Prognostic Significance of TLR2, SMAD3 and Localization-dependent SATB1 in Stage I and II Non–Small-Cell Lung Cancer Patients

This study aimed to explore the prognostic value of SATB1, SMAD3, and TLR2 expression in non–small-cell lung carcinoma patients with clinical stages I-II. To investigate, we evaluated immunohistochemical staining to each of these markers using tissue sections from 69 patients from our cohort and gene expression data for The Cancer Genome Atlas (TCGA) cohort. We found that, in our cohort, high expression levels of nuclear SATB1n and SMAD3 were independent prognostic markers for better overall survival (OS) in NSCLC patients. Interestingly, expression of cytoplasmic SATB1c exhibited a significant but inverse association with survival rate, and it was an independent predictor of unfavorable prognosis. Likewise, TLR2 was a negative outcome biomarker for NSCLC even when adjusting for covariates. Importantly, stratification of NSCLCs with respect to combined expression of the three biomarkers allowed us to identify subgroups of patients with the greatest difference in duration of survival. Specifically, expression profile of SATB1n-high/SMAD3high/TLR2low was associated with the best OS, and it was superior to each single protein alone in predicting patient prognosis. Furthermore, based on the TCGA dataset, we found that overexpression of SATB1 mRNA was significantly associated with better OS, whereas high mRNA levels of SMAD3 and TLR2 with poor OS. In conclusion, the present study identified a set of proteins that may play a significant role in predicting prognosis of NSCLC patients with clinical stages I-II.

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Prognostic Significance of N-Glycolyl GM3 Ganglioside Expression in Non-Small Cell Lung Carcinoma Patients: New Evidences

Pathology Research International ◽

10.1155/2015/132326 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Rancés Blanco ◽

Elizabeth Domínguez ◽

Orlando Morales ◽

Damián Blanco ◽

Darel Martínez ◽

...

Keyword(s):

Growth Factor ◽

Epidermal Growth Factor ◽

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Prognostic Significance ◽

Small Cell ◽

Small Cell Lung ◽

Cell Lung Carcinoma ◽

Epidermal Growth ◽

Nsclc Patients

The prognostic role of N-glycolyl GM3 ganglioside (NeuGcGM3) expression in non-small cell lung carcinoma (NSCLC) still remains controversial. In this study, the NeuGcGM3 expression was reevaluated using an increased number of NSCLC cases and the 14F7 Mab (a highly specific IgG1 raised against NeuGcGM3). An immunohistochemical score integrating the percentage of 14F7-positive cells and the intensity of reaction was applied to reassess the relationship between NeuGcGM3 expression, some clinicopathological features, and the overall survival (OS) of NSCLC patients. The double and the triple expression of NeuGcGM3 with the epidermal growth factor receptor (EGFR) and/or its ligand, the epidermal growth factor (EGF), were also evaluated. NeuGcGM3 expression correlates with both S-Phase fraction (p=0.006) and proliferation index (p=0.000). Additionally, NeuGcGM3 expression was associated with a poor OS of patients in both univariate (p=0.020) and multivariate (p=0.010) analysis. Moreover, the double and/or the triple positivity of tumors to NeuGcGM3, EGFR, and/or EGF permitted us to identify phenotypes of NSCLC with a more aggressive biological behavior. Our results are in agreement with the negative prognostic significance of NeuGcGM3 expression in NSCLC patients. However, standardization of techniques to determine the expression of NeuGcGM3 in NSCLC as well as the implementation of a universal scoring system is recommended.

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Rates and Risk of Pneumonitis in Non-small Cell Lung Carcinoma (NSCLC) Patients (pts) Treated with Concurrent Chemoradiation

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2008.06.408 ◽

2008 ◽

Vol 72 (1) ◽

pp. S117-S118 ◽

Cited By ~ 2

Author(s):

R.B. Barriger ◽

J.C. Aseneau ◽

M. Yu ◽

C. Reynolds ◽

P. Mantravadi ◽

...

Keyword(s):

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Small Cell ◽

Concurrent Chemoradiation ◽

Small Cell Lung ◽

Cell Lung Carcinoma ◽

Nsclc Patients

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Prognostic significance of PD-L1 in advanced non-small cell lung carcinoma

Medicine ◽

10.1097/md.0000000000023172 ◽

2020 ◽

Vol 99 (45) ◽

pp. e23172

Author(s):

Yanjie Zhao ◽

Feng Shi ◽

Quan Zhou ◽

Yuchen Li ◽

Jiangping Wu ◽

...

Keyword(s):

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Prognostic Significance ◽

Small Cell ◽

Small Cell Lung ◽

Cell Lung Carcinoma

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Non-small cell lung carcinoma (NSCLC) patients with baseline brain imaging: A prospective observational study

Annals of Oncology ◽

10.1093/annonc/mdz066.002 ◽

2019 ◽

Vol 30 ◽

pp. ii20

Author(s):

N. Gundu ◽

P.S. Malik ◽

S. Khurana ◽

S. Pathy ◽

M. Yadav ◽

...

Keyword(s):

Observational Study ◽

Brain Imaging ◽

Lung Carcinoma ◽

Prospective Observational Study ◽

Small Cell Lung Carcinoma ◽

Small Cell ◽

Small Cell Lung ◽

Cell Lung Carcinoma ◽

Nsclc Patients

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Identification of expression signatures for non-small-cell lung carcinoma subtype classification

Bioinformatics ◽

10.1093/bioinformatics/btz557 ◽

2019 ◽

Cited By ~ 4

Author(s):

Ran Su ◽

Jiahang Zhang ◽

Xiaofeng Liu ◽

Leyi Wei

Keyword(s):

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Lung Squamous Cell Carcinoma ◽

Small Cell ◽

The Cancer Genome Atlas ◽

Classification Model ◽

Recursive Feature Elimination ◽

Small Cell Lung ◽

Cell Lung Carcinoma ◽

Subtype Classification

Abstract Motivation Non-small-cell lung carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes. Results In this study, we designed a classification model integrating both mRNA and long non-coding RNA (lncRNA) expression data to effectively classify the subtypes of NSCLC. A gene selection algorithm, named WGRFE, was proposed to identify the most discriminative gene signatures within the recursive feature elimination (RFE) framework. GeneRank scores considering both expression level and correlation, together with the importance generated by classifiers were all taken into account to improve the selection performance. Moreover, a module-based initial filtering of the genes was performed to reduce the computation cost of RFE. We validated the proposed algorithm on The Cancer Genome Atlas (TCGA) dataset. The results demonstrate that the developed approach identified a small number of expression signatures for accurate subtype classification and particularly, we here for the first time show the potential role of LncRNA in building computational NSCLC subtype classification models. Availability and implementation The R implementation for the proposed approach is available at https://github.com/RanSuLab/NSCLC-subtype-classification.

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