Methylation-driven genes and their prognostic value in cervical squamous cell carcinoma

Promising predictive molecular biomarkers for cervical cancer (Review)

Cervical cancer (CC) constitutes a serious public health problem. Vaccination and screening programs have notably reduced the incidence of CC worldwide by >80%; however, the mortality rate in low‑income countries remains high. The staging of CC is a determining factor in therapeutic strategies: The clinical management of early stages of CC includes surgery and/or radiotherapy, whereas radiotherapy and/or concurrent chemotherapy are the recommended therapeutic strategies for locally advanced CC. The histopathological characteristics of tumors can effectively serve as prognostic markers of radiotherapy response; however, the efficacy rate of radiotherapy may significantly differ among cancer patients. Failure of radiotherapy is commonly associated with a higher risk of recurrence, persistence and metastasis; therefore, radioresistance remains the most important and unresolved clinical problem. This condition highlights the importance of precision medicine in searching for possible predictive biomarkers to timely identify patients at risk of treatment response failure and provide tailored therapeutic strategies according to genetic and epigenetic characteristics. The present review aimed to summarize the evidence that supports the role of several proteins, methylation markers and non‑coding RNAs as potential predictive biomarkers for CC.

The Haematopoietically-expressed homeobox transcription factor: roles in development, physiology and disease

The Haematopoietically expressed homeobox transcription factor (Hhex) is a transcriptional repressor that is of fundamental importance across species, as evident by its evolutionary conservation spanning fish, amphibians, birds, mice and humans. Indeed, Hhex maintains its vital functions throughout the lifespan of the organism, beginning in the oocyte, through fundamental stages of embryogenesis in the foregut endoderm. The endodermal development driven by Hhex gives rise to endocrine organs such as the pancreas in a process which is likely linked to its role as a risk factor in diabetes and pancreatic disorders. Hhex is also required for the normal development of the bile duct and liver, the latter also importantly being the initial site of haematopoiesis. These haematopoietic origins are governed by Hhex, leading to its crucial later roles in definitive haematopoietic stem cell (HSC) self-renewal, lymphopoiesis and haematological malignancy. Hhex is also necessary for the developing forebrain and thyroid gland, with this reliance on Hhex evident in its role in endocrine disorders later in life including a potential role in Alzheimer's disease. Thus, the roles of Hhex in embryological development throughout evolution appear to be linked to its later roles in a variety of disease processes.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures

Background

Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.

Methods

The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.

Results

A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.

Conclusion

This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient’s prognosis and accurate immunotherapy.

Uncovering of potential molecular markers for cervical squamous cell carcinoma (CESC) based on analysis of methylated-differentially expressed genes

OBJECTIVE

Cervical squamous cell carcinoma (CESC) is a cancer with high mortality caused by human papillomavirus. The aim of this study was to uncover potential CESC biomarkers to help early diagnosis and treatment.

MATERIALS AND METHODS

The mRNA transcriptome data and DNA methylation data were downloaded from database for the identification of differentially expressed mRNAs (DEmRNAs) and DNA methylation analysis. Functional analysis was used to reveal the molecular functions. Then, the association between differential methylation and DEmRNA was analyzed. Protein-protein interaction (PPI) network analysis was performed on the selected differentially methylated genes (DEGs). Subsequently, we analyzed the prognosis and constructed a prognostic risk model. We also performed diagnostic analyses of risk model genes. In addition, we verified the protein expression level of identified DEGs.

RESULTS

1438 DEmRNAs, 1669 differentially methylated sites (DMSs), 46 differentially methylated CpG islands and 53 differential methylation genes (DMGs) were obtained. In PPI, the highest interaction scores were MX2 and IRF8, and their interaction score was 0.928. Interestingly, 5 DMGs were found to be associated with CESC prognosis. In addition, our results demonstrated that high risk score was associated with poor prognosis of CESC. Furthermore, it was found that ZIK1, ZNRF2, HHEX, VCAM1 could be diagnostic molecular markers for CESC.

CONCLUSION

Analysis of methylated-differentially expressed genes may contribute to the identification of early diagnosis and therapeutic targets of CESC. In addition, a prognostic model based on 5 DMGs can be used to predict the prognosis of CESC.

Construction of a four-mRNA prognostic signature with its ceRNA network in CESC

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC. In this study, the RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas. Differentially expressed mRNAs, lncRNAs and miRNAs were identified with the edgeR R package. A four-mRNA prognostic signature was developed by multivariate Cox regression analysis. Kaplan–Meier survival with the log-rank tests was performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed with the multiMiR package and miRcode database. Kyoto encyclopedia of genes and genomes (KEGG) analysis and gene ontology analyses were used to identify the functional role of the ceRNA network in the prognosis of CESC. A total of 298 differentially expressed mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. A prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was developed, and the prognostic ability of this signature was indicated by the AUC of 0.726. Patients in the high-risk group exhibited significantly worse OS. The KEGG pathways, TGF-β and Cell adhesion molecules, were significantly enriched. In this study, a CESC-associated ceRNA network was constructed, and a multi-mRNA-based prognostic model for CESC was developed based on the ceRNA network, providing a new perspective for cancer pathogenesis research.

