In Silico Variant Analysis Identifies Mutations in TP53 Gene Increasing The Risk of Cancer in COVID-19 Patients
Bibliographic record
Abstract
Introduction: The Coronavirus 2019 (COVID-19) disease caused by SARS-CoV-2 emerged as a global pandemic due to its lethal nature and great potential to spread rapidly. It is predicted that the coronavirus infection can leave detrimental consequences in infected patients even after recovery. Therefore considering the ability of the virus to induce lethal mutations in the host genome, we aimed to identify variants in COVID-19 patients irrespective of their origin. Methods: For the present study, transcriptomic data of patients were retrieved from the GEO dataset of the National Center for Biotechnology Information (NCBI) (GSE216811 and GSE197976 (India), GSE174786 (Germany), GSE171052 (Canada), GSE 206264 (Spain) and GSE206677 (Japan)). Each dataset was assessed independently for Single nucleotide polymorphisms (SNPs). After quality check and removing low-quality reads the samples were normalized using Bcf-tools norms. Further, SnpEff eff and SIFT4g annotator was used to identify and annotate variants occurring in each sample. The common variants occurring in all the patients of COVID-19. Were identified through a Venn diagram. The systems biology approach was used to identify hub genes out of common mutated genes. Results and Discussion: CytoHUBBA plug-in of Cytoscape identified TP53 as the hub gene mutated due to COVID- 19 infection. TP53 is a tumor suppressor gene that plays an important role in DNA damage repair, cell cycle regulation, and inducing apoptosis. Conclusion: Therefore, we conclude that mutation in TP53 causes loss of suppression potential of the gene increasing the risk of cancer in such individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".