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Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients
Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the c...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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D.A. Spandidos
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652232/ https://www.ncbi.nlm.nih.gov/pubmed/29098036 http://dx.doi.org/10.3892/ol.2017.6835 |
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author | Tian, Suyan |
author_facet | Tian, Suyan |
author_sort | Tian, Suyan |
collection | PubMed |
description | Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required. |
format | Online Article Text |
id | pubmed-5652232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-56522322017-11-02 Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients Tian, Suyan Oncol Lett Articles Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required. D.A. Spandidos 2017-11 2017-08-28 /pmc/articles/PMC5652232/ /pubmed/29098036 http://dx.doi.org/10.3892/ol.2017.6835 Text en Copyright: © Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Tian, Suyan Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title | Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title_full | Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title_fullStr | Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title_full_unstemmed | Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title_short | Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
title_sort | classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652232/ https://www.ncbi.nlm.nih.gov/pubmed/29098036 http://dx.doi.org/10.3892/ol.2017.6835 |
work_keys_str_mv | AT tiansuyan classificationandsurvivalpredictionforearlystagelungadenocarcinomaandsquamouscellcarcinomapatients |