Fangzhu Xiao

Background: Esophageal cancer ranks as the 11th most diagnosed cancer worldwide and the 7th leading cause of cancer-related deaths, mainly due to late-stage diagnosis. Identifying novel biomarkers is essential for enhancing prognostic evaluations and targeting patients for immunotherapy. Methods: We used the DepMap database to identify survival-dependent genes in esophageal carcinoma cells. A prognostic model was developed using univariate and multivariate Cox regression and LASSO, validated with the GEO dataset. WGCNA and GSEA analyses were conducted to explore mechanisms, alongside ESTIMATE and ssGSEA for prognosis. Results: We constructed a novel four-gene prognostic signature (CPSF6, IGBP1, MTG2, TCP1) based on SDG expression and survival data. This signature stratified esophageal cancer patients into high- and low-risk groups with significantly different survival, with the high-risk group showing shorter survival. WGCNA and GSEA analyses linked prognosis to immune pathways, including interferon-γ response and IL6-JAK-STAT3 signaling. ssGSEA revealed reduced infiltration of 19 immune cell types in high-risk patients, and ESTIMATE analysis confirmed the association between immune infiltration and poor prognosis. Conclusion: This study establishes a four-gene survival signature for esophageal cancer that distinguishes high-risk from low-risk populations, providing novel prognostic indicators. Immune response pathways were downregulated in high-risk patients, offering potential targets for understanding esophageal cancer mechanisms and developing immunotherapeutic strategies.

Hyperlipidemia, a significant contributor to cardiovascular ailments, have a close association with obesity, is closely linked to obesity. Traditional anthropometric indices (BMI, WHR, WHtR) are widely used to assess obesity, but their nonlinear relationships with hyperlipidemia and gender-specific risk thresholds remain poorly understood. Leveraging data from 2,739 participants in the NHANES 20072020, this research utilized multivariable logistic regression analysis along with restricted cubic splines (RCS) to investigate both linear and nonlinear relationships between various obesity indices and the presence of hyperlipidemia. Nonlinear dose-response relationships were observed for BMI, WHR, and WHtR with hyperlipidemia (P for nonlinear < 0.001). Gender-specific risk thresholds were identified: males: WHR > 0.94, WHtR > 0.76, BMI >28.0; Females: WHR 0.910.99, WHtR 0.610.69, BMI 29.434.39. Gender differences in effect sizes were significant (e.g., WHR : males 6.986, P<0.001 vs. females 5.666, P=0.023). Subgroup analysis showed stronger associations in younger individuals (50 years) without hypertension or diabetes. WHR remained an independent predictor in metabolic comorbidities, while BMI and WHtR were confounded by metabolic disturbances. This study highlights the importance of gender-specific risk thresholds for obesity indices, particularly WHR, in predicting hyperlipidemia. The results demonstrate that WHR serves as a more reliable predictor for metabolic disorder risks compared to other anthropometric indices, particularly highlighting the importance of implementing preventive measures in populations under 50 years of age. Future research should focus on developing precision stratification models to optimize obesity management.