Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Revealing active constituents within traditional Chinese Medicine used for treating bacterial pneumonia, with emphasis on the mechanism of baicalein against multi-drug resistant Klebsiella pneumoniae

ETHNOPHARMACOLOGICAL RELEVANCE

The emergence of antibiotic-resistant bacteria has rendered it more challenging to treat bacterial pneumonia. Traditional Chinese medicine (TCM) has superior efficacy in the treatment of pneumonia, and it has the unique advantage of antibacterial resistance against multi-drug resistant (MDR) bacteria, but the medication rule and pharmacological mechanism of its antibacterial activity are not clear.

AIM OF THE STUDY

This study aims to reveal Chinese medication patterns in treating bacterial pneumonia to select bioactive constituents in core herbs, predict their pharmacological mechanisms and further explore their antibacterial ability against clinically isolated MDR Klebsiella pneumoniae (KP) and their antibacterial mechanisms.

MATERIALS AND METHODS

The high-frequency medicinal herbs to treat lung diseases were first screened from Pharmacopoeia of the People's Republic of China (ChP.), and then bioactive compounds in core herbs and targets for compounds and disease were collected. Potential targets, signaling pathways, and drugs' core components were determined by constructing protein-protein interaction network, enrichment analysis and "component-target-pathway-disease" network were mapped by Cytoscape 3.8.2, and the potential therapeutic value of selected core components was verified by comparing the disease targets in the GEO database with the herbal component targets in the ITCM database. The clinically isolated KP were screened by drug sensitivity tests with meropenem (MEM), polymyxin E (PE), and tigecycline and biofilm-forming assay; broth microdilution, chessboard methods and biofilm morphology and permeability experiments were employed to determine the antibacterial, bactericidal and biofilm inhibition ability of selected bioactive constituents alone and in combination with antibiotics; The mechanism of bioactive components on quorum sensing (QS) genes LuxS and LuxR was predicted by molecular docking and tested by RT-PCR.

RESULTS

The 13 core Chinese medicines were obtained by mining ChP., and 615 potential targets of core herbal medicine were screened, and the PI3K-Akt signaling pathway might play crucial roles in the therapeutic process. In-vitro experiments revealed that the selected core compounds, including forsythoside B, baicalin, baicalein, and forsythin, all have antibacterial activity, in which baicalein had the strongest ability and a synergistic effect in combination with MEM or PE. Their synergy exhibited a stronger effect on biofilms of MDR KP, inhibiting biofilm formation, disrupting formed biofilms, and removing the residual structures of dead bacteria. Baicalein was predicted to have stable binding capacity to LuxS and LuxR genes by molecular docking, and RT-PCR results verified that the combination of baicalein with MEM or PE was effective in inhibiting the expression of QS genes (LuxS and LuxR) and consequently suppressing biofilm formation.

CONCLUSION

The core Chinese herbal medicine in the ChP. to treat lung diseases has a multi-component, multi-target, and multi-pathway synergy to improve bacterial pneumonia. Experimental studies have confirmed that the bioactive compound baicalein was able to combat MDR KP alone and synergistic with MEM or PE, inhibited and disrupted biofilms via regulating LuxS and LuxR genes, and further disturbed quorum sensing system to promote the therapeutic efficacy, which provides a new pathway and rationale for treating MDR KP-induced bacterial pneumonia.

Promoter hypermethylation of neural-related genes is compatible with stemness in solid cancers

BACKGROUND

DNA hypermethylation is an epigenetic feature that modulates gene expression, and its deregulation is observed in cancer. Previously, we identified a neural-related DNA hypermethylation fingerprint in colon cancer, where most of the top hypermethylated and downregulated genes have known functions in the nervous system. To evaluate the presence of this signature and its relevance to carcinogenesis in general, we considered 16 solid cancer types available in The Cancer Genome Atlas (TCGA).

RESULTS

All tested cancers showed significant enrichment for neural-related genes amongst hypermethylated genes. This signature was already present in two premalignant tissue types and could not be explained by potential confounders such as bivalency status or tumor purity. Further characterization of the neural-related DNA hypermethylation signature in colon cancer showed particular enrichment for genes that are overexpressed during neural differentiation. Lastly, an analysis of upstream regulators identified RE1-Silencing Transcription factor (REST) as a potential mediator of this DNA methylation signature.

CONCLUSION

Our study confirms the presence of a neural-related DNA hypermethylation fingerprint in various cancers, of genes linked to neural differentiation, and points to REST as a possible regulator of this mechanism. We propose that this fingerprint indicates an involvement of DNA hypermethylation in the preservation of neural stemness in cancer cells.

Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

Dual-specificity phosphatase 26-dificient neurons are susceptible to oxygen-glucose deprivation/reoxygenation-evoked apoptosis and proinflammatory response by affecting the TAK1-medaited JNK/P38 MAPK pathway

Dual-specificity phosphatase 26 (DUSP26) is linked to a broad range of human disorders as it affects numerous signaling cascades. However, the involvement of DUSP26 in ischemic stroke has not been explored. Here, we investigated DUSP26 as a key mediator of oxygen-glucose deprivation/reoxygenation (OGD/R)-associated neuronal injury, an in vitro model for investigating ischemic stroke. A decline in DUSP26 occurred in neurons suffering from OGD/R. A deficiency in DUSP26 rendered neurons more susceptible to OGD/R by aggravating neuronal apoptosis and inflammation, while the overexpression of DUSP26 blocked OGD/R-evoked neuronal apoptosis and inflammation. Mechanistically, enhanced phosphorylation of transforming growth factor-β-activated kinase 1 (TAK1), c-Jun N-terminal kinase (JNK) and P38 mitogen-activated protein kinase (MAPK) was evidenced in DUSP26-deficient neurons suffering from OGD/R, whereas the opposite effects were observed in DUSP26-overexpressed neurons. Moreover, the inhibition of TAK1 abolished the DUSP26-deficiency-elicited activation of JNK and P38 MAPK and exhibited anti-OGD/R injury effects in DUSP26-deficiency neurons. Results from these experiments show that DUSP26 is essential for neurons in defending against OGD/R insult, while neuroprotection is achieved by restraining the TAK1-mediated JNK/P38 MAPK pathway. Therefore, DUSP26 may serve as a therapeutic target for the management of ischemic stroke.

Iroquois Family Genes in Gastric Carcinogenesis: A Comprehensive Review

Gastric cancer (GC) is the fifth leading cause of cancer-associated death worldwide, accounting for 768,793 related deaths and 1,089,103 new cases in 2020. Despite diagnostic advances, GC is often detected in late stages. Through a systematic literature search, this study focuses on the associations between the Iroquois gene family and GC. Accumulating evidence indicates that Iroquois genes are involved in the regulation of various physiological and pathological processes, including cancer. To date, information about Iroquois genes in GC is very limited. In recent years, the expression and function of Iroquois genes examined in different models have suggested that they play important roles in cell and cancer biology, since they were identified to be related to important signaling pathways, such as wingless, hedgehog, mitogen-activated proteins, fibroblast growth factor, TGFβ, and the PI3K/Akt and NF-kB pathways. In cancer, depending on the tumor, Iroquois genes can act as oncogenes or tumor suppressor genes. However, in GC, they seem to mostly act as tumor suppressor genes and can be regulated by several mechanisms, including methylation, microRNAs and important GC-related pathogens. In this review, we provide an up-to-date review of the current knowledge regarding Iroquois family genes in GC.

Transcriptomics Based Network Analyses and Molecular Docking Highlighted Potentially Therapeutic Biomarkers for Colon Cancer

In this study, machine learning-based multiple bioinformatics analysis was carried out for the purpose of the deep and efficient mining of high-throughput transcriptomics data from the TCGA database. Compared with normal colon tissue, 2469 genes were significantly differentially expressed in colon cancer tissue. Gene functional annotation and pathway analysis suggested that most DEGs were functionally related to the cell cycle and metabolism. Weighted gene co-expression network analysis revealed a significant module and the enriched genes that were closely related to fatty acid degradation and metabolism. Based on colon cancer progression, the trend analysis highlighted that several gene sets were significantly correlated with disease development. At the same time, the most specific genes were functionally related to cancer cell features such as the high performance of DNA replication and cell division. Moreover, survival analysis and target drug prediction were performed to prioritize reliable biomarkers and potential drugs. In consideration of a combination of different evidence, four genes (ACOX1, CPT2, CDC25C and PKMYT1) were suggested as novel biomarkers in colon cancer. The potential biomarkers and target drugs identified in our study may provide new ideas for colonic-related prevention, diagnosis, and treatment; therefore, our results have high clinical value for colon cancer.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

Identifying Genes that Affect Differentiation of Human Neural Stem Cells and Myelination of Mature Oligodendrocytes

