PCNA in Cervical Intraepithelial Neoplasia and Cervical Cancer: An Interaction Network Analysis of Differentially Expressed Genes

Abstract

The investigation of differentially expressed genes (DEGs) and their interactome could provide valuable insights for the development of markers to optimize cervical intraepithelial neoplasia (CIN) screening and treatment. This study investigated patients with cervical disease to identify gene markers whose dysregulated expression and protein interaction interface were linked with CIN and cervical cancer (CC). Literature search of microarray datasets containing cervical epithelial samples was conducted in Gene Expression Omnibus and Pubmed/Medline from inception until March 2021. Retrieved DEGs were used to construct two protein-protein interaction (PPI) networks. Module DEGs that overlapped between CIN and CC samples, were ranked based on 11 topological algorithms. The highest-ranked hub gene was retrieved and its correlation with prognosis, tissue expression and tumor purity in patients with CC, was evaluated. Screening of the literature yielded 9 microarray datasets (GSE7803, GSE27678, GSE63514, GSE6791, GSE9750, GSE29570, GSE39001, GSE63678, GSE67522). Two PPI networks from CIN and CC samples were constructed and consisted of 1704 and 3748 DEGs along 21393 and 79828 interactions, respectively. Two gene clusters were retrieved in the CIN network and three in the CC network. Multi-algorithmic topological analysis revealed PCNA as the highest ranked hub gene between the two networks, both in terms of expression and interactions. Further analysis revealed that while PCNA was overexpressed in CC tissues, it was correlated with favorable prognosis (log-rank P=0.022, HR=0.58) and tumor purity (P=9.86 × 10-4, partial rho=0.197) in CC patients. This study identified that cervical PCNA exhibited multi-algorithmic topological significance among DEGs from CIN and CC samples. Overall, PCNA may serve as a potential gene marker of CIN progression. Experimental validation is necessary to examine its value in patients with cervical disease.

Primary Melanoma of the Cervix Uteri: A Systematic Review and Meta-Analysis of the Reported Cases

“…The FIGO staging system has also been characterized as the most reliable prognostic factor for primary MM of the cervix by Min et al [ 9 ]. Cervical MM is considered to be human papillomavirus (HPV)-independent and distinct compared to other cervical epithelial malignancies [ 109 , 110 , 111 , 112 ], based on findings from matched controlled studies that examined the presence of different HPV subtypes in patients with malignant melanomas [ 113 ]. While the co-existence of cervical MM with other histological types of cervical cancer is pathophysiologically possible, it is rare based on the currently available literature.…”

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome–Interactome Signature for Predicting Non-Small Cell Lung Cancer

“…It has been suggested that early surgical resection of NSCLC can increase the 5-year survival by up to 70%; unfortunately, almost 75% of cases are detected at the time of advanced disease (Stages III/IV), making it difficult to manage the disease despite significant modern advancements in oncology practice [ 51 ]. Furthermore, past works have identified various diagnostic and prognostic signatures in cancers for patient stratification using gene expression and omics data; however, this approach has failed to capture the synergistic effects of gene expression [ 17 , 18 , 19 ]. Therefore, applying AI and MLT to omics data could be a promising approach for identifying and developing better prognostic and diagnostic models in cancers.…”

“…The prognostic markers might be helpful for evaluating treatment outcomes and monitoring and selecting suitable therapeutic strategies in NSCLC [ 17 ]. Integrated bioinformatic analysis of differentially expressed genes was successfully used to identify the potential prognostic gene signatures in other cancers, including esophageal squamous cell carcinoma and cervical cancer [ 18 , 19 ].…”

Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics

“…Central gene elements of the aging rat hippocampal network were inferred by measuring network features from their complex interactome and by identifying sub-networks and hub objects ( Giannos et al, 2021a , b , 2022 ). Highly clustered DEGs or densely connected modules in the PPI network were identified using the molecular complex detection ( Bader and Hogue, 2003 ).…”