Aberrantly DNA Methylated-Differentially Expressed Genes in Pancreatic Cancer Through an Integrated Bioinformatics Approach

Pancreatic cancer remains one of the chief contributors to cancer related deaths on a global scale, with its diagnosis often associated with poor prognosis and high mortality. Accumulating literature continues to highlight the role of aberrant DNA methylation in relation to pancreatic cancer progression. Integrated bioinformatics approaches in the characterization of methylated-differentially expressed genes (MeDEGs) in pancreatic cancer were employed to enhance our understanding of the potential underlying molecular mechanisms of this cancer. We initially identified differentially expressed genes (DEGs) between 178 pancreatic cancer samples and 4 normal samples and differentially methylated genes (DMGs) based on 185 pancreatic cancer samples as well as 10 normal samples by analyzing RNA sequencing data in the TCGA database. Eventually, 31 MeDEGs including 5 hypomethylated/upregulated genes and 26 hypermethylated/downregulated genes were identified. Univariate Cox model and Kaplan–Meier method revealed that, among 31 MeDEGs, 5 hypermethylated/downregulated genes (ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42) were correlated with poor survival of patients with pancreatic cancer. KEGG pathway enrichment analysis by GSEA 3.0 and the protein–protein interaction (PPI) network revealed that these 5 MeDEGs were enriched in numerous cancer-related pathways in addition to interacting with each other, highlighting a significant role in the development of pancreatic cancer. Taken together, the key findings of the current study demonstrate that ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42 are hypermethylated/downregulated genes in pancreatic cancer and may be associated, through their modulation of specific pathways, with unfavorable pancreatic cancer prognosis.

Identification of an immune-based mRNA-lncRNA signature for overall survival in cervical squamous cell carcinoma

Aim: To better predict the survival of cervical squamous cell carcinoma (CESC) patients, we aimed to construct a signature according to different immune infiltration. Methods: We downloaded the RNA sequences of CESC patients from the Cancer Genome Atlas database. By using single-sample gene set enrichment analysis, we separated the samples into high- and low-immunity groups. Then we separated the samples into training and testing datasets and performed the following analyses: univariate, least absolute shrinkage and selection operator analysis, multivariate Cox regression analyses and weighted gene coexpression network analysis using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes studies were performed using the Database for Annotation, Visualization and Integrated Discovery website. Results & conclusion: We finally identified a signature with three mRNAs and two lncRNAs: ADGRG5, HSH2D, ZMAT4, RBAKDN and LINC00200. In short, our study constructed an mRNA-lncRNA signature related to immune infiltration to better predict the survival of CESC patients.

Development and validation of a nomogram to predict the mortality risk in elderly patients with ARF

Background

Acute respiratory failure (ARF) is a life-threatening complication in elderly patients. We developed a nomogram model to explore the risk factors of prognosis and the short-term mortality in elderly patients with ARF.

Methods

A total of 759 patients from MIMIC-III database were categorized into the training set and 673 patients from our hospital were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. A 30-day follow-up was performed for all patients.

Results

Multivariate logistic regression analysis showed that the heart rate, respiratoryrate, systolic pressure, SPO₂, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had a C-index of 0.741 (95% CI [0.7058-0.7766]), and the C-index was 0.687 (95% CI [0.6458-0.7272]) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality.

Conclusion

Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

Epigenetic Regulatory Enzymes: mutation Prevalence and Coexistence in Cancers

Epigenetic regulation is an important layer of transcriptional control with the particularity to affect the broad spectrum of genome. Over the years, largely due to the substantial number of recurrent mutations, there have been hundreds of novel driver genes characterized in various cancers. Additionally, the relative contribution of two dysregulated epigenomic entities (DNA methylation and histone modifications) that gradually drive the cancer phenotype remains in the research focus. However, a complex scenario arises when the disease phenotype does not harbor any relevant mutation or an abnormal transcription level. Although the cancer landscape involves the contribution of multiple genetic and non-genetic factors, herein, we discuss specifically the mutation spectrum of epigenetically-related enzymes in cancer. In addition, we address the coexistence of these two epigenetic entities in malignant human diseases, especially cancer. We suggest that the study of epigenetically-related somatic mutations in the early cellular differentiation stage of embryonic development might help to understand their later-staged footprints in the cancer genome. Furthermore, understanding the co-occurrence and/or inverse association of different disease types and redefining the general definition of "healthy" controls could provide insights into the genome reorganization.

Six-lncRNA Immune Prognostic Signature for Cervical Cancer

Background

This study searched for immune-related long noncoding RNAs (lncRNAs) to predict the prognosis of patients with cervical cancer.

Method

We obtained immunologically relevant lncRNA expression profiles and clinical follow-up data from cervical cancer patients from The Cancer Genome Atlas database and the Molecular Signatures Database. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The immune prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator Cox regression, prognosis was analyzed by Kaplan-Meier curves between different groups, and the accuracy of the prognostic model was assessed by receiver operating characteristic-area under the curve (ROC-AUC) analysis.

Results

A six-lncRNA immune prognostic signature (LIPS) was constructed to predict the prognosis of cervical cancer. The six lncRNAs are as follows: AC009065.8, LINC01871, MIR210HG, GEMIN7-AS1, GAS5-AS1, and DLEU1. A ROC-AUC analysis indicated that the model could predict the prognosis of cervical cancer patients in different subgroups. A Kaplan-Meier analysis showed that patients with high risk scores had a poor prognosis; these results were equally meaningful in the subgroup analyses. Risk scores differed depending on the clinical pathology and tumor grade and were independent risk factors for cervical cancer prognosis. Gene set enrichment analysis revealed an association between the LIPS and the immune response, Wnt signaling pathway, and TGF beta signaling pathway.

Conclusion

Our study shows that the six-LIPS can predict the prognosis of cervical cancer and contribute to decisions regarding the immunotherapeutic strategy.