Molecular Insights into Gastric Cancer: A Comparative Analysis of Asian and White Populations
Bibliographic record
Abstract
Background/Aim: Gastric cancer exhibits significant molecular differences across racial and ethnic groups, influencing prognosis and treatment response. This study aimed to compare the molecular characteristics of gastric cancer between Asian and White populations using data from The Cancer Genome Atlas (TCGA). Patients and Methods: TCGA data for gastric cancer patients were analyzed to identify differences in genetic mutations, copy number variations, and transcriptomic profiles between Asian and White populations. Bioinformatics tools and statistical analyses were used to assess molecular alterations and pathway enrichment. Results: Distinct molecular patterns were observed between the two populations. Asian patients exhibited a higher prevalence of mutations in genes such as TP53 and ARID1A, while White patients showed increased alterations in KRAS and PIK3CA. Differences in immune-related gene expression and tumor microenvironment signatures were also noted, suggesting potential implications for targeted therapies and immunotherapy response. Conclusion: Significant molecular differences exist in gastric cancer between Asian and White populations, showing the need for population-specific treatment strategies. These findings may inform personalized therapeutic approaches and contribute to the advancement of precision oncology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".