Identification of prognostic genes associated with innate lymphoid cells in cervical cancer through bioinformatics analyses and experimental assays
- 1Department of Gynecology, Shanxi Provincial People’s Hospital, Shanxi Medical University, Taiyuan, 030001 China
Abstract
The functions of innate lymphoid cells (ILCs) remain ambiguous in cervical cancer (CC). Thus, this study sought to pinpoint prognostic genes linked to ILCs in CC through both bioinformaticis analyses and experiments. First, single-cell analysis was conducted to identify ILCs, subsequently followed by hdWGCNA to identify key module genes, and Least absolute shrinkage and selection operator (Lasso)+StepCox [both] was determined to the optimum model. Subsequently, differential expression analysis coupled with univariate Cox regression was used to discover CC survival-related genes, which were then intersected with key module genes. A prognostic model was identified using 101 combinations derived from 10 machine learning algorithms, AGPAT4, UCP2, NDUFB7, SELL, TNF, WDR45, TFRC, FCRL3, TBX21, and AKNA were identified as prognostic genes. The area under the curve (AUC) for prognostic model exceeded 0.7, and Kaplan-Meier curve demonstrated that the low-risk cohort had an extended survival duration, suggesting the prognostic model’s predictive capacity for CC survival. Additionally, reverse transcription real-time polymerase chain reaction (RT-qPCR) and Western blot were employed to verify prognostic gene expression in tissue samples. Expression study revealed that FCRL3 shown significantly increased expression in CC samples at the levels of bioinformatics, mRNA, and protein. This study identified ten ILC-related prognostic genes in CC, which offer preliminary candidate molecular markers for CC survival stratification, further large-scale clinical validation and functional research are required prior to clinical prognostic or therapeutic translation.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13027-026-00788-x.
Related
- Identification of Crohn’s disease-associated molecular signatures by integrative bioinformatics analysis and experimental validation
- Identification of Endoplasmic reticulum Homeostasis-associated diagnostic biomarkers for Sarcopenia through integrated Bioinformatics and Experimental Validation
- Indole-3-propionic acid nano-prodrugs sustain increased lymphoid exposure and alleviate experimental autoimmune arthritis
- Single-cell and bulk transcriptome-based identification of myeloid-derived suppressor cell-associated immune evasion signatures and a prognostic model for gastric cancer.
- Identification of SGK1 as a candidate regulator associated with myeloid cell senescence in ME/CFS: An integrative transcriptomic and experimental study
BibTeX
@article{sun2026identification,
title = {{Identification of prognostic genes associated with innate lymphoid cells in cervical cancer through bioinformatics analyses and experimental assays}},
author = {Jinrui Sun and Yingying Xu and Kaining Guo and Qinmei Feng},
journal = {Infectious Agents and Cancer},
year = {2026},
month = aug,
doi = {10.1186/s13027-026-00788-x},
url = {https://doi.org/10.1186/s13027-026-00788-x},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Identification of prognostic genes associated with innate lymphoid cells in cervical cancer through bioinformatics analyses and experimental assays
AU - Jinrui Sun
AU - Yingying Xu
AU - Kaining Guo
AU - Qinmei Feng
PY - 2026
DA - 2026/08/01
JO - Infectious Agents and Cancer
DO - 10.1186/s13027-026-00788-x
UR - https://doi.org/10.1186/s13027-026-00788-x
AB - The functions of innate lymphoid cells (ILCs) remain ambiguous in cervical cancer (CC). Thus, this study sought to pinpoint prognostic genes linked to ILCs in CC through both bioinformaticis analyses and experiments. First, single-cell analysis was conducted to identify ILCs, subsequently followed by hdWGCNA to identify key module genes, and Least absolute shrinkage and selection operator (Lasso)+StepCox [both] was determined to the optimum model. Subsequently, differential expression analysis coupled with univariate Cox regression was used to discover CC survival-related genes, which were then intersected with key module genes. A prognostic model was identified using 101 combinations derived from 10 machine learning algorithms, AGPAT4, UCP2, NDUFB7, SELL, TNF, WDR45, TFRC, FCRL3, TBX21, and AKNA were identified as prognostic genes. The area under the curve (AUC) for prognostic model exceeded 0.7, and Kaplan-Meier curve demonstrated that the low-risk cohort had an extended survival duration, suggesting the prognostic model’s predictive capacity for CC survival. Additionally, reverse transcription real-time polymerase chain reaction (RT-qPCR) and Western blot were employed to verify prognostic gene expression in tissue samples. Expression study revealed that FCRL3 shown significantly increased expression in CC samples at the levels of bioinformatics, mRNA, and protein. This study identified ten ILC-related prognostic genes in CC, which offer preliminary candidate molecular markers for CC survival stratification, further large-scale clinical validation and functional research are required prior to clinical prognostic or therapeutic translation. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13027-026-00788-x.
SN - 1750-9378
ER -