Human neural stem cells (NSCs) are self-renewing, multipotent cells of the central nervous system (CNS). They are characterized by their ability to differentiate into a range of cells, including oligodendrocytes (OLs), neurons, and astrocytes, depending on exogenous stimuli. An efficient and easy directional differentiation method was developed for obtaining large quantities of high-quality of human OL progenitor cells (OPCs) and OLs from NSCs. RNA sequencing, immunofluorescence staining, flow cytometry, western blot, label-free proteomic sequencing, and qPCR were performed in OL lines differentiated from NSC lines. The changes in the positive rate of typical proteins were analyzed expressed by NSCs, neurons, astrocytes, OPCs, and OLs. We assessed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differentially expressed (DE) messenger RNAs (mRNAs) related to the differentiation of NSCs and the maturation of OLs. The percentage of NSCs differentiated into neurons, astrocytes, and OLs was 82.13%, 80.19%, and 90.15%, respectively. We found that nestin, PAX6, Musashi, and vimentin were highly expressed in NSCs; PDGFR-α, A2B5, NG2, OLIG2, SOX10, and NKX2-2 were highly expressed in OPCs; and CNP, GALC, PLP1, and MBP were highly expressed in OLs. RNA sequencing, western blot and qPCR revealed that ERBB4 and SORL1 gradually increased during NSC–OL differentiation. In conclusion, NSCs can differentiate into neurons, astrocytes, and OLs efficiently. PDGFR-α, APC, ID4, PLLP, and other markers were related to NSC differentiation and OL maturation. Moreover, we refined a screening method for ERBB4 and SORL1, which may underlie NSC differentiation and OL maturation.

Graphical Abstract

Potential unreported genes and proteins may regulate differentiation of human neural stem cells into oligodendrocyte lineage. Neural stem cells (NSCs) can differentiate into neurons, astrocytes, and oligodendrocyte (OLs) efficiently. By analyzing the DE mRNAs and proteins of NSCs and OLs lineage, we could identify reported markers and unreported markers of ERBB4 and SORL1 that may underlie regulate NSC differentiation and OL maturation.

Network Pharmacology and Data Mining Approach Reveal the Medication Rule of Traditional Chinese Medicine in the Treatment of Premenstrual Syndrome/Premenstrual Dysphoric Disorder

Premenstrual syndrome (PMS) is a common disorder that affects women of reproductive age. It is characterized by periodic mental and somatic symptoms such as irritability, depression, and breast pain during the luteal phase. Premenstrual dysphoric disorder (PMDD) is the most severe form of PMS. In recent years, the incidence of PMS/PMDD has been increasing year after year. However, due to the complex symptoms and ambiguous classification of PMS/PMDD, the limitations of present treatments, such as their poor efficacy rate, have become increasingly apparent. With its unique benefits such as syndrome differentiation and high cure rate, traditional Chinese medicine (TCM) has sparked new diagnosing and treating of PMS/PMDD. This study uses data mining methods, and statistical analysis revealed that Xiaoyao San and Chaihu Shugan San were the commonly used TCM to treat PMS/PMDD. A detailed investigation of regularly used single herbs revealed that most TCM is used as cold herbs that penetrate the liver meridian, with predominant bitter, sweet, and pungent flavors. The network pharmacology method analyzes the interactions between diseases, targets, and herbs. Meanwhile, the deep action targets and molecular mechanisms of 10 commonly used herbs for the treatment of PMS/PMDD are studied, revealing that it involves several ingredients, many targets, and different pathways. This interaction provides insight into the mechanism of action of TCM in the synergistic treatment of PMS/PMDD. It is now clear that we can begin treating PMS/PMDD with TCM using the target and mechanism revealed by the abovementioned findings in the future. This serves as an essential reference for future research and clinical application of TCM in the treatment of PMS/PMDD.

The prognostic effect of PNN in digestive tract cancers and its correlation with the tumor immune landscape in colon adenocarcinoma

Background

The present study investigated the expression, mutation, and methylation profile of PNN and its prognostic value in digestive tract cancers. The disparities in signaling pathways and the immune landscape in colon adenocarcinoma (COAD) based on PNN expression were specifically explored.

Methods

The expression, mutation, methylation levels of PNN, and survival data in esophageal cancer, gastric adenocarcinoma, COAD, and rectal adenocarcinoma were evaluated using several bioinformatic databases. Gene Ontology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were performed to investigate the enriched biological functions and pathways in COAD. Several acknowledged bioinformatic algorithms were employed to assess the correlation between PNN expression and the tumor immune landscape in COAD.

Results

PNN was upregulated and remarkably related to tumor stage in digestive tract cancers. High expression of PNN was positively associated with poor progression‐free survival and overall survival time, specifically in COAD. PNN expression was identified as an independent prognostic factor in COAD. GO and GSEA analyses revealed that PNN participates in multiple biological processes underlying carcinogenicity in COAD. Further investigation showed that PNN expression was significantly associated with tumor‐infiltrating immune cells, immune cell functions, and several immune checkpoints in COAD. The PNN low expression group had a lower tumor immune dysfunction and exclusion (TIDE) score and a higher immunophenoscore (IPS), indicating a better response to immunotherapy.

