An expression signature model to predict lung adenocarcinoma-specific survival

Shi, Xiaoshun; Tan, Haoming; Le, Xiaobing; Xian, Hua; Li, Xiaoxiang; Huang, Kuo Cheng; Luo, Viola Yingjun; Liu, Qing; Wu, Ziying; Mo, Hong-ying; Chen, Allen M.; Liang, Ying; Zhang, Jiexia

doi:10.2147/cmar.s159563

Cited by 38 publications

(37 citation statements)

References 38 publications

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“…Study has shown that the great expression level of RP11-108K3.2 correlated with low overall survival of CRC, and this result was consistent with our study [15]. In a study on lung adenocarcinoma, the researchers study 346 differentially expressed lncRNAs, of which RP11-108K3.2 is highly expressed in lung cancer tissues [16]. ACADL GFRA3 CLVS2 ALPI CCNO NGB RASSF8 RBPMS2 PNPLA3 ACSBG1 FOLR2 RASSF10 MB MCIDAS AP1S2 GRB7 RBPMS LIPG FERMT1 FERMT2 SLC26A2 SLC13A2 KIF5C LMO3 HAND2 FAM13C CPXM2 TUBB2B CSRP1 PHOX2B FIGF CLU LYVE1 PRRT2 C7 COL6A2 LEPREL1 COL21A1 COL9A1 COL10A1 CRTAP CHL1 COLGALT2 PRKAA2 FABP4 ITGA7 MYOT LEPR EBF1 FABP6 TMTC1 IL32 ACKR1 METTL7A CHI3L1 HBB CXCL1 CCL2 HBG1 ITGA2 ITGA5 PTGER3 ADIPOQ SLC2A4 LCN2 FLNC ADAMTS14 THSD7A COL11A1 PCOLCE2 TMEM179 CKS2 E2F7 KIF24 RECQL4 REEP2 CA9 SMPX IP6K3 KCNIP3 ASB5 ZBTB16 FBXO41 HJURP Figure 3: The protein-protein interaction network of differentially expressed mRNAs.…”

Section: Discussionsupporting

confidence: 90%

Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

Zhao

Wang

Yang

et al. 2019

Preprint

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Objective. To screen some RNAs that correlated with colorectal cancer (CRC). Methods. Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functional enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed which correlated with CRC. RNAs in this network were subjected to analyze the relationship with the patient prognosis. Results. A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis. Conclusion. The identified RNAs and constructed ceRNA network could provide great sources for the researches of therapy of the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve as a novel prognostic predictor of CRC.

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Section: Discussionsupporting

confidence: 90%

Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

Zhao

Wang

Yang

et al. 2019

Preprint

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“…In addition, previous studies have shown that RP11-108K3.1 could serve as a potential independent biomarker for the prognostic prediction of colorectal cancer [18] . RP11-108K3.1 is one of the DElncRNAs in lung adenocarcinoma [19] . However, no study so far has reported any association of RP11-109M17.2 with cancer, which means that it may serve as a novel therapeutic target in TSCC.…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Prognostic Biomarkers for Tongue Squamous Cell Carcinoma

Zhang

et al. 2020

Preprint

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Background: Tongue squamous cell carcinoma (TSCC) is one of the most common types of oral cancer and has a poor prognosis owing to a limited understanding of the pathogenesis mechanisms. The purpose of this study was to explore and identify potential biomarkers in TSCC by integrated bioinformatics analysis. Methods: The RNA sequencing data and clinical characteristics of TSCC patients were downloaded from The Cancer Genome Atlas (TCGA), and then differentially expressed RNAs (DERNAs), including differentially expressed long noncoding RNAs (DElncRNAs) and differentially expressed messenger RNAs (DEmRNAs), were identified in TSCC by bioinformatics analysis. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmark pathway analyses were used to analyze the DERNAs. Using univariate and multivariate Cox regression analyses, four-lncRNA and two-mRNA signatures were developed and used for predicting survival in TSCC patients. We established a risk model to predict the overall survival (OS) of TSCC patients based on the DERNAs with Kaplan–Meier analysis and the log-rank p test. Furthermore, weighted gene coexpression network analysis (WGCNA) was performed in Cytoscape, and a protein-protein interaction (PPI) network was established in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Results: A total of 2,006 DEmRNAs and 1,001 DElncRNAs were found to be dysregulated in TSCC. A total of 417 DERNAs were used to construct the coexpression network, and the PPI network included 103 DEmRNAs. Univariate regression analysis of the DERNAs revealed 51 DElncRNAs and 90 DEmRNAs that were associated with OS in TSCC patients. Multivariate Cox regression analysis demonstrated that four of those lncRNAs (MGC32805, RP1-35C21.2, RP11-108K3.1, RP11-109M17.2) and two mRNAs (CA9, GTSF1L) had significant prognostic value, and their cumulative risk score indicated that these four-lncRNA and two-mRNA signatures independently predicted OS in TSCC patients. Conclusions: The current findings provide novel insights into the molecular mechanisms of TSCC and identify four lncRNAs and two mRNAs that are potential biomarkers that may be independent prognostic signatures for TSCC diagnosis and treatment.

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“…These long molecules are dysregulated among cancers (Yan et al, 2015) and play key roles in gene regulation and carcinogenesis, including proliferation, survival, migration, and genomic stability (Gutschner et al, 2013;Castro-Oropeza et al, 2018). It is believed that the clinical value of lncRNA is not confined to candidate biomarkers for diagnostic and prognostic purposes (Shi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma

Wang

2020

Front. Bioeng. Biotechnol.

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Lung adenocarcinoma (LUAD) is one of the most fatal malignant tumors harmful to human health. The complexity and behavior characteristics of long-non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) network in LUAD patients are still unclear. The purpose of this study was to elucidate the regulatory networks of dysregulated RNAs, view, and identify potential prognosis signatures involved in LUAD. The expression profiles of mRNAs, lncRNAs, and miRNAs were obtained from the TCGA database. In total, 2078 DEmRNAs, 257 DElncRNAs, and 101 DEmiRNAs were sorted out. A PPI network including 45 DEmRNAs was constructed. Ten hub genes in the PPI network associated with cell cycle-related pathways were identified and they played key roles in regulating cell proliferation. A total of three DEmiRNAs, seven DElncRNAs, and six DEmRNAs were enrolled in the ceRNA network. Except for certain genes without any published study reports, all the genes in the ceRNA network played an essential role in controlling tumor cell proliferation and were associated with prognosis in LUAD. Finally, based on step regression and Cox regression survival analysis, we identified four candidate biomarkers, including miR490, miR1293, LINC01740, and IGF2BP1, and established a risk model based on the four genes. Our study provided a global view and systematic dissection of the lncRNA-associated ceRNA network, and the identified four genes might be novel important prognostic factors involved in LUAD pathogenesis.

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An expression signature model to predict lung adenocarcinoma-specific survival

Cited by 38 publications

References 38 publications

Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

Identification of Potential Prognostic Biomarkers for Tongue Squamous Cell Carcinoma

Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma

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