A DNA methylation signature to improve survival prediction of gastric cancer

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Circular RNA LDLRAD3 promotes gastric cancer progression by upregulating COL4A5 through sponging miR-h37

BACKGROUND

Circular RNAs play an important role in the development of gastric cancer (GC). circ-low-density lipoprotein receptor class A domain containing 3 (LDLRAD3) has been confirmed to be related to GC progression. miR-137 is also a suppressor in GC. However, the impact of the interaction between circ-LDLRAD3 and miR-137 on the progression of GC remains unclear at present.

METHODS

The study identified expression level differences of circ-LDLRAD3, miR-137, and COL4A5 in GC pathological specimens compared to normal tissue samples. Furthermore, through in vitro experiments, including flow cytometry, cell counting kit-8 (CCK-8) assays, wound healing, Western blotting, and colony formation assays, we further explored the molecular regulatory mechanisms by which these factors promote the progression of GC.

RESULTS

In this study, circ-LDLRAD3 was confirmed to have higher expression, and miR-137 had lower expression in GC tissues and cell lines. circ-LDLRAD3 knockdown and miR-137 overexpression promoted apoptosis and inhibited proliferation, migration, and invasion in GC cell lines. Further experiments validated that COL4A5 had a positive relationship with GC and that circ-LDLRAD3 promoted the expression of COL4A5. circ-LDLRAD3 could be sponged and inhibited by miR-137 in GC cells. As a result, the promotional effect of circ-LDLRAD3 on COL4A5 was counteracted by miR-137.

CONCLUSION

Our study showed that the knockdown of circ-LDLRAD3 suppressed the development of GC by regulating the miR-137/COL4A5 axis.

Hypermethylation of the sodium channel beta subunit gene promoter is associated with colorectal cancer

AIMS

To better understand the role of sodium channel beta subunit (SCNN1B) in the initiation and progression of colorectal cancer (CRC) and to identify potential biomarkers for the early detection and prognosis of CRC.

METHODS

A total of 74 pairs of CRC tissues and their adjacent normal tissues were collected between October 2016 and November 2017. The methylation levels of the SCNN1B promoter region in CRC tissues and their adjacent normal tissues were investigated by pyrosequencing. The expression of both SCNN1B mRNA and protein were detected by RT‒qPCR and immunohistochemistry, respectively.

RESULTS

The results showed that the methylation levels of the SCNN1B promoter region were significantly higher in CRC tissues than in adjacent normal tissues. The expression levels of SCNN1B mRNA and protein were significantly lower in the CRC tissues than in their adjacent normal tissues. Moreover, Pearson's correlation analysis showed that the methylation levels of the SCNN1B promoter were negatively correlated with the SCNN1B mRNA levels in CRC tissues. In addition, the high methylation levels and low mRNA expression of SCNN1B showed a significant association with advanced tumour stage, increased risk of lymph node metastasis and poor prognosis of CRC patients.

CONCLUSION

This study suggested that the decreased expression of SCNN1B due to its promoter hypermethylation may play an important role in the progression and prognosis of CRC, and the methylation levels of the SCNN1B promoter may serve as an effective molecular marker for predicting the progression and prognosis of CRC.

New insight into the CNC-bZIP member, NFE2L3, in human diseases

Nuclear factor erythroid 2 (NF-E2)-related factor 3 (NFE2L3), a member of the CNC-bZIP subfamily and widely found in a variety of tissues, is an endoplasmic reticulum (ER) membrane-anchored transcription factor that can be released from the ER and moved into the nucleus to bind the promoter region to regulate a series of target genes involved in antioxidant, inflammatory responses, and cell cycle regulation in response to extracellular or intracellular stress. Recent research, particularly in the past 5 years, has shed light on NFE2L3's participation in diverse biological processes, including cell differentiation, inflammatory responses, lipid homeostasis, immune responses, and tumor growth. Notably, NFE2L3 has been identified as a key player in the development and prognosis of multiple cancers including colorectal cancer, thyroid cancer, breast cancer, hepatocellular carcinoma, gastric cancer, renal cancer, bladder cancer, esophageal squamous cell carcinoma, T cell lymphoblastic lymphoma, pancreatic cancer, and squamous cell carcinoma. Furthermore, research has linked NFE2L3 to other cancers such as lung adenocarcinoma, malignant pleural mesothelioma, ovarian cancer, glioblastoma multiforme, and laryngeal carcinoma, indicating its potential as a target for innovative cancer treatment approaches. Therefore, to gain a better understanding of the role of NFE2L3 in disease, this review offers insights into the discovery, structure, function, and recent advancements in the study of NFE2L3 to lay the groundwork for the development of NFE2L3-targeted cancer therapies.

Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology

PURPOSE OF REVIEW

Epigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease. Being able to quantify to which extent epigenomic processes are mediators of the association between environmental exposures and diseases is of interest for epidemiologic research. In this review, we summarize the proposed mediation analysis methods with applications to epigenomic data.

RECENT FINDINGS

The ultra-high dimensionality and high correlations that characterize omics data have hindered the precise quantification of mediated effects. Several methods have been proposed to deal with mediation in high-dimensional settings, including methods that incorporate dimensionality reduction techniques to the mediation algorithm. Although important methodological advances have been conducted in the previous years, key challenges such as the development of sensitivity analyses, dealing with mediator-mediator interactions, including environmental mixtures as exposures, or the integration of different omic data should be the focus of future methodological developments for epigenomic mediation analysis.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Integrated analysis of transcriptome and epigenome reveals ENSR00000272060 as a potential biomarker in gastric cancer

Background: Enhancer RNAs (eRNAs) are involved in gene expression regulation. Although functional roles of eRNAs in the pathophysiology of neoplasms have been reported, their involvement in gastric cancer (GC) is less known. Materials & methods: A network-based integrative approach was utilized for analyzing transcriptome and epigenome alterations in GC, and an eRNA was selected for experimental validation. Survival analysis and clinicopathological associations were also performed. Results: A hub eRNA, ENSR00000272060, showed significantly increased expression in tumor versus nontumor tissues, as well as an association with clinicopathological features. A seven-gene prognostic model was also constructed. Conclusion: The constructed network provides a comprehensive understanding of the underlying processes implicated in the progression of GC, along with a starting point from which to derive potential diagnostic/prognostic biomarkers.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC.

METHODS

RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8).

RESULTS

The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups.

