ArticleOncology letters2017
Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients.
Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Association of resection type, nodal evaluation, and survival in early-stage lung squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.Thoracic cancer · 2026Article
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- METTL16 accelerates lung adenocarcinoma progression by inducing N6-methyladenosine modification of GTSE1 to regulate p53 pathway and cell cycle.Cell division · 2025Article
- Translating premalignant biology to accelerate non-small-cell lung cancer interception.Nature reviews. Cancer · 2025Review
- One-carbon metabolism is distinct metabolic signature for proliferative intermediate exhausted T cells of ICB-resistant cancer patients.Cell death discovery · 2025Article
- Article
- Sex-Based Differences in Lung Cancer Incidence: A Retrospective Analysis of Two Large US-Based Cancer Databases.Cancers · 2024Article
- Immune-active tumor-adjacent tissues are associated with favorable prognosis in stage I lung squamous cell carcinoma.iScience · 2023Article
- The value of AGR2 and KRT5 as an immunomarker combination in distinguishing lung squamous cell carcinoma from adenocarcinoma.American journal of translational research · 2021Article
- Lung squamous cell carcinoma and lung adenocarcinoma differential gene expression regulation through pathways of Notch, Hedgehog, Wnt, and ErbB signalling.Scientific reports · 2020Article
- A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma.International journal of molecular medicine · 2020Article
- A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.BMC genomics · 2019Article
- Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods.Oncology letters · 2019Article
- Identification of monotonically differentially expressed genes for non-small cell lung cancer.BMC bioinformatics · 2019Article
- Prognostic significance of the combination of preoperative fibrinogen and the neutrophil-lymphocyte ratio in patients with non-small cell lung cancer following surgical resection.Oncology letters · 2019Article
- A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning.Frontiers in oncology · 2019Article
- Identification of subtype-specific prognostic signatures using Cox models with redundant gene elimination.Oncology letters · 2018Article
Corrections and comments
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1 author.
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Abstract
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.
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