Bioinformatic Analysis Revealing Mitotic Spindle Assembly regulated NDC80 and MAD2L1 as Prognostic Biomarkers in Non-Small Cell Lung Cancer Development

Abstract

Background Lung cancer has been the leading cause of tumor related death, and 80%~85% of it is non-small cell lung cancer (NSCLC). Even with the rising molecular targeted therapies, for example EGFR, ROS1 and ALK, the treatment is still challenging. The study is to identify credible responsible genes during the development of NSCLC using bioinformatic analysis, developing new prognostic biomarkers and potential gene targets to the disease. Methods Firstly, three genes expression profiles GSE44077, GSE18842 and GSE33532 were picked from Gene Expression Omnibus (GEO) to analyze the genes with different expression level (GDEs) between NSCLC and normal lung samples, and the cellular location, molecular function and the biology pathways the GDEs enriched in were analyzed. Then, gene function modules of GDEs were explored based on the protein-protein interaction network (PPI), and the top module which contains most genes was identified, followed by containing genes annotation and survival analysis. Moreover, multivariate cox regression analysis was performed in addition to the Kaplan meier survival to narrow down the key genes scale. Further, the clinical pathological features of the picked key genes were explored using TCGA data. Results Three GEO profiles shared a total of 664 GDEs, including 232 up-regulated and 432 down-regulated genes. Based on the GDEs PPI network, the top function module containing a total of 69 genes was identified, and 31 of 69 genes were mitotic cell cycle regulation related. And survival analysis of the 31 genes revealed that 17/31 genes statistical significantly related to NSCLC overall survival, including 4 spindle assembly checkpoints, namely NDC80, BUB1B, MAD2L1 and AURKA. Further, multivariate cox regression analysis identified NDC80 and MAD2L1 as independent prognostic indicators in lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) respectively. Interestingly, pearson correlation analysis indicated strong connection between the four genes NDC80, BUB1B, MAD2L1 and AURKA, and their clinical pathological features were addressed. Conclusions Using bioinformatic analysis of GEO combined with TCGA data, we revealed two independent prognostic indicators in LUAD and LUSC respectively and analyzed their clinical features. However, more detailed experiments and clinical trials are needed to verify their drug targets role in clinical medical use.

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“…After analyzing clinical samples, the experimental model was utilized to search for further support. We found that knockdown of MAD2L1 signi cantly decreased proliferation and promoted apoptosis of ES cells in vitro and in vivo, in line with previous reports that MAD2L1 acts as an oncogene in various cancers, including gastric cancer, rhabdomyosarcoma, and colorectal cancer [25,29,30]. Xia et al revealed that MAD2L1 promotes the proliferation and migration of rhabdomyosarcoma cells [14].…”

Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma

“…GEO database has been one of the most commonly used public databases for worldwide researchers to explore the genetic abnormalities in various cancers [ 36 – 39 ]. In the study, we firstly picked four different cDNA expression profiles GSE53757, GSE53000, GSE71963 and GSE68417 from GEO database to analyze the differently expressed genes in ccRCC comparing to normal renal tissue, and the result revealed 192 genes that were shared in four profiles including 39 up-regulated and 153 down-regulated genes.…”

Bioinformatic Analysis Identifing FGF1 Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma 

“…GEO and TCGA database have been two most commonly used public databases for worldwide researchers to explore the genetic abnormalities in various cancers [36][37][38][39]. In the study, four different cDNA expression pro les GSE53757, GSE53000, GSE71963 and GSE68417 were picked from GEO database based on their sample number for further analyzing the differently expressed genes in ccRCC comparing to normal renal tissue, and the result revealed 192 genes that were shared in four pro les including 39 up-regulated and 153 down-regulated genes.…”