Prognostic value of the mRNA expression of members of the HSP90 family in non‑small cell lung cancer

Liu, Kang; Kang, Min; Li, Jixi; Wen, Qiuyuan; Wang, Rensheng

doi:10.3892/etm.2019.7228

Cited by 26 publications

(22 citation statements)

References 44 publications

(35 reference statements)

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“…The enhancement of tumor suppressing function of ACVR1B might be one possible strategy for treating high-risk LUAD patients. On the other hand, HSP90AB1 overexpression was found associated with poor prognosis both in our study and other studies [36,37]. Whether inhibiting such expression could improve current standard therapies for higher-risk subgroup might warrants further studies.…”

Section: Discussionsupporting

confidence: 67%

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

Wang

Guo

et al. 2020

Preprint

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Background: Integrating phenotypic and genotypicinformation to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS)and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression and mutation data. Methods: We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features, and constructed 14 different input feature sets for predictivemodel development.We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance and time-dependent AUC under competing risks scenario.We stratified patients into higher and lower-risk subgroups by the final risk score, and further investigated underlying immune phenotyping variations associated with the differential risk.Results: The model integrating all three types of data achieved the bestprediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically-definedsubgroups. The score could account for extra EFS-related variationsthat were not captured by clinicopathologic scores. Being validated forRFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p-value< 0.001). The higher-risk patients were characterized with transcriptionalaberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells. Conclusions: We developed a novel prognostic risk score with improved prediction accuracy,using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was an significant predictor of both EFS and RFS.Trail registration: This study was based on public open data from TCGA and hence the study objects were retrospectively registered.

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Section: Discussionsupporting

confidence: 67%

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

Wang

Guo

et al. 2020

Preprint

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“…Xu et al reported that MiR-199b-5p can promote tumor proliferation through regulating KLK10 in cervical cancer [35]. Liu et al reported that Heat Shock Protein 90 Beta Family Member 1 (HSP90B1) is significantly related with worse overall survival in lung cancer [36]. Cawthorn et al reported that up-regulated HSP90B1 is related with distant metastasis [37].…”

Section: Discussionmentioning

confidence: 99%

Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

Zhang

Huang

et al. 2019

J Transl Med

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BackgroundThe current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC).MethodsDifferentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs.ResultsFifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores.ConclusionThe current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.

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“…Microarray technology has been widely applied in the examination of general genetic abnormalities (11–13). Furthermore, the Gene Expression Omnibus (GEO) database is increasingly recognized by researchers with an increasing interest in data mining and bioinformatics and a number of tumor-associated genes, including for NPC, being discovered (14,15).…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

et al. 2019

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The aim of the present study was to identify genes associated with and the underlying mechanisms in nasopharyngeal carcinoma (NPC) using microarray data. GSE12452 and GSE34573 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to obtain differentially expressed genes (DEGs). In addition, the Database for Annotation, Visualization and Integrated Discovery was used to perform pathway enrichment analyses for DEGs using the Gene Ontology (GO) annotation along with the Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, Cytoscape was used to perform module analysis of the protein-protein interaction (PPI) network and pathways of the hub genes were studied. A total of 298 genes were ascertained as DEGs in the two datasets. To functionally categorize these DEGs, we obtained 82 supplemented GO terms along with 7 KEGG pathways. Subsequently, a PPI network consisting of 10 hub genes with high degrees of interaction was constructed. These hub genes included cyclin-dependent kinase (CDK) 1, structural maintenance of chromosomes (SMC) 4, kinetochore-associated (KNTC) 1, kinesin family member (KIF) 23, aurora kinase A (AURKA), ATAD (ATPase family AAA domain containing) 2, NDC80 kinetochore complex component, enhancer of zeste 2 polycomb repressive complex 2 subunit, BUB1 mitotic checkpoint serine/threonine kinase and protein regulator of cytokinesis 1. CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 presented with high areas under the curve in receiver operator curves, suggesting that these genes may be diagnostic markers for nasopharyngeal carcinoma. In conclusion, it was proposed that CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 may be involved in the tumorigenesis of NPC. Furthermore, they may be utilized as molecular biomarkers in early diagnosis of NPC.

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Prognostic value of the mRNA expression of members of the HSP90 family in non‑small cell lung cancer

Cited by 26 publications

References 44 publications

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

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