Dataset Information

Comprehensive bioinformatics analysis of lncRNAs in gastric cancer.

ABSTRACT: Long non-coding RNAs (lncRNAs) have been generally considered to serve important roles in various types of cancer, including gastric cancer. However, a comprehensive understanding of lncRNAs in gastric cancer requires further study. The present study performed an in-depth study revealed 50 differently expressed lncRNAs. The changed cellular pathways and biological process in gastric cancer were determined. To further confirm the functions of the differently expressed lncRNAs, co-expression networks were constructed between the lncRNAs and mRNA; this lead to the identification of 6 modules, which participated in various cellular pathways. In addition, 2 lncRNAs were identified which were associated with clinical outcome. The biological analysis and experimental evidence suggested that LINC00982 inhibited, while LL22NC03-N14H11.1 promoted the proliferation of gastric cancer cells. These lncRNAs may be considered as potential prognostic factor in gastric cancer.

SUBMITTER: Qi D

PROVIDER: S-EPMC6313033 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Comprehensive bioinformatics analysis of lncRNAs in gastric cancer.

Qi Dongdong D Wang Qiang Q Wu Meiqing M Zhang Xiong X

Oncology letters 20181115 1

Long non-coding RNAs (lncRNAs) have been generally considered to serve important roles in various types of cancer, including gastric cancer. However, a comprehensive understanding of lncRNAs in gastric cancer requires further study. The present study performed an in-depth study revealed 50 differently expressed lncRNAs. The changed cellular pathways and biological process in gastric cancer were determined. To further confirm the functions of the differently expressed lncRNAs, co-expression netwo ...[more]

PMID: 30655896

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Project description:BackgroundN6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approaches of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between m6A and lncRNAs enrolling in the shaping of TIME remains unclear.MethodsThe RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering method was implemented to classify GC patients into two clusters. Survival analysis, the infiltration level of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. A competing endogenous RNA (ceRNA) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which m6A-related lncRNAs enriched. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal lncRNAs, and risk model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the response to immune checkpoint inhibitors (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect expression patterns of the selected lncRNAs in the 35 tumor tissues and their paired adjacent normal tissues, and validated the prognostic value of risk model in our cohort (N = 35).ResultsThe expression profiles of 15 lncRNAs were included to cluster patients into 2 subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and lower mutation rates of the genes. Different immune cell infiltration patterns were also displayed between the two clusters. GSEA showed that cluster1 preferentially enriched in tumor hallmarks and tumor-related biological pathways. KEGG pathway analysis found that the target mRNAs which m6A-related lncRNAs regulated by sponging miRNAs mainly enriched in vascular smooth muscle contraction, cAMP signaling pathway and cGMP-PKG signaling pathway. Next, eight lncRNAs were selected by LASSO regression algorithm to construct risk model. Patients in the high-risk group had poor prognoses, which were consistent in our cohort. As for predicting responses to ICIs therapy, patients from high-risk group were found to have lower tumor mutation burden (TMB) scores and account for large proportion in the Microsatellite Instability-Low (MSI-L) subtype. Moreover, patients had distinct immunophenoscores in different risk groups.ConclusionOur study revealed that the interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to ICIs therapy.

Dataset Information

Comprehensive bioinformatics analysis of lncRNAs in gastric cancer.

Publications

Comprehensive bioinformatics analysis of lncRNAs in gastric cancer.

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