Identification and validation of a five-lncRNA signature for predicting survival with targeted drug candidates in ovarian cancer

Bioengineered. 2021 Dec;12(1):3263-3274. doi: 10.1080/21655979.2021.1946632.

Abstract

The dysregulation of long non-coding RNAs (lncRNAs) plays a crucial role in ovarian cancer (OC). In this study, we screened out five differentially expressed lncRNAs (AC092718.4, AC138035.1, BMPR1B-DT, RNF157-AS1, and TPT1-AS1) between OC and normal ovarian based on TCGA and GTEx RNA-seq databases by using Kaplan-Meier analysis and univariate Cox, LASSO, and multivariate Cox regression. Then, a risk signature was constructed, with 1, 3, 5-year survival prediction accuracy confirmed by ROC curves, and an online survival calculator for easier clinical use. With lncRNA-microRNA-mRNA regulatory networks established, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, suggesting the involvement of a variety of cancer-related functions and pathways. Finally, five candidate small-molecule drugs (thioridazine, trifluoperazine, loperamide, LY294002, and puromycin) were predicted by Connectivity Map. In conclusion, we identified a 5-lncRNA signature of prognostic value with its ceRNA networks, and five candidate drugs against OC.[Figure: see text].

Keywords: Ovarian cancer; computational biology; long non-coding rnas; risk signature; small molecular drugs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics*
  • Computational Biology
  • Female
  • Gene Regulatory Networks / genetics
  • Humans
  • MicroRNAs / genetics
  • Models, Statistical
  • Ovarian Neoplasms* / diagnosis
  • Ovarian Neoplasms* / genetics
  • Ovarian Neoplasms* / mortality
  • Prognosis
  • RNA, Long Noncoding / genetics*
  • Transcriptome / genetics

Substances

  • Biomarkers, Tumor
  • MicroRNAs
  • RNA, Long Noncoding

Grants and funding

This work was supported by the National Natural Science Foundation of China [Grant No. 81870432] ; Natiaonal Natural Science Foundation of China [Grant No. 81570567]; National Natural Science Foundation of China [Grant No. 81571994]; the Li Ka Shing Foundation [Grant No. L11112008] .