The Expression Pattern of miR-17, −24, −124 and −145 as Diagnostic Factor for Metastatic Gastric Cancer; a Lesson from Gastric Cancer Stem cells
Bibliographic record
Abstract
Abstract Background Distant metastasis of Gastric Cancer (GC) causes more than 700 000 deaths worldwide. Cancer Stem Cells (CSCs) are a subpopulation of cancer cells responsible for aggressiveness and chemoresistance in clinical settings. MicroRNAs (miRNAs) emerge as important players in regulating self-renewal and metastasis in CSCs. Understanding the role of miRNAs in CSCs offer a potential diagnostic tool for GC patients. This study is aimed to identify miRNAs that target both stemness and metastasis in gastric cancer stem cells (GCSCs) and differentially expressed in metastatic GC patients as diagnostic biomarkers for GC metastasis. Methods We investigate the gene expression profile of patients using the GEO database and Rstudio software. To obtain the regulatory networks and miRNAs, the STRING and miRwalk database used. The gastric cancer tissues were obtained from Iranian National Tumor Bank (INTB) to validate the results. Results Our results indicated three important regulatory cores affecting the immune system’s regulation, tumor progress, and metastasis. Based on the bioinformatics results, four miRNAs miR-17-5p, miR-24-3p, miR-124-3p, and miR-145-5p, were selected, and their expression pattern was evaluated in 10 patients’ metastatic tumors compared to 10 nonmetastatic tumors by real-time PCR. The expression level of mir-17, −24, and −124 was upregulated about 8, 10, 60 folds, respectively, and miR-145 was down-regulated 4.5 folds in metastatic tumors compared to nonmetastatic tumors. Conclusion the high expression level of miR-17, −24, −124, and low level of miR-145 in GC patients’ samples could be a potential biomarker for the presence of GCSCs and the diagnosis of metastasis.
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.
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 | 0.001 | 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".