Mechanisms of Therapeutic Resistance in Cancer (Stem) Cells with Emphasis on Thyroid Cancer Cells
Bibliographic record
Abstract
Tissue invasion, metastasis and therapeutic resistance to anti-cancer treatments are common and main causes of death in cancer patients. Tumor cells mount complex and still poorly understood molecular defense mechanisms to counteract and evade oxygen deprivation, nutritional restrictions as well as radio- and chemotherapeutic treatment regimens aimed at destabilizing their genomes and important cellular processes. In thyroid cancer, as in other tumors, such defense strategies include the reactivation in cancer cells of early developmental programs normally active exclusively in stem cells, the stimulation of cancer stem-like cells resident within the tumor tissue and the recruitment of bone marrow-derived progenitors into the tumor (Thomas et al., 2008;Klonisch et al., 2009;Derwahl, 2011). Metastasis and therapeutic resistance in cancer (stem) cells involves the epithelial-to-mesenchymal transition- (EMT-) mediated enhancement in cellular plasticity, which includes coordinated dynamic biochemical and nuclear changes (Ahmed et al., 2010). The purpose of the present review is to provide an overview of the role of DNA repair mechanisms contributing to therapeutic resistance in thyroid cancer and highlight the emerging roles of autophagy and damage associated molecular pattern (DAMP) responses in EMT and chemoresistance in tumor cells. Finally, we use the stem cell factor and nucleoprotein High Mobility Group A2 (HMGA2) as an example to demonstrate how factors intended to protect stem cells are wielded by cancer (stem) cells to gain increased transformative cell plasticity which enhances metastasis, therapeutic resistance and cell survival. Wherever possible, we have included information on these cellular processes and associated factors as they relate to thyroid cancer cells.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".