Modules of Correlated Genes in a Gene Expression Regulatory Network of CDDP-Resistant Cancer Cells
Bibliographic record
Abstract
Chemotherapeutic drugs have been used as important strategies in cancer treatment. However, chemotherapy-resistant tumors arise especially in relapsing and progressive disease. Understanding of mechanisms underlaying Cisplatin-CDDP chemotherapy resistance may help find new therapeutic targets to revert this phenotype. The aim of this work, through an integrative Systems Biology approach, is to optimize an in silico model of TFs-miRNAs gene expression regulatory network of CDDP-chemoresistant cancer cell lines. By identifying modules of co-expressed genes in this regulatory network we expect further understanding of CDDP-chemoresistant phenotype. A set of deregulated genes was determined for two CDDP-chemoresistant cancer cell lines by considering gene copy number and transcriptomics. These genes were used as input targets for the construction and fitting of a large scale ordinary differential equations (ODE) model using our biocomputational platform previously reported. Model optimization was performed using COPASI and modules of correlated genes were determined using WGCNA. A model of 108 deregulated target genes, 44 transcription factors and 21 miRNAs was successfully constructed and optimized. Eleven modules of correlated genes were determined along with their gene product annotation. This report contributes to the understanding of the complex regulatory networks of CDDP-resistance and the future design of therapeutic strategies to overcome drug resistance.
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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 | 0.000 | 0.000 |
| 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".