Molecular Landscape of LncRNAs in Prostate Cancer: A focus on pathways and therapeutic targets for intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: One of the most malignant tumors in men is prostate cancer that is still incurable due to its heterogenous and progressive natures. Genetic and epigenetic changes play significant roles in its development. The RNA molecules with more than 200 nucleotides in length are known as lncRNAs and these epigenetic factors do not encode protein. They regulate gene expression at transcriptional, post-transcriptional and epigenetic levels. LncRNAs play vital biological functions in cells and in pathological events, hence their expression undergoes dysregulation. AIM OF REVIEW: The role of epigenetic alterations in prostate cancer development are emphasized here. Therefore, lncRNAs were chosen for this purpose and their expression level and interaction with other signaling networks in prostate cancer progression were examined. KEY SCIENTIFIC CONCEPTS OF REVIEW: The aberrant expression of lncRNAs in prostate cancer has been well-documented and progression rate of tumor cells are regulated via affecting STAT3, NF-κB, Wnt, PI3K/Akt and PTEN, among other molecular pathways. Furthermore, lncRNAs regulate radio-resistance and chemo-resistance features of prostate tumor cells. Overexpression of tumor-promoting lncRNAs such as HOXD-AS1 and CCAT1 can result in drug resistance. Besides, lncRNAs can induce immune evasion of prostate cancer via upregulating PD-1. Pharmacological compounds such as quercetin and curcumin have been applied for targeting lncRNAs. Furthermore, siRNA tool can reduce expression of lncRNAs thereby suppressing prostate cancer progression. Prognosis and diagnosis of prostate tumor at clinical course can be evaluated by lncRNAs. The expression level of exosomal lncRNAs such as lncRNA-p21 can be investigated in serum of prostate cancer patients as a reliable biomarker.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it