Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis.

Abstract

BACKGROUNDS:

The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

METHODS:

We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

RESULTS:

A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

CONCLUSION:

: This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.

• Authors: Yan Li, Fan-Fan Huo, Ying-Ying Wen, Miao Jiang
• Journal: BioMed research international (Biomed Res Int) Vol. 2021 Pg. 6680036 ( 2021) ISSN: 2314-6141 [Electronic] United States
• PMID: 33997040 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Yan Li et al.

Chemical References

• RNA, Long Noncoding

Topics

• Computational Biology
• Female
• Humans
• Ovarian Neoplasms (genetics, immunology, metabolism, mortality)
• Prognosis
• RNA, Long Noncoding (genetics, immunology, metabolism)
• Signal Transduction (genetics, immunology)
• Transcriptome (genetics, immunology)