Using UCSC Xena ( https://xena.ucsc.edu/ ), genomic data commons (GDC) downloads of the expression, mutation, and survival data of CESC patients, as well as Pan-cancer clinical data were obtained from TCGA. MiRNA data from CESC patients (three normal samples and 309 tumor samples) was also downloaded. Data on lncRNAs that can bind with miRNA was obtained from the ENCORI database. These data were sorted and standardized for subsequent analysis.

Using the limma package in R (edge) software, the p-value was adjusted to the false discovery rate (FDR), and the differentially expressed genes (DEGs) were screened using |log2 fold change (FC)| > 1 and fdr < 0.05 as the cut-off criteria. Survival analysis was conducted on the screened DEGs using the R survival package, and the conditions were set as km < 0.05, HR > 1, and cox P-value < 0.05 using the GEPIA database ( http://gepia.cancer-pku.cn/ ). The filter conditions were set as logfc > 1.0 and P-value < 0.01 to determine whether target gene expression differed in CESC.

Survival analysis was performed using the R survival package to evaluate the prognostic value of DEGs in CESC patients. Survival curves were drawn using the Kaplan-Meier method. The Log-rank test was used to evaluate statistical significance. Significance was defined as P < 0.05.

MiRNA that bound the target gene was screened using ENCORI. The R limma, ggpubr, and ggextra packages were used to assess the correlation between miRNA and the target genes, conduct differential expression analysis on the samples, and carry out visual processing to screen DEmiRNAs. The filtering conditions were corfilter <−0.2 and P-value < 0.01. To evaluate the prognostic value of DEmiRNAs in CESC patients, survival analysis was performed using the R survival package.

LncRNA that specifically binds to miRNA was screened using ENCORI ( https://starbase.sysu.edu.cn/ ). Co-expression analysis of lncRNA and miRNA, correlation analysis of lncRNA and the target gene, and differential expression analysis of lncRNA in the samples were conducted using the limma, ggpubr, and ggExtra packages in R (edge). Visual processing was carried out using R to screen the differentially expressed lncRNAs (DElncRNAs). The filtering conditions were corfilter < −0.2, P-value < 0.01, and diff P-value < 0.05.

Using the obtained miRNA-mRNA and miRNA-lncRNA relationship pairs to predict miRNA targeting and co-expression, the screened DEmRNA was shown to interact with DEmiRNAs, and the DEmiRNAs were shown to interact with DElncRNAs. A ceRNA network of the DElncRNAs-DEmiRNAs-DEmRNA was constructed and visualized using Cytoscape v3.8.2 software, an open-source software platform for visualizing molecular interaction networks and biological pathways and integrating annotations, gene expression profiles, and other state data into these networks. Cytoscape plug-in (BINGO) 18 provides researchers with a comprehensive set of functional annotation tools to understand the biological significance behind gene ontology (GO) and the gene list of the Kyoto Encyclopedia. To further understand the functions of the DEGs, BiNGO was used for functional enrichment analysis of the DEG molecular functions (MF), biological processes (BP), and cellular components (CC). For the graphene oxide condition, p < 0.05 was used as the cut-off [12] .

TIMER is a network resource used to systematically evaluate the clinical impact of different immune cells in diverse cancer types ( https://cistrome.shinyapps.io/timer/ ). Using the TIMER “SCNA” module, the relationship between the target gene copy number and the composition of the immune infiltrate was analyzed. The distribution of immune cells in different somatic copy number alteration (SCNA) states of the target gene was analyzed using a violin diagram. SCNAs are a marker of tumorigenesis and progression that can impact the immune treatment response. The correlation between target gene expression and the infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells, was assessed using the TIMER “GENE” module, and the relationship between the target gene and the number of each cell type was determined using a scatter diagram. R software was used to analyze the correlation between the target gene and the immune cell marker gene. A corfilter >0 and P-value <0.05 were used as the screening conditions. Visual analysis was determined using a scatter diagram. Three immune checkpoint genes (ICGs) were identified and the correlation between ICGs and the target genes was analyzed using the Gene-Corr module. A scatter diagram was used for visual analysis.

