Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer

Yao Fu, Xiubing Zhang, Lili Yu, Guiping Zhang, Xinyu Liu and Wei Ren

Computer Methods in Biomechanics and Biomedical Engineering, 2025, vol. 28, issue 8, 1333-1353

Abstract: The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028–1.320; p = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685–0.983; p = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10255842.2025.2475483 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gcmbxx:v:28:y:2025:i:8:p:1333-1353

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gcmb20

DOI: 10.1080/10255842.2025.2475483

Access Statistics for this article

Computer Methods in Biomechanics and Biomedical Engineering is currently edited by Director of Biomaterials John Middleton

More articles in Computer Methods in Biomechanics and Biomedical Engineering from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().