CONCLUSION

The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

Construction of novel 7 integrin-related gene signatures in thyroid cancer construction of model based on integrin genes

Advanced and metastatic THCA patients usually have a poor prognosis. Thus, this study aimed to establish a risk model to discriminate the high risk population. The expression and clinical data were obtained from TCGA database. The cluster analysis, lasso, univariate and multivariate cox analyses were used to construct risk model. K-M, ROC and DCA were applied to validate the efficiency and stability of the model. GO, KEGG, and ssGSEA analysis were performed to identify the potential mechanism of signatures. The 7-gene prognosis model was constructed, including FAM27E3, FIGN, GSTM4, BEX5, RBPMS2, PHF13, and DCSTAMP. ROC and DCA results showed our model had a better prognosis prediction performance than other risk models. The high risk score was associated with the poor prognosis of THCA patients with different clinical characteristics. The risk score was closely related to cell cycle. Further, we found that the expressions of signatures were significantly dysregulated in THCA and associated with prognosis. These gene expressions were affected by some clinical characteristics, methylation and CNV. Some signatures played a role in drug sensitivity and pathway activation. We constructed a 7-gene signature model based on the integrin-related genes, which showed a great prognostic value in THCA.

Comprehensive analysis of subtypes and risk model based on complement system associated genes in ccRCC

BACKGROUND

Immune therapy is widely used in treating clear cell renal cell carcinoma (ccRCC), yet identifying patient subgroups that are expected to response remains challenging. As complement system can mediate immune effects, including the progression of tumors, a correlation between complement system and immune therapy may exist.

METHODS

Based on 11 complement system associated genes (CSAGs) identified from The Cancer Genome Atlas (TCGA), we performed unsupervised clustering and classified the tumors into two different complement system (CS) patterns. The clinical significance, tumor microenvironment (TME), functional enrichment, and immune infiltration were further analyzed. A novel scoring system named CSscore was developed based on the expression levels of the 11 CSAGs.

RESULTS

Two distinct CS patterns were identified, classified as Cluster1 and Cluster2, and Cluster1 showed poor clinical outcome. Further analysis of functional enrichment, immune cell infiltration, and genetic variation revealed that Cluster1 had high infiltration of TME immune cells, but also exhibited high immune escape. The novel prognostic model, CSscore could act as an independent prognostic factor and effectively predict patients' prognosis and distinguish the therapeutic efficacy of different immune treatment strategies. The pan-cancer analysis of the CSscore indicates its potential to be further generalized to other types of cancer.

CONCLUSIONS

Two distinct CS patterns were identified and were further analyzed in terms of infiltration of TME immune cells and immune escape, providing potential explanations for the impact on prognosis of ccRCC. Our CSscore prognostic model may offer a novel perspective in the management of ccRCC patients, and potentially other types of cancer as well.

Predictive value of DNA methylation in the efficacy of chemotherapy for gastric cancer

Background

Gastric cancer (GC) is one of the most common causes of cancer-related death. Drug resistance in chemotherapy often occurs in patients with GC, leading to tumor recurrence and poor survival. DNA methylation is closely related to the development of cancer.

Methods

To investigate the role of DNA methylation in chemotherapy resistance in GC patients, we conducted a comprehensive analysis using DNA methylation data and survival information obtained from The Cancer Genome Atlas. Univariate Cox analysis was performed to screen for differential DNA methylation of chemotherapy response in patients who did and did not receive chemotherapy. Multivariate Cox analysis was then performed to identify the independent prognostic genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to explore the biological function of the signature genes.

Results

Patients receiving adjuvant chemotherapy for GC survived longer. 308 differentially methylated genes were demonstrated to be associated with prognosis. Six genes were optimally chosed for establisehing the risk model, including C6orf222, CCNL1, CREBZF, GCKR, TFCP2, and VIPR2. It was constructed based on the DNA methylation levels of these six genes: risk score = 0.47123374*C6orf222 + 9.53554803*CCNL1 + 10.40234138* CREBZF + 0.07611856* GCKR + 18.87661557*TFCP2 - 0.46396254* VIPR2. According to the risk score, patients receiving chemotherapy were divided into high- and low-risk groups, and the prognosis of the two groups was compared. The high-risk group had a shorter survival; however, this association was not present in patients without chemotherapy. The accuracy and predictive efficacy of the risk score in predicting the 1-, 3-, and 5-year survival of patients was evaluated with the receiver operating characteristic curve. In patients receiving chemotherapy, the area under the curve of the risk score for 1-, 3-, and 5-year survival was 0.841, 0.72, and 0.734, respectively. In patients who did not receive chemotherapy, the area under the curve was 0.406, 0.585, and 0.585, respectively. A nomogram model was constructed based on the risk score and clinical indicators. The model showed good consistency in the predicted probabilities and actual probabilities. Gene Ontology functional enrichment of these candidate methylated genes showed the following molecular functions: RNA binding, protein binding, mRNA binding, and nucleic acid binding; that they were mediated mainly through the following cell components: nuclear speck, nucleoplasm, nucleus, catalytic step 2 spliceosome, and the transcription factor AP-1 complex; and that they were involved in the following biological processes: mRNA processing, mRNA splicing, and RNA polymerase II promoter transcription. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment results revealed that the signaling pathways mainly enriched were transcriptional misregulation in cancer, spliceosome, and the IL-17 signaling pathway.

Conclusion

Our work identifies a six DNA methylated expression signature as a promising biomarker of chemo-resistance in GC, which provides new insights into the development of new strategies to overcome chemo-resistance in GC.

Inactivation of epithelial sodium ion channel molecules serves as effective diagnostic biomarkers in clear cell renal cell carcinoma

BACKGROUND

Non-voltage-gated sodium channel, also known as the epithelial sodium channel (ENaC), formed by heteromeric complexes consisting of SCNN1A, SCNN1B, and SCNN1G, is responsible for maintaining sodium ion and body fluid homeostasis in epithelial cells. However, no systematic study of SCNN1 family members has been conducted in renal clear cell carcinoma (ccRCC) to date.

OBJECTIVE

To investigate the abnormal expression of SCNN1 family in ccRCC and its potential correlation with clinical parameters.

METHODS

The transcription and protein expression levels of SCNN1 family members in ccRCC were analyzed based on the TCGA database, and were confirmed by quantitative RT-PCR and immunohistochemical staining assays, respectively. The area under curve (AUC) was used to evaluate the diagnostic value of SCNN1 family members for ccRCC patients.

RESULTS

The mRNA and protein expression of SCNN1 family members was significantly downregulated in ccRCC compared with normal kidney tissues, which might be due to DNA hypermethylation in the promoter region. It is worth noting that the AUC of SCNN1A, SCNN1B, and SCNN1G were 0.965, 0.979, and 0.988 based on the TCGA database (p < 0.0001), respectively. The diagnostic value was even higher when combing these three members together (AUC = 0.997, p < 0.0001). Intriguingly, the mRNA level of SCNN1A was significantly lower in females compared with males, while SCNN1B and SCNN1G were increased with the progression of ccRCC and remarkably associated with a worse outcome for patients.

