The cox-filter method identifies respective subtype-specific lncRNA prognostic signatures for two human cancers

Suyan Tian, Chi Wang, Jing Zhang, Dan Yu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: The most common histological subtypes of esophageal cancer are squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). It has been demonstrated that non-marginal differences in gene expression and somatic alternation exist between these two subtypes; consequently, biomarkers that have prognostic values for them are expected to be distinct. In contrast, laryngeal squamous cell cancer (LSCC) has a better prognosis than hypopharyngeal squamous cell carcinoma (HSCC). Likewise, subtype-specific prognostic signatures may exist for LSCC and HSCC. Long non-coding RNAs (lncRNAs) hold promise for identifying prognostic signatures for a variety of cancers including esophageal cancer and head and neck squamous cell carcinoma (HNSCC). Methods: In this study, we applied a novel feature selection method capable of identifying specific prognostic signatures uniquely for each subtype - the Cox-filter method - to The Cancer Genome Atlas esophageal cancer and HSNCC RNA-Seq data, with the objectives of constructing subtype-specific prognostic lncRNA expression signatures for esophageal cancer and HNSCC. Results: By incorporating biological relevancy information, the lncRNA lists identified by the Cox-filter method were further refined. The resulting signatures include genes that are highly related to cancer, such as H19 and NEAT1, which possess perfect prognostic values for esophageal cancer and HNSCC, respectively. Conclusions: The Cox-filter method is indeed a handy tool to identify subtype-specific prognostic lncRNA signatures. We anticipate the method will gain wider applications.

Original languageEnglish
Article number18
JournalBMC Medical Genomics
Volume13
Issue number1
DOIs
StatePublished - Feb 5 2020

Bibliographical note

Funding Information:
This study was supported by the Education Department of Jilin Province (grant No. JJKH20190032KJ) and the Natural Science Foundation of China (grant No. 31401123).

Publisher Copyright:
© 2020 The Author(s).

Keywords

  • Cox regression model
  • Esophageal cancer
  • Head and neck squamous cell carcinoma (HNSCC)
  • Long non-coding RNA (lncRNA)
  • Prognostic signature

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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