Dataset Information

Landscape of associations between long non-coding RNAs and infiltrating immune cells in liver hepatocellular carcinoma.

ABSTRACT: Liver hepatocellular carcinoma (LIHC) can be detected by the immune system; however, it acquires features for evasion of immune surveillance during its origin and development. Long non-coding RNAs (lncRNAs) are critical as immune regulators in cancers; nevertheless, the biological functions and mechanisms of lncRNAs in evasion of immune system by LIHC remain unclear. In this study, an integrated and computational approach was developed to identify immune-related lncRNAs and to divide LIHC patients into diverse immune-related risk groups based on the expression profiles of coding genes and lncRNAs. LIHC-specific genes and lncRNAs in 17 immune cell populations were identified and analysed. Gene and lncRNA co-expressing networks for diverse immune cell populations were constructed and analysed. Some imported immune-related lncRNAs, such as MIR9-3HG, were also identified. The LIHC patients comprised three different groups based on immune-related risk. LIHC patients possessing a greater diversity of immune cell populations had better survival prognosis. The collective data are evidence of a credible computational model that can prioritize novel immune-related lncRNAs and depict the atlas of immune-related lncRNAs in LIHC. These findings will further the understanding of lncRNA function and advance the identification of immunotherapy targets in LIHC.

SUBMITTER: Li L

PROVIDER: S-EPMC7576285 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Landscape of associations between long non-coding RNAs and infiltrating immune cells in liver hepatocellular carcinoma.

Li Li L Song Xiaowei X Lv Yanju Y Jiang Qiuying Q Fan Chengjuan C Huang Dayong D

Journal of cellular and molecular medicine 20200910 19

Liver hepatocellular carcinoma (LIHC) can be detected by the immune system; however, it acquires features for evasion of immune surveillance during its origin and development. Long non-coding RNAs (lncRNAs) are critical as immune regulators in cancers; nevertheless, the biological functions and mechanisms of lncRNAs in evasion of immune system by LIHC remain unclear. In this study, an integrated and computational approach was developed to identify immune-related lncRNAs and to divide LIHC patien ...[more]

PMID: 32910548

Similar Datasets

Project description:BackgroundLiver hepatocellular carcinoma (LIHC) is the third largest cause of cancer mortality. Exosomes are vital regulators in the development of cancer. However, the mechanisms regarding the association of exosome-related long non-coding RNAs (lncRNAs) in LIHC are not clear.MethodsLIHC RNA sequences and exosome-associated genes were collected according to The Cancer Genome Atlas (TCGA), Hepatocellular Carcinoma Cell DataBase (HCCDB) and ExoBCD databases, and exosome-related lncRNAs with prognostic differential expression were screened as candidate lncRNAs using Spearman's method and univariate Cox regression analysis. Candidate lncRNAs were then used to construct a prognostic model and mRNA-lncRNA co-expression network. Differentially expressed genes (DEGs) in low- and high-risk groups were identified and enrichment analysis was performed for up- and down-regulated DEGs, respectively. The expression of immune checkpoint-related genes, immune escape potential and microsatellite instability among different risk groups were further analyzed. Quantitative real-time polymerase chain reaction (qRT-PCR) and transwell assay were applied for detecting gene expression levels and invasion and migration ability.ResultsBased on 17 prognostical exosome-associated lncRNAs, four hub lncRNAs (BACE1_AS, DSTNP2, PLGLA, and SNHG3) were selected for constructing a prognostic model, which was demonstrated to be an independent prognostic variable for LIHC. High risk score was indicative of poorer overall survival, lower anti-tumor immune cells, higher genomic instability, higher immune escape potential, and less benefit for immunotherapy. The qRT-PCR test verified the expression level of the lncRNAs in LIHC cells, and the inhibitory effect of BACE1_AS on immune checkpoint genes levels. BACE1_AS silence also depressed the ability of migration and invasion of LIHC cells.ConclusionThe Risk model constructed by exosome-associated lncRNAs could well predict immunotherapy response and prognostic outcomes for LIHC patients. We comprehensively reveal the clinical features of prognostical exosome-related lncRNAs and their potential ability to predict immunotherapeutic response of patients with LIHC and their prognosis.

Project description:Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored. Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively. Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74-8.70], p < 0.001), which outperforms those traditional clinicopathological factors. Furthermore, patients with higher risk scores also showed more advanced levels of a proinflammatory senescence-associated secretory phenotype (SASP), higher infiltration of regulatory T (Treg) cells and lower infiltration of naïve B cells, suggesting the regulatory effects of OIS on immune microenvironment. Additionally, we identified NRAV as a representative OIS-related lncRNA, which is over-expressed in HCC tumors mainly driven by DNA hypomethylation. Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.

Dataset Information

Landscape of associations between long non-coding RNAs and infiltrating immune cells in liver hepatocellular carcinoma.

Publications

Landscape of associations between long non-coding RNAs and infiltrating immune cells in liver hepatocellular carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets