Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms
Bibliographic record
Abstract
Background: Almost all patients treated with androgen deprivation therapy (ADT) eventually develop castration-resistant prostate cancer (CRPC). Our research aims to elucidate the potential biomarkers and molecular mechanisms that underlie the transformation of primary prostate cancer into CRPC. Methods: We collected three microarray datasets (GSE32269, GSE74367, and GSE66187) from the Gene Expression Omnibus (GEO) database for CRPC. Differentially expressed genes (DEGs) in CRPC were identified for further analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. We conducted virtual screening of drugs using AutoDock Vina. In vitro experiments were performed using the Cell Counting Kit-8 (CCK-8) assay to evaluate the inhibitory effects of the drugs on CRPC cell viability. Scratch and transwell invasion assays were employed to assess the effects of the drugs on the migration and invasion abilities of prostate cancer cells. Results: Overall, a total of 719 DEGs, consisting of 513 upregulated and 206 downregulated genes, were identified. The biological functional enrichment analysis indicated that DEGs were mainly enriched in pathways related to the cell cycle and metabolism. CCNA2 and CKS2 were identified as promising biomarkers using a combination of WGCNA, LASSO logistic regression, SVM-RFE, and Venn diagram analyses. These potential biomarkers were further validated and exhibited a strong predictive ability. The results of the virtual screening revealed Aprepitant and Dolutegravir as the optimal targeted drugs for CCNA2 and CKS2, respectively. In vitro experiments demonstrated that both Aprepitant and Dolutegravir exerted significant inhibitory effects on CRPC cells (p < 0.05), with Aprepitant displaying a superior inhibitory effect compared to Dolutegravir. Discussion: The expression of CCNA2 and CKS2 increases with the progression of prostate cancer, which may be one of the driving factors for the progression of prostate cancer and can serve as diagnostic biomarkers and therapeutic targets for CRPC. Additionally, Aprepitant and Dolutegravir show potential as anti-tumor drugs for CRPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".