CONCLUSION

The aberrantly decrease of SCNN1 family members might serve as valuable biomarkers for the diagnosis of ccRCC.

Methylation of nonessential genes in cutaneous melanoma - Rule Out hypothesis

Differential methylation plays an important role in melanoma development and is associated with survival, progression and response to treatment. However, the mechanisms by which methylation promotes melanoma development are poorly understood. The traditional explanation of selective advantage provided by differential methylation postulates that hypermethylation of regulatory 5'-cytosine-phosphate-guanine-3' dinucleotides (CpGs) downregulates the expression of tumor suppressor genes and therefore promotes tumorigenesis. We believe that other (not necessarily alternative) explanations of the selective advantages of methylation are also possible. Here, we hypothesize that melanoma cells use methylation to shut down transcription of nonessential genes - those not required for cell survival and proliferation. Suppression of nonessential genes allows tumor cells to be more efficient in terms of energy and resource usage, providing them with a selective advantage over the tumor cells that transcribe and subsequently translate genes they do not need. We named the hypothesis the Rule Out (RO) hypothesis. The RO hypothesis predicts higher methylation of CpGs located in regulatory regions (CpG islands) of nonessential genes. It also predicts the higher methylation of regulatory CpGs linked to nonessential genes in melanomas compared to nevi and lower expression of nonessential genes in malignant (derived from melanoma) versus normal (derived from nonaffected skin) melanocytes. The analyses conducted using in-house and publicly available data found that all predictions derived from the RO hypothesis hold, providing observational support for the hypothesis.

Sodium channel 1 subunit alpha SCNN1A exerts oncogenic function in pancreatic cancer via accelerating cellular growth and metastasis

The identification of new diagnostic and therapeutic biomarkers might be helpful to understand molecular mechanism of cancer pathogenesis and develop anti-cancer targets. This study reported the alteration of Sodium channel 1 subunit alpha (SCNN1A) expression, its prognostic significance and biological roles in pancreatic cancer. Bioinformatics database was searched to explore the expression of SCNN1A in pancreatic cancer specimens and analysis results were further validated by qRT-PCR and Western blot assay. The correlation between SCNN1A expression and clinicopathological characteristics and its impact on survival outcome of pancreatic cancer patients were investigated using GEPIA database and Kaplan-Meier plotter. Loss- and gain-of-functional experiments in vitro were done to investigate the biological function of SCNN1A in pancreatic cancer. Bioinformatics analysis and validation experiment showed that SCNN1A was frequently overexpressed in pancreatic cancer specimens and cell lines (P < 0.001), and there were significant relevance between high SCNN1A expression and TP53 mutation (P < 0.05) as well as unfavorable prognosis of pancreatic cancer patients (HR for overall survival: 1.9, P = 0.003 and HR for disease-free survival: 1.7, P = 0.014). The silencing of SCNN1A suppressed cell proliferation, migration and invasion and induced cell apoptosis (P < 0.05), while its overexpression promoted aggressive phenotypes of pancreatic cancer cells in vitro (P < 0.05). SCNN1A possessed oncogenic function and its dysregulation could be implicated in the development and metastasis of pancreatic cancer.

A newly defined basement membrane-related gene signature for the prognosis of clear-cell renal cell carcinoma

Background: Basement membranes (BMs) are associated with cell polarity, differentiation, migration, and survival. Previous studies have shown that BMs play a key role in the progression of cancer, and thus could serve as potential targets for inhibiting the development of cancer. However, the association between basement membrane-related genes (BMRGs) and clear cell renal cell carcinoma (ccRCC) remains unclear. To address that gap, we constructed a novel risk signature utilizing BMRGs to explore the relationship between ccRCC and BMs.

Methods: We gathered transcriptome and clinical data from The Cancer Genome Atlas (TCGA) and randomly separated the data into training and test sets to look for new potential biomarkers and create a predictive signature of BMRGs for ccRCC. We applied univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to establish the model. The risk signature was further verified and evaluated through principal component analysis (PCA), the Kaplan-Meier technique, and time-dependent receiver operating characteristics (ROC). A nomogram was constructed to predict the overall survival (OS). The possible biological pathways were investigated through functional enrichment analysis. In this study, we also determined tumor mutation burden (TMB) and performed immunological analysis and immunotherapeutic drug analysis between the high- and low-risk groups.

Results: We identified 33 differentially expressed genes and constructed a risk model of eight BMRGs, including COL4A4, FREM1, CSPG4, COL4A5, ITGB6, ADAMTS14, MMP17, and THBS4. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. The K-M and ROC analysis demonstrated that the model could predict the prognosis well from the areas under the curves (AUCs), which was 0.731. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for ccRCC. Furthermore, immune cell infiltration, immunological checkpoints, TMB, and the half-inhibitory concentration varied considerably between high- and low-risk groups.

Conclusion: Employing eight BMRGs to construct a risk model as a prognostic indicator of ccRCC could provide us with a potential progression trajectory as well as predictions of therapeutic response.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets

A previous research study on differentiating gastric cancer (GC) into distinct subtypes or prognostic models was mostly based on GC tissues, which neglected the role of nontumour tissues in GC subtypes. The purpose of the research was to identify GC subtypes on the basis of tumour and adjacent nontumour tissues to assess the prognosis of GC patients. We characterized three GC subtypes on the basis of the immunologic and hallmark gene sets in GC and adjacent nontumour tissues: among them, the GC patients with subtype I had the longest survival time compared to patients with other subtypes. The classification was closely associated with T stage and pathological stage of GC patients. A prognostic model containing two gene sets was constructed by LASSO analysis. Kaplan–Meier analysis showed that patients in the high-risk group survived longer than those in the low-risk group and the two prognostic genes sets in the model were strongly correlated with survival status. Then, GO and KEGG analyses and PPI network show that nontumour and tumour tissues are influencing the prognosis of GC patients in separate manners. In summary, we emphasized the prognostic value of nontumour tissue in GC patients and proposed a novel insight that both changes in tumour and nontumour tissues should be taken into account when selecting a treatment strategy for GC.

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

Background

This study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.

Methodology

Tumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.

Results

Based on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.

Conclusion

A prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.

