HERG and STAT1 Interaction in Prostate Cancer Proliferation: A Potential Novel Therapeutic Target
Bibliographic record
Abstract
Prostate cancer is the second most common cancer type in Canadian males and the third leading cause of cancer death in Canadian men. Currently, androgen deprivation therapy (ADT) is the most commonly used therapy for prostate cancer. However, ADT has several side effects due to the blockade of normal androgen signaling. Therefore, it is of considerable significance to find the novel anti-cancer targets, which could specifically inhibit the prostate cancer cells. In recent years, it has been well established that human ether-a-go-go related gene (HERG) ex- presses aberrantly in different kinds of cancer cells and contributes to carcinogenic process such as cancer cell proliferation; therefore, HERG could be a potential target for cancer therapy. However, the therapeutic availability of HERG inhibitors in cancer is mostly limited because HERG blocking might result in long QT syndrome, which is a severe cardiac arrhythmia, due to the regulatory role of HERG in cardiac action potential repolarization. Recently, our lab discovered that in an andro- gen-dependent prostate cancer cell line (LNCap), the androgen receptor (AR) agonist (R1881) in- creased cell proliferation by promoting the interaction between HERG and signal transducer and activator of transcription 1 (STAT1). In addition, disrupting the HERG-STAT1 complex formation with a 28 amino acid-peptide fragment (FR peptide), which is a mimetic of the SH2 domain of STAT1, decreased both HERG and STAT1 expression as well as subsequent R1881-induced cancer proliferation in LNCap. In contrast, these effects have not been observed in androgen-independent cell line C4-2. Thus, we conclude that in LNCap, the AR signaling increases HERG-STAT1 com- plex which plays an essential role in cancer cell proliferation, and the HERG-STAT1 complex rep- resents a potential novel anti-cancer target in androgen-sensitive prostate cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".