Cause and Consequences of Genetic and Epigenetic Alterations in Human Cancer
Bibliographic record
Abstract
Both genetic and epigenetic changes contribute to development of human cancer. Oncogenomics has primarily focused on understanding the genetic basis of neoplasia, with less emphasis being placed on the role of epigenetics in tumourigenesis. Genomic alterations in cancer vary between the different types and stages, tissues and individuals. Moreover, genomic change ranges from single nucleotide mutations to gross chromosomal aneuploidy; which may or may not be associated with underlying genomic instability. Collectively, genomic alterations result in widespread deregulation of gene expression profiles and the disruption of signalling networks that control proliferation and cellular functions. In addition to changes in DNA and chromosomes, it has become evident that oncogenomic processes can be profoundly influenced by epigenetic mechanisms. DNA methylation is one of the key epigenetic factors involved in regulation of gene expression and genomic stability, and is biologically necessary for the maintenance of many cellular functions. While there has been considerable progress in understanding the impact of genetic and epigenetic mechanisms in tumourigenesis, there has been little consideration of the importance of the interplay between these two processes. In this review we summarize current understanding of the role of genetic and epigenetic alterations in human cancer. In addition we consider the associated interactions of genetic and epigenetic processes in tumour onset and progression. Furthermore, we provide a model of tumourigenesis that addresses the combined impact of both epigenetic and genetic alterations in cancer cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".