Prognostic and Immunological Role of PPP1R14A as a Pan-Cancer Analysis Candidate

Despite emerging evidence revealing the remarkable roles of protein phosphatase 1 regulatory inhibitor subunit 14A (PPP1R14A) in cancer tumorigenesis and progression, no pan-cancer analysis is available. A comprehensive investigation of the potential carcinogenic mechanism of PPP1R14A across 33 tumors using bioinformatic techniques is reported for the first time. PPP1R14A is downregulated in major malignancies, and there is a significant correlation between the PPP1R14A expression and the prognosis of patients. The high expression of PPP1R14A in most cases was associated with poor overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) across patients with various malignant tumors, including adrenocortical carcinoma (ACC) and bladder urothelial carcinoma (BLCA), indicated through pan-cancer survival analysis. Receiver operating characteristic (ROC) analysis subsequently exhibited that the molecule has high reference significance in diagnosing a variety of cancers. The frequency of PPP1R14A genetic changes including genetic mutations and copy number alterations (CNAs) in uterine carcinosarcoma reached 16.07%, and these alterations brought misfortune to the survival and prognosis of cancer patients. In addition, methylation within the promoter region of PPP1R14A DNA was enhanced in a majority of cancers. Downregulated phosphorylation levels of phosphorylation sites including S26, T38, and others in most cases took place in several tumors, such as breast cancer and colon cancer. PPP1R14A remarkably correlated with the levels of infiltrating cells and immune checkpoint genes. Our research on the carcinogenic effect of PPP1R14A in different tumors is comprehensively summarized and analyzed and provides a theoretical basis for future therapeutic and immunotherapy strategies.

Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Background

DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients.

Methods

The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification.

Results

A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7–9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2–3.1, P = 0.0072).

Conclusion

The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

Downregulation of epithelial sodium channel (ENaC) activity in cystic fibrosis cells by epigenetic targeting

The pathogenic mechanism of cystic fibrosis (CF) includes the functional interaction of the cystic fibrosis transmembrane conductance regulator (CFTR) protein with the epithelial sodium channel (ENaC). The reduction of ENaC activity may constitute a therapeutic option for CF. This hypothesis was evaluated using drugs that target the protease-dependent activation of the ENaC channel and the transcriptional activity of its coding genes. To this aim we used: camostat, a protease inhibitor; S-adenosyl methionine (SAM), showed to induce DNA hypermethylation; curcumin, known to produce chromatin condensation. SAM and camostat are drugs already clinically used in other pathologies, while curcumin is a common dietary compound. The experimental systems used were CF and non-CF immortalized human bronchial epithelial cell lines as well as human bronchial primary epithelial cells. ENaC activity and SCNN1A, SCNN1B and SCNN1G gene expression were analyzed, in addition to SCNN1B promoter methylation. In both immortalized and primary cells, the inhibition of extracellular peptidases and the epigenetic manipulations reduced ENaC activity. Notably, the reduction in primary cells was much more effective. The SCNN1B appeared to be the best target to reduce ENaC activity, in respect to SCNN1A and SCNN1G. Indeed, SAM treatment resulted to be effective in inducing hypermethylation of SCNN1B gene promoter and in lowering its expression. Importantly, CFTR expression was unaffected, or even upregulated, after treatments. These results open the possibility of CF patients’ treatment by epigenetic targeting.

Hormones and Sex-Specific Medicine in Human Physiopathology

A prodigious increment of scientific evidence in both preclinical and clinical studies is narrowing a major gap in knowledge regarding sex-specific biological responses observed in numerous branches of clinical practices. Some paradigmatic examples include neurodegenerative and mental disorders, immune-related disorders such as pathogenic infections and autoimmune diseases, oncologic conditions, and cardiovascular morbidities. The male-to-female proportion in a population is expressed as sex ratio and varies eminently with respect to the pathophysiology, natural history, incidence, prevalence, and mortality rates. The factors that determine this scenario incorporate both sex-associated biological differences and gender-dependent sociocultural issues. A broad narrative review focused on the current knowledge about the role of hormone regulation in gender medicine and gender peculiarities across key clinical areas is provided. Sex differences in immune response, cardiovascular diseases, neurological disorders, cancer, and COVID-19 are some of the hints reported. Moreover, gender implications in occupational health and health policy are offered to support the need for more personalized clinical medicine and public health approaches to achieve an ameliorated quality of life of patients and better outcomes in population health.

Methylation- and homologous recombination deficiency-related mutant genes predict the prognosis of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a lung cancer subtype with poor prognosis. We investigated the prognostic value of methylation‐ and homologous recombination deficiency (HRD)‐associated gene signatures in LUAD.

Methods

Data on RNA sequencing, somatic mutations, and methylation were obtained from TCGA database. HRD scores were used to stratify patients with LUAD into high and low HRD groups and identify differentially mutated and expressed genes (DMEGs). Pearson correlation analysis between DMEGs and methylation yielded methylation‐associated DMEGs. Cox regression analysis was used to construct a prognostic model, and the distribution of clinical features in the high‐ and low‐risk groups was compared.

Results

Patients with different HRD scores showed different DNA mutation patterns. There were 272 differentially mutated genes and 6294 differentially expressed genes. Fifty‐seven DMEGs were obtained; the top 10 upregulated genes were COL11A1, EXO1, ASPM, COL12A1, COL2A1, COL3A1, COL5A2, DIAPH3, CAD, and SLC25A13, while the top 10 downregulated genes were C7, ERN2, DLC1, SCN7A, SMARCA2, CARD11, LAMA2, ITIH5, FRY, and EPHB6. Forty‐two DMEGs were negatively correlated with 259 methylation sites. Gene ontology and pathway enrichment analysis of the DMEGs revealed enrichment of loci involved in extracellular matrix‐related remodeling and signaling. Six out of the 42 methylation‐associated DMEGs were significantly associated with LUAD prognosis and included in the prognostic model. The model effectively stratified high‐ and low‐risk patients, with the high‐risk group having more patients with advanced stage disease.

Conclusion

We developed a novel prognostic model for LUAD based on methylation and HRD. Methylation‐associated DMEGs may function as biomarkers and therapeutic targets for LUAD. Further studies are needed to elucidate their roles in LUAD carcinogenesis.

Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma

Background: DNA methylation (MET)–mediated transcriptomic disturbance and copy number variations (CNVs) exert a significant influence in stimulating the heterogeneous progression of stomach adenocarcinoma (STAD). Nevertheless, the relation of DNA MET with CNVs, together with its impact on tumor occurrence, is still unclear.

Methods: The messenger RNA (mRNA) expression (EXP) profiles, DNA MET, and DNA copy numbers, together with STAD mutation data, were collected from the TCGA official data portal. We employed circular binary segmentation algorithm in “DNAcopy.” library of R package for mapping DNA CNV data at genetic level for all samples based on the segmented CNV data. Stable clusters of samples were recognized using negative matrix factorization cluster analysis based on 50 iterations and the “brunet” method using the MET-correlated (METcor) and CNV-correlated (CNVcor) genes. The R package “iCluster” method was utilized to comprehensively analyze the EXP, MET, and DNA CNV profiles.