CESC patient data was downloaded from UCSC Xena and sorted into standardized original data ( Table 1 ) for subsequent analysis. A total of 2277 DEGs, including 1163 up-regulated and 1114 down-regulated genes were screened. Survival analysis was conducted to screen a total of 38 DEGs. The GEPIA database ( http://gepia.cancer-pku.cn/ ) was used to define differences in the expression of CESC target genes ( Figure 1 ). Of these, 16 DEGs were up-regulated, 17 were down-regulated, and five were unchanged in CESC. A total of 19 DEGs correlated significantly with both overall and disease-free survival. One of the target genes, MSRB3, was selected for subsequent analysis ( Figure 2 ).

MiRNA from CESC patients (three normal and 309 tumor samples) was downloaded from TCGA, and standardized to develop a miRNA expression matrix. DEmiRNAs were paired with DEmRNA and 93 miRNAs were predicted to interact with DEmRNA ( Table 2 ). Hsa-miR-33b-5p, hsa-miR-32-5p, and hsa-miR-363-3p co-expressed with MSRB33 were screened using the miRNA expression matrix downloaded from TCGA ( Table 3 ). A miRNA and MSRB3 correlation scatter diagram ( Figure 3 ) and a miRNA differential expression diagram in normal and tumor samples ( Figure 4 ) were obtained. Survival analysis was performed and visualized to evaluate the prognostic characteristics of DEmiRNAs ( Figure 5 ). Cytoscape 3.8.2 was used to integrate the interaction pairs and construct the miRNA-mRNA regulatory network, including nodes and edges ( Figure 6 ).

Among the identified DEmiRNAs, hsa-miR-33b-5p was selected for follow-up analysis, and the DEmiRNAs and DElncRNA were paired. A total of 59 lncRNAs were predicted to interact with DEmiRNA ( Table 4 ). The DElncRNAs (MSC-AS1) were screened using the DEmiRNA expression matrix, DEmiRNA, and miRNA data. The hsa-miR-33b-5p and MSC-AS1 co-expression and the MSC-AS1 and MSRB3 correlation analysis files were obtained ( Table 5 ) and a correlation scatter diagram of MSC-AS1 and hsa-mia-33b-5p ( Figure 7(a) ), a correlation scatter diagram of MSC-AS1 and MSRB3 ( Figure 7(b) ), and a box diagram of differential MSC-AS1 expression in normal and tumor samples ( Figure 7(c) ) were developed. Survival analysis was performed on the screened lncRNA to evaluate the prognostic characteristics of differentially expressed lncRNA (MSC-AS1) ( Figure 8 ).

The screened DEmRNA, DEmiRNAs, and DElncRNA were integrated to construct a lncRNA-miRNA-mRNA ceRNA network ( Figure 9 ), including three nodes and two edges. A DEmiRNA (hsa-miR-33b-5p) and a DElncRNA (MSC-AS1) were obtained. The results indicated that these RNA are particularly important in CESC occurrence, development, and prognosis.

The estimated degree of CD8 T cell infiltration was compared between tumors with different MSRB3 gene SCNA statuses in CESC. The loss of MSRB3 expression was significantly correlated with the level of immune cell infiltration in CESC ( Figure 10 ). MSRB3 expression was positively correlated with B cell, CD4⁺ T cell, macrophage, and dendritic cell infiltration and negatively correlated with CD8⁺ T cell and neutrophil infiltration ( Figure 11 ). MSRB3 expression was also positively correlated with the expression of the marker gene, PTGS2, on the surface of macrophage M1 cells, the marker gene, CD163, on the surface of macrophage M1 cells, and the expression of VSIG4, MS4A4A, and marker gene, NRP1, on the surface of dendritic cells ( Figure 12 ). MSRB3 expression was also found to correlate with significantly high expression of several immune checkpoints, including programmed death 1 (PD-1), programmed death ligand 1 (PD-L1), and cytotoxic T lymphocyte-associated protein 4 (CTLA4). These findings indicate that anti-PD-1/L1 or anti-CTLA4 immunotherapy may be an effective treatment option for CESC patients ( Figure 13 ). In addition, MSRB3 expression was higher in cervical cancer tissues than in normal tissues ( Figure 14 ), and disease prognosis was worse for patients with a high MSRB3 expression than those with low expression ( Figure 15 ).