The role of folate receptor α in cancer development, progression and treatment: Cause, consequence or innocent bystander?
Bibliographic record
Abstract
Folate receptor alpha (FRalpha) is a membrane-bound protein with high affinity for binding and transporting physiologic levels of folate into cells. Folate is a basic component of cell metabolism and DNA synthesis and repair, and rapidly dividing cancer cells have an increased requirement for folate to maintain DNA synthesis, an observation supported by the widespread use of antifolates in cancer chemotherapy. FRalpha levels are high in specific malignant tumors of epithelial origin compared to normal cells, and are positively associated with tumor stage and grade, raising questions of its role in tumor etiology and progression. It has been suggested that FRalpha might confer a growth advantage to the tumor by modulating folate uptake from serum or by generating regulatory signals. Indeed, cell culture studies show that expression of the FRalpha gene, FOLR1, is regulated by extracellular folate depletion, increased homocysteine accumulation, steroid hormone concentrations, interaction with specific transcription factors and cytosolic proteins, and possibly genetic mutations. Whether FRalpha in tumors decreases in vivo among individuals who are folate sufficient, or whether the tumor's machinery sustains FRalpha levels to meet the increased folate demands of the tumor, has not been studied. Consequently, the significance of carrying a FRalpha-positive tumor in the era of folic acid fortification and widespread vitamin supplement use in countries such as Canada and the United States is unknown. Epidemiologic and clinical studies using human tumor specimens are lacking and increasingly needed to understand the role of environmental and genetic influences on FOLR1 expression in tumor etiology and progression. This review summarizes the literature on the complex nature of FOLR1 gene regulation and expression, and suggests future research directions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".