Association of high insulin receptor expression with poor prognosis in patients with breast cancer
Bibliographic record
Abstract
Background: The aim of this study was to investigate the expression of insulin receptor (IR) in the blood vessels of patients with breast cancer (BC) with or without type 2 diabetes mellitus (DM2) and its relationship with histopathological features of BC tissues and patient prognosis. Methods: A total of 124 patients with BC diagnosed and treated at The Affiliated Hospital of Putian University between January 2018 and January 2019 were eligible for this study. According to the presence or absence of DM2, they were then divided into 2 groups: patients with BC and DM2 (DBC group, n=26) and patients with BC and without DM2 (BC group, n=98). The expression of IR in the cancer and adjacent tissues was detected using immunohistochemistry. The patients were followed up for 1 year. Kaplan-Meier analysis was used to compute the overall survival (OS) of the patients with BC. Furthermore, Cox regression was employed to investigate the correlation of IR expression with DM2, pathological tissue, TNM stage, and OS. Results: IR expression in cancer tissues (34.7%) was significantly higher than that in adjacent normal tissues (15.3%). Among cancer tissues, IR was highly expressed in DBC tissues (57.7%) compared with BC tissues (28.6%). IR was also highly expressed in patients with tumor infiltration and lymphatic metastasis. Its expression was significantly correlated with T stage and N stage, but not with M stage. In addition, patients with high IR expression had significantly lower survival than did those with low IR expression. Moreover, univariate and multivariate Cox regression analysis indicated that tumor infiltration, lymphatic metastasis, tumor size, T stage, and high IR expression were independent risk factors for BC prognosis. Conclusions: High IR expression was associated with poor prognosis of patients with BC. The expression of IR may be a promising indicator to assess the survival of patients with BC.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".