Results: A total of 313 STAD samples were isolated for checking DNA copy numbers and MET and for measuring EXP. In accordance with our results, we discovered obvious co-regulation of CNVcor genes and METcor counterparts. Apart from that, these genes were subject to multi-omics integration. Meanwhile, three subtypes of STAD were detected and confirmed based on independent data. Among them, the subtype with increased aggressiveness was related to decreased mutation frequencies of ARID1A, PIK3CA, ZFHX3, SPECC1, OBSCN, KMT2D, FSIP2, ZBTB20, TTN, and RANBP2, together with the abnormal levels of JPH3, KCNB1, and PLCXD3.

Conclusion: According to the results, these aforementioned genes exerted crucial roles in the development of invasive STAD. Our findings on transcriptomic regulation genomically and epigenetically facilitate the understanding of the STAD pathology from different aspects, which help to develop efficient anti-STAD therapy.

DNA methylation of RNA-binding protein for multiple splicing 2 functions as diagnosis biomarker in gastric cancer pathogenesis and its potential clinical significance

Higher methylation levels of RNA-binding protein for multiple splicing 2 (RBPMS2) was reported to be related with unfavorable outcome in gastric cancer (GC). However, molecular function and diagnostic significance of DNA methylation of RBPMS2 remains indistinct. Here we aimed to whether DNA methylation of RBPMS2 acts as a diagnosis biomarker in GC pathogenesis and its potential clinical significance. Western blot and immunochemistry assays were carried out to explore the level of RBPMS2. GC malignancy behaviors were determined by cell counting kit-8, Transwell, flow cytometry analysis and terminal-deoxynucleoitidyl transferase mediated nick end labeling staining. The inflammatory cell infiltration in xenograft model was observed by hematoxylin and eosin staining. CpG Islands was predicted by MethPrimer and the DNA methylation of RBPMS2 was evaluated by methylation-specific polymerase chain reaction. The results showed that RBPMS2 was downregulated in GC specimens. Poor survival rates were associated with low RBPMS2 expression. Overexpression of RBPMS2 inhibited GC growth while facilitated apoptosis in GC cells. In addition, level of DNA methylation of RBPMS2 in GC tissues was increased and DNA methylation of RBPMS2 was strongly associated with tumor invasion, Borrmann classification and TNM stage. We also observed that DNA methylation inhibitors counteracted the role of RBPMS2 in restraining GC development and tumorigenesis. To sum, our data demonstrated that DNA methylation of RBPMS2 was responsible for its downregulation in GC and promoted tumor progression, indicating DNA methylation of RBPMS2 might serve as a valuable potential parameter in GC pathogenesis.

Identification of four novel hub genes as monitoring biomarkers for colorectal cancer

Background

It must be admitted that the incidence of colorectal cancer (CRC) was on the rise all over the world, but the related treatment had not caught up. Further research on the underlying pathogenesis of CRC was conducive to improving the survival status of current CRC patients.

Methods

Differentially expressed genes (DEGs) screening were conducted based on “limma” and “RobustRankAggreg” package of R software. Weighted gene co-expression network analysis (WGCNA) was performed in the integrated DEGs that from The Cancer Genome Atlas (TCGA), and all samples of validation were from Gene Expression Omnlbus (GEO) dataset.

Results

The terms obtained in the functional annotation for primary DEGs indicated that they were associated with CRC. The MEyellow stand out whereby showed the significant correlation with clinical feature (disease), and 4 hub genes, including ABCC13, AMPD1, SCNN1B and TMIGD1, were identified in yellow module. Nine datasets from Gene Expression Omnibus database confirmed these four genes were significantly down-regulated and the survival estimates for the low-expression group of these genes were lower than for the high-expression group in Kaplan-Meier survival analysis section. MEXPRESS suggested that down-regulation of some top hub genes may be caused by hypermethylation. Receiver operating characteristic curves indicated that these genes had certain diagnostic efficacy. Moreover, tumor-infiltrating immune cells and gene set enrichment analysis for hub genes suggested that there were some associations between these genes and the pathogenesis of CRC.

Conclusion

This study identified modules that were significantly associated with CRC, four novel hub genes, and further analysis of these genes. This may provide a little new insights and directions into the potential pathogenesis of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00216-7.

NFE2L3 promotes tumor progression and predicts a poor prognosis of bladder cancer

The high incidence and vulnerability to recurrence of bladder urothelial carcinoma (BLCA) is a challenge in the clinical. Recent studies have revealed that NFE2L3 plays a vital role in the carcinogenesis and progression of different human tumors. However, the role of NFE2L3 in bladder cancer has not been elucidated. In this study, NFE2L3 expression was significantly increased in bladder cancer samples. Its high expression was associated with advanced clinicopathological characteristics and was an independent prognostic factor for overall survival (OS) and metastasis-free survival (MFS) in 106 patients with BLCA. In vitro and in vivo experiments demonstrated that NFE2L3 knockdown inhibited bladder cancer cells proliferation by inducing the cell cycle arrest and cell apoptosis. Meanwhile, NFE2L3 overexpression promotes BLCA cell migration and invasion in vitro cell lines and in vivo xenografts. Moreover, we identified many genes and pathway alterations associated with tumor progression and metastasis by performing RNA-Seq analysis and functional enrichment of NFE2L3 overexpressing BLCA cells. Mechanistic investigation reveals that overexpression of NFE2L3 promoted epithelial-mesenchymal transition (EMT) in bladder cancer cells with decreased expression of gap junction-associated protein ZO-1 and epithelial marker E-cadherin with the elevation of transcription factors Snail1 and Snail2. Finally, we performed a comprehensive proteomics analysis to explore more potential molecular mechanisms. Our findings revealed that NFE2L3 might serve as a valuable clinical prognostic biomarker and therapeutic target in BLCA.

Alterations in the Ca2+ toolkit in oesophageal adenocarcinoma

Aim:

To investigate alterations in transcription of genes, encoding Ca²⁺ toolkit proteins, in oesophageal adenocarcinoma (OAC) and to assess associations between gene expression, tumor grade, nodal-metastatic stage, and patient survival.

Methods:

The expression of 275 transcripts, encoding components of the Ca²⁺ toolkit, was analyzed in two OAC datasets: the Cancer Genome Atlas [via the University of Alabama Cancer (UALCAN) portal] and the oesophageal-cancer, clinical, and molecular stratification [Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)] dataset. Effects of differential expression of these genes on patient survival were determined using Kaplan-Meier log-rank tests. OAC grade- and metastatic-stage status was investigated for a subset of genes. Adjustment for the multiplicity of testing was made throughout.

