Identification of Differentially Expressed Genes Related to the Lipid Metabolism of Esophageal Squamous Cell Carcinoma by Integrated Bioinformatics Analysis
Bibliographic record
Abstract
Purpose: In recent years, lipid metabolism has been reprogrammed to meet the energy and substrate needs of tumorigenesis and development and is a potential new target for cancer treatment. However, the regulatory mechanism of lipid metabolism in esophageal squamous cell carcinoma is not well understood. Methods: We first downloaded the esophageal squamous cell carcinoma (ESCC) gene dataset in the GEO and TCGA databases and analyzed the central differentially expressed genes (DEGs) of ESCC through bioinformatics. Afterwards, the GSEA method was used to analyze the lipid metabolism-related pathway of the central gene in the pathological process of ESCC, and it was determined that the central gene OIP5 was significantly related to the fatty acid metabolism pathway. Our heatmap also revealed that the enrichment of the ACSL family in ESCC tissues was more pronounced than in normal tissues. We hypothesized that OIP5 can regulate the fatty acid metabolism process in ESCC cells and affect the tumorigenic ability of ESCC. Further statistical analysis and experiment were conducted to determine the lipid metabolism-related gene, OIP5′s, expression pattern and clinical significance in ESCC, analyze the effect of OIP5 expression on fatty acid metabolism-related enzymes in ESCC, revealing the specific mechanism of OIP5 that promotes ESCC development. Conclusions: Our study established a correlation between OIP5 expression and clinicopathological factors (tumor size, T stage, N stage, and clinical grade) in esophageal squamous cell carcinoma (p < 0.05). We have also experimentally demonstrated that OIP5 regulates ESCC fatty acid metabolism by influencing the expression of the key enzyme ACSL1 in lipid metabolism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".