDUFB6), were verified by reverse transcription polymerase chain reaction. RESULTS:A total of eight upregulated and one downregulated lncRNAs and 25 upregulated and 20 downregulated protein-coding genes were found to be involved in metabolism in gastric cancer. Within the lncRNAs-miRNAs-mRNAs metabolic network, 78 miRNA-target links, 546 positive coexpression relationships, and 191 protein-protein interactions were found. The expression of TOPORS-AS1 and its associated gene, NDUFB6 in gastric cancer tissues was significantly lower than that in adjacent nontumor tissues. Moreover, NDUFB6 expression was associated with the invasion and distal metastasis of gastric cancer. CONCLUSIONS:The metabolism-associated lncRNAs play important roles in the occurrence of gastric cancer.

SUBMITTER: Mo X

PROVIDER: S-EPMC6160698 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Identification and functional annotation of metabolism-associated lncRNAs and their related protein-coding genes in gastric cancer.

Mo Xiaoyan X Li Tianwen T Xie Yi Y Zhu Linwen L Xiao Bingxiu B Liao Qi Q Guo Junming J

Molecular genetics & genomic medicine 20180710 5

<h4>Background</h4>Long noncoding RNAs (lncRNAs) play important roles in carcinogenesis. However, the roles of metabolism-associated lncRNAs in cancers are still unclear.<h4>Methods</h4>A microarray of metabolism-associated lncRNAs was used to detect their expression patterns between gastric cancer and paired nontumorous tissues. Its results and gastric cancer differential gene expression data from public databases were used to screen the metabolic pathway-associated lncRNAs. A metabolic network ...[more]

PMID: 29992774

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Project description:Gastric cancer is a common malignant tumor with high occurrence and recurrence and is the leading cause of death worldwide. However, the prognostic value of protein-coding and non-coding RNAs in stage III gastric cancer has not been systematically analyzed. In this study, using TCGA data, we identified 585 long noncoding RNAs (lncRNAs) and 927 protein-coding genes (PCGs) correlated with the overall survival rate of gastric cancer. Functional enrichment analysis revealed that the prognostic genes positively correlated with death rates were enriched in pathways, including gap junction, focal adhesion, cell adhesion molecules (CAMs), and neuroactive ligand-receptor interaction, that are involved in the tumor microenvironment and cell-cell communications, suggesting that their dysregulation may promote the tumor progression. To evaluate the performance of the prognostic genes in risk prediction, we built three multivariable Cox models based on prognostic genes selected from the prognostic PCGs and lncRNAs. The performance of the three models based on features from only PCGs or lncRNAs or from all prognostic genes were systematically compared, which revealed that the features selected from all the prognostic genes showed higher performance than the features selected only from lncRNAs or PCGs. Furthermore, the multivariable Cox regression analysis revealed that the stratification with the highest performance was an independent prognostic factor in stage III gastric cancer. In addition, we explored the underlying mechanism of the prognostic lncRNAs in the Cox model by predicting the lncRNA and protein interaction. Specifically, CTD-2218G20.2 was predicted to interact with PSG4, PSG5, and PSG7, which could also interact with cancer-related proteins, including KISS1, TIMP2, MMP11, IGFBP1, EGFR, and CDKN1C, suggesting that CTD-2218G20.2 might participate in the cancer progression via these cancer-related proteins. In summary, the systematic analysis of the prognostic lncRNAs and PCGs was of great importance to the understanding of the progression of stage III gastric cancer.

Project description:Background: Although the cucumber reference genome and its annotation were published several years ago, the functional annotation of predicted genes, particularly protein-coding genes, still requires further improvement. In general, accurately determining orthologous relationships between genes allows for better and more robust functional assignments of predicted genes. As one of the most reliable strategies, the determination of collinearity information may facilitate reliable orthology inferences among genes from multiple related genomes. Currently, the identification of collinear segments has mainly been based on conservation of gene order and orientation. Over the course of plant genome evolution, various evolutionary events have disrupted or distorted the order of genes along chromosomes, making it difficult to use those genes as genome-wide markers for plant genome comparisons. Results: Using the localized LASTZ/MULTIZ analysis pipeline, we aligned 15 genomes, including cucumber and other related angiosperm plants, and identified a set of genomic segments that are short in length, stable in structure, uniform in distribution and highly conserved across all 15 plants. Compared with protein-coding genes, these conserved segments were more suitable for use as genomic markers for detecting collinear segments among distantly divergent plants. Guided by this set of identified collinear genomic segments, we inferred 94,486 orthologous protein-coding gene pairs (OPPs) between cucumber and 14 other angiosperm species, which were used as proxies for transferring functional terms to cucumber genes from the annotations of the other 14 genomes. In total, 10,885 protein-coding genes were assigned Gene Ontology (GO) terms which was nearly 1,300 more than results collected in Uniprot-proteomic database. Our results showed that annotation accuracy would been improved compared with other existing approaches. Conclusions: In this study, we provided an alternative resource for the functional annotation of predicted cucumber protein-coding genes, which we expect will be beneficial for the cucumber's biological study, accessible from http://cmb.bnu.edu.cn/functional_annotation. Meanwhile, using the cucumber reference genome as a case study, we presented an efficient strategy for transferring gene functional information from previously well-characterized protein-coding genes in model species to newly sequenced or "non-model" plant species.

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Dataset Information

Identification and functional annotation of metabolism-associated lncRNAs and their related protein-coding genes in gastric cancer.

Publications

Identification and functional annotation of metabolism-associated lncRNAs and their related protein-coding genes in gastric cancer.

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