ArticleBMC cancer2018
Signaling protein signature predicts clinical outcome of non-small-cell lung cancer.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base.Functional & integrative genomics · 2025Review
- Review
- A scoping review of artificial intelligence applications in thoracic surgery.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2022Article
- Effect of depression disorder on the efficacy and quality of life of first-line chemotherapy combined with immunotherapy in oncogene-driver negative NSCLC patients.Frontiers in oncology · 2022Article
- Molecular biomarkers in early stage lung cancer.Translational lung cancer research · 2021Review
- Novel prognostic molecular markers in lung cancer.Oncology letters · 2020Review
- A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network.BioMed research international · 2019Article
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Authors and funding
29 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundNon-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application.
methodsA total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort.
resultsA 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P < 0.001) and SCC patients (27 months vs. not reached, HR 9.97; P < 0.001). Cox regression analysis showed that both signatures are independent prognostic indicators and outperformed TNM staging (ADC: adjusted HR 3.07 vs. 2.43, SCC: adjusted HR 7.84 vs. 2.24). Particularly, we found that only the ADC patients in high-risk group significantly benefited from adjuvant chemotherapy (P = 0.018).
conclusionsBoth ADC and SCC protein signatures could effectively stratify the prognosis of NSCLC patients, and may support patient selection for adjuvant chemotherapy.
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