How generative AI may broaden the computational scope of tumor-anchored antigen discovery, support tumor-context-integrated optimization of neoantigen candidates, facilitate the engineering of multi-epitope and mRNA vaccine constructs, and incorporate tumor-specific constraints such as antigen-presentation defects, clonal architecture, and the state of the tumor immune microenvironment is discussed.
Xin-Jie Hu, Xin-Yu Liu, Tao Wang et al.· Biotechnology Advances· 0 citations
Background The heterogeneity of hepatocellular carcinoma (HCC) complicates diagnosis and treatment, and previous prognostic models have failed to yield satisfactory results. Given that the metabolic reprogramming of cholesterol plays a crucial role in the pathogenic mechanism of hepatocellular carcinoma, this study was conducted as a breakthrough point. Methods Utilizing multi-cohort data from TCGA, ICGC, and GEO, we screened Cholesterol metabolism-related genes (CMRGs) to construct a CMRG-derived prognostic model based on two key genes: KIF20A and NDC80. Predictive performance was evaluated via Kaplan-Meier and ROC analyses. Additionally, the tumor immune microenvironment (TME) was characterized using CIBERSORT and ESTIMATE algorithms. Results The two-gene signature stratified patients into high- and low-risk groups, with high-risk patients showing poorer survival (HR = 2.71, P < 0.001). Elevated risk scores correlated with an immunosuppressive TME enriched in monocytes/macrophages. Exploratory anti-PD-1 analysis suggested higher risk scores were associated with non-response, warranting further validation. Conclusion The integrated nomogram achieved AUC values of 0.747 (95% CI: 0.665-0.830), 0.728 (95% CI: 0.652-0.804), and 0.778 (95% CI: 0.706-0.851) for 1-, 2-, and 3-year predictions. This KIF20A/NDC80-based model is a reliable prognostic tool for HCC; its immunotherapeutic utility remains exploratory and requires prospective validation.
Yu Yan, Zhen-Guo Liu, Dawei Zhou et al.· Cancer Biomarkers· 0 citations
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