Integrating multi-tissue expression and splicing data to prioritise anatomical subsite- and sex-specific colorectal cancer susceptibility genes with therapeutic potential
Bibliographic record
Abstract
Abstract Numerous potential susceptibility genes have been identified for colorectal cancer (CRC). However, it remains unclear which genes have a causal role in CRC risk, whether these genes are associated with specific types of CRC, and if they have potential for therapeutic targeting. We performed a multi-tissue transcriptome-wide association study (TWAS) across six relevant normal tissues (n=187-670) and applied a causal framework (involving Mendelian randomisation and genetic colocalisation) to prioritise causal associations between gene expression or splicing events and CRC risk (52,775 cases; 45,940 controls), incorporating sex- and anatomical subsite-specific analyses. We identified 35 genes with robust evidence for a potential causal role in CRC, including ten genes not previously identified by TWAS. Among these genes, SEMA4D emerged as a significant discovery; it is not located at any established CRC genome-wide association study (GWAS) risk locus and its encoded protein is targeted by an antibody currently being clinically studied for CRC treatment. Several genes showed increased expression associated with CRC risk and evidence of CRC cell dependency in CRISPR screen analyses, highlighting their potential as targets for therapeutic inhibition. A female-specific association with CRC risk was observed for CCM2 expression, which is involved in progesterone signalling pathways. Subsite-specific associations were also found, including a link between rectal cancer risk and expression of LAMC1 , which encodes a target for a clinically approved drug. Additionally, we performed a focused analysis of established drug targets to further identify potential therapies for CRC, revealing PDCD1 , the product of which (PD-1) is targeted by a clinically approved CRC immunotherapy. In summary, our comprehensive analysis provides valuable insights into the molecular underpinnings of CRC and supports promising avenues for therapeutic intervention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".