Differential gene expression patterns in Niemann-Pick Type C and Tay-Sachs diseases: Implications for neurodegenerative mechanisms
Bibliographic record
Abstract
Lysosomal storage disorders (LSDs) are a group of rare genetic conditions characterized by the impaired function of enzymes responsible for lipid digestion. Among these LSDs, Tay-Sachs disease (TSD) and Niemann-Pick type C (NPC) may share a common gene expression profile. In this study, we conducted a bioinformatics analysis to explore the gene expression profile overlap between TSD and NPC. Analyses were performed on RNA-seq datasets for both TSD and NPC from the Gene Expression Omnibus (GEO) database. Datasets were subjected to differential gene expression analysis utilizing the DESeq2 package in the R programming language. A total of 147 differentially expressed genes (DEG) were found to be shared between the TSD and NPC datasets. Enrichment analysis was then performed on the DEGs. We found that the common DEGs are predominantly associated with processes such as cell adhesion mediated by integrin, cell-substrate adhesion, and urogenital system development. Furthermore, construction of protein-protein interaction (PPI) networks using the Cytoscape software led to the identification of four hub genes: APOE, CD44, SNCA, and ITGB5. Those hub genes not only can unravel the pathogenesis of related neurologic diseases with common impaired pathways, but also may pave the way towards targeted gene therapy of LSDs.In addition, they serve as the potential biomarkers for related neurodegenerative diseases warranting further investigations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".