Restoring the epigenetic landscape of lung microbiome: potential therapeutic approach for chronic respiratory diseases.

BACKGROUND: Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchiectasis, present significant threats to global health. Recent studies have revealed the crucial role of the lung microbiome in the development of these diseases. Pathogens have evolved complex strategies to evade the immune response, with the manipulation of host cellular epigenetic mechanisms playing a pivotal role. There is existing evidence regarding the effects of Pseudomonas on epigenetic modifications and their association with pulmonary diseases. Therefore, this study aims to directly assess the connection between Pseudomonas abundance and chronic respiratory diseases. We hope that our findings will shed light on the molecular mechanisms behind lung pathogen infections. METHODS: We analyzed data from 366 participants, including individuals with COPD, acute exacerbations of COPD (AECOPD), bronchiectasis, and healthy individuals. Previous studies have given limited attention to the impact of Pseudomonas on these groups and their comparison with healthy individuals. Two independent datasets from different ethnic backgrounds were used for external validation. Each dataset separately analyzed bacteria at the genus level. RESULTS: The study reveals that Pseudomonas, a bacterium, was consistently found in high concentrations in all chronic lung disease datasets but it was present in very low abundance in the healthy datasets. This suggests that Pseudomonas may influence cellular mechanisms through epigenetics, contributing to the development and progression of chronic respiratory diseases. CONCLUSIONS: This study emphasizes the importance of understanding the relationship between the lung microbiome, epigenetics, and the onset of chronic pulmonary disease. Enhanced recognition of molecular mechanisms and the impact of the microbiome on cellular functions, along with a better understanding of these concepts, can lead to improved diagnosis and treatment.

Spectroscopic investigation and structural simulation in human serum albumin with hydroxychloroquine/Silybum marianum and a possible potential COVID-19 drug candidate.

In this study, the interaction between human serum albumin (HSA) and the hydroxychloroquine/Silybum marianum (HCQ/SM) mixture was investigated using various techniques. The observed high binding constant (Kb) and Stern-Volmer quenching constant (KSV) indicate a strong binding affinity between the HCQ/SM mixture and HSA. The circular dichroism (CD) analysis revealed that HCQ/SM induced conformational changes in the secondary structure of HSA, leading to a decrease in the α-helical content. UV-Vis analysis exhibited a slight redshift, indicating that the HCQ/SM mixture could adapt to the flexible structure of HSA. The experimental results demonstrated the significant conformational changes in HSA upon binding with HCQ/SM. Theoretical studies were carried out using molecular dynamics simulation via the Gromacs simulation package to explore insights into the drug interaction with HSA-binding sites. Furthermore, molecular docking studies demonstrated that HCQ/SM-HSA exhibited favorable docking scores with the receptor (5FUZ), suggesting a potential therapeutic relevance in combating COVID-19 with a value of -6.24 kcal mol-1. HCQ/SM exhibited stronger interaction with both SARS-CoV-2 virus main proteases compared to favipiravir. Ultimately, the experimental data and molecular docking analysis presented in this research offer valuable insights into the pharmaceutical and biological properties of HCQ/SM mixtures when interacting with serum albumin.

Cohort profile update: Tehran cardiometabolic genetic study.

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.

BACKGROUND: Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors. METHODS: This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance. RESULTS: 50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS. CONCLUSION: Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

Revealing transcriptional and post-transcriptional regulatory mechanisms of γ-glutamyl transferase and keratin isoforms as novel cooperative biomarkers in low-grade glioma and glioblastoma multiforme.

Gamma-glutamyltransferase (GGT) and keratins (KRT) are key factors in regulating tumor progression rely on emerging evidence. However, the prognostic values of GGT and KRT isoforms and their regulation patterns at transcriptional and post-transcriptional levels have been rarely studