scholarly journals Identification of a four long non-coding RNA (lncRNA) Signature for Predicting Prognosis of Patients with Non-Small Cell Lung Cancer: a Multicenter Study in China

2020 ◽  
Author(s):  
Rui-Qi Wang ◽  
Xiao-Ran Long ◽  
Chun-Lei Ge ◽  
Mei-Yin Zhang ◽  
Long Huang ◽  
...  
Keyword(s):  
Staging System ◽  
Tnm Staging ◽  
Small Cell ◽  
Small Cell Lung ◽  
Discovery Cohort ◽  
Non Coding Rna ◽  
Tnm Staging System ◽  
Nsclc Patients ◽  
Long Non Coding Rna ◽  
Independent Cohort

Abstract Background:This study aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in non-small-cell lung carcinoma (NSCLC) patients and providing additional prognostic information to the tumor node metastasis (TNM) staging system. Methods: NSCLC cases from a hospital were divided into a discovery cohort (n=194) and validation cohort (n=172) and analyzed using a custom lncRNA microarray. Another 73 cases obtained from another hospital were assayed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The differentially expressed lncRNAs were detected by significance analysis of microarrays (SAM) program and used for identifying those associated with survival in the discovery cohort, which were then employed to construct a prognostic lncRNA signature using a risk-score method. The signature was then confirmed in the validation and independent cohort as well. Results: The discovery cohort was found to comprise of 305 lncRNAs, which showed differential expression between the NSCLC and the corresponding normal lung tissues, a 4-lncRNA signature was identified that was found to significantly correlate with the survival of the NSCLC patients. This signature was further validated in the validation and independent cohort. Moreover, multivariate Cox analysis demonstrates that the 4-lncRNA signature is independent of the TNM staging system.as a risk-score model. The receiver operating characteristic (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of TNM staging alone in all the cohorts. Conclusions:This study identified a 4-lncRNA signature, which is a powerful prognostic biomarker which related to patient survival in addition to the traditional TNM staging system.

scholarly journals Prognostic impact of cell type under the seventh TNM staging system in resected non-small cell lung cancer

2012 ◽  
Vol 3 (3) ◽  
pp. 249-254
Author(s):  
Tatsuro Okamoto ◽  
Hironobu Wada ◽  
Teruaki Mizobuchi ◽  
Hidehisa Hoshino ◽  
Yasumitsu Moriya ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Staging System ◽  
Tnm Staging ◽  
Small Cell ◽  
Prognostic Impact ◽  
Cell Type ◽  
Small Cell Lung ◽  
Tnm Staging System

Immunoscore classifier:A gene based prognostic tool in early non-small cell lung cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21030-e21030
Author(s):  
Meiying Guo ◽  
Xindong Sun ◽  
Jinming Yu ◽  
Linlin Wang
Keyword(s):  
Prognostic Value ◽  
Immune Status ◽  
Staging System ◽  
Tnm Staging ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung ◽  
Immune Genes ◽  
Tnm Staging System ◽  
Immune Group

e21030 Background: The clinical benefits of immunotherapy in patients with stage I non-small cell lung cancer (NSCLC) is still controversial. Immune status plays critical role in the development and progression of NSCLC, and is associated with the patient survival outcomes. The analysis of immune features is thus valuable for the determination of immunotherapy. However, one single immune feature cannot reflect the complex immune status, and its prognostic value is extremely limited. In this study, we aimed to construct an immunoscore classifier based on multiple immuno-genes to predict the prognosis of patients with early NSCLC. Methods: A total of 522 patients with stage I NSCLC were included in this study. All patients' follow-up records and gene expression data were completely preserved. A least absolute shrinkage and selection operator (LASSO) algorithm was used to screen immune-related genes, and a COX proportional hazard regression model was used to construct the immunoscore classifier based on multiple immune-genes. Besides, the net reclassification improvement (NRI) calculation and concordance index (C-index) were applied to quantify the improvement of usefulness added by the immunoscore classifier compared to TNM staging system. Results: The immunoscore classifier including CCL5, CD8A, CXCL9, HLA-DQA1, LAG3, STAT1, and CD276 was significantly correlated with OS (HR: 2.785 CI: 1.809-4.289 P < 0.001) in patients with stage I NSCLC. With the optimal cut-off value of 4.32, all patients can be divided into a low-risk immune group and a high-risk immune group. The 10-year survival rates of the two groups were 36.8% and 12.3%, respectively. Besides, the immunoscore classifier was superior to the traditional TNM staging system in terms of distinguishing ability (C-index improvement by 0.075) and net reclassification ability (NRI improvement by 11.29%), indicating that the immunoscore classifier plays an important role in improving prognostic value. Conclusions: Multiple immune-genes based immunoscore classifiers can effectively predict the prognosis of patients with stage I NSCLC, and is significantly superior to the traditional TNM staging system in terms of prediction effectiveness and accuracy. As a new assessment tool, the immunoscore classifier may be helpful for determining the immune status of patients with stage I NSCLC and screening patients suitable for subsequent immunotherapy.

Sign in / Sign up

Export Citation Format

Share Document