Conclusion

PNN was highly expressed in digestive tract cancers and could act as an independent prognostic factor and a response predictor for immunotherapy in COAD.

DNA methylation and miRNA expression in colon adenomas compared with matched normal colon mucosa and carcinomas

Dysregulation of DNA methylation patterns and non-coding RNA, including miRNAs, has been implicated in colon cancer, and these changes may occur early in the development of carcinoma. In this study, the role of epigenetics as early changes in colon tumorigenesis was examined through paired sample analysis of patient-matched normal, adenoma and carcinoma samples. Global methylation was assessed by genomic 5-methyl cytosine (5-mC) and long interspersed nuclear element-1 (LINE-1) promoter methylation by pyrosequencing. KRAS mutations were also assessed by pyrosequencing. Expression of miRNA, specifically, two microRNA genes-miR-200a and let-7c-was analysed using RT-qPCR. Differences in global methylation in adenomas were not observed, compared with normal tissue. However, LINE-1 methylation was decreased in adenomas (p = .056) and carcinomas (p = .011) compared with normal tissue. Expressions of miRNA, miR-200a and let-7c were significantly higher in adenomas than normal tissues (p = .008 and p = .045 respectively). Thus the significant changes in LINE-1 methylation and microRNA expression in precancerous lesions support an early role for epigenetic changes in the carcinogenic process. Epigenetic characteristics in adenomas may provide potential diagnostic and prognostic therapeutic targets early in cancer development at the adenoma stage.

Colon Cancer-Related Genes Identification and Function Study Based on Single-Cell Multi-Omics Integration

Transcriptomes and DNA methylation of colon cancer at the single-cell level are used to identify marker genes and improve diagnoses and therapies. Seven colon cancer subtypes are recognized based on the single-cell RNA sequence, and the differentially expressed genes regulated by dysregulated methylation are identified as marker genes for different types of colon cancer. Compared with normal colon cells, marker genes of different types show very obvious specificity, especially upregulated genes in tumors. Functional enrichment analysis for marker genes indicates a possible relation between colon cancer and nervous system disease, moreover, the weak immune system is verified in colon cancer. The heightened expression of markers and the reduction of methylation in colon cancer promote tumor development in an extensive mechanism so that there is no biological process that can be enriched in different types.

Biological Function and Clinical Value of VPS13A in Pan-Cancer Based on Bioinformatics Analysis

Purpose

Vacuolar protein sorting-associated protein 13A (VPS13A) has been shown to be associated with rhabdomyosarcoma, gastric cancer and ovarian cancer, but the pan cancer analysis of VPS13A is still lacking, and the bioinformatics function of VPS13A has not been studied yet.

Methods

We used TCGA and GEO databases to investigate the distribution, expression and prognosis of VPS13A in 33 tumors for the first time. We used TIMER2, ULCAN databases to obtain the expression differences of VPS13A in tumor tissues and corresponding normal tissues, and further obtain the gene expression in different pathological stages of tumors from the GEPIA database. Mutation types and survival analysis of VPS13A were obtained from cBioPortal database. The relationship between VPS13A and immune infiltration was explored using TIMER2. We used the String website to obtain VPS13A binding proteins and draw the protein–protein interaction network map. JVENN was used for cross analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for gene enrichment analysis.

Results

VPS13A is highly expressed in most tumors, and gene expression is associated with prognosis in patients with tumors. The expression level of VPS13A was correlated with the infiltration depth of CD8+T cells in DLBC, LUAD, SKCM and TGCT, and was correlated with carcinoma-associated fibroblasts in BRCA, CESC, LIHC and THYM. Compared with normal tissue, VPS13A methylation levels were higher in some primary tumors. KEGG gene enrichment indicates that VPS13A is involved in RNA degradation, autophagy, cell senescence, cell cycle, apoptosis and other pathways.

Conclusion

VPS13A is closely related to the occurrence and progression of tumors and can be used as a biomarker for tumor screening and diagnosis. The level of VPS13A expression and the presence of mutations affect the prognosis of patients with certain cancers, which can be determined by early genetic testing.

Heterozygous missense mutation of the RELN gene is one of the causes of epilepsy

OBJECTIVES

Genetic factors play an important role in the onset of epilepsy, and the involvement of the RELN gene was recently discovered. This paper reports a family with a history of epilepsy caused by a heterozygous missense mutation in the RELN gene.

METHODS

After a clear diagnosis was made in the proband with a family history of epilepsy, gene sequencing was performed on the proband and his family members.

