scholarly journals MicroRNA signatures as clinical biomarkers in lung cancer

2015 ◽  
pp. 35 ◽  
Author(s):  
Evi S. Lianidou ◽  
Athina Markou ◽  
Martha Zavridou
Keyword(s):  
Lung Cancer ◽  
Clinical Biomarkers

Abstract 4943: microRNAs as clinical biomarkers for lung cancer classification

2011 ◽  
Author(s):  
Tina Edmonston ◽  
Michal Kushnir ◽  
Gila Lithwick Yanai ◽  
Hila Benjamin ◽  
Marluce Bibbo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Classification ◽  
Clinical Biomarkers

scholarly journals Comprehensive Analysis of Prognostic Value and Immune Infiltration of ATⅡ-associated Genes in Non-small Cell Lung Cancer

2021 ◽  
Author(s):  
Xiaoxia Wen ◽  
Yao Xiao ◽  
Ping Leng ◽  
Huaichao Luo
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Mrna Expression ◽  
Immune Cells ◽  
Interaction Analysis ◽  
Progression Free Survival ◽  
Normal Lung ◽  
Alveolar Type ◽  
Mesenchymal Transition ◽  
Clinical Biomarkers

Abstract Background: Lung cancer is one of the most commonly diagnosed cancer and the leading cause of cancer-related death in the world. ATⅡ（alveolar type II cells, ATⅡ ）are a key structure of the distal lung epithelium and have a secretory function that is essential to maintain normal lung homeostasis. ATⅡ cells dedifferentiate into a cell stem-like state, which can continuous differentiation, proliferation, repair and damage, and helps initiate and maintain tumor progression. However, the potential mechanistic value of ATⅡ-associated genes as a clinical biomarker and therapeutic target of NSCLC has not been fully elucidated.Methods: We used the Gene Expression Profile Interaction Analysis (GEPIA) and Oncomine database to explore the expression of ATⅡ-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1 and PGC) in NSCLCpatients. Then we euse the Kaplan Meierplotter and the GEPIA website to evaluate the prognosis of survival impact of differential expression of these genes. Finally, we analyzed the correlation between eight ATⅡ-associated genes and infiltration of immune cells using the TIMER website.Results: The expression levels of AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1 and PGC were remarkably reduced in lung cancer tissues, and also observably related to clinical cancer stages. Low mRNA expression of AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1 and PGC were associated with short overall survival (OS) in NSCLS patients and the low expression of CLDN18, FOXA2, NKX2-1, PGC, SFTPB, SFTPC, SFTPD were significantly related to a reduced progression-free survival (FP), and low CLDN18, FOXA2 and SFTPD mRNA expression led to a short post-progression survival (PPS). Moreover, the functions of the differentially expressed eight ATⅡ-associated genes were primarily related to lung development, regulation of epithelial to mesenchymal transition, late endosome, antibacterial humoral response. Finally, the expression of AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1 and PGC in LUAD and LUSC patients were significantly correlated with the infiltration of diverse immune cells, including six types of CD4+ T cells, macrophages, neutrophils, B cells, CD8+ T cells, and dendritic cells.Conclusion: Our study provided strong evidence of the values of ATⅡ-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1 and PGC) as clinical biomarkers and therapeutic targets in NSCLC and might provide some new inspirations to assist in the design of new immunotherapies.

scholarly journals Clinical Significance of MicroRNA Expression Profiles and Polymorphisms in Lung Cancer Development and Management

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Francesca Megiorni ◽  
Antonio Pizzuti ◽  
Luigi Frati
Keyword(s):  
Lung Cancer ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Development ◽  
Industrialized Countries ◽  
Lung Cancers ◽  
Novel Treatments ◽  
Novel Mirna ◽  
Clinical Biomarkers ◽  
Development And Management

Lung cancers account for a huge percentage of death in industrialized countries, and hence there is an increasing call for the development of novel treatments. These malignancies are caused by a combination of environmental factors, principally cigarette smoking and genetic alterations. MicroRNAs (miRNAs) are a recently discovered class of regulatory noncoding small RNAs with a significance in numerous biological processes. Strong evidence links miRNA impaired expression profiles and pathways to the etiology of several diseases, including neoplasia. This paper focuses on the emerging role of miRNA function in lung cancer development with particular highlighting on the use of miRNA profiles and polymorphisms for the molecular and biological characterization of tumor pulmonary growth and progression. Furthermore, we underline the potential utility of lung cancer-associated miRNAs as clinical biomarkers with a diagnostic, prognostic, and therapeutic significance and give emphasis to the promising novel miRNA-based curative strategies.

