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GOLM1 predicts poor prognosis of patients with NSCLC and is associated with the proliferation and chemo‐sensitivity of cisplatin in NSCLC cells: bioinformatics analysis and laboratory validation

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Abstract

Lung cancer is one of the deadliest malignant tumors with non-small cell lung cancer (NSCLC) being the most prevalent type. Patients with NSCLC usually were diagnosed at the advance clinical stages, and these patients often had high rate of tumor-recurrence, thus leading to poor prognosis. Yet, the molecular mechanisms underlying NSCLC progression and recurrence are largely unknown. This study aimed to identify potential hub genes associated with the pathophysiology of NSCLC by bioinformatics analysis and laboratory validation. The GSE51852, GSE52248 and GSE75037 datasets were downloaded from the Gene Expression Omnibus database. The overlap** differentially expressed genes (DEGs) were analyzed by GEO2R tool. Gene Ontology (GO) and KEGG pathway enrichment analysis were performed on these overlap** DEGs. The protein-protein interaction network was constructed to identify hub genes from DEGs. The expression and survival analysis of these hub genes were performed by using the integrated bioinformatics tools. Finally, the effects of GOLM1 on the proliferation and chemo-sensitivity of NSCLC cells were determined by in vitro functional assays. A total of 197 overlap** DEGs (37 up-regulated and 160 down-regulated) were identified from the microarray datasets. Furthermore, the PPI network with 89 nodes and 768 edges was constructed and 17 hub genes were identified from PPI network by using MCODE analysis. The survival analysis revealed that the expression of 5 hub genes (FGF2, GOLM1, GPC3, IL6 and SPP1) were significantly correlated with overall survival of patients with lung cancer. Furthermore, the in vitro functional studies showed that GOLM1 overexpression promoted the NSCLC cell proliferation and colony formation; while GOLM1 knockdown exerted the opposite effects. Importantly, GOLM1 overexpression reduced the chemo-sensitivity of cisplatin in NSCLC cells by attenuating the inhibitory effects of cisplatin on the cell proliferation and colony formation. In conclusion, the present study showed that 5 hub genes including FGF2, GOLM1, GPC3, IL6 and SPP1 were deregulated in NSCLC tissues and may predict the prognosis of patients with NSCLC. GOLM1 may play an important role in regulating the cell proliferation and chemo-sensitivity of cisplatin in NSCLC.

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All the data are available upon request from the corresponding author.

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Funding

This study was supported by Hainan Province Health and Family Planning Industry Research Project (18A200148) and Sanya Medical and Health Project (2019YW09).

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Correspondence to Mei Zhao.

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Supplementary Information

Supplementary Figure S1

PPI network construction of overlap** DEGs derived from STRING database. (PNG 631 kb)

High Resolution Image (TIF 7280 kb)

Supplementary Figure S2

The correlation of hub gene expression levels and the overall survival of patients with NSCLC was analyzed by KM Plotter. The analyzed hub genes include (A) FGF2, (B) GPBAR1, (C) GPC3, (D) PTH1R, (E) RAMP2, (F) RAMP3 and (G) SSTR1. The red lines represent patients with high gene expression, and black lines represent patients with a low gene expression. HR = hazard ratio. (PNG 445 kb)

High Resolution Image (TIF 8981 kb)

Supplementary Figure S3

The correlation of hub gene expression levels and the overall survival of patients with NSCLC was analyzed by Human Protein Atlas database. The analyzed hub genes include (A) CALCRL, (B) CXC13, (C) GNG11, (D) VIPR1, (E) S1PR1, (F) FPR2, (G) GOLM1, (H) IL6 and (I) SPP1. The pink lines represent patients with high gene expression, and blue lines represent patients with a low gene expression. (PNG 681 kb)

High Resolution Image (TIF 770 kb)

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Supplementary Table S2

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Zhao, M., Li, X. & Chen, X. GOLM1 predicts poor prognosis of patients with NSCLC and is associated with the proliferation and chemo‐sensitivity of cisplatin in NSCLC cells: bioinformatics analysis and laboratory validation. J Bioenerg Biomembr 53, 177–189 (2021). https://doi.org/10.1007/s10863-021-09875-7

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  • DOI: https://doi.org/10.1007/s10863-021-09875-7

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