Genes In Cervical Cancer Research Articles

Novel biomarkers of inflammation-associated immunity in cervical cancer.

Cervical cancer (CC) is a highly malignant gynecological cancer with a direct causal link to inflammation, primarily resulting from persistent high-risk human papillomavirus (HPV) infection. Given the challenges in early detection and mid to late-stage treatment, our research aims to identify inflammation-associated immune biomarkers in CC. Using a bioinformatics approach combined with experimental validation, we integrated two CC datasets (GSE39001 and GSE63514) in the Gene Expression Omnibus (GEO) to eliminate batch effects. Immune-related inflammation differentially expressed genes (DGEs) were obtained by R language identification. This analysis identified 37 inflammation-related DEGs. Subsequently, we discussed the different levels of immune infiltration between CC cases and controls. Weighted gene co-expression network analysis (WGCNA) identified seven immune infiltration-related modules in CC. We identified 15 immune DEGs associated with inflammation at the intersection of these findings. In addition, we constructed a protein interaction network using the String database and screened five hub genes using "CytoHubba": CXC chemokine ligand 8 (CXCL8), CXC chemokine ligand 10 (CXCL10), CX3C chemokine receptor 1 (CX3CR1), Fc gamma receptors 3B (FCGR3B), and SELL. The expression of these five genes in CC was determined by PCR experiments. In addition, we assessed their diagnostic value and further analyzed the association of immune cells with them. Five inflammation- and immune-related genes were identified, aiming to provide new directions for early diagnosis and mid to late-stage treatment of CC from multiple perspectives.

Identification of Immunogenic Cell Death-Associated Long Non-Coding RNA in the Prognosis of Cervical Cancer

Cervical cancer (CC) is a malignant tumor with a high incidence in women. It is urgent to develop new diagnostic and predictive markers of CC to improve the prognosis of CC. Programmed cell death (PCD) of cells has been proven to play an essential role in the occurrence and development of tumors. From the perspective of bioinformatics, we discussed the role of 12 PCD-related genes in the CC radiation-sensitive group and radiation-resistant group and establishes the CC risk model accordingly. At first, we identified two hub genes related to prognosis: PRKAA2 and MLLT11. There was an indirect correlation between MLLT11 and CC, and the effect of PRKAA2 on cervical cancer needs further study. Subsequently, we constructed the risk model about CC and confirmed that there was a significant difference in prognosis between the two groups, and the Top pathway involved was closely related to CC. Finally, by discussing the immune landscape of prognosis-related genes in CC, it was found that there were significant differences between the two groups in the abundance of immune cell infiltration, the expression value of immune inspection sites, and the expression values of PD-L1 and PD-L2. In addition, we performed RT-qPCR to validate the levels of hub genes. This study is helpful for the individualized treatment of CC patients.

Combined single cell and spatial transcriptome analysis reveals hedgehog pathway-related genes as potential therapeutic targets for cervical cancer

Cervical cancer (CC) remains one of the most common and deadly malignancies among women worldwide, with exceptionally high morbidity and mortality rates. The aberrant activation of the hedgehog pathway is intimately associated with tumor development and progression. Nevertheless, the potential therapeutic targets within the hedgehog pathway in CC have yet to be clearly identified. In this study, we conducted an in-depth investigation of hedgehog pathway-related genes in CC, integrating single-cell sequencing data and spatial transcriptomics. Utilizing a comprehensive scoring algorithm, we identified that myofibroblasts within CC tissue exhibit a highly enriched hedgehog pathway. Our analysis of the myofibroblast development process revealed that MYH9 plays a crucial role. Further exploration using spatial transcriptome data allowed us to delve into the role of MYH9 in myofibroblasts. We discovered that MYH9-negative and MYH9-positive myofibroblasts display distinct profiles. Validation using extensive transcriptome data demonstrated that a high infiltration of MYH9-positive myofibroblasts is a risk factor for CC patients, significantly impacting prognosis and immunotherapeutic efficacy. Our study provides unique insights into the relationship between CC and the hedgehog pathway, offering new directions for cancer treatment strategies.

Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway.

In the dynamic landscape of cervical cancer (CC) pathophysiology, this study aimed to elucidate the role of necroptosis in modulating tumor proliferation, invasion, and the immune microenvironment in CC. In this study, the impact of necroptosis on CC was evaluated through a series of bioinformatical analyses and experimental approaches. The impact of necroptosis on CC was illustrated by analyzing its effects on tumor aggression, immune responses, and the JAK2-STAT3 signaling pathway. Bevacizumab, a monoclonal antibody targeting vascular endothelial growth factor (VEGF), was also evaluated for its potential induction of necroptosis in CC cells and its interaction with necroptosis inhibitors. Additionally, the study assessed the influence of necroptosis on the immune microenvironment, particularly in T-cell-related pathways and the expression of tumor suppressor genes in CC. Necroptosis was found to enhance VEGFA expression through the activation of the JAK2-STAT3 pathway, promoting tumor proliferative and invasive capabilities in CC. Bevacizumab induced necroptosis in CC cells, potentially leading to resistance to therapy. The combination of bevacizumab with necroptosis inhibitors attenuated VEGFA expression, suggesting a novel therapeutic strategy. Additionally, necroptosis activated T-cell-related pathways and promoted the infiltration and activation of Jurkat T cells. CD3D-a tumor suppressor gene in CC-was identified as a critical marker and its expression could be upregulated by necroptosis via the JAK2-STAT3 pathway in Jurkat T cells. Treatment of CC cells with supernatants from necroptosis-induced Jurkat cells resulted in reduced tumor cell proliferation and invasion. This study reveals a complex interaction between necroptosis, tumor progression, and the immune response in CC. The findings propose a nuanced approach to leveraging necroptosis for therapeutic interventions, highlighting the potential of combining necroptosis inhibitors with existing therapies to improve treatment outcomes in CC.

Comprehensive analysis of autophagy associated genes and immune infiltrates in cervical cancer.

Cervical cancer (CC) is the most common gynecological malignant tumor and the fourth leading cause of cancer-related death in women. The progression of CC is significantly affected by autophagy. Our objective was to use bioinformatics analysis to explore the expression, prognostic significance, and immune infiltration of autophagy-related genes in CC. We identified a set of autophagy-related differentially expressed genes (ARDEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ARDEGs were further validated by The Human Protein Atlas (HPA), GSE52903, and GSE39001 dataset. Hub genes were found by the STRING network and Cytoscape. We performed Gene Set Enrichment Analysis (GSEA), Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis to further understand the functions of the hub genes. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used to check the hub genes. A total of 10 up-regulated (CXCR4, BAX, SPHK1, EIF2AK2, TBK1, TNFSF10, ITGB4, CDKN2A, IL24, and BIRC5) and 19 down-regulated (PINK1, ATG16L2, ATG4D, IKBKE, MLST8, MAPK3, ERBB2, ULK3, TP53INP2, MTMR14, BNIP3, FOS, CCL2, FAS, CAPNS1, HSPB8, PTK6, FKBP1B , and DNAJB1) ARDEGs were identified. The ARDEGs were enriched in cell growth, apoptosis, human papillomavirus infection, and cytokine-mediated. Then, we found that low expression of MAPK3 was associated with poor prognosis in CC patients and was significantly enriched in immune pathways. In addition, the expression of MAPK3 was significantly positively correlated with the infiltration levels of macrophages, B cells, mast cell activation, and cancer-associated fibroblasts. Furthermore, MAPK3 was positively correlated with LGALS9, and negatively correlated with CTLA4 and CD40. Our results show that MAPK3 can be used as a new prognostic biomarker to predict the prognosis of patients with CC.

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study

Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease.
 Objective: This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC.
 Materials and Methods: In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened.
 Results: Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, and KIF2C are the key genes, of which 5 genes (KIF11, TTK, PBK, MELK, and TOP2A) were druggable.
 Conclusion: This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments.
 Key words: Protein interactions, Cervical cancer, Hub genes, Gene expression, DEGs.

Identification of a prognostic model based on immune and hypoxia-related gene expressions in cervical cancer

Background Tumour immune microenvironment (TIME) has long been a key direction of tumour research. Understanding the occurrence, metastasis and other processes of cervical cancer (CC) is of great significance in the diagnosis and prognosis of tumours. Methods Here, this study applied the univariate Cox regression model to determine the prognostic association of immune and hypoxia signature genes in CC, and used Least Absolute Shrinkage and Selection Operator (LASSO) Cox method to build immune and hypoxia related risk score model to uncover the immune signature of the TIME of CC. Moreover, we used in vitro experiment to validate the expression level of signature genes. Notably, we assessed the predictive effect of anti-PD1/PDL1 immunotherapy using risk score model. Results Through the LASSO Cox regression model, we obtained 12 characteristic genes associated with the prognosis of CC, and also associated with immunity and hypoxia. Interestingly, the high-risk group had the properties of high hypoxia and low immunity, while the low-risk group had the properties of low hypoxia and high immunity. In the low-risk group, patients lived longer and had a significant therapeutic advantage of anti-PD-1 immunotherapy. Conclusions Established risk scores model can help predict response to anti-PD-1 immunotherapy of CC.

