Five genes may predict metastasis in non‑small cell lung cancer using bioinformatics analysis

Sun, Ruiying; Xia, Meng; Wang, Wei; Liu, Boxuan; Lv, Xin; Yuan, Jianbo; Zeng, Lizhong; Chen, Yang; Yuan, Bo; Yang, Shuanying

doi:10.3892/ol.2019.10498

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…Overexpression of ZBTB16 in lung cancer cell lines inhibited proliferation and increased apoptosis while the depletion of cytoplasmic PLZF was correlated with the high tumor grade, lymph node metastasis, the higher tumor stage and the shorter overall survival [44,45]. ZBTB16 was also down-regulated in never smoker patients with lung adenocarcinoma [46] and non-small cell lung cancer high-metastatic cell line compared with the low-metastatic cell line [47].…”

Section: Discussionmentioning

confidence: 92%

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Zengin

Önal-Süzek

2020

BMC Bioinformatics

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Background Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. Results We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. Conclusions This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

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Section: Discussionmentioning

confidence: 92%

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Zengin

Önal-Süzek

2020

BMC Bioinformatics

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“…In order to assess the potential utilities of these 5 immunocompetent metastatic HNC mouse models for HNC metastasis research, we first assembled an “HNC invasion/metastatic candidate genome database”, and then compared candidate invasion/metastatic events between TCGA-HNC tumors and these 5 models. The database collected genes from the cancer hallmark “activating invasion and metastasis” gene set [ 52 ], as well as genes with demonstrated experimental evidence [ 55 , 56 , 57 , 58 , 59 ] (manually curated) or from the GeneRIF bioinformatics resource ( ; with keyword filtering of “metastasis”, “HNSCC”, “OPSCC”, “OSCC”, etc.). The compiled “HNC invasion/metastatic candidate genome database” comprises a total of 1283 human genes, and their corresponding mouse gene names are also listed for referencing ( Table S6 ).…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Exome Analysis of Immunocompetent Metastatic Head and Neck Cancer Models Reveals Patient Relevant Landscapes

Ngan

Liu

et al. 2020

Cancers

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Immunocompetent metastatic head and neck cancer (HNC) models, although scarce, can help understanding cancer progression and therapy responses in vivo. Their comprehensive genome characterizations are essential for translational research. We first exome-sequenced the two most widely used spontaneous metastatic immunocompetent models, namely AT-84 and SCC VII, followed by comprehensive genomic analyses with three prior-sequenced models (MOC2, MOC2-10, and 4MOSC2), together with patient tumors for utility assessment. AT-84 and SCC VII bear high HNC tumor resemblance regarding mutational signatures—Trp53, Fanconi anemia, and MAPK and PI3K pathway defects. Collectively, the five models harbor genetic aberrations across 10 cancer hallmarks and 14 signaling pathways and machineries (metabolic, epigenetic, immune evasion), to extents similar in patients. Immune defects in HLA-A (H2-Q10, H2-Q4, H2-Q7, and H2-K1), Pdcd1, Tgfb1, Il2ra, Il12a, Cd40, and Tnfrsf14 are identified. Invasion/metastatic genome analyses first highlight potential druggable ERBB4 and KRAS mutations, for advanced/metastatic oral cavity cancer, as well as known metastasis players (Muc5ac, Trem3, Trp53, and Ttn) frequently captured by all models. Notable immunotherapy and precision druggable targets (Pdcd1, Erbb4, Fgfr1, H/Kras, Jak1, and Map2k2) and three druggable hubs (RTK family, MAPK, and DNA repair pathways) are frequently represented by these models. Immunocompetent metastatic HNC models are worth developing to address therapy- and invasion/metastasis-related questions in host immunity contexts.

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“…Lung cancer is a very harmful disease with an average 5-year relative survival rate of only 18% (Siegel, Miller & Jemal, 2018). In recent years, numerous diagnostic molecular markers related to lung tumor have been identified (Xie et al, 2019a;Bao et al, 2017;Tepeli et al, 2012;Jan et al, 2019;Sun et al, 2019), but it is difficult to achieve accurate early-stage detection. This is probably the most important cause of high mortality in lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

Gao

et al. 2020

PeerJ

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Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.

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Five genes may predict metastasis in non‑small cell lung cancer using bioinformatics analysis

Cited by 13 publications

References 39 publications

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Comprehensive Exome Analysis of Immunocompetent Metastatic Head and Neck Cancer Models Reveals Patient Relevant Landscapes

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

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