Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

Meijing Wu; Xiaobin Shang; Yue Sun; Jing Wu; Guoyan Liu

doi:10.7717/peerj.8961

Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

PeerJ. 2020 May 7:8:e8961. doi: 10.7717/peerj.8961. eCollection 2020.

Authors

Meijing Wu^#¹, Xiaobin Shang^#², Yue Sun¹, Jing Wu¹, Guoyan Liu¹

Affiliations

¹ Department of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, China.
² Department of Esophageal Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

^# Contributed equally.

Abstract

Background: Abnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs' potential mechanism of in lymphocyte infiltration.

Methods: The RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan-Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman's correlation after removing the influence of tumor purity.

Results: A total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion.

Conclusion: We defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

Keywords: ceRNA; lncRNA; Lymphocytes, Tumor-Infiltrating; Ovarian cancer.

Grants and funding

This work was supported by the National Natural Science Foundation of China (No. 81472761). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.