LINC02544 modulates the RAB/RAS signaling pathway by potential regulation of DENND2A expression level in the breast cancer patients: integrated bioinformatics and experimental analyses
Bibliographic record
Abstract
Abstract Background: One of the most common female malignancies is breast cancer (BC) and is recognized as a second death factor for women population among other cancer-related diseases. Dysregulation of RNA expression levels can lead human status to some different pathological statuses, including breast cancer. In this study, we have investigated the expression level of DENND2A and lncRNA LINC02544 in the BC tissue samples among Iranian population. Also, the clinicopathological and biomarker analysis of these RNAs were investigated. Method: Microarray analysis was performed on GSE61304 in order to find the differentially expressed genes in the BC samples compared to controls. Multiple online and offline software, including R programming language and several relative statistical and visualization packages, demonstrated the differential expression level of genes,relative lncRNAs, and single nucleotide polymorphisms. The Real-time PCR results have demonstrated that the expression level of DENND2A has been reduced in the breast cancer samples compared with controls. In order to investigate the genotype frequency of rs6852 region in the DENND2A gene, high-resolution melt (HRM) method has been used. Results of microarray and Real-time PCR analysis have been shown that the expression of DENND2A and lncRNA LINC02544 have significantly down-regulated in the breast cancer samples. These data suggested that these two RNAs are likely to respond to alter the expression level of each other. Therefore, DENND2A could be an important prognostic biomarker in breast cancer samples among Iranian population. lncRNA LINC02544 could have an activator effect on the DENND2A expression. There is strong evidence that both of these RNAs could perform as tumor suppressors in breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".