RESULTS

The proband was a 19-year-old male who presented with general convulsions during sleep lasting for about 1 min and was relieved spontaneously. His father and grandmother also experienced seizures. The gene sequencing results of the proband, his mother, and his grandmother showed that both the proband and his grandmother carried the same heterozygous missense mutation in the RELN gene (c.7909 C > T), unlike the proband's mother.

DISCUSSION

Mutations in the RELN gene can lead to the occurrence of benign epilepsy, though the specific type of seizures that it can cause is still unclear, and may increase the susceptibility to epilepsy. In addition, it may have potential anticancer effects.

Expression mode and prognostic value of FXYD family members in colon cancer

The FXYD gene family comprises seven members that encode a class of small-membrane proteins characterized by an FXYD motif and interact with Na⁺/K⁺-ATPase. Until now, the expression patterns and prognostic roles of the FXYD family in colon cancer (CC) have not been systematically reported. Gene expression, methylation, clinicopathological features and the prognoses of CC patients were obtained from The Cancer Genome Atlas (TCGA) database. The expression feature and prognostic values of FXYD members were identified. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism underlying the function of the FXYD family in CC. Tumor Immune Estimation Resource (TIMER) and CIBERSORT analysis were used to assess the correlations between FXYD family members and tumor immune infiltrating cells (TIICs). FXYD family members were differentially expressed in CC except for FXYD2. FXYD2, FXYD3 and FXYD4 were revealed as independent prognostic factors for recurrence, while FXYD3 and FXYD7 were identified as prognostic factors for survival according to univariate and multivariate analyses with Cox regression. GSEA revealed that FXYD family members were involved in complicated biological functions underlying cancer progression. TIMER and CIBERSORT analyses showed significant associations between FXYD family genes and TIICs. The present study comprehensively revealed the expression mode and prognostic value of FXYD members in CC, providing insights for further study of the FXYD family as potential clinical biomarkers in CC.

Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma

Background

This study aimed to systematically investigate gene signatures for hepatoblastoma (HB) and identify potential biomarkers for its diagnosis and treatment.

Materials and Methods

GSE131329 and GSE81928 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between hepatoblastoma and normal samples were identified using the Limma package in R. Then, the similarity of network traits between two sets of genes was analyzed by weighted gene correlation network analysis (WGCNA). Cytoscape was used to visualize and select hub genes. PPI network of hub genes was construed by Cytoscape. GO enrichment and KEGG pathway analyses of hub genes were carried out using ClueGO. The random forest classifier was constructed based on the hub genes using the GSE131329 dataset as the training set, and its reliability was validated using the GSE81928 dataset. The resulting core hub genes were combined with the InnateDB database to identify the innate core genes.

Results

A total of 4244 DEGs in HB were identified. WGCNA identified four modules that were significantly correlated with the disease status. A total of 114 hub genes were obtained within the top 20 genes of each node rank. 6982 relation pairs and 3700 nodes were contained in the PPI network of 114 hub genes. GO enrichment and KEGG pathway analyses of hub genes were focused on MAPK, cell cycle, p53, and other crucial pathways involved in HB. A random forest classifier was constructed using the 114 hub genes as feature genes, resulting in a 95.5% true positive rate when classifying HB and normal samples. A total of 35 core hub genes were obtained through the mean decrease in accuracy and mean decrease Gini of the random forest model. The classification efficiency of the random forest model was 81.4%. Finally, CDK1, TOP2A, ADRA1A, FANCI, XRCC1, TPX2, CCNB2, CDK4, GLYATL1, and CFHR3 were identified by cross-comparison with the InnateDB database.

Conclusion

Our study established a random forest classifier that identified 10 core genes in HB. These findings may be beneficial for the diagnosis, prediction, and targeted therapy of HB.

To Determine Pivotal Genes Driven by Methylated DNA in Obstructive Sleep Apnea Hypopnea Syndrome

Obstructive sleep apnea syndrome (OSAHS) is a widespread respiratory dysfunction that has attracted more and more attention in recent years. Recently, a large number of studies have shown that abnormal DNA methylation epigenetically silences genes necessary for the pathogenesis of human diseases. However, the exact mechanism of abnormal DNA methylation in OSAHS is still elusive. In this study, we downloaded the OSAHS data from the GEO database. Our data for the first time revealed 520 hypermethylated genes and 889 hypomethylated genes in OSAHS. Bioinformatics analysis revealed that these abnormal methylated genes exhibited an association with the regulation of angiogenesis, apoptosis, Wnt, and ERBB2 signaling pathways. PPI network analysis displayed the interactions among these genes and validated several hub genes, such as GPSM2, CCR8, TAS2R20, TAS2R4, and TAS2R5, which were related to regulating liganded Gi-activating GPCR and the transition of mitotic metaphase/anaphase. In conclusion, our study offers a new hint of understanding the molecular mechanisms in OSAHS progression and will provide OSAHS with newly generated innovative biomarkers.

