Predisposition of an Intronic Duplication in CHEK2 Gene in the Cases of Breast Cancer from Balochistan
Bibliographic record
Abstract
OBJECTIVE: After BRCA 1&2, CHEK2 is the most frequently predisposing altered gene causing breast cancer in female. The prime objective of the current study was to analyze germline CHEK2 variants and their association with breast cancer in Balochistani population. METHODS: Breast cancer is among the most prevalent cancers worldwide and most common diagnosed cancer in women. Mutations in many proto-oncogenes and tumor suppressor genes lead to the development of cancer. Along with the highly penetrance genes BRCA1 and BRCA2 increase the risk of breast cancer, mutations in other genes including CHEK2, a tumor suppressor gene have also been reported to be associated with breast cancer. In current study CHEK2 gene was analyzed for variation in breast cancer patients and controls of Balochistani population. Sequencing results of the DNA samples of the registered cases of breast cancer in CENAR were analyzed using Chromas software and bioinformatics tools including BLAT and RNAfold web server. RESULTS: An intronic variant c.319+38-43dupA falling 38 nucleotide away at splice donor site of the CHEK2 gene exon 2, in 9% breast cancer cases, was identified which has also been previously reported in non-Hodgkin Lymphoma cases. All the cases with identified variant, were affected with invasive ductal carcinoma. Higher tumor grade (III) was reported in >50% of the patients and > 70% of patients diagnosed with advanced stage of cancer. The RNA prediction results revealed the variant falling in the intronic region may code for miRNA that could play an important role in cancer progression. CONCLUSION: Our results suggest that the intronic variant identified in breast cancer cases as well as reported previously may act as a cancer marker and causing a splice site disruption or altering the posttranscriptional modification of mRNA encoded by CHEK2 gene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".