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

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 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.

Bioinformatics Analysis of Dysregulated MicroRNA-Messenger RNA Networks in Small Cell Lung Cancer

2021 ◽  
Vol 11 (4) ◽  
pp. 595-604
Author(s):  
Xingsheng Liu ◽  
Yi Zhang
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Messenger Rna ◽  
Target Genes ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung ◽  
Hub Genes ◽  
Clinical Biomarkers

The present study aimed to identify a key module of differentially expressed miRNAs (DE-miRNAs) together with the corresponding differentially expressed mRNAs (DE-mRNAs) within small cell lung cancer (SCLC). Linear models were applied to ascertain the DE-miRNAs and DE-mRNAs in SCLC versus matched non-carcinoma samples obtained from the RNA expression datasets of GSE19945, GSE74190 and GSE6044. The common DE-miRNAs were identified using the Venn plot. Then, 3 databases were used to retrieve the DE-miRNAs target genes, and the intersection was taken for validating the shared target genes. Besides, Cytoscape was utilized for constructing the miRNAmRNA network for SCLC. Finally, a key module of five DE-miRNAs and four hub genes was determined based on the degree. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted for exploring those hub genes in terms of their functions along with the involved signal transduction pathways. Altogether 106 shared DE-miRNAs were identified, which were used to predict 63 common target genes. In addition, a key module of five DE-miRNAs (hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-93-5p and hsa-miR- 106b-5p) and four hub genes (SOX4, DPYSL2, TGFBR2 and F3) were extracted from the miRNAmRNA network according to their degree. Finally, the hub genes were subjected to GO as well as KEGG analysis, which revealed that cell cycle G1/S phase transition, the extracellular matrix, and cellular senescence signaling pathways exerted vial parts during SCLC progression. A key module of five DE-miRNAs and four hub genes may be potentially used as clinical biomarkers to predict SCLC.

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