Association of <i>p53</i> Gene Mutation With <i>Helicobacter pylori</i> Infection in Gastric Cancer Patients and Its Correlation With Clinicopathological and Environmental Factors
Bibliographic record
Abstract
Background: Gastric cancer is also a leading cancer in Bangladesh like that of the global incidences. It is speculated that environmental, bacterial infection and molecular factors might have been carrying the key role of rising trend of the disease. This study was aimed to investigate the association of mutated p53 gene with of Helicobacter pylori ( H. pylori ) infection, clinicopathological and some environmental factors of the gastric cancer patients. Methods: This cross-sectional study was carried out from January 2015 to December 2016 in a specialized cancer hospital of Bangladesh. Patients were selected randomly who were admitted for surgical intervention after diagnosis as adenocarcinoma of the stomach and physically fit for surgery. After admission proper evaluation of the patients was done. Tissue sample from the gastrectomy specimen along with the blood sample was sent to the related laboratories. After DNA extraction for p53 , exons 5 and 6, they were adjusted for proper primer designing. Appropriate sequencing analysis of the result was done. Status of p53 was investigated to see their association with the result of the H. pylori , age and sex, tumor status, smoking and extra salt intake of the patients. Result of the study was calculated and analyzed by Chi-square and binomial logistic regression to find the association amongst them. Results: Among the 71 patients, mean age was 52.96 years old, male: female ratio were 48:23, age group above 41 years were 53 (74.6%), proliferative and ulceroproliferative group of the tumor dominated (87.3%). There were 52 cases with (73.2%) p53 mutation. Among the 51 H. pylori positive cases, 41 (80%) had p53 mutation (P = 0.033). Tumor size and lymph node status were found to be associated with the gene mutation (P = 0.05). Age also had strong correlation with the mutation (P = 0.015). Gene mutation was found mostly among the younger (<= 40 years) group of patients (94.4%). Patient with extra salt intake was also found related with the mutation (P = 0.03). Conclusions: Environmental and genetic factors seem to be risk factors for gastric cancer in Bangladesh. Nationwide anti H. pylori drive and further molecular research could elicit the other risk factors which might help to reduce the gastric cancer incidences in the country after taking appropriate measures. World J Oncol. 2019;10(1):46-54 doi: https://doi.org/10.14740/wjon1087
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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".