SPP1 inhibition improves the cisplatin chemo-sensitivity of cervical cancer cell lines

Xing, Chengzhong; Xiong, Dongsheng; Ye, Liya; Yang, Hui-Chun; Mei, Shuangshuang; Wu, Jinhong; Chen, Shanshan; Ruoran, MI

doi:10.1007/s00280-018-3759-5

Cited by 47 publications

(36 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous bioinformatics analysis showed that SPP1 is closely related to the incidence and poor prognosis of CC [27], which is consistent with our ndings. Moreover, SPP1 down regulation improves the cisplatin sensitivity of HeLa cells by inhibiting the activity of the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway [28].…”

Section: Discussionmentioning

confidence: 99%

A novel four-gene prognostic signature as a risk biomarker in cervical cancer

Wang

Zheng

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC by a bioinformatics-based analysis using the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)-CC cohort. Methods: RNA-Seq data from four GEO datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan–Meier survival analysis were used for in-depth screening for hub DEGs. Cox regression was then used to develop prognostic signature, which was in turn used to create a nomogram. Results: A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values of 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. Conclusions: We developed a four-gene signature that can accurately predict the prognosis, in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

A novel four-gene prognostic signature as a risk biomarker in cervical cancer

Wang

Zheng

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The protein encoded by SPP1 helps osteoclasts to bind to the mineralized bone matrix, which plays an important role in tumorigenesis, invasion and metastasis [10,62]. Overexpressed SPP1 expression had been con rmed in various types of cancers [63][64][65][66][67]. A study based on gastric cancer cell lines indicated that the elevated expression of SPP1 is a critical determinant of poor prognosis [68].…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Hub Genes Associated With Gastric Cancer Using Integrated Bioinformatics Analysis

Zhang

Qiao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.Methods: Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytoHubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs. Results: Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.Discussion: We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

show abstract

“…Xu F et al found that hypoxia and TGF-β1 increased PLOD2 expression to facilitate epithelial-tomesenchymal transition (EMT) and focal adhesion formation, thus promoting the migration and invasion of cervical cancer cells [35]. Chen X et al found SPP1 inhibition enhanced the chemosensitivity of cervical cancer cell lines to cisplatin [36]. Using microarray analysis, Song JY et al found that APOD played in the invasion of cervical cancer [37].…”

Section: Discussionmentioning

confidence: 99%

SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

Jiang

Cao

Gao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC= 0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003). Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients..

show abstract

SPP1 inhibition improves the cisplatin chemo-sensitivity of cervical cancer cell lines

Cited by 47 publications

References 29 publications

A novel four-gene prognostic signature as a risk biomarker in cervical cancer

A novel four-gene prognostic signature as a risk biomarker in cervical cancer

Identification of Novel Hub Genes Associated With Gastric Cancer Using Integrated Bioinformatics Analysis

SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

Contact Info

Product

Resources

About