Microarray expression profiling of dysregulated long non-coding RNAs in triple-negative breast cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) represents a collection of malignant breast tumors that are often aggressive and have an increased risk of metastasis and relapse. Long non-coding RNAs are generally defined as RNA transcripts measuring 200 nucleotides or longer that do not encode for any protein. During the past decade, increasing evidence has shown that lncRNAs play important roles in oncogenesis and tumor suppression; however, the roles of lncRNAs in TNBC are poorly understood. To address this issue, we used Agilent human lncRNA microarray chips and bioinformatics tools, including Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), to assess lncRNA expression in 3 pairs of TNBC tissues. A dysregulated lncRNA expression profile was identified by microarray and verified by qRT-PCR in 48 pairs of breast cancer subtype tissues. Metastasis is the major cause of cancer-related deaths, including those in TNBC, and the presence of dormant residual disseminated tumor cells (DTC) may be a key factor leading to metastasis. ANKRD30A, a potential target for breast cancer immunotherapy, is currently one of the most used DTC markers. Notably, we found the expression levels of the novel intergenic lncRNA LINC00993 to be associated with the expression levels of ANKRD30A. Furthermore, our qRT-PCR data indicated that the expression of LINC00993 was also associated with the expression of the estrogen receptor. In conclusion, our study identified a set of lncRNAs that were consistently aberrantly expressed in TNBC, and these dysregulated lncRNAs may be involved in the development and/or progression of TNBC.
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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 | 0.001 | 0.001 |
| 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".