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.

SOCS2 is a potential prognostic marker that suppresses the viability of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality worldwide. Thus, there is an urgent requirement to identify novel diagnostic and prognostic biomarkers for this disease. The present study aimed to identify the hub genes associated with the progression and prognosis of patients with HCC. A total of three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus (GEO) database, followed by the identification of differentially expressed genes (DEGs) using the GEO2R method. The identified DEGs were assessed for survival significance using Kaplan-Meier analysis. Among the 15 identified DEGs in HCC tissues [cytochrome P450 family 39 subfamily A member 1, cysteine rich angiogenic inducer 61, Fos proto-oncogene, forkhead transcription factor 1 (FOXO1), growth arrest and DNA damage inducible β, Inhibitor of DNA binding 1, interleukin-1 receptor accessory protein, metallothionein-1M, pleckstrin homology-like domain family A member 1, Rho family GTPase 3, serine dehydratase, suppressor of cytokine signaling 2 (SOCS2), tyrosine aminotransferase (TAT), S100 calcium-binding protein P and serine protease inhibitor Kazal-type 1 (SPINK1)]. Low expression levels of FOXO1, SOCS2 and TAT and high SPINK1 expression indicated poor survival outcomes for patients with HCC. In addition, SOCS2 was associated with distinct stages of HCC progression in patients and presented optimal diagnostic value. In vitro functional experiments indicated that overexpression of SOCS2 inhibited HCC cell proliferation and migration. Taken together, the results of the present study suggest that SOCS2 may act as a valuable prognostic marker that is closely associated with HCC progression.

Cysteine metabolic circuitries: druggable targets in cancer

To enable survival in adverse conditions, cancer cells undergo global metabolic adaptations. The amino acid cysteine actively contributes to cancer metabolic remodelling on three different levels: first, in its free form, in redox control, as a component of the antioxidant glutathione or its involvement in protein s-cysteinylation, a reversible post-translational modification; second, as a substrate for the production of hydrogen sulphide (H2S), which feeds the mitochondrial electron transfer chain and mediates per-sulphidation of ATPase and glycolytic enzymes, thereby stimulating cellular bioenergetics; and, finally, as a carbon source for epigenetic regulation, biomass production and energy production. This review will provide a systematic portrayal of the role of cysteine in cancer biology as a source of carbon and sulphur atoms, the pivotal role of cysteine in different metabolic pathways and the importance of H2S as an energetic substrate and signalling molecule. The different pools of cysteine in the cell and within the body, and their putative use as prognostic cancer markers will be also addressed. Finally, we will discuss the pharmacological means and potential of targeting cysteine metabolism for the treatment of cancer.

A Review of DUSP26: Structure, Regulation and Relevance in Human Disease

Dual specificity phosphatases (DUSPs) play a crucial role in the regulation of intracellular signalling pathways, which in turn influence a broad range of physiological processes. DUSP malfunction is increasingly observed in a broad range of human diseases due to deregulation of key pathways, most notably the MAP kinase (MAPK) cascades. Dual specificity phosphatase 26 (DUSP26) is an atypical DUSP with a range of physiological substrates including the MAPKs. The residues that govern DUSP26 substrate specificity are yet to be determined; however, recent evidence suggests that interactions with a binding partner may be required for DUSP26 catalytic activity. DUSP26 is heavily implicated in cancer where, akin to other DUSPs, it displays both tumour-suppressive and -promoting properties, depending on the context. Here we review DUSP26 by evaluating its transcriptional patterns, protein crystallographic structure and substrate binding, as well as its physiological role(s) and binding partners, its role in human disease and the development of DUSP26 inhibitors.

An Eight-CpG-based Methylation Classifier for Preoperative Discriminating Early and Advanced-Late Stage of Colorectal Cancer

Aim

To develop and validate a CpG-based classifier for preoperative discrimination of early and advanced-late stage colorectal cancer (CRC).

Methods

We identified an epigenetic signature based on methylation status of multiple CpG sites (CpGs) from 372 subjects in The Cancer Genome Atlas (TCGA) CRC cohort, and an external cohort (GSE48684) with 64 subjects by LASSO regression algorithm. A classifier derived from the methylation signature was used to establish a multivariable logistic regression model to predict the advanced-late stage of CRC. A nomogram was further developed by incorporating the classifier and some independent clinical risk factors, and its performance was evaluated by discrimination and calibration analysis. The prognostic value of the classifier was determined by survival analysis. Furthermore, the diagnostic performance of several CpGs in the methylation signature was evaluated.

