Identifying Long Non-coding RNA of Prostate Cancer Associated With Radioresponse by Comprehensive Bioinformatics Analysis
Bibliographic record
Abstract
Although radiotherapy is greatly successful in the treatment of prostate cancer (PCa), radioresistance is still a major challenge in the treatment. To our knowledge, this study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify the differentially expressed lncRNAs and protein-coding genes (PCGs) between complete response (CR) and non-complete response (non-CR) groups in radiotherapy. Statistical methods were applied to identify the correlation between lncRNAs and radioresponse as well as lncRNAs and PCGs. The correlation between PCGs and radioresponse was analyzed using weighted gene co-expression network analysis (WGCNA). The three online databases were used to predict the potential target miRNAs of lncRNAs and the miRNAs that might regulate PCGs. RT-qPCR was utilized to detect the expression of lncRNAs and PCGs in our PCa patients. A total of 65 differentially expressed lncRNAs and 468 differentially expressed PCGs were found between the two groups of PCa. After the chi-square test, LINC01600 was selected to be highly correlated with radioresponse from the 65 differentially expressed lncRNAs. Pearson correlation analysis found 558 PCGs co-expressed with LINC01600. WGCNA identified the darkred module associated with radioresponse in PCa. After taking the intersection of the darkred module of WGCNA, differentially expressed PCGs between the two groups of PCa, and the PCGs co-expressed with LINC01600, three PCGs, that is, JUND, ZFP36, and ATF3 were identified as the potential target PCGs of LINC01600. More importantly, we detected the expression of LINC01600 and three PCGs using our PCa patients, and finally verified that LINC01600 and JUND were differentially expressed between CR and non-CR groups, excluding ZFP36 and ATF3. Meantime, the potential regulation ability of LINC01600 for JUND in PCa cell lines was initially explored. In addition, we constructed the competing endogenous RNA (ceRNA) network of LINC01600-miRNA-JUND. In conclusion, we initially reveal the association of LINC01600 with radioresponse in PCa and identify its potential target PCGs for further basic and clinical research.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".