Research Paper Volume 16, Issue 8 pp 7293—7310
A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma
- 1 Department of Gynecology and Obstetrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, Guangdong, China
- 2 Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, Yunnan, China
- 3 Yantian District Maternal and Child Health Hospital, Shenzhen, Guangdong, China
- 4 Department of Dermatology, The First People’s Hospital of Foshan, Foshan, Guangdong, China
- 5 Department of Burn and Plastic Surgery, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, Guangdong, China
Received: October 11, 2023 Accepted: March 28, 2024 Published: April 23, 2024
https://doi.org/10.18632/aging.205757How to Cite
Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism.
Methods: The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA). We construct a stemness-related metabolic gene signature through shrinkage estimation and univariate analysis, thereby calculating the corresponding risk scores. Moreover, we selected corresponding DEGs between groups with high- and low-risk score and conducted routine bioinformatic analyses. Furthermore, we validated the expression of four hub genes at the protein level through immunohistochemistry (IHC) in samples obtained from our patient cohort.
Results: According to the findings, it was found that six genes—AKR1B10, GNA15, ALDH1B1, PLOD2, LPCAT1, and GPX8— were differentially expressed in both TCGA-CSEC and GEO datasets among 23 differentially expressed metabolism-related genes (DEMRGs). mRNAsi exhibited a notable association with the extent of key oncogene mutation. The results showed that the AUC values for forecasting survival at 1, 3, and 5 years are 0.715, 0.689, and 0.748, individually. We observed a notable association between the risk score and different immune cell populations, along with enrichment in crucial signaling pathways in CESC. Four genes differentially expressed between different risk score groups were validated by IHC to be highly expressed in the CESC samples at the protein level.
Conclusion: The current investigation indicated that a 3-gene signature based on stemness-related metabolic and 4 hub genes with differential expression between high and low-risk score subgroups may serve as valuable prognostic markers and potential therapeutic targets in CESC.
Abbreviations
CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; MRGs: metabolism-related genes; OCLR: One-class logistic regression; mRNAsi: stemness indexes; AUC: area under the time-dependent ROC curve; DEGs: differentially expressed genes; WGCNA: weighted gene co-expression network analysis; DEMRGs: diferentially expressed metabolism-related genes; CSCs: Cancer stem cells; IHC: immunohistochemistry; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; REOs: relative expression sequence; ROC: receiver operating characteristic; LASSO: Least absolute shrinkage and selection operator; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; ESTIMATE: Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data; CIBERSORT: Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts; ssGSEA: Single-sample Gene Set Enrichment Analysis.