microRNAs as clinical biomarkers for lung cancer classification

2010 ◽  
Author(s):  
Tina B. Edmonston ◽  
Michal Kushnir ◽  
Ranit Aharonov ◽  
Gila Lithwick Yanai ◽  
Hila Benjamin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Classification ◽  
Clinical Biomarkers

scholarly journals Montreal prognostic score: estimating survival of patients with non-small cell lung cancer using clinical biomarkers

2013 ◽  
Vol 109 (8) ◽  
pp. 2066-2071 ◽  
Author(s):  
B Gagnon ◽  
J S Agulnik ◽  
I Gioulbasanis ◽  
G Kasymjanova ◽  
D Morris ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Prognostic Score ◽  
Small Cell ◽  
Small Cell Lung ◽  
Clinical Biomarkers

scholarly journals Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3722 ◽  
Author(s):  
Nasrullah Nasrullah ◽  
Jun Sang ◽  
Mohammad S. Alam ◽  
Muhammad Mateen ◽  
Bin Cai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Nodule ◽  
Gradient Boosting ◽  
Final Decision ◽  
Multiple Strategies ◽  
Lung Nodule Detection ◽  
Clinical Biomarkers ◽  
Nodule Detection ◽  
Learned Features

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

scholarly journals Association of Sarcopenia with and Efficacy of Anti-PD-1/PD-L1 Therapy in Non-Small-Cell Lung Cancer

2019 ◽  
Vol 8 (4) ◽  
pp. 450 ◽  
Author(s):  
Naoya Nishioka ◽  
Junji Uchino ◽  
Soichi Hirai ◽  
Yuki Katayama ◽  
Akihiro Yoshimura ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Group Performance ◽  
Objective Response ◽  
Eastern Cooperative Oncology Group ◽  
Small Cell ◽  
Small Cell Lung ◽  
Change Rate ◽  
Clinical Biomarkers

Secondary sarcopenia is defined as a decrease in muscle mass due to disease or malnutrition. Several studies have reported that secondary sarcopenia is an indicator of postoperative recurrence. We hypothesized that there is a correlation between the effect of immune checkpoint inhibitors (ICIs) and sarcopenia. We retrospectively analyzed 38 patients with advanced non-small cell lung cancer (NSCLC) who were treated with ICIs between February 2016 and April 2018. Patients were divided into two groups according to the change rate of the psoas major muscle area (PMMA) at the L2–L3 position and investigated the correlation between the change rate of the PMMA and the efficacy of ICIs was investigated. The objective response and disease control rates were lower in patients with sarcopenia than in those without sarcopenia. Patients with sarcopenia exhibited a significantly shorter median progression-free survival (PFS) than non-sarcopenia patients. Moreover, focusing on good Eastern Cooperative Oncology Group performance status patients, sarcopenia patients showed a shorter PFS than non-sarcopenia patients. Patients with sarcopenia are associated with poor outcomes for immunotherapy among those with advanced NSCLC, based on retrospective analysis. Further research is needed to validate the clinical biomarkers involved in ICI responders.

scholarly journals Exosomes in the lung cancer microenvironment: biological functions and potential use as clinical biomarkers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runzhi Qi ◽  
Yuwei Zhao ◽  
Qiujun Guo ◽  
Xue Mi ◽  
Mengqi Cheng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Suppressor Cells ◽  
Tumour Microenvironment ◽  
Diagnostic Value ◽  
Cancer Microenvironment ◽  
Malignant Tumours ◽  
Mesenchymal Transition ◽  
Clinical Biomarkers ◽  
Treatment Responses

AbstractLung cancer is one of the most common malignant tumours worldwide. however, emerging immunotherapy and targeted therapies continue to show limited efficacy. In the search for new targets for lung cancer treatment, exosomes have become a major focus of research. Exosomes play an important role in the tumour microenvironment (TME) of lung cancer and affect invasion, metastasis, and treatment responses. This review describes our current understanding of the release of exosomes derived from different cells in the TME, the effects of exosomes on T/Tregs, myeloid-derived suppressor cells, tumour-associated macrophages, dendritic cells, and natural killer cells, and the role of exosomes in the endothelial–mesenchymal transition, angiogenesis, and cancer-associated fibroblasts. In particular, this review focuses on the potential clinical applications of exosomes in the lung cancer microenvironment and their prognostic and diagnostic value.

scholarly journals Clinical Biomarkers and Prognosis in Taiwanese Patients with Non-Small Cell Lung Cancer (NSCLC)

2012 ◽  
Vol 03 (04) ◽  
pp. 412-423 ◽  
Author(s):  
Yixia Li ◽  
Yea-Jyh Chen ◽  
Li-Jung Chang ◽  
Michael Hendryx ◽  
Juhua Luo
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Clinical Biomarkers
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