Integrative <scp>miRNOMe</scp> profiling reveals the <scp>miR</scp> ‐195‐5p– <scp>CHEK1</scp> axis and its impact on luminal breast cancer outcomes
Bibliographic record
Abstract
The luminal subtype (estrogen receptor-positive, ER+) is the most common and the most heterogeneous type of breast carcinoma (BC) in women. During our study, we determined expression levels of all microRNAs (miRNome) in 101 ER+ BC samples and identified 25 miRNAs being associated with proliferative markers. Using comprehensive in silico analyses we prioritized CHEK1, CDC25A, and CCNE1 as candidate genes affecting the proliferation of ER+ BC, with two microRNAs from the miR-497∼195 cluster identified as their potential regulators. In a cohort of 217 patients, we found a significant association between high expression of CHEK1 and shorter relapse-free survival (RFS) in luminal BC patients treated with adjuvant chemotherapy, especially in patients with luminal A subtype. In patients treated with neoadjuvant therapy, the opposite role for RFS was observed for hsa-miR-195-5p. Subsequently, we confirmed the potency of hsa-miR-195-5p to inhibit the expression of CHEK1 in vitro. Moreover, the specific Chk1 inhibitor rabusertib (LY2603618) significantly enhanced the efficacy of doxorubicin in both ER+ and ER- cell lines. In summary, we have identified the association of a specific miRNA profile with highly proliferative luminal BCs and demonstrated the ability of hsa-miR-195-5p to inhibit CHEK1 expression in BC in vitro, underlining the importance of CHEK1 expression and its inhibition for prognosis and treatment of patients with luminal BCs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".