Identification of a new microRNA expression profile as a potential cancer screening tool
Bibliographic record
Abstract
PURPOSE: Small non-coding microRNAs (miRNAs) are key components of cancer development and are considered as potential biomarkers for cancer diagnosis and treatment monitoring. This study investigated miRNA expression profiles of human cancer cells in order to develop a screening method for lung cancer. METHODS: A series of lung cancer related miRNAs (miR-21, miR-145, miR-155, miR-205, miR-210, miR-92, miR-17-5p, miR-143, miR-182, miR-372, let-7a) were selected as candidates for miRNA expression profiles of human lung cancer cell lines (A549, SK-mes-1). MicroRNA u6 was the endogenous control. Cancer cell lines for positive controls; breast MCF-7, prostate Du-145, and glioblastoma U118. The negative control was normal lung fibroblast cell line MRC-5. RT-PCR was performed on StepOnePlus (Applied Biosystem, USA). MiRNA expressions of malignant cells were compared with normal fibroblast cells as well as endogenous control (u6) using the thermal cycle at threshold. Assessment of miRNA expression profiles were then performed using agglomerative hierarchical cluster analysis software (SPSS13, USA). RESULTS: We demonstrated that miR-21, miR-182 and let7-5a were over-expressed, and miR-145 and miR-155 were under-expressed in all cancer cell lines. Combined with the cluster analysis we were able to clearly distinguish cell lines for normal fibroblasts, breast cancer, prostate cancer, glioblastoma, and lung cancer. CONCLUSION: There is potential utility of screening for lung cancer with miRNA expression profiles. Future work will focus on the sensitivity of such miRNA expression profiles in screening sputum for lung cancer, which can be performed in real time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".