Cervical cancer, the third most common cancer worldwide with 50% increase in mortality, is a growing public health concern. Recent research on non-coding RNAs including long coding RNAs (lncRNAs) and circular RNAs (circRNAs) highlights their key roles in carcinogenesis across different cancers; but the studies on lncRNA-circRNA-mRNA regulatory networks (tripartite network) are still lacking in cervical cancer. This study integrates transcriptomic data to analyse circRNA-lncRNA-mRNA interactions, focusing on immune signaling pathways. To further understand the biological relevance of these interactions, differentially expressed genes were identified using DESeq2/limma, followed by functional enrichment. A competing endogenous RNA (ceRNA) network was constructed in Cytoscape, with miRNA binding serving as the central connecting factor. The immune-focused subnetwork comprised 7 mRNAs, 14 circRNAs, and 80 lncRNAs. Different machine learning prognostic models based on the ncRNA network and individual RNAs including differentially expressed coding and non-coding RNAs were developed to assess prognostic performance. Among these, the LASSO model based on the circRNA-lncRNA-mRNA network showed the best overall performance, with AUC values of 0.77 in the training set and 0.89 in the test set. Model performance was also assessed using bootstrap resampling for internal validation to ensure robustness. DLEU1, ITPR1, hsa_circRNA_101206, and miR-210 were identified as key prognostic biomarkers. Docking, HPA validation, and immune infiltration analyses confirmed the miR-210:DLEU1:ITPR1 axis as a key immune regulator in cervical cancer. This network modelling approach establishes that combinatorial modelling of coding and non-coding elements outperforms individual approaches, providing a robust framework for prognostic stratification and therapeutic targeting in cervical cancer.
V. Navya, Geethu Muraleedharan, P. Fasna et al.· Biochemical and Biophysical...· 0 citations
Polycystic Ovary Syndrome (PCOS) is a complex reproductive disorder characterized by irregular menstrual cycles, polycystic ovaries, and hyperandrogenism.
Diagnosis is often complicated by phenotype variability and inconsistent biomarker use. This metaanalysis, conducted according to PRISMA 2020 guidelines and based on studies published between
2010 and May 2023, investigates the heterogeneity in key PCOS biomarkers, testosterone and
LH/FSH ratio, using a random-effects model.
Thirteen studies were included in the meta-analysis. Biomarker-specific
analyses were conducted using R to evaluate variations in testosterone and LH/FSH ratios. Sample
sizes consisted of 500 PCOS cases and 592 controls for testosterone, and 1184 PCOS cases and
1410 controls for the LH/FSH ratio. Heterogeneity and subgroup variability were assessed independently for each biomarker.
Marked heterogeneity was observed across studies, with I² values of 95% for testosterone
and 99% for the LH/FSH ratio. Contributing factors included variability in population demographics, diagnostic criteria, and assay methodologies. Traditional assays often fail to sensitively
detect testosterone in women, while visual scoring methods are vulnerable to observer bias
The observed heterogeneity underscores the limitations of conventional biomarkers in
diagnosing PCOS. A move toward standardised, sensitive, and phenotype-specific diagnostic tools
is crucial. Multi-steroid panels and integrative omics-based approaches can address variability and
enhance diagnostic accuracy.
Integrating novel biomarkers such as DAPK2, S100A9, Bacteroides vulgatus, and microRNA-6767-5p holds promise for improved diagnosis of PCOS subtypes. AGP may help identify
normoandrogenic presentations. Embracing immune-metabolic insights supports the development
of diverse, accessible, and validated diagnostics aligned with the goals of precision medicine and
improved women's reproductive health.
Ashitha Washington, Heera T Shenoy, Ravindra Kumar· Current Women s Health Revie...· 0 citations