Transglutaminase 3 suppresses proliferation and cisplatin resistance of cervical cancer cells by inactivation of the PI3K/AKT pathway.

Recent studies have shown that dysregulation of transglutaminase 3 (TGM3) is related to the aggressive progression of several cancer types. Our study aimed to determine the function of TGM3 in cervical cancer (CC) tumorigenesis. Gene expression profiles GSE63514, GSE9750, GSE46857 and GSE67522 were obtained from the Gene Expression Omnibus (GEO) database. Overlapping differential expressed genes (DEGs) in CC were screened using GEO2R online tool and Venn diagram software. The Kaplan-Meier plotter was used to determine overall survival. TGM3 expression was analyzed based on GEO and The Cancer Genome Atlas (TCGA) databases, qRT-PCR and western blot analyses. Cell proliferation was evaluated by CCK-8 and EdU incorporation assays. The half-maximal inhibitory concentration (IC50) value of cisplatin and cell apoptosis was assessed by CCK-8 and TUNEL assays, respectively. P-glycoprotein (P-gp) expression and the changes of phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway were examined using western blot analysis. We identified 3 overlapping DEGs, including TGM3, glutathione peroxidase 3 (GPX3), and alpha B-crystallin (CRYAB), which were downregulated in CC tissues. TGM3 expression was reduced in CC cells and related to the poor prognosis of CC patients. TGM3 overexpression retarded the proliferation, reduced IC50 value of cisplatin, accelerated cisplatin-induced apoptosis, and inhibited cisplatin-induced P-gp level in CC cells. Furthermore, TGM3 overexpression suppressed the PI3K/Akt pathway in CC cells. Moreover, treatment with 740Y-P, a PI3K activator, abolished the effect of TGM3 overexpression on proliferation and cisplatin resistance in CC cells. In conclusion, overexpression of TGM3 suppressed proliferation and cisplatin resistance in CC cells by blocking the PI3K/Akt pathway.

The prognostic, immunological and single-cell features of m6A molecules in cervical cancer.

Cervical cancer (CC) is a growing health concern, emphasizing the need for reliable biomarkers in treatment selection and prognosis assessment. We analyzed gene expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) for CC. Using Consensus Cluster Plus, we applied machine learning to cluster the CC cohort. Differential analysis was performed using the edge R package, while weighted correlation network analysis (WGCNA) was conducted using the WGCNA package. Single-sample gene set enrichment analysis (ssGSEA) evaluated immune cell abundance and computed the m6Ascore. Western blot and Q-PCR validated the m6A score in CC. Common copy number variation alterations were observed in the 23 m6A-related genes in CC, and their mutation frequency was summarized in a waterfall chart. Patients were grouped into two clusters, m6AclusterA and m6AclusterB. Improved clinical outcomes were observed in m6AclusterA, while m6AclusterB exhibited higher infiltration of 14 immune cell types. WGCNA analysis generated seven integrated modules, enriched in several biological processes. Prognostic differential genes were used to generate two gene clusters (gene Cluster I and gene Cluster II). Using ssGSEA, the m6Ascore was calculated for each patient. Lower m6Ascore correlated with better clinical outcomes, lower gene mutation frequency, and wild-type status. We investigated the sensitivity of high and low m6Ascore to immunotherapy, visualized through violin and UMAP diagrams showcasing crosstalk among single-cell clusters. The key gene PFKFB4 showed higher expression in CC cell lines and tumor tissues compared to normal cells and tissue. Our study elucidates the role of m6A molecules in predicting prognosis, biological features, and appropriate treatment for CC patients.

