Transient Receptor Potential Channels in Prostate Cancer: Associations with ERG Fusions and Survival
Bibliographic record
Abstract
Calcium movement and concentration in the cell plays significant roles in normal physiology and in diseases such as cancer. The significance of this ion in oncogenesis suggests that membrane-relevant proteins are involved in its regulation and are deregulated in various cancers. These channels and transporters could be targets for therapeutic interventions. An evaluation of the expression of transient receptor potential (TRP) channels in prostate cancer was performed using publicly available genomic and proteome data. Two TRP family members with high expression in prostate cancers, TRPML2 and TRPM4, were chosen for further analysis the uncover the associations of their level of expression with clinical and pathologic prostate cancer characteristics. Several TRP channels were expressed in prostate cancers at the protein level including TRPM4, TRPML1, TRPML2, TRPC1 and TRPP3. At the mRNA level, MCOLN2 and TRPM4 were strongly expressed in a sub-set of prostate cancers. Cases with high MCOLN2 mRNA expression were associated with frequent ERG fusions and a trend for better survival outcomes. In contrast, prostate cancer cases with high TRPM4 mRNA expression were associated with lower ERG fusion frequency than cases with low TRPM4 mRNA expression. The prognosis of prostate cancers with high TRPM4 expression was not different from the prognosis with counterparts having low TRPM4 mRNA expression. TRP channels were expressed in sub-sets of prostate cancers. The two well-expressed channels of the super family, TRPML2 and TRPM4, have divergent associations with the most prevalent prostate cancer molecular aberrations, ERG fusions. These results imply diverse regulations of the TRP channels that would have to be taken into consideration when devising therapeutic interventions targeting individual channels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".