Tumor suppressor miR‐193a‐3p enhances efficacy of BRAF/MEK inhibitors in <i>BRAF</i>‐mutated colorectal cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patients with BRAF-mutated colorectal cancer (CRC) have a poor prognosis despite recent therapeutic advances such as combination therapy with BRAF, MEK, and epidermal growth factor receptor (EGFR) inhibitors. To identify microRNAs (miRNAs) that can improve the efficacy of BRAF inhibitor dabrafenib (DAB) and MEK inhibitor trametinib (TRA), we screened 240 miRNAs in BRAF-mutated CRC cells and identified five candidate miRNAs. Overexpression of miR-193a-3p, one of the five screened miRNAs, in CRC cells inhibited cell proliferation by inducing apoptosis. Reverse-phase protein array analysis revealed that proteins with altered phosphorylation induced by miR-193a-3p were involved in several oncogenic pathways including MAPK-related pathways. Furthermore, overexpression of miR-193a-3p in BRAF-mutated cells enhanced the efficacy of DAB and TRA through inhibiting reactivation of MAPK signaling and inducing inhibition of Mcl1. Inhibition of Mcl1 by siRNA or by Mcl1 inhibitor increased the antiproliferative effect of combination therapy with DAB, TRA, and anti-EGFR antibody cetuximab. Collectively, our study demonstrated the possibility that miR-193a-3p acts as a tumor suppressor through regulating multiple proteins involved in oncogenesis and affects cellular sensitivity to MAPK-related pathway inhibitors such as BRAF inhibitors, MEK inhibitors, and/or anti-EGFR antibodies. Addition of miR-193a-3p and/or modulation of proteins involved in the miR-193a-3p-mediated pathway, such as Mcl1, to EGFR/BRAF/MEK inhibition may be a potential therapeutic strategy against BRAF-mutated CRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it