Prediction of prognosis and immune landscape in cervical cancer based on heat shock protein-related genes

Objective: Heat shock proteins (HSPs) play key roles in the malignant transformation and progression of many tumors. However, the effectiveness of using HSP-related genes to predict the prognosis of patients with cervical cancer (CC) remains elusive. We aimed to delineate the prognosis and biological significance of HSP-related genes in CC. Methods: We collected the transcriptional and clinical data of CC patients from The Cancer Genome Atlas (TCGA) and searched for HSP-related genes in the literature. LASSO and univariate/multivariate Cox regression analyses were utilized to screen genes; 12 genes were found to be related to CC survival, and a prediction model was built. The effectiveness of the model was confirmed using TCGA and GEO, and it was found to be an independent predictor of CC. The nomogram is plotted. The prognostic model was further visualized using calibration curves, which showed good agreement with the predicted outcomes at 1-, 3, and 5 years. Results: We found that low-risk patients had higher immune cell infiltration and stronger immune function, and according to the immunophenoscore and TIDE scores, the low-risk group tended to respond more to immunotherapy. Additionally, we used the GDSC database to predict drug sensitivity in patients with different prognostic risks. Conclusion: In summary, we built a good model to help predict the prognosis of CC patients and provide a reference for personalized treatment and medication for different patients.

A comprehensive study of oxidative stress-related effects on the prognosis and drug therapy of cervical cancer.

Cervical cancer (CC) is a serious global disease with poor prognoses and a significant recurrence rate in patients with advanced disease. Oxidative stress (OS) greatly influences many types of human cancers, making it crucial to understand the functional mechanisms of OS-related genes in CC. The transcriptome and clinical data of three normal samples and 306 patients with CC were obtained from The Cancer Genome Atlas dataset. The GSE44001 dataset was acquired from the Gene Expression Omnibus database. OS-related subtypes in the cohort with CC were identified using unsupervised hierarchical clustering, univariate Cox analysis, gene set enrichment analysis (GSEA), and least absolute shrinkage and selection operator regression analysis. Additionally, molecular pathways that differ across subtypes were determined and OS-related genes linked to the prognosis of patients of CC were determined. Finally, a clinical prognostic gene signature was developed and validated. The relative infiltration level of immune cell subpopulations in different risk groups and subtypes was evaluated using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERPORT) algorithm and single-sample GSEA (ssGSEA) techniques. The present study established two distinct OS subtypes (OS clusters A and B). Analysis using ssGSEA and CIBERSPORT revealed that OS cluster B exhibited a significant level of immune infiltration. A clinical prognostic gene signature was established using OS-related characteristic genes identified by examining the differentially expressed genes across both subtypes. Furthermore, patients with CC were grouped into high- and low-risk groups, with the low-risk group showing higher survival rates. Additionally, these individuals exhibited significant advantages in terms of survival and immunotherapy. Receiver operating characteristic curve analysis demonstrated the higher predictive value of the clinical prognostic gene signature. The outcomes of the validation group depicted congruence with those recorded in the training group. A new model was constructed based on eight OS-related characteristic genes to aid the prediction of the survival rates of individuals with CC. The present study contributes to the existing literature on the mechanisms of OS genes in CC and offers a fresh perspective for future advancements in immunotherapy for such individuals.

Systematic Analysis of the Therapy Resistance Genes and their Prognostic Relevance in Cervical Cancer.

Critical issues in the therapeutic management of cervical cancer (CC) include therapy resistance and treatment failure. The development of therapy resistance is a multifaceted, progressive process, including genetic and epigenetic abnormalities. The present study aimed to identify genes that may contribute to therapy resistance in CC. We have created an extensive list of the genes in cancer that are therapy-resistant using a text-mining approach. The list was compared with the TCGA-CESC dataset to identify the differentially expressed therapy resistance genes (DETRGs) in CC. We used online resources (UALCAN, DNMIVD, cBio- Portal, HCMDB, OncoDB, ShinyGO, HPA, KM Plotter, TIMER, and DGIdb) to determine the potential association between methylation and expression of therapy resistance genes with the prognosis and clinical outcomes in CC. The systematic analysis identified 71 out of 91 DETRGs showed aberrant DNA methylation. The overlapping analysis identified 25 genes to show an inverse correlation between methylation and expression. Further, differential expression or methylation could be helpful in CC staging, HPV association, prediction of metastasis and prognosis. The study identified seven driver genes in CC. The PPIN identifies ten hub genes (HGs) associated with CC staging, cancer hallmarks, and prognosis to affect long-term survival. Our thorough investigation uncovered several novel genes and pathways that might contribute to therapy resistance in CC. The genes identified in our study may serve as a biomarker, prognostic indicator, and therapeutic target in CC.

