TRPM channels in human cancers: regulatory mechanism and therapeutic prospects
Bibliographic record
Abstract
The transient receptor potential melastatin (TRPM) channel family has been previously implicated in various diseases, including those related to temperature sensing, cardiovascular health, and neurodegeneration. Nowadays, increasing evidence indicates that TRPM family members also play significant roles in various types of cancers, exhibiting both pro- and anti-tumorigenic functions. They are involved in tumor cell proliferation, survival, invasion, and metastasis, serving as potential diagnostic and prognostic biomarkers for cancer. This paper begins by describing the structure and physiological functions of the TRPM family members. It then outlines their roles in several common malignancies, including pancreatic, prostate, colorectal, breast, brain cancer, and melanoma. Subsequently, we focused on investigating the specific mechanisms by which TRPM family members are involved in tumorigenesis and development from both the tumor microenvironment (TME) and intracellular signaling. TRPM channels not only transmit signals from the TME to regulate tumor cell functions, but also mediate extracellular matrix remodeling, which is conducive to the malignant transformation of tumor cells. Importantly, TRPM channels depend on the regulation of the inflow of various ions in cells, and participate in key signaling pathways involved in tumor progression, such as Wnt/β-catenin, MAPK, PI3K/AKT, p53, and autophagy. Finally, we summarize the current strategies and challenges of targeting TRPM channels in tumor treatment, and discuss the feasibility of combining targeted TRPM channel drugs with cancer immunotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".