RESUMEN
Sunitinib resistance is, nowadays, the major challenge for advanced renal cell carcinoma patients. Illuminating the potential mechanisms and exploring effective strategies to overcome sunitinib resistance are highly desired. We constructed a reliable gene signature which may function as biomarkers for prediction of sunitinib sensitivity and clinical prognosis. The gene expression profiles were obtained from The Cancer Genome Atlas database. By performing GEO2R analysis, numerous differentially expressed genes (DEGs) were found to be associated with sunitinib resistance. To acquire more precise DEGs, we integrated three different microarray datasets. Functional analysis revealed that these DEGs were mainly involved in Rap1 signaling pathway, p53 signaling pathway and Ras signaling pathway. Then, top five hub genes, BIRC5, CD44, MUC1, TF, CCL5, were identified from protein-protein interaction (PPI) network. Sub-network analysis carried out by MCODE plugin revealed that key DEGs were related with PI3K-Akt signaling pathway, Rap1 signaling pathway and VEGF signaling pathway. Next, we established sunitinib-resistant OS-RC-2 and 786-O cell lines and validated the expression of five hub genes in cell lines. To further evaluate the potentials of five-gene signature for predicting clinical prognosis, we analyzed RCC patients with gene expressions and overall survival information from two independent patient datasets. The Kaplan-Meier estimated the OS of RCC patients in the low- and high-risk groups according to gene expression signature. Multivariate Cox regression analysis indicated that the prognostic power of five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature which can predict sunitinib sensitivity and OS for advanced RCC patients, providing novel insights into understanding of sunitinib-resistant mechanisms and identification of RCC patients with poor prognosis.
Asunto(s)
Biomarcadores de Tumor/genética , Carcinoma de Células Renales/tratamiento farmacológico , Resistencia a Antineoplásicos/genética , Sunitinib/administración & dosificación , Adulto , Anciano , Anciano de 80 o más Años , Antígenos de Carbohidratos Asociados a Tumores/genética , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Quimiocina CCL5/genética , Femenino , Regulación Neoplásica de la Expresión Génica/genética , Humanos , Receptores de Hialuranos/genética , Estimación de Kaplan-Meier , Masculino , Persona de Mediana Edad , Mucina-1/genética , Pronóstico , Sunitinib/efectos adversos , Survivin/genética , Transcriptoma/genéticaRESUMEN
Purpose Clear cell renal cell carcinoma(ccRCC) is the most common type of renal cell carcinoma. While it is curable when detected at an early stage, some patients presented with advanced disease have poor prognosis. We aimed to identify key genes and miRNAs associated with clinical prognosis in ccRCC. Methods The microarray datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were analyzed by using GEO2R. Then, Functional enrichment analysis was performed using the DAVID. A retrospective series of 254 ccRCC patients with complete clinical information was included in this study. Kaplan-Meier analysis and multivariate cox regression analysis were used for prognostic analysis. Wound healing assay and transwell assay were designed to evaluate the migration and invasion ability of ccRCC cell lines. Results miRNA-18a was identified to be related with prognosis of ccRCC by using Kaplan-Meier analysis and multivariate cox regression analysis demonstrated that the prognostic value of miRNA-18a was independent of clinical features. Further studies showed that up-regulation of miRNA-18a had a positive effect on migration and invasion of ccRCC cells. The target gene (HIF1A) of the miRNA-18a was predicted by using the miRPathDB database. The transcription factors of DEGs were identified by using the i-cisTarget. Luckily, HIF1A was found to be one of the transcription factors of DEGs. Among these DEGs, PVT1 may be regulated by HIF1A and be related with prognosis of ccRCC. Finally, validation of miRNA18a/HIF1A/PVT1 pathway was checked via reverse transcription-polymerase chain reaction (RT-PCR) assay in both cell lines and clinical tumor samples. Conclusion Our research revealed that miRNA18a/HIF1A/PVT1 pathway might play a crucial role in ccRCC progression, providing novel insights into understanding of ccRCC molecular mechanisms. Importantly, miRNA-18a could serve as a potential diagnostic biomarker and therapeutic targets for ccRCC patients.