scholarly journals Identification of potential biomarkers of lung adenocarcinoma brain metastases via microarray analysis of cDNA expression profiles

2018 ◽  
Author(s):  
Haiyang Su ◽  
Zhenyang Lin ◽  
Weicheng Peng ◽  
Zhiqiang Hu
Keyword(s):  
Brain Metastases ◽  
Lung Adenocarcinoma ◽  
Microarray Analysis ◽  
Expression Profiles ◽  
Cdna Expression ◽  
Potential Biomarkers

Three differential expression profiles of miRNAs as potential biomarkers for lung adenocarcinoma

2018 ◽  
Vol 507 (1-4) ◽  
pp. 377-382 ◽  
Author(s):  
Rongjiong Zheng ◽  
Wenjie Mao ◽  
Zhennan Du ◽  
Jun Zhang ◽  
Mingming Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Expression Profiles ◽  
Potential Biomarkers

Circular RNAs expression profiles in plasma exosomes from early‐stage lung adenocarcinoma and the potential biomarkers

2019 ◽  
Vol 121 (3) ◽  
pp. 2525-2533 ◽  
Author(s):  
Falin Chen ◽  
Chunli Huang ◽  
Qiumei Wu ◽  
Lili Jiang ◽  
Shaoting Chen ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Expression Profiles ◽  
Circular Rnas ◽  
Potential Biomarkers

TRIM58 is a prognostic biomarker remodeling tumor microenvironment in KRAS-driven lung adenocarcinoma

2021 ◽  
Author(s):  
Xiaowei Chen ◽  
Yu Wang ◽  
Xiao Qu ◽  
Fenglong Bie ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Follicular Helper ◽  
Follicular Helper T Cell ◽  
Cancer Genome Atlas ◽  
Genome Atlas ◽  
Potential Biomarkers

Aim: To comprehensively analyze the expression profiles of ubiquitin-related genes (URGs) and determine potential biomarkers in KRAS-driven lung adenocarcinoma (LUAD). Materials & methods: Differential expression analyses were performed between KRAS-wild and KRAS-mutant LUAD samples from The Cancer Genome Atlas database, and 34 URGs were screened out. ESTIMATE and CIBERSORT methods were used to calculate the ratio of immune and stromal components. Results & conclusion: TRIM58 was positively correlated with abundances of M2 macrophages and resting mast cells and negatively correlated with follicular helper T-cell abundances in KRAS-driven LUAD. TRIM58 was a potential prognosis-associated indicator for tumor microenvironment modulation and played a key role in TME-specific AS landscapes alterations in KRAS-driven LUAD.

scholarly journals Long Noncoding RNA Expression Profiles of Lung Adenocarcinoma Ascertained by Microarray Analysis

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e104044 ◽  
Author(s):  
Gang Xu ◽  
Jie Chen ◽  
Qinshi Pan ◽  
Keta Huang ◽  
Jingye Pan ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Microarray Analysis ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
Rna Expression

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Gu ◽  
Ying Sun ◽  
Xiong Zheng ◽  
Jin Ma ◽  
Xiao-Ying Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Microarray Analysis ◽  
Cancer Progression ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Circular Rnas ◽  
Target Mrnas

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar’s home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

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