Pengfei Zhao

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

Resensitizing Paclitaxel-Resistant Ovarian Cancer via Targeting Lipid Metabolism Key Enzymes CPT1A, SCD and FASN

Epithelial ovarian cancer (EOC) is a lethal gynecological cancer, of which paclitaxel resistance is the major factor limiting treatment outcomes, and identification of paclitaxel resistance-related genes is arduous. We obtained transcriptomic data from seven paclitaxel-resistant ovarian cancer cell lines and corresponding sensitive cell lines. Define genes significantly up-regulated in at least three resistant cell lines, meanwhile they did not down-regulate in the other resistant cell lines as candidate genes. Candidate genes were then ranked according to the frequencies of significant up-regulation in resistant cell lines, defining genes with the highest rankings as paclitaxel resistance-related genes (PRGs). Patients were grouped based on the median expression of PRGs. The lipid metabolism-related gene set and the oncological gene set were established and took intersections with genes co-upregulated with PRGs, obtaining 229 co-upregulated genes associated with lipid metabolism and tumorigenesis. The PPI network obtained 19 highly confidential synergistic targets (interaction score > 0.7) that directly associated with CPT1A. Finally, FASN and SCD were up-stream substrate provider and competitor of CPT1A, respectively. Western blot and qRT-PCR results confirmed the over-expression of CPT1A, SCD and FASN in the A2780/PTX cell line. The inhibition of CPT1A, SCD and FASN down-regulated cell viability and migration, pharmacological blockade of CPT1A and SCD increased apoptosis rate and paclitaxel sensitivity of A2780/PTX. In summary, our novel bioinformatic methods can overcome difficulties in drug resistance evaluation, providing promising therapeutical strategies for paclitaxel-resistant EOC via taregting lipid metabolism-related enzymes.