Glycine Decarboxylase Suppresses the Renal Cell Carcinoma Growth and Regulates Its Gene Expressions and Functions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".