Trans-ancestry transcriptome-wide association and functional studies to uncover novel susceptibility genes and therapeutic targets for colorectal cancer
Bibliographic record
Abstract
Abstract By integrating findings from large-scale omics analyses with experimental tests, this study aims to decipher susceptibility genes and the underlying biological mechanisms involved in the development of colorectal cancer (CRC). We first conducted a trans-ancestry transcriptome-wide association study (TWAS) among 57,402 CRC cases and 119,110 controls, aiming to examine how altered gene expression influences CRC risk in European and Asian populations. Then, functional experiments in (i) CRC cell lines and (ii) tumor xenografts were conducted to examine potential underlying mechanisms involved in colorectal carcinogenesis. Further, a drug sensitivity test was employed to explore possible clinical implications for CRC treatment. The TWAS identified 67 genes highly associated with CRC risk, 23 of which were novel findings. Functional annotation of variants within TWAS-identified loci revealed that the majority (93.6%) showed evidence of transcriptional regulatory mechanisms via proximal promoter or distal enhancer-promoter interactions. Among the identified susceptibility genes, splicing factor 3a subunit 3 (SF3A3) may act as an oncogene on the basis that overexpression of this gene was significantly associated with increased risk of CRC (P = 5.75 × 10−11). Further cell and animal experiments confirmed that SF3A3 plays an oncogenic role in CRC development, and the underlying biological mechanism is likely to be related to its anti-apoptosis effect. The drug sensitivity test suggested that phenethyl isothiocyanate (PEITC) targeting SF3A3 can inhibit CRC progression. This study identified novel CRC susceptibility genes and potential biological mechanisms of SF3A3 involved in CRC development, providing important insight into the etiology and potential leads to the treatment of CRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".