Bioinformatics analysis illustrates the functions of miR-377-5p in cervical cancer

ABSTRACT Cervical cancer (CC) is a frequent disease in women whose development is related with miRNA disorder. MiR-377-5p plays a negative role in the development of some tumors, while few studies have revealed its role in CC. In this study, the functions of miR-377-5p in CC were investigated by bioinformatics. Briefly, the expression and survival curve of miR-377-5p in CC was analyzed with the Cancer Genome Atlas (TCGA) database, and the abundance of miR-377-5p in clinical samples and CC cell lines were measured by qRT-PCR. Moreover, the MicroRNA Data Integration Portal (miRDIP) database was used to predict targets of miR-377-5p, and the Database for Annotation Visualization and Integrated Discovery (David) was used for enrichment analysis of the functions of the miR-377-5p. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to screen the hub targets of miR-377-5p. Moreover, the Gene Expression Profiling Interactive Analysis (GEPIA) database was used to analyze the abundance of the genes in CC. Results showed that decreased miR-377-5p was found in the CC tissues and cell lines, and low miR-377-5p was connected with poor prognosis of patients. Besides, the targets of miR-377-5p were enriched in the PI3K/AKT, MAPK and RAS signaling pathways. Moreover, CDC42, FLT1, TPM3 and CAV1 were screened as hub nodes in the targets of miR-377-5p, and increased CDC42, FLT1, TPM3 and CAV1 also indicated the poor survival rates of the patients in the long term. In conclusion, this study suggests that miR-377-5p downregulation is a biomarker event for CC progression.

SHCBP1 contributes to the proliferation and self‑renewal of cervical cancer cells and activation of the NF‑κB signaling pathway through EIF5A.

Cervical cancer (CC) is the most common human papillomavirus-related disease. Continuous activation of the NF-κB signaling pathway has been observed in CC. SHC binding and spindle associated 1 (SHCBP1) contributes to tumorigenesis and activation of the NF-κB pathway in multiple cancer types, while its function in CC remains unclear. In the present study, three Gene Expression Omnibus datasets were used to identify differentially expressed genes (DEGs) in CC. Loss- and gain-of-function experiments were performed using stable SHCBP1-silenced and SHCBP1-overexpressing CC cells. To further explore the molecular mechanism of SHCBP1 in CC, small interfering RNA targeting eukaryotic translation initiation factor 5A (EIF5A) was transfected into stable SHCBP1-overexpressing CC cells. The results demonstrated that SHCBP1 was an upregulated DEG in CC tissues compared with healthy control cervical tissues. Functional experiments revealed the pro-proliferative and pro-stemness role of SHCBP1 in CC cells (CaSki and SiHa cells), in vitro. Furthermore, the NF-κB signaling pathway in CC cells was activated by SHCBP1. Increases in cell proliferation, stemness and activation of NF-κB, induced by SHCBP1 overexpression in CC cells, were reversed by EIF5A knockdown. Taken together, the results indicated that SHCBP1 serves an important role in regulation of CC cell proliferation, self-renewal and activation of NF-κB via EIF5A. The present study demonstrated a potential molecular mechanism underlying the progression of CC.

Immune infiltration and a necroptosis-related gene signature for predicting the prognosis of patients with cervical cancer.

Background: Cervical cancer (CC), the fourth most common cancer among women worldwide, has high morbidity and mortality. Necroptosis is a newly discovered form of cell death that plays an important role in cancer development, progression, and metastasis. However, the expression of necroptosis-related genes (NRGs) in CC and their relationship with CC prognosis remain unclear. Therefore, we screened the signature NRGs in CC and constructed a risk prognostic model. Methods: We downloaded gene data and clinical information of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from The Cancer Genome Atlas (TCGA) database. We performed functional enrichment analysis on the differentially expressed NRGs (DENRGs). We constructed prognostic models and evaluated them by Cox and LASSO regressions for DENRGs, and validated them using the International Cancer Genome Consortium (ICGC) dataset. We used the obtained risk score to classify patients into high- and low-risk groups. We employed the ESTIMATE and single sample gene set enrichment analysis (ssGSEA) algorithms to explore the relationship between the risk score and the clinical phenotype and the tumor immune microenvironment. Results: With LASSO regression, we established a prognostic model of CC including 16 signature DENRGs (TMP3, CHMP4C, EEF1A1, FASN, TNF, S100A10, IL1A, H1.2, SLC25A5, GLTP, IFNG, H2AC13, TUBB4B, AKNA, TYK2, and H1.5). The risk score was associated with poor prognosis in CC. Survival was lower in the high-risk group than the low-risk group. The nomogram based on the risk score, T stage, and N stage showed good prognostic predictive power. We found significant differences in immune scores, immune infiltration analysis, and immune checkpoints between the high- and low-risk groups (p < 0.05). Conclusion: We screened for DENRGs based on the TCGA database by using bioinformatics methods, and constructed prognostic models based on the signature DENRGs, which we confirmed as possibly having important biological functions in CC. Our study provides a new perspective on CC prognosis and immunity, and offers a series of new targets for future treatment.

