Acid-base transport in pancreatic cancer: Molecular mechanisms and clinical potential
Bibliographic record
Abstract
Solid tumors are characterized by a microenvironment that is highly acidic, while intracellular pH (pHi) is normal or even elevated. This is the result of elevated metabolic rates in the highly proliferative cancer cells, in conjunction with often greatly increased rates of net cellular acid extrusion. Studies in various cancers have suggested that while the acid extrusion mechanisms employed are generally the same as those in healthy cells, the specific transporters upregulated vary with the cancer type. The main such transporters include Na(+)/H(+) exchangers, various HCO3(-) transporters, H(+) pumps, and lactate-H(+) cotransporters. The mechanisms leading to their dysregulation in cancer are incompletely understood but include changes in transporter expression levels, trafficking and membrane localization, and posttranslational modifications. In turn, accumulating evidence has revealed that in addition to supporting their elevated metabolic rate, their increased acid efflux capacity endows the cancer cells with increased capacity for invasiveness, proliferation, and chemotherapy resistance. The pancreatic duct exhibits an enormous capacity for acid-base transport, rendering pHi dysregulation a potentially very important topic in pancreatic ductal adenocarcinoma (PDAC). PDAC - accounting for about 90% of all pancreatic cancers - has one of the highest cancer mortality rates known, and new diagnostic and treatment options are highly needed. However, very little is known about whether pH regulation is altered in PDAC and, if so, the possible role of this in cancer development. Here, we review current models for pancreatic acid-base transport and pH homeostasis and summarize current views on acid-base dysregulation in cancer, focusing where possible on the few studies to date in PDAC. Finally, we present new data-mining analyses of acid-base transporter expression changes in PDAC and discuss essential directions for future work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".