ArticlePeerJ2025
Sialyltransferase-related genes as predictive factors for therapeutic response and prognosis in cervical cancer.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Writers and readers of sialylation in immunoregulation in cancer.The Journal of biological chemistry · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cancer-associated hypersialylation is believed to be related to the metastatic cell phenotype and the suppression of sialyltransferases (SiaTs) has been suggested to be a potent preventive strategy against metastasis. The present research discovered SiaTs-related genes for cervical cancer (CC). Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were applied to obtain the relevant samples. Mutation dataset were processed using mutect2 software. The gene modules were obtained Results: Mutation of 14 SiaTs was seen in CC. Subsequently, WGCNA-based identification of SiaTs-related gene modules was significantly enriched in metabolism-related pathways. The established RiskScore model manifested excellent prognostic classification efficiency. A poorer prognosis and occurrence of both immune evasion and reduced immunoreactivity may be seen in high-risk patients yet relatively higher immune cell scores were noticeable in low-risk patients. Angiogenesis and MYC target V2 may be the differentially activated pathways in high-risk patients, while those in low-risk patients were KRAS Signaling DN and Interferon alpha response. In addition, most immune checkpoint-correlated genes were identified to express higher in low-risk patients, while higher sensitivities to chemotherapy drugs was discovered in high-risk patients. Cellular assays have revealed that Conclusion: In this study, we systematically constructed and validated a risk scoring model based on SiaTs-related genes, which can effectively predict the prognosis and potential response to immunotherapy and chemotherapy in CC patients. This provides a new molecular basis and clinical reference for achieving individualized treatment.
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