Construction of prognostic risk markers for cervical cancer combined with anoikis-related genes and their clinical significance.

Several studies have demonstrated that anoikis affects the development, metastasis and prognosis of cancer. This study aimed to identify anoikis-related marker genes in cervical cancer (CC). Least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to construct a prognostic model and analyse the independent prognostic ability of riskscore. Receiver operating characteristic curve (ROC) and survival curves were used to evaluate and verify the performance and accuracy of the model. The nomogram of CC prognostic model was drawn using riskscore combined with clinical information. We analysed the relationship between prognostic riskscore and immune infiltration level and analysed immunophenoscore. Finally, qRT-PCR assay was used to verify the feature genes. By Cox analysis, we found that the prognostic risk model could effectively predict the risk of CC in patients independently of other clinical factors. Both the levels of immune infiltration and the immunophenoscore were significantly lower in high-risk CC patients than those in low-risk patients, revealing that high-risk patients were likely to have bad response to immunotherapy. The qRT-PCR results of the feature genes were consistent with the results of gene expression in the database. The prognostic model constructed, based on anoikis-related genes in CC, could predict the prognosis of CC patients. The model described here can provide effective support for assessing prognostic risk and devising personalised protocols during clinical treatment.

Monitoring methylation‑driven genes as prognostic biomarkers for cervical cancer

Aberrant DNA methylations are markedly associated with the development of cervical cancer (CC); however, only a limited number of studies have focused on identifying the DNA methylation‑driven genes in CC by integrative bioinformatics analysis to predict the prognosis of CC. In the present study, DNA methylation and transcriptome profiling data were downloaded from The Cancer Genome Atlas database. DNA methylation‑driven genes were obtained using the MethylMix R package. The Database for Annotation, Visualization and Integrated Discovery and ConsensusPathDB were used to perform Gene Ontology and pathway analyses, respectively. The survival R package was used to analyze overall survival rates associated with methylation‑driven genes. In total, data for 125 methylation‑driven mRNAs and 38 methylation‑driven long‑coding RNAs (lncRNAs) were obtained. Based on the univariate and multivariate Cox regression models, it was demonstrated that FLT1 (fms‑related tyrosine kinase 1) mRNA, MKI67 (marker of proliferation Ki‑67), PLEKHG6 (pleckstrin homology domain containing family G with RhoGef domain member 6) and POLE2 (DNA polymerase epsilon 2) lncRNAs were predictors of the overall survival of patients with CC. According to DNA methylation and gene expression, FLT1 mRNA, and the MKI67, PLEKHG6 and POLE2 lncRNAs functioned as independent biomarkers for the prognosis of CC. DNA methylation assays revealed that the promoter methylation levels of FLT1 were significantly upregulated in CC and cervical adenocarcinoma compared with normal controls. The results of immunohistochemical analysis revealed that the expression level of FLT1 in CC tissues was higher than that in normal tissues; however, the PLEKHG6 gene was expressed at high levels in normal tissues. On the whole, the present study demonstrates that methylation‑driven lncRNAs and mRNAs contribute to the survival of patients with CC, and FLT1 mRNA, and the MKI67, PLEKHG6 and POLE2 lncRNAs may be potential biomarkers for the prognosis of CC.

The clinical value of miRNA-21 in cervical cancer: A comprehensive investigation based on microarray datasets.

