RESUMEN
AIMS: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease. Chemotherapy based on gemcitabine (GEM) remains the first-line drug for patients with advanced PDAC. However, GEM resistance impairs its therapeutic effectiveness. Therefore, identifying effective therapeutic targets are urgently needed to overcome GEM resistance. METHODS: The clinical significance of Tripartite Motif Containing 29 (TRIM29) was identified by exploring GEO datasets and TCGA database and its potential biological functions were predicted by GSEA analysis. The regulatory axis was established by bioinformatics analysis and validated by mechanical experiments. Then, in vitro and in vivo assays were performed to validate the roles of TRIM29 in PDAC GEM resistance. RESULTS: High TRIM29 expression was associated with poor prognosis of PDAC and functional experiments demonstrated that TRIM29 promoted GEM resistance in PDAC GEM-resistant (GR) cells. Furthermore, we revealed that circRPS29 promoted TRIM29 expression via competitive interaction with miR-770-5p and then activated MEK/ERK signaling pathway. Additionally, both in vitro and in vivo functional experiments demonstrated that circRPS29/miR-770-5p/TRIM29 axis promoted PDAC GEM resistance via activating MEK/ERK signaling pathway. CONCLUSION: Our results identify the significance of the signaling axis, circRPS29/miR-770-5p/TRIM29-MEK/ERK, in PDAC GEM resistance, which will provide novel therapeutic targets for PDAC treatment.
Asunto(s)
Carcinoma Ductal Pancreático , Resistencia a Antineoplásicos , Gemcitabina , Sistema de Señalización de MAP Quinasas , Neoplasias Pancreáticas , Factores de Transcripción , Animales , Humanos , Ratones , Antimetabolitos Antineoplásicos/farmacología , Antimetabolitos Antineoplásicos/uso terapéutico , Carcinoma Ductal Pancreático/tratamiento farmacológico , Carcinoma Ductal Pancreático/genética , Carcinoma Ductal Pancreático/patología , Línea Celular Tumoral , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacología , Desoxicitidina/uso terapéutico , Proteínas de Unión al ADN/metabolismo , Proteínas de Unión al ADN/genética , Resistencia a Antineoplásicos/genética , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Sistema de Señalización de MAP Quinasas/efectos de los fármacos , Ratones Desnudos , MicroARNs/genética , MicroARNs/metabolismo , Neoplasias Pancreáticas/tratamiento farmacológico , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/patología , Pronóstico , ARN Circular/genética , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , Ensayos Antitumor por Modelo de XenoinjertoRESUMEN
Alzheimer's disease (AD) is a common neurodegenerative disease. The major problems that exist in the diagnosis of AD include the costly examinations and the high-invasive sampling tissue. Therefore, it would be advantageous to develop blood biomarkers. Because AD's pathological process is considered tightly related to autophagy; thus, a diagnostic model for AD based on ATGs may have more predictive accuracy than other models. We obtained GSE63060 dataset from the GEO database, ATGs from the HADb and screened 64 differentially expressed autophagy-related genes (DE-ATGs). We then applied them to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as DisGeNET and PaGenBase enrichment analyses. By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3. We combined them with sex and age to establish a nomogram model. To evaluate the model's distinguishability, consistency, and clinical applicability, we applied the receiver operating characteristic (ROC) curve, C-index, calibration curve, and on the validation datasets GSE63061, GSE54536, GSE22255, and GSE151371 from GEO database. The results show that our model demonstrates good prediction performance. This AD diagnosis model may benefit both clinical work and mechanistic research.