Identification of a panel of mitotic spindle-related genes as a signature predicting survival in lung adenocarcinoma
JOURNAL OF CELLULAR PHYSIOLOGY
Authors: Zhang, Liwen; He, Miao; Zhu, Wenjing; Lv, Xuemei; Zhao, Yanyun; Yan, Yuanyuan; Li, Xueping; Jiang, Longyang; Zhao, Lin; Fan, Yue; Su, Panpan; Gao, Mengcong; Ma, Heyao; Li, Kai; Wei, Minjie
Abstract
Lung adenocarcinoma (LUAD) is one of the most malignant tumor types worldwide. Our objective was to identify a genetic signature that could predict the prognosis of patients with LUAD. We extracted gene data sets from The Cancer Genome Atlas and obtained differentially expressed genes that were highly expressed at every stage. These genes were analyzed using gene set enrichment analysis to obtain four biological processes associated with LUAD. Subsequently, Cox univariate and multivariate analyses were performed to generate four optimized models (G2M checkpoint, E2F targets, mitotic spindle, and glycolysis). We identified a mitotic spindle-related signature (KIF15, BUB1, CCNB2, CDK1, KIF4A, DLGAP5, ECT2, and ANLN), which could be an independent prognostic indicator, to predict the prognosis of patients with LUAD. This new discovery should offer opportunities to explore the pathogenesis of LUAD and prove clinically useful in predicting LUAD patient prognosis.
Network-based meta-analysis for the identification of potential target for human anaplastic thyroid carcinoma
META GENE
Authors: Naorem, Leimarembi Devi; Pathak, Ella; Muthaiyan, Mathavan; Venkatesan, Amouda
Abstract
Anaplastic thyroid carcinoma (ATC) is a highly malignant undifferentiated tumor in adults. The study aimed to discover potential therapeutic targets for ATC patient's treatment. For this purpose, 4 mRNA expression profiles consist of 36 ATC and 81 normal thyroid samples were considered. 738 differentially expressed genes (DEGs) were identified which consist of 402 up-regulated and 336 down-regulated genes. The enrichment analysis was performed by DAVID tool and protein-protein interaction (PPI) network was constructed using STRING. Subsequently, the potential hubs, significant modules, gene regulatory network and network motifs were identified. 4 genes namely, CDK1, CDC20, CCNA2 and CCNB2 are found to be common among hubs, modules and the important motif of the regulatory network. Gene regulatory network reveals the important transcription factors, LPOLYA, TCF4, AIRE, PU1 and E2F1; miRs such as hsa-miR-146a, and hsa-miR-590. Altogether, the results suggested that CDK1, CDC20, CCNA2 and CCNB2 and associated miRNAs and TFs could aid as a biomarker for the ATC treatment. However, further experimental validations need to be performed for the obtained biomarkers.