Previous work has demonstrated that the expression of microRNA-21 (miR-21) is implicated in cervical cancer (CC). However, little is known regarding its associations with clinical parameters. We first conducted a meta-analysis using data from Gene Expression Omnibus (GEO) microarrays and The Cancer Genome Atlas (TCGA). Then, enrichment analysis and hub gene screening were performed by bioinformatic methods. Finally, the role of the screened target genes in CC was explored. According to the meta-analysis, the expression of miR-21 in cancer tissues was higher than in adjacent nontumor tissues (P < 0.05). In addition, 46 genes were predicted as potential targets of miR-21. After enrichment analyses, it was detected that these genes were enriched in various cancer pathways, including the phosphatidylinositol signaling system and mammalian target of rapamycin (mTOR) signaling pathway. In this study, bioinformatic tools and meta-analysis validated that miR-21 may function as a highly sensitive and specific marker for the diagnosis of CC, which may provide a novel approach to the diagnosis and treatment of CC.

A Pyroptosis-Related Gene Panel for Predicting the Prognosis and Immune Microenvironment of Cervical Cancer.

Cervical cancer (CC) is one of the most common malignant tumors of the female reproductive system. And the immune system disorder in patients results in an increasing incidence rate and mortality rate. Pyroptosis is an immune system-related programmed cell death pathway that produces systemic inflammation by releasing pro-inflammatory intracellular components. However, the diagnostic significance of pyroptosis-related genes (PRGs) in CC is still unclear. Therefore, we identified 52 PRGs from the TCGA database and screened three Differentially Expressed Pyroptosis-Related Genes (DEPRGs) in the prognosis of cervical cancer: CHMP4C, GZMB, TNF. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate COX regression analysis were then used to construct a gene panel based on the three prognostic DEPRGs. The patients were divided into high-and low-risk groups based on the median risk score of the panel. According to the Kaplan-Meier curve, there was a substantial difference in survival rates between the two groups, with the high-risk group’s survival rate being significantly lower than the low-risk group’s. The PCA and t-SNE analyses revealed that the panel was able to differentiate patients into high-and low-risk groups. The area under the ROC curve (AUC) shows that the prognostic panel has high sensitivity and specificity. The risk score could then be employed as an independent prognostic factor using univariate and multivariate COX regression analyses paired with clinical data. The analyses of GO and KEGG functional enrichment of differentially expressed genes (DEGs) in the high-and low-risk groups revealed that these genes were primarily engaged in immune response and inflammatory cell chemotaxis. To illustrate immune cell infiltration in CC patients further, we used ssGSEA to compare immune-related cells and immune pathway activation between the high-and low-risk groups. The link between three prognostic DEPRGs and immune-related cells was still being discussed after evaluating immune cell infiltration in the TCGA cohort with “CIBERSORT.” In addition, the GEPIA database and qRT-PCR analysis were used to verify the expression levels of prognostic DEPRGs. In conclusion, PRGs are critical in tumor immunity and can be utilized to predict the prognosis of CC.

PMEPA1 Serves as a Prognostic Biomarker and Correlates with Immune Infiltrates in Cervical Cancer.

Emerging studies have demonstrated that Prostate transmembrane protein androgen induced 1 (PMEPA1) plays crucial roles in the carcinogenesis of many developing human tumors. However, the clinical significance of PMEPA1 expression in cervical cancer (CC) and its contribution to cancer immunity have not been investigated. In this study, we identified PMEPA1 as a survival-related gene in CC based on TCGA datasets. Univariate and multivariate analysis showed that PMEPA1 expression was an independent predictor for overall survival in CC patients. We could observe a strong negative correlation between PMEPA1 expression and PMEPA1 methylation. Two CpG sites of PMEPA1 were associated with overall survival, and one CpG site of PMEPA1 was associated with progression-free survival. The low level of PMEPA1 methylation was associated with advanced clinical stage of CC patients. KEGG assays revealed the genes associated with PMEPA1 expression were mainly enriched in several tumor-related pathways. Increased PMEPA1 expressions were observed to be positively related to high immune infiltration levels in several immune cells. Finally, the pan-cancer assays revealed that PMEPA1 expression was associated with the overall survival of UVM, PAAD, LUSC, BLCA, CESC, and LUAD. Taken together, PMEPA1 is a prognosis-related biomarker for multiple cancer types, especially CC. PMEPA1 is involved in tumor immunity, suggesting PMEPA1 may be a potential immunotherapeutic target in CC.