Multimodal Gene Expression and Methylation Profiling Reveals Misclassified Tumors Beyond Histological Diagnosis
Bibliographic record
Abstract
Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation data to improve classification accuracy and detect misclassified tumors. Using 6216 samples from The Cancer Genome Atlas (TCGA), we applied Support Vector Machines (SVMs) and hierarchical clustering to evaluate classification accuracy across single and integrated platforms. mRNA and methylation data alone achieved accuracies of 97% and 95.4%, respectively. Their integration further reduced false positives and improved the identification of outliers, including histologically misclassified cases such as papillary renal cell carcinoma samples clustering with bladder cancer. The integrated approach also revealed molecular subtypes correlated with somatic mutations and patient survival, offering clinically relevant insights. Our findings highlight the value of combining genetic and epigenetic profiles to refine cancer diagnostics. This framework enhances diagnostic precision, supports treatment decisions, and provides a scalable quality control tool for molecular 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".