Integrating Single‐Cell Transcriptome‐Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune‐Related Drug Targets for Cancers
Bibliographic record
Abstract
Single-cell expression quantitative trait loci data offer promising opportunities to inform immune-related drug development in cancer. However, pleiotropy can complicate causal inference. We introduce MR-DEG, a framework that integrates Mendelian randomization (MR) and differential expressed gene (DEG) to strengthen causal inference. Using eight conventional MR and colocalization methods, we estimated effects of 11 021 dynamic gene expression profiles during CD4+ T cell activation on the risk of six cancer types. This identified 1000 gene-cancer pairs with putative effects (https://www.omicsharbour.com/sc-eqtl-mr). Of these 1000 pairs, 517 involved 205 unique genes that were differentially expressed in relevant cancer tissues based on single-cell RNA-sequencing data. Of these 517 pairs, 265 were classified as likely causal using the conventional MR methods. After applying MR-DEG to the remaining 252 potentially pleiotropic pairs, an additional 89 were classified as likely causal. Sixty-four and 391 of the 1000 original pairs exhibited time- and non-time dependent effects on cancer risk, respectively. Integrating the 1000 gene-cancer pairs of MR findings and clinical trial evidence, we identified 200 pairs corresponding to 33 unique genes that encode drug targets under clinical investigation. These results demonstrate how combining genetic, transcriptomic and clinical trial evidence can reduce pleiotropic bias, and prioritize immune-related drug targets for cancer prevention.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".