Visualization-aided Classification Ensembles Discriminate Lung Adenocarcinoma and Squamous Cell Carcinoma Samples Using Their Gene Expression Profiles

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2014 Oct 22

PMID 25335090

Citations 10

Authors

Ao Zhang

Chi Wang

Shiji Wang

Liang Li

Zhongmin Liu

Suyan Tian

Affiliations

Soon will be listed here.

Abstract

Introduction: The widespread application of microarray experiments to cancer research is astounding including lung cancer, one of the most common fatal human tumors. Among non-small cell lung carcinoma (NSCLC), there are two major histological types of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC).

Results: In this paper, we proposed to integrate a visualization method called Radial Coordinate Visualization (Radviz) with a suitable classifier, aiming at discriminating two NSCLC subtypes using patients' gene expression profiles. Our analyses on simulated data and a real microarray dataset show that combining with a classification method, Radviz may play a role in selecting relevant features and ameliorating parsimony, while the final model suffers no or least loss of accuracy. Most importantly, a graphic representation is more easily understandable and implementable for a clinician than statistical methods and/or mathematic equations.

Conclusion: To conclude, using the NSCLC microarray data presented here as a benchmark, the comprehensive understanding of the underlying mechanism associated with NSCLC and of the mechanisms with its subtypes and respective stages will become reality in the near future.

Citing Articles

Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma.

Wu X, Wang L, Feng F, Tian S J Int Med Res. 2019; 48(3):300060519893837.

PMID: 31854219 PMC: 7607763. DOI: 10.1177/0300060519893837.

A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.

Li X, Shi G, Chu Q, Jiang W, Liu Y, Zhang S BMC Genomics. 2019; 20(1):881.

PMID: 31752667 PMC: 6868745. DOI: 10.1186/s12864-019-6086-2.

A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer.

Karlsson A, Cirenajwis H, Ericson-Lindquist K, Brunnstrom H, Reutersward C, Jonsson M Sci Rep. 2019; 9(1):5207.

PMID: 30914778 PMC: 6435686. DOI: 10.1038/s41598-019-41585-4.

Measurement of quality of life in second-line patients with advanced NSCLC without targetable mutations: a review.

Heigener D, Kuchler T Lung Cancer Manag. 2019; 5(2):105-116.

PMID: 30643554 PMC: 6310313. DOI: 10.2217/lmt-2016-0010.

Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.

Guan Q, Yan H, Chen Y, Zheng B, Cai H, He J BMC Genomics. 2018; 19(1):99.

PMID: 29378509 PMC: 5789529. DOI: 10.1186/s12864-018-4446-y.

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