Results:

Of the 275 Ca²⁺-toolkit genes analyzed, 75 displayed consistent changes in expression between OAC and normal tissue in both datasets. The channel-encoding genes, N-methyl-D-aspartate receptor 2D (GRIN2D), transient receptor potential (TRP) ion channel classical or canonical 4 (TRPC4), and TRP ion channel melastatin 2 (TRPM2) demonstrated the greatest increase in expression in OAC in both datasets. Nine genes were consistently upregulated in both datasets and were also associated with improved survival outcomes. The 6 top-ranking genes for the weighted significance of altered expression and survival outcomes were selected for further analysis: voltage-gated Ca²⁺ channel subunit α 1D (CACNA1D), voltage-gated Ca²⁺ channel auxiliary subunit α2 δ4 (CACNA2D4), junctophilin 1 (JPH1), acid-sensing ion channel 4 (ACCN4), TRPM5, and secretory pathway Ca²⁺ ATPase 2 (ATP2C2). CACNA1D, JPH1, and ATP2C2 were also upregulated in advanced OAC tumor grades and nodal-metastatic stages in both datasets.

Conclusions:

This study has unveiled alterations of the Ca²⁺ toolkit in OAC, compared to normal tissue. Such Ca²⁺ signalling findings are consistent with those from studies on other cancers. Genes that were consistently upregulated in both datasets might represent useful markers for patient diagnosis. Genes that were consistently upregulated, and which were associated with improved survival, might be useful markers for patient outcome. These survival-associated genes may also represent targets for the development of novel chemotherapeutic agents.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.

Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).

Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.

Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.

Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

Clinical significance and prognostic role of an immune-related gene signature in gastric adenocarcinoma

Limited progress has been made in the treatment of gastric adenocarcinoma (GAC) in recent years, but the potential of immunotherapy in GAC is worthy of consideration. The purpose of this study was to develop a reliable, personalized signature based on immune genes to predict the prognosis of GAC. Here, we identified two groups of patients with significantly different prognoses by performing unsupervised clustering analysis of The Cancer Genome Atlas (TCGA) database based on 881 immune genes. The immune signature was constructed with a training set composed of 350 GAC samples from the TCGA and subsequently validated with 431 samples from GSE84437, 432 samples from GSE26253, and 145 GAC samples from real-time quantitative reverse transcription polymerase chain reaction data. This classification system can also be used to predict prognosis in different clinical subgroups. Further analysis suggested that high-risk patients were characterized by low immune scores, distinctive immune cell proportions, different immune checkpoint profiles, and a low tumor mutational burden. Ultimately, the signature was identified as an independent prognostic factor. In general, the signature can accurately predict recurrence and overall survival in patients with GAC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.

Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes

Evidence suggests that abnormal DNA methylation patterns play a crucial role in the etiology and pathogenesis of colon adenocarcinoma (COAD). In this study, we identified a total of 97 methylation-driven genes (MDGs) through a comprehensive analysis of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis identified four MDGs (CBLN2, RBM47, SLCO4C1, and TMEM220) associated with overall survival (OS) in COAD patients. A risk prediction model was then developed based on these four MDGs to predict the prognosis of COAD patients. We also created a nomogram that incorporated risk scores, age, and TNM stage to promote a personalized prediction of OS in COAD patients. Compared with the traditional TNM staging system, our new nomogram was better at predicting the OS of COAD patients. In cell experiments, we confirmed that the mRNA expression levels of CLBN2 and TMEM220 were regulated by the methylation of their promoter regions. Moreover, immunohistochemistry showed that CBLN2 and TMEM220 were potential prognostic biomarkers for COAD patients. In summary, we have established a risk prediction model and nomogram that might be effectively utilized to promote the prediction of OS in COAD patients.

A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

DNA methylation signatures associated with prognosis of gastric cancer

Background

Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.

Methods

Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.

Results

We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed.

Conclusions

We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08389-0.

Prognostic value of noggin protein expression in patients with resected gastric cancer

Background

Noggin and RNA-binding protein for multiple splicing 2 (RBPMS2) are known to regulate the expression of smooth muscle cells, endothelial cells, and osteoblasts. However, the prognostic role of combined Noggin and RBPMS2 expression in resected gastric cancer (GC) is unclear.

Methods

A total of 163 patients with GC who underwent gastrectomy were included in this study. The expression of Noggin and RBPMS2 proteins in tumor cells at the tumor center and invasive front of resected GC was evaluated by immunohistochemistry, and in conjunction with clinicopathological parameters the patient survival was analyzed.

Results

RBPMS2 protein expression was high at the tumor center (n = 86, 52.8%) and low at the invasive front (n = 69, 42.3%), while Noggin protein expression was high in both tumor center (n = 91, 55.8%) and the invasive front (n = 90, 55.2%). Noggin expression at the invasive front and tumor center was significantly decreased in advanced T stage, non-intestinal-type (invasive front, P = 0.008 and P <  0.001; tumor center lesion, P = 0.013 and P = 0.001). RBPMS2 expression at the invasive front was significantly decreased in non-intestinal-type and positive lymphatic invasion (P <  0.001 and P = 0.013). Multivariate analysis revealed that high Noggin protein expression of the invasive front was an independent prognostic factor for overall survival (hazard ratio [HR], 0.58; 95% confidence interval [CI]; 0.35–0.97, P <  0.036), but not at the tumor center (HR, 1.35; 95% CI; 0.81–2.26, P = 0.251).

Conclusions

Our study indicates that high Noggin expression is a crucial prognostic factor for favorable outcomes in patients with resected GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08273-x.

Identification of a methylomics-associated nomogram for predicting overall survival of stage I-II lung adenocarcinoma

The aim of this paper was to identify DNA methylation based biomarkers for predicting overall survival (OS) of stage I–II lung adenocarcinoma (LUAD) patients. Methylation profile data of patients with stage I–II LUAD from The Cancer Genome Atlas (TCGA) database was used to determine methylation sites-based hallmark for stage I–II LUAD patients’ OS. The patients were separated into training and validation datasets by using median risk score as cutoff. Univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to develop a DNA methylation signature for OS of patients with stage I–II LUAD. As a result, an 11-DNA methylation signature was determined to be critically associated with the OS of patients with stage I–II LUAD. Analysis of receiver operating characteristics (ROC) suggested a high prognostic effectiveness of the 11-DNA methylation signature in patients with stage I–II LUAD (AUC at 1, 3, 5 years in training set were (0.849, 0.879, 0.831, respectively), validation set (0.742, 0.807, 0.904, respectively), entire TCGA dataset (0.747, 0.818, 0.870, respectively). Kaplan–Meier survival analyses exhibited that survival was significantly longer in the low-risk cohort compared to the high-risk cohort in the training dataset (P = 7e − 07), in the validation dataset (P = 1e − 08), and in the all-cohort dataset (P = 6e − 14). In addition, a nomogram was developed based on molecular factor (methylation risk score) as well as clinical factors (age and cancer status) (AUC at 1, 3, 5 years entire TCGA dataset were 0.770, 0.849, 0.979, respectively). The result verified that our methylomics-associated nomogram had a strong robustness for predicting stage I–II LUAD patients’ OS. Furthermore, the nomogram combined clinical and molecular factors to determine an individualized probability of recurrence for patients with stage I–II LUAD, which stood for a major advance in the field of personalized medicine for pulmonary oncology. Collectively, we successfully identified a DNA methylation biomarker and a DNA methylation-based nomogram to predict the OS of patients with stage I–II LUAD.

