Bioinformatics Analysis Reveals Biomarkers With Cancer Stem Cell Characteristics in Lung Squamous Cell Carcinoma

Liao, Yi; Cheng, Mengqing; Fan, Xiaoxuan

doi:10.3389/fgene.2020.00427

Cited by 45 publications

(39 citation statements)

References 67 publications

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“…Of available databases, the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/ ) database is an authoritative oncology database, and the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) database stores curated gene expression datasets. Many studies have constructed a multi-queue verification model based on these two databases, such as non-small cell lung cancer [ 6 , 7 ], and ovarian cancer [ 8 ]. Prognostic models provide effective guidance for doctors and patients to make optimal treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Lin

Yang

et al. 2020

Cancer Cell Int

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Background Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Method In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan–Meier analysis, receiver operating characteristic curve, and univariate/multivariate Cox analysis were used to evaluate the prognostic model. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model. Results A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model’s predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst. Conclusion The proposed 11-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.

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Section: Introductionmentioning

confidence: 99%

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Lin

Yang

et al. 2020

Cancer Cell Int

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Section: Discussionmentioning

confidence: 99%

“…If not treated promptly and effectively, RA can seriously reduce the quality of life and even cause disability. There is no doubt that understanding diseases at the molecular level will help to improve their diagnosis and treatment [ 26 , 27 ]. Up to now, various biomarkers have been identified to be associated with rheumatoid arthritis and may be selected as therapeutic targets, but the detailed mechanism of gene regulation leading to disease progression remains elusive [ 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Ren

Du³

et al. 2020

BioMed Research International

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Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis.

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“…Lung cancer remains the leading malignancy worldwide, and non-small-cell lung cancer (NSCLC) accounts for approximately 80% of cases. 1,2 Chromosome rearrangements involving the ROS proto-oncogene 1 receptor tyrosine kinase (ROS1) gene generally define a unique molecular subset and have been identified in 1-2% of NSCLC patients. 3 The morbidity is slightly higher in East Asian populations, which reports a frequency of 2.4%.…”

Section: Introductionmentioning

confidence: 99%

<p>Identification of a Novel MPRIP-ROS1 Fusion and Clinical Efficacy of Crizotinib in an Advanced Lung Adenocarcinoma Patient: A Case Report</p>

Yun¹,

Li²,

Shang³

et al. 2020

OTT

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ROS1 fusions have been identified in 1-2% of non-small-cell lung cancer (NSCLC) patients; they are validated as a driver of carcinogenesis and could be subjected to inhibition by crizotinib. However, previous studies suggested a variable progression-free survival (PFS) ranging from 9.1 to 20.0 months for crizotinib treatment in ROS1-rearranged NSCLC. Here, we reported a 45-year-old female diagnosed with stage IVB lung adenocarcinoma with multiple lymph nodes and bone metastasis carrying a novel MPRIP-ROS1 fusion, which was identified by RNA-based NGS (next-generation sequencing) and was sensitive to crizotinib treatment. Materials and Methods: A targeted NGS panel was used to analyze genomic DNA and total RNA isolated from formalin-fixed paraffin-embedded (FFPE) tissue block of the patient. An RNA fusion panel based on amplicon sequencing was designed for detection fusion variation. Fusion results were validated using reverse transcriptase polymerase chain reaction and Sanger sequencing. Results: We reported a novel MPRIP-ROS1 fusion identified in this advanced lung adenocarcinoma case. The rearrangement was generated by exons 1-21 of MPRIP at chr17: p11.2 joined to exons 35-43 of ROS1 at chr6: q22.1, which retained an intact kinase domain of ROS1. The primary tumor and metastatic lymph nodes were eliminated on computed tomographic (CT) scan imaging after 2 months' crizotinib treatment, and the multiple bone metastatic lesions were significantly relieved according to bone scintigraphy images. To date, the treatment has lasted 16 months, and the patient is still in follow-up showing sustained response to crizotinib. Conclusion: The study identified a novel MPRIP-ROS1 fusion that was sensitive to crizotinib, which provided a new driver of lung adenocarcinoma and potential therapeutic target for crizotinib. It also expanded NSCLC treatment of ROS1 rearrangement and highlighted the importance of genetic testing for precise treatment decision-making.

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Bioinformatics Analysis Reveals Biomarkers With Cancer Stem Cell Characteristics in Lung Squamous Cell Carcinoma

Cited by 45 publications

References 67 publications

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

<p>Identification of a Novel MPRIP-ROS1 Fusion and Clinical Efficacy of Crizotinib in an Advanced Lung Adenocarcinoma Patient: A Case Report</p>

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