Results

The eight-CpG-based methylation signature discriminated early stage from advanced-late stage CRC, with a satisfactory AUC of more than 0.700 in both the training and validation sets. This methylation classifier was identified as an independent predictor for CRC staging. The nomogram showed favorable predictive power for preoperative staging, and the C-index reached 0.817 (95% CI: 0.753–0.881) and 0.817 (95% CI: 0.721–0.913) in another training set and validation set respectively, with good calibration. The patients stratified in the high-risk group by the methylation classifier had significantly worse survival outcome than those in the low-risk group. Combination diagnosis utilizing only four of the eight specific CpGs performed well, even in CRC patients with low CEA level or at early stage.

Conclusions

Our classifier is a valuable predictive indicator that can supplement established methods for more accurate preoperative staging and also provides prognostic information for CRC patients. Besides, the combination of multiple CpGs has a high value in the diagnosis of CRC.

Colon cancer-specific diagnostic and prognostic biomarkers based on genome-wide abnormal DNA methylation

Abnormal DNA methylation is a major early contributor to colon cancer (COAD) development. We conducted a cohort-based systematic investigation of genome-wide DNA methylation using 299 COAD and 38 normal tissue samples from TCGA. Through conditional screening and machine learning with a training cohort, we identified one hypomethylated and nine hypermethylated differentially methylated CpG sites as potential diagnostic biomarkers, and used them to construct a COAD-specific diagnostic model. Unlike previous models, our model precisely distinguished COAD from nine other cancer types (e.g., breast cancer and liver cancer; error rate ≤ 0.05) and from normal tissues in the training cohort (AUC = 1). The diagnostic model was verified using a validation cohort from The Cancer Genome Atlas (AUC = 1) and five independent cohorts from the Gene Expression Omnibus (AUC ≥ 0.951). Using Cox regression analyses, we established a prognostic model based on six CpG sites in the training cohort, and verified the model in the validation cohort. The prognostic model sensitively predicted patients’ survival (p ≤ 0.00011, AUC ≥ 0.792) independently of important clinicopathological characteristics of COAD (e.g., gender and age). Thus, our DNA methylation analysis provided precise biomarkers and models for the early diagnosis and prognostic evaluation of COAD.

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC

Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC.

Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes.

Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients.

Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses

BACKGROUND

Ulcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimentation rate, but these indicators have an unsatisfactory specificity. In this study, we performed bioinformatics analysis using data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) databases and verified the selected core genes in a mouse model of dextran sulfate sodium (DSS)-induced colitis.

AIM

To identify UC-related differentially expressed genes (DEGs) using a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC.

METHODS

Two microarray datasets from the NCBI-GEO database were used, and DEGs between patients with UC and healthy controls were analyzed using GEO2R and Venn diagrams. We annotated these genes based on their functions and signaling pathways, and then protein-protein interactions (PPIs) were identified using the Search Tool for the Retrieval of Interacting Genes. The data were further analyzed with Cytoscape software and the Molecular Complex Detection (MCODE) app. The core genes were selected and a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed. Finally, colitis model mice were established by administering DSS, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction (PCR).

RESULTS

One hundred and seventy-seven DEGs, 118 upregulated and 59 downregulated, were initially identified from the GEO2R analysis and predominantly participated in inflammation-related pathways. Seven clusters with close interactions in UC formed: Seventeen core genes were upregulated [C-X-C motif chemokine ligand 13 (CXCL13), C-X-C motif chemokine receptor 2 (CXCR2), CXCL9, CXCL5, C-C motif chemokine ligand 18, interleukin 1 beta, matrix metallopeptidase 9, CXCL3, formyl peptide receptor 1, complement component 3, CXCL8, CXCL1, CXCL10, CXCL2, CXCL6, CXCL11 and hydroxycarboxylic acid receptor 3] and one was downregulated [neuropeptide Y receptor Y1 (NYP1R)] in the top cluster according to the PPI and MCODE analyses. These genes were substantially enriched in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The top three core genes (CXCL13, NYP1R, and CXCR2) were selected and verified in a mouse model of colitis using real-time PCR Increased expression was observed compared with the control mice, but only CXCR2 expression was significantly different.

CONCLUSION

Core DEGs identified in UC are related to inflammation and immunity inflammation, indicating that these reactions are core features of the pathogenesis of UC. CXCR2 may reflect the degree of inflammation in patients with UC.

