The genomic landscape of <scp>Cronkhite–Canada</scp> syndrome: Possible clues for pathogenesis
Bibliographic record
Abstract
OBJECTIVES: Cronkhite-Canada syndrome (CCS) is a rare hamartomatous polyposis syndrome with a proposed association with chronic autoimmune inflammation. To date, genetic background of patients with CCS remains less investigated. In this study we aimed to explore the genomic landscape of CCS. METHODS: Whole exome sequencing was performed on peripheral blood samples extracted from 18 patients with CCS. Potential function-impacting germline variants were filtered by R software. Through systematic data analysis, a number of genetic variants were identified. Enrichment analysis was performed using the R package ClusterProfiler. RESULTS: Overall, 3960 low-frequency (<0.05 or not reported in the Exome Aggregation Consortium East Asian, 1000 Genomes, or ESP6500 database) potentially function-impacting germline variants were identified, with 18 genes (FDFT1, LOC400863, MUC3A, MUC4, ZNF806, GXYLT1, MUC6, PABPC3, PSPH, ZFPM1, CIC, LOC283710, ARSD, GOLGA6L2, LOC388282, SLC25A5, TMEM247, WDR89) involved over half the patients. Functional enrichment of these genes revealed several biological processes in relation to innate immune responses and glycosylation. Only one likely pathogenic germline variant of an hamartomatous polyposis syndrome-associated gene, PTCH1, was detected in one patient. CONCLUSIONS: CCS has genomic alteration patterns completely distinct from those of traditional hamartomatous polyposis syndrome. The germline mutation landscape indicates potential roles of innate immune responses and glycosylation in the pathogenesis of CCS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".