Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment

Abstract

In developing countries, cervical cancer is still the major cause of cancer-related death among women. To better understand the correlation between tumor microenvironment (TME) and prognosis of cervical cancer, we screened 1367 differentially expressed genes (DEGs) of cervical cancer samples in The Cancer Genome Atlas (TCGA) database using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm-derived immune scores. Then, we extracted 401 tumor immune microenvironment (TIME)-related DEGs that related to patients' survival outcomes. Protein-protein interaction (PPI) network and functional enrichment analysis revealed that the prognostic genes mainly participated in myeloid leukocyte activation, adaptive immune response regulation, and receptor signaling pathways. A total of 79 key prognostic DEGs were obtained through PPI network. A TF-lncRNA-miRNA-mRNA regulatory network was constructed to explore the potential regulatory mechanism. 4 genes (CCR7, PD-1, ZAP70, and CD28) were validated in another independent cohort of cervical cancer from the Gene Expression Omnibus (GEO) database. Finally, potential drugs for key prognostics DEGs were predicted using DrugBank. In conclusion, we obtained a list of potential prognostic TIME-related genes and potential predicted drugs by integrative bioinformatics approaches. A comprehensive understanding of prognostic genes within the TIME may provide new strategies for cervical cancer treatment.

Development of a Novel Immune Infiltration-Based Gene Signature to Predict Prognosis and Immunotherapy Response of Patients With Cervical Cancer

“…In this study, we attempted to establish a predictive model with four immune-related genes with an expression matrix of 1-or-2 to replace their previous specific expression values. To a certain extent, we avoided the problem caused by platform differences and detecting technique to a certain extent, so as to make up for the limitations of previous studies (20)(21)(22)(23)(24)(25).…”

Multi-Omics Data Analyses Construct a Six Immune-Related Genes Prognostic Model for Cervical Cancer in Tumor Microenvironment

“…Multiple studies have investigated the underlying mechanisms of CC, particularly with regard to the TME, therapeutic targets, and prognostic factors ( Che et al, 2020 ; Lu et al, 2020 ; Zhao et al, 2020 ). However, the current understanding remains unsatisfactory and insufficient.…”

“…However, the current understanding remains unsatisfactory and insufficient. Numerous previous research studies concentrated mainly on the RNA-seq profiles, which are characterized by some disadvantages in comprehensively clarifying the potential mechanisms involved ( Sun et al, 2019 ; Liu et al, 2020 ; Ma J. et al, 2020 ; Zhao et al, 2020 ; Yang et al, 2021 ). Many researchers have recently concentrated on investigating prognostic factors by integrative multi-omics data analysis, which yielded more powerful results and provided a more comprehensive view of the TME ( Gu et al, 2020 ; Ma H. et al, 2020 ; Yan et al, 2020 ; Zhang Y. et al, 2020 ; Xu et al, 2021 ).…”

Integrated Bioinformatical Analysis Identifies GIMAP4 as an Immune-Related Prognostic Biomarker Associated With Remodeling in Cervical Cancer Tumor Microenvironment

“…Among them, the immune component seems to contribute more to immunotherapy-response of CC, including the T cells, macrophages, and neutrophils ( Langers et al, 2014 ; Krishnan et al, 2018 ; Chen X. J. et al, 2019 ; Ohno et al, 2020 ). Many researchers have currently investigated the correlation of the prognosis in CC patients with the critical immunological biomarkers ( De Jaeghere et al, 2020 ; Yuan et al, 2020 ; Zhao et al, 2020 ). These studies showed significant heterogeneity of the immune component and immune response in CC patients, which might play a decisive role in the ultimate clinical outcomes of patients.…”