Epigenetics and therapeutic targets in gastrointestinal malignancies

Gastrointestinal (GI) malignancies account for substantial mortality and morbidity worldwide. They are generally promoted by dysregulated signal transduction and epigenetic pathways, which are controlled by specific enzymes. Recent studies demonstrated that histone deacetylases (HDACs) together with DNA methyltransferases (DNMTs) have crucial roles in the signal transduction/epigenetic pathways in GI regulation. In this review, we discuss various enzyme targets and their functional mechanisms responsible for the regulatory processes of GI malignancies. We also discuss the epigenetic therapeutic targets that are mainly facilitated by DNMT and HDAC inhibitors, which have functional consequences and clinical outcomes for GI malignancies.

DNA Methylation Patterns Correlate with the Expression of SCNN1A, SCNN1B, and SCNN1G (Epithelial Sodium Channel, ENaC) Genes

The interplay between the cystic fibrosis transmembrane conductance regulator (CFTR) and the epithelial sodium channel (ENaC) in respiratory epithelia has a crucial role in the pathogenesis of cystic fibrosis (CF). The comprehension of the mechanisms of transcriptional regulation of ENaC genes is pivotal to better detail the pathogenic mechanism and the genotype-phenotype relationship in CF, as well as to realize therapeutic approaches based on the transcriptional downregulation of ENaC genes. Since we aimed to study the epigenetic transcriptional control of ENaC genes, an assessment of their expression and DNA methylation patterns in different human cell lines, nasal brushing samples, and leucocytes was performed. The mRNA expression of CFTR and ENaC subunits α, β and γ (respectively SCNN1A, SCNN1B, and SCNN1G genes) was studied by real time PCR. DNA methylation of 5'-flanking region of SCNN1A, SCNN1B, and SCNN1G genes was studied by HpaII/PCR. The levels of expression and DNA methylation of ENaC genes in the different cell lines, brushing samples, and leukocytes were very variable. The DNA regions studied of each ENaC gene showed different methylation patterns. A general inverse correlation between expression and DNA methylation was evidenced. Leukocytes showed very low expression of all the 3 ENaC genes corresponding to a DNA methylated pattern. The SCNN1A gene resulted to be the most expressed in some cell lines that, accordingly, showed a completely demethylated pattern. Coherently, a heavy and moderate methylated pattern of, respectively, SCNN1B and SCNN1G genes corresponded to low levels of expression. As exceptions, we found that dexamethasone treatment appeared to stimulate the expression of all the 3 ENaC genes, without an evident modulation of the DNA methylation pattern, and that in nasal brushing a considerable expression of all the 3 ENaC genes were found despite an apparent methylated pattern. At least part of the expression modulation of ENaC genes seems to depend on the DNA methylation patterns of specific DNA regions. This points to epigenetics as a controlling mechanism of ENaC function and as a possible therapeutic approach for CF.

Genome-wide DNA methylome and whole-transcriptome landscapes of spontaneous intraductal papilloma in tree shrews

Background

Breast intraductal papilloma (IP) is mainly caused by the abnormal proliferation of ductal epithelial cells. Tree shrews have potential as an animal model for the study of breast tumours; however, little is known regarding the transcriptome and DNA methylome landscapes of breast IP in tree shrews. In this research, we conducted whole-genome DNA methylation and transcriptome analyses of breast IP and normal mammary glands in tree shrews.

Methods

DNA methylation profiles were generated from the whole-genome bisulfite sequencing and whole-transcriptome landscapes of IP and control groups of tree shrews through strand-specific library construction and RNA sequencing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses and gene set enrichment analysis were performed. Spearman’s correlation analysis was used to identify statistical relationships between gene expression and DNA methylation.

Results

A genome-wide perspective of the epigenetic regulation of protein-coding genes in breast IP in tree shrews was obtained. The methylation levels at CG sites were considerably higher than those at CHG or CHH sites, and were highest in gene body regions. In total, 3,486, 82 and 361 differentially methylated regions (DMRs) were identified in the context of CG, CHG, and CHH, respectively, and 701 differentially methylated genes (DMGs) were found. Further, through transcriptomic analysis, 62 differentially expressed genes, 50 long noncoding RNAs, and 32 circular RNAs were identified in breast IP compared to normal mammary glands. Correlation analysis between the DNA methylation and transcriptome data revealed that 25 DMGs were also differentially expressed genes, among which the expression levels of 9 genes were negatively correlated with methylation levels in gene body regions. Importantly, integrated analysis identified 3 genes (PDZ domain-containing 1, ATPase plasma membrane Ca²⁺ transporting 4 and Lymphocyte cytosolic protein 1) that could serve as candidates for further study of breast IP in tree shrews.

Conclusions

This research has unearthed the comprehensive landscape of the transcriptome and DNA methylome of spontaneous IP in tree shrews, as well as candidate tumorigenesis related genes in IP. These results will contribute to the use of tree shrews in animal models of breast tumours.

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.

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

Extracellular genetic materials and their application in clinical practice

This study reviews the possible origins, functional roles, and diagnostic applications of 'extracellular genetic material' (EGM), a novel term introduced to cover DNA, RNA, and DNA/RNA-related molecules released from all types of cells into the extracellular region. The literature on EGMs shows them to play a dual role in diverse, fine-tuning mechanisms involved in both homeostasis and pathological events, including cancerogenesis and genometastasis. Recent developments in the next-generation technology have provided successful applications of low quantities of genomic materials into the diagnostic field, yielding high sensitivity and specificity in test results. Also, the successful application of EGMs into diagnostics has afforded promising outcomes for researchers and clinicians. This study of EGM provides a deeper understanding of the subject as an area of interest, especially cell-free DNA, aiming toward the eventual development of new therapeutic applications and diagnostic strategies.

Development of a five-protein signature for predicting the prognosis of head and neck squamous cell carcinoma

Currently no reliable indicators are available for predicting the clinical outcome of head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a protein-based model to improve the prognosis prediction of HNSCC. The proteome data of HNSCC cohort was downloaded from The Cancer Proteome Atlas (TCPA) portal. The TCPA HNSCC cohort was randomly divided into the discovery and validation cohort. A protein-based risk signature was developed with the discovery cohort, and then verified with the validation cohort. The prognostic value of HER3_pY1289 was further determined. We have constructed a five-protein risk signature which was strongly associated with the overall survival (OS) in the discovery cohort. Similar findings were observed in the validation cohort. The protein-based risk signature was identified as an independent prognostic factor for HNSCC. A nomogram model built on the protein-based risk signature exhibited good performance for predicting OS. Our immunohistochemistry (IHC) analysis showed that higher HER3_pY1289 staining intensity was closely associated with unfavorable prognosis of HNSCC. HER3 suppression inhibited the proliferation and invasion capacity of HNSCC cells. Collectively, we have developed a protein-based risk signature for accurately predicting the prognosis of HNSCC, which might provide valuable information for optimal individualized treatment regimens.

