Analysis of the interlink between glucose-6-phosphate dehydrogenase (G6PD) and lung cancer through multi-omics databases
Bibliographic record
Abstract
Glucose-6-Phosphate Dehydrogenase (G6PD) is a crucial enzyme that executes the pentose phosphate pathway. Due to its critical nodal position in the metabolic network, it is associated with different forms of cancer tumorigeneses and progression. Nonetheless, its functional role and molecular mechanism in lung cancer remain unknown. The present study provides intricate information associated with G6PD and Lung Cancer. Varieties of public datasets were retrieved by us, including UALCAN, TCGA, cBioPortal, and the UCSC Xena browser. The data obtained were used to assess the expression of G6PD, its clinical features, epigenetic regulation, relationship with tumour infiltration, tumour mutation burden, microsatellite instability, tumour microenvironment, immune checkpoint genes, genomic alteration, and patient's overall survival rate. The present study revealed that the G6PD expression was correlated with the clinical features of lung cancer including disease stage, race, sex, age, smoking habits, and lymph node metastasis. Moreover, the expression profile of G6PD also imparts epigenetic changes by modulating the DNA promoter methylation activity. Methylation of promoters changes the expression of various transcription factors, genes leading to an influence on the immune system. These events linked with G6PD-related mutational gene alterations (FAM3A, LAG3, p53, KRAS). The entire circumstance influences the patient's overall survival rate and poor prognosis. Functional investigation using STRING, GO, and KEGG found that G6PD primarily engages in hallmark functions (metabolism, immunological responses, proliferation, apoptosis, p53, HIF-1, FOXO, PI3K-AKT signaling). This work provides a wide knowledge of G6PD's function in lung cancer, as well as a theoretical foundation for possible prognostic therapeutic markers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".