Expression-based analyses indicate a central role for hypoxia in driving tumor plasticity through microenvironment remodeling and chromosomal instability
Bibliographic record
Abstract
Can transcriptomic alterations drive the evolution of tumors? We asked if changes in gene expression found in all patients arise earlier in tumor development and can be relevant to tumor progression. Our analyses of non-mutated genes from the non-amplified regions of the genome of 158 triple-negative breast cancer (TNBC) cases identified 219 exclusively expression-altered (EEA) genes that may play important role in TNBC. Phylogenetic analyses of these genes predict a "punctuated burst" of multiple gene upregulation events occurring at early stages of tumor development, followed by minimal subsequent changes later in tumor progression. Remarkably, this punctuated burst of expressional changes is instigated by hypoxia-related molecular events, predominantly in two groups of genes that control chromosomal instability (CIN) and those that remodel tumor microenvironment (TME). We conclude that alterations in the transcriptome are not stochastic and that early-stage hypoxia induces CIN and TME remodeling to permit further tumor evolution.
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 | 0.000 |
| 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) | 0.001 | 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".