A Prognostic Prediction Model Developed Based on Four CpG Sites and Weighted Correlation Network Analysis Identified DNAJB1 as a Novel Biomarker for Pancreatic Cancer

Background

The prognosis of pancreatic cancer, which is among the solid tumors associated with high mortality, is poor. There is a need to improve the overall survival rate of patients with pancreatic cancer.

Materials and Methods

The Cancer Genome Atlas (TCGA) dataset with 153 samples and the International Cancer Genome Consortium (ICGC) dataset with 235 samples were used as the discovery and validation cohorts, respectively. The least absolute shrinkage and selection operator regression was used to construct the prognostic prediction model based on the DNA methylation markers. The predictive efficiency of the model was evaluated based on the calibration curve, concordance index, receiver operating characteristic curve, area under the curve, and decision curve. The xenograft model and cellular functional experiments were used to investigate the potential role of DNAJB1 in pancreatic cancer.

Results

A prognostic prediction model based on four CpG sites (cg00609645, cg13512069, cg23811464, and cg03502002) was developed using TCGA dataset. The model effectively predicted the overall survival rate of patients with pancreatic cancer, which was verified in the ICGC dataset. Next, a nomogram model based on the independent prognostic factors was constructed to predict the overall survival rate of patients with pancreatic cancer. The nomogram model had a higher predictive value than TCGA or ICGC datasets. The low-risk group with improved prognosis exhibited less mutational frequency and high immune infiltration. The brown module with 247 genes derived from the WGCNA analysis was significantly correlated with the prognostic prediction model, tumor grade, clinical stage, and T stage. The bioinformatic analysis indicated that DNAJB1 can serve as a novel biomarker for pancreatic cancer. DNAJB1 knockdown significantly inhibited the proliferation, migration, and invasion of pancreatic cancer cells in vivo and in vitro.

Conclusion

The prognostic prediction model based on four CpG sites is a new method for predicting the prognosis of patients with pancreatic cancer. The molecular characteristic analyses, including Gene Ontology, Gene Set Enrichment Analysis, mutation spectrum, and immune infiltration of the subgroups, stratified by the model provided novel insights into the initiation and development of pancreatic cancer. DNAJB1 may serve as diagnostic and prognostic biomarkers for pancreatic cancer.

Roles of NRF3 in the Hallmarks of Cancer: Proteasomal Inactivation of Tumor Suppressors

Simple Summary

This review summarizes recent advances in our understanding of the physiological roles of the NFE2-related factor 2 (NRF2)-related transcription factor NRF3 in cancer. NRF3 confers cells with six so-called “hallmarks of cancer” through upregulating gene expression of specific target genes, leading to tumorigenesis and cancer malignancy. These driver gene-like functions of NRF3 in cancer are distinct from those of NRF2.

Abstract

The physiological roles of the NRF2-related transcription factor NRF3 (NFE2L3) have remained unknown for decades. The remarkable development of human cancer genome databases has led to strong suggestions that NRF3 has functional significance in cancer; specifically, high NRF3 mRNA levels are induced in many cancer types, such as colorectal cancer and pancreatic adenocarcinoma, and are associated with poor prognosis. On the basis of this information, the involvement of NRF3 in tumorigenesis and cancer malignancy has been recently proposed. NRF3 confers cancer cells with selective growth advantages by enhancing 20S proteasome assembly through induction of the chaperone gene proteasome maturation protein (POMP) and consequently promoting degradation of the tumor suppressors p53 and retinoblastoma (Rb) in a ubiquitin-independent manner. This new finding offers insight into the proteasomal but not the genetic inactivation mechanism of tumor suppressors. Moreover, NRF3 promotes cancer malignancy-related processes, including metastasis and angiogenesis. Finally, the molecular mechanisms underlying NRF3 activation have been elucidated, and this knowledge is expected to provide many insights that are useful for the development of anticancer drugs that attenuate NRF3 transcriptional activity. Collectively, the evidence indicates that NRF3 confers cells with six so-called “hallmarks of cancer”, implying that it exhibits cancer driver gene-like function. This review describes recent research advances regarding the newly discovered addiction of cancer cells to NRF3 compared to NRF2.

Epigenetic Mechanisms in Gastric Cancer: Potential New Therapeutic Opportunities

Gastric cancer (GC) is one of the deadliest malignancies worldwide. Complex disease heterogeneity, late diagnosis, and suboptimal therapies result in the poor prognosis of patients. Besides genetic alterations and environmental factors, it has been demonstrated that alterations of the epigenetic machinery guide cancer onset and progression, representing a hallmark of gastric malignancies. Moreover, epigenetic mechanisms undergo an intricate crosstalk, and distinct epigenomic profiles can be shaped under different microenvironmental contexts. In this scenario, targeting epigenetic mechanisms could be an interesting therapeutic strategy to overcome gastric cancer heterogeneity, and the efforts conducted to date are delivering promising results. In this review, we summarize the key epigenetic events involved in gastric cancer development. We conclude with a discussion of new promising epigenetic strategies for gastric cancer treatment.

Pharmacogenetics in the treatment of gastrointestinal stromal tumors - an updated review

INTRODUCTION

Gastrointestinal stromal tumors (GIST) are the best example of a targeted therapy in solid tumors. The introduction of tyrosine kinase inhibitors (TKIs) deeply improved the prognosis of this tumor. However, a degree of inter-patient variability is still reported in response rates and pharmacogenetics may play an important role in the final clinical outcome.

AREAS COVERED

In this review, the authors provide an updated overview of the pharmacogenetic literature analyzing the role of polymorphisms in both GIST treatment efficacy and toxicity.

EXPERT OPINION

Besides the primary role of somatic DNA in dictating the clinical response to TKIs, several polymorphisms influencing their pharmacokinetics and pharmacodynamics have been identified as being potentially involved. In the last 10 years, many potential biomarkers have been proposed to predict clinical response and toxicity after TKI administration. However, the evidence is still too limited to promote a clinical translation. To date, the somatic mutational status represents the main player in clinical response to TKIs in GIST treatment; however, pharmacogenetics could still explain the degree of inter-patient variability observed in GIST patients. A combination of different theoretical approaches, experimental model systems, and statistical methods is clearly needed, in order to translate pharmacogenetics to clinical practice in the near future.