Yina Guo

Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions

Ovarian cancer represents one of the most lethal gynecologic malignancies, characterized by low early detection rates and challenging prognostic assessment. Conventional diagnostic modalities demonstrate limited sensitivity and specificity for early-stage disease identification. Recent research has begun to explore causal inference methodologies as complementary approaches that may enhance diagnostic precision and prognostic capability. This systematic review evaluates the current state and future prospects of causal inference methodologies in enhancing ovarian cancer diagnosis and prognosis. We performed a comprehensive systematic review focusing on causal inference methodologies applied to ovarian cancer research. The analysis encompassed biomarker identification, pathogenic mechanism elucidation, and multimodal data integration. Additionally, we analyzed the synergistic combination of causal inference with machine learning approaches across genomic, transcriptomic, proteomic, and imaging datasets. Causal inference methods have shown effectiveness in identifying crucial biomarkers and revealing underlying pathogenic mechanisms of ovarian cancer. The integration of machine learning with causal inference has enhanced model interpretability, clinical applicability, and diagnostic-prognostic accuracy. These approaches have achieved improved predictions of disease progression and optimization of treatment strategies by leveraging clinical, genetic, and imaging data. Causal inference shows considerable potential in advancing precision medicine for ovarian cancer, offering robust frameworks for addressing confounding factors and establishing causal relationships. As these methodologies evolve and data volumes expand, their application may become increasingly valuable in oncology practice.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

Abstract Purpose This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis. Methods SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses. Results Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets. Conclusion GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.