Glycine Decarboxylase Suppresses the Renal Cell Carcinoma Growth and Regulates Its Gene Expressions and Functions
Notice bibliographique
Résumé
Background: Glycine decarboxylase (GLDC), a key metabolic enzyme, participates in the regulation of the glycine metabolic pathway. Differential expression of GLDC is linked to the malignant growth of renal cell carcinoma (RCC) and may regulate tumor progression through other genes. However, the regulatory function of GLDC in RCC is currently unknown. The purpose of this work was to evaluate the roles of GLDC in the invasion, proliferation, and migration of RCC cells and elucidate the processes underlying RCC development. Methods: The expression of GLDC in RCC cell lines and tissues was identified by quantitative reverse transcription polymerase chain reaction (PCR) and western blot. A stably transfected cell line overexpressing GLDC was constructed using a lentiviral vector. Cell proliferation was detected using Cell Counting Kit-8 (CCK8) and EdU experiments, and scratch and transwell assays were used to determine migration and invasion capabilities. Furthermore, differential proteins were identified and obtained using high-performance liquid chromatography (HPLC)-tandem mass spectrometry (MS/MS) analysis. Finally, these differential proteins were analyzed by bioinformatics, including cluster analysis, subcellular localization, domain annotation, annotation of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, and study of protein-protein interactions. Results: GLDC expression was found to be lower in six RCC cell lines (786-O, A498, Caki-1, 769-P, OSRC-2, and ACHN) than in 293T cells and decreased in kidney cancer tissues compared to neighboring normal tissues. Overexpression of GLDC inhibited the proliferation of RCC cells as well as their migration and invasion abilities. Tandem mass tag analysis showed that 317 and 236 genes were downregulated and upregulated, respectively, when GLDC was overexpressed in A498 cells. Tandem mass tag analysis showed that 317 and 236 genes were downregulated and upregulated, respectively, when GLDC was overexpressed in A498 cells. Volcano plot showed these upregulated and downregulated proteins. Cluster analysis showed that differentially expressed protein screening can represent the effect of biological treatment on samples. Subcellular localization analysis showed differential proteins are mainly distributed in the nucleus, cytoplasm, mitochondria, plasma membrane, extracellular matrix, and lysosome. GO annotation showed many biological processes in the cells were changed, including "positive regulation of histone H3-K4 methylation", "cofactor binding", and "nuclear body". KEGG pathway analysis showed key pathways have all undergone considerable alterations, such as "cell cycle", "glyoxylate and dicarboxylate metabolism", and "threonine, glycine, and serine metabolism". Finally, highly aggregated proteins with the same or similar functions were acquired by analysis of the protein-protein interaction (PPI) network. Conclusions: These studies indicate that GLDC overexpression suppresses the invasion, proliferation, and migration of RCC cells and leads to the upregulation and downregulation of 236 and 317 genes, respectively.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».