Back

Prognostic Value of IL21, CXCL9 and CD1A in Cervical Cancer

xu, y.; liu, y.; GUO, Z.

2026-01-11 allergy and immunology
10.64898/2026.01.08.26343702 medRxiv
Show abstract

BackgroundCervical cancer is one of the most common malignant tumors of the female reproductive system. Existing treatments provide limited benefit for patients with advanced, recurrent or metastatic disease, and reliable prognostic markers are lacking. In this study we integrated multi-omic data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Protein-coding genes meeting the criteria of an adjusted P value < 0.05 and |log2 fold-change| > 5 were screened; 693 genes were identified. We further focused on three genes related to the tumor microenvironment--interleukin 21 (IL21), C-X-C motif chemokine ligand 9 (CXCL9) and cluster of differentiation 1A (CD1A)--and performed differential expression analysis, survival analysis, clinical stage analysis and immune infiltration correlation analysis to clarify their prognostic value and potential mechanisms in cervical cancer. Results(1) CXCL9 and CD1A were highly expressed in cervical cancer tissues, and all three genes showed high expression across different pathological stages without stage-dependent differences; (2) high expression of IL21, CXCL9 and CD1A improved patient prognosis and was positively associated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI); (3) expression of IL21, CXCL9 and CD1A was closely correlated with infiltration of multiple immune cells: IL21 correlated with total T cells, helper T cells and B cells, CXCL9 correlated with T cells and activated dendritic cells, and CD1A correlated with immature dendritic cells. ConclusionIL21, CXCL9 and CD1A are potential prognostic biomarkers and key immunomodulatory factors in cervical cancer. This study provides a new direction for immunotherapy and individualized precision treatment of cervical cancer.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS ONE
5266 papers in training set
Top 11%
16.1%
2
Medicine
31 papers in training set
Top 0.1%
14.0%
3
Journal of Cellular and Molecular Medicine
20 papers in training set
Top 0.1%
7.2%
4
Frontiers in Immunology
638 papers in training set
Top 2%
7.2%
5
Scientific Reports
3612 papers in training set
Top 28%
3.7%
6
Journal of Medical Virology
140 papers in training set
Top 0.6%
3.7%
50% of probability mass above
7
eLife
5828 papers in training set
Top 32%
3.4%
8
Genomics
64 papers in training set
Top 0.5%
2.3%
9
Journal of Translational Medicine
57 papers in training set
Top 0.5%
2.3%
10
Microbial Pathogenesis
17 papers in training set
Top 0.1%
1.8%
11
Frontiers in Oncology
103 papers in training set
Top 2%
1.6%
12
Cancer Medicine
26 papers in training set
Top 0.6%
1.6%
13
Journal of Biomedical Science
17 papers in training set
Top 0.1%
1.5%
14
Cancers
213 papers in training set
Top 3%
1.5%
15
Cells
249 papers in training set
Top 4%
1.2%
16
The Lancet Regional Health - Western Pacific
15 papers in training set
Top 0.1%
1.2%
17
PeerJ
308 papers in training set
Top 7%
1.2%
18
Communications Medicine
113 papers in training set
Top 4%
1.0%
19
Frontiers in Genetics
230 papers in training set
Top 5%
0.9%
20
Communications Biology
993 papers in training set
Top 28%
0.9%
21
International Journal of Cancer
49 papers in training set
Top 1%
0.9%
22
Genes to Cells
25 papers in training set
Top 0.4%
0.9%
23
Bioscience Reports
27 papers in training set
Top 1%
0.9%
24
Biomedicines
67 papers in training set
Top 2%
0.9%
25
BMJ Open
601 papers in training set
Top 13%
0.7%
26
Biomedicine & Pharmacotherapy
42 papers in training set
Top 2%
0.7%
27
BMC Cancer
67 papers in training set
Top 2%
0.7%
28
International Journal of Molecular Sciences
494 papers in training set
Top 16%
0.7%
29
Journal of the American Heart Association
140 papers in training set
Top 4%
0.5%
30
Annals of Translational Medicine
18 papers in training set
Top 0.7%
0.5%