Genetic variants in the Folic acid Metabolic Pathway Genes predict outcomes of metastatic Colorectal Cancer patients receiving first-line Chemotherapy

Background: The association between genetic variants in the folic acid metabolic pathway genes and survival, as well as the responses to chemotherapy of metastatic colorectal cancer (mCRC) patients has not been reported. Methods: The association between genetic variants in the folic acid metabolic pathway genes and progression-free survival (PFS) and overall survival (OS) of mCRC patients were analyzed using Cox regression model. The false discovery rate (FDR) correction method was conducted. The logistic regression model was used to explore the effects of the interested genetic variants on disease control rate (DCR). The Cancer Genome Atlas (TCGA) database was applied to compare gene expression differences. Results: We found that rs3786362 G allele of thymidylate synthase (TYMS) gene was significantly associated with PFS (P = 1.10 × 10^-2), OS (P = 2.50 × 10^-2) and DCR (P = 5.00 × 10^-3). The expression of TYMS was overexpressed in CRC tissues compared with adjacent normal tissues. Furthermore, TYMS expression level decreased with respect to younger age and advanced tumor stage. Conclusion: Genetic variants in the folic acid metabolic pathway genes might serve as potential prognostic biomarkers for mCRC patients.

A novel epigenetic signature to predict recurrence-free survival in patients with colon cancer

BACKGROUND

DNA methylation plays an important role in the initiation and progression of colon cancer. The aim of the present study was to perform a comprehensive analysis of DNA methylation and gene expression profiles in order to develop a signature to predict recurrence-free survival (RFS) of colon cancer.

METHODS

DNA methylation and mRNA expression data were obtained from TCGA database, and were analyzed using an R package MethylMix. Functional enrichment analysis was performed on statistically significant genes identified by MethylMix criteria. The epigenetic signature and nomogram associated with the RFS of colon cancer were established by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Additionally, a joint survival analysis of gene expression and methylation was performed to identify potential prognostic factors for patients with colon cancer.

RESULTS

A total of 179 differentially methylated genes were obtained using MethylMix algorithm. An epigenetic signature for RFS was developed using LASSO. Patients with high-risk had significantly worse RFS than those with low-risk. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables in patients with colon cancer. Moreover, joint survival analysis of gene expression and methylation revealed that seven methylated genes could be independent prognostic factors for RFS in colon cancer.

CONCLUSIONS

Our proposed epigenetic signature presents potential prognostic significance in assessing recurrence risk stratification for patients with colon cancer.

SEPSIS. Educational and Best Practice Frontiers. Beyond the Boundaries of Fatality, Enhancing Clinical Skills and Precision Medicine

Dissemination and exploitation of knowledge regarding affordable clinical skills and innovative precision medicine, two current topics in active development in medicine, may contribute to improve also sepsis management. Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection. Sepsis is strongly related to all body organs or to systemic diseases and to the quality of the best-practice in use, which is particularly critical in surgical or intervention techniques. Trauma, surgical and mini-invasive procedures, vascular or endoscopic interventions, otolaryngology, obstetrics-gynecological and urological procedures, malnutrition, dental, skin, chronic liver, kidney and respiratory disease are frequently involved. Accordingly, apart from the clinical risk analysis and management of the process of care, the actual factors that may be easily neglected are the techniques used, the personal skills of the health professionals and the quality of the equipment. The quest for biomarkers consistent with the unmet needs of medical doctors and of their patient and the efforts for overcoming bacterial antibiotic resistances are currently the main foci of medical research. In addition, in this regard, research and innovation would benefit from greater knowledge, skills and use of bioinformatics and omics. The caveats related to in-silico approaches must be flagged: algorithms may equally warrant scientific innovations or hide the lack of them; a patient is more than a set of covariates. Epidemiology and prevention includes all the actions suitable for achieving an adequate hygiene and immunization of populations and for safer hospital policies and procedures during Patients’ stays. In any subset, the most unresolved critical point in sepsis is a timely diagnosis. This is impaired by low degrees of suspicion for the possibility of emerging sepsis, by the shortage of use of the simplest microbiological testing but, equally or more, by the insufficient diffusion of non-invasive imaging skills suitable to detect and monitor the emerging sites and sources of infection. In primary care, in emergency facilities, in hospital wards and in intensive care units, inclusion of appropriate knowledge, skills, expertise and imaging equipment must be extended as much as possible. The low cost of UltraSound machines and of increasing bioinformatics literacy by e-learning, makes such investments affordable even in limited-resources contexts. Frontier educational and best practice intervention enhancing affordable clinical skills and innovative precision medicine may lead beyond the boundaries of fatal outcomes in sepsis.

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Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

BACKGROUND

The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis.

METHODS

The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database.

RESULTS

We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA.

CONCLUSIONS

Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.