Unraveling Racial Disparities in Papillary Thyroid Cancer: A Comparative Bulk RNA-Sequencing Gene Expression Analysis
Bibliographic record
Abstract
Papillary thyroid cancer (PTC) is the most common thyroid malignancy, with significant racial/ethnic disparities in incidence and survival. Asians have the highest incidence, and recurrence, while African Americans experience the lowest survival rates, suggesting contributions from genetic, environmental, and healthcare-related factors. While socioeconomic disparities play a role, emerging evidence highlights genetic and molecular mechanisms underlying these differences. This study examines differentially expressed genes (DEGs) to identify potential molecular drivers of PTC disparities. Bulk RNA-sequencing (RNA-seq) data from 20 PTC tumors (5 White, 5 African American, 5 Hispanic, and 5 Asian) were analyzed using the UseGalaxy platform. Preprocessing included quality control, adapter trimming, and genome alignment. Differential expression analysis identified genes with p < 0.01 and fold change ≥ 2.5. Volcano plots visualized significant DEGs. Gene Set Enrichment Analysis (GSEA) via eVITTA identified enriched pathways. TCGA data analysis validated racial/ethnic differences in gene expression. Ethnic groups exhibited distinct gene expression profiles. GSEA revealed differences in cell proliferation, immune regulation, and thyroid hormone metabolism. African Americans showed immune suppression and reduced tumor suppressor activity, while Asians exhibited enriched cell cycle and DNA repair pathways. Significant differences were confirmed in some of the genes in TCGA data analysis. This study identifies genetic factors contributing to racial disparities in PTC, emphasizing the need for further validation in larger cohorts and functional studies. Understanding these molecular differences may inform personalized treatment strategies and improve PTC outcomes across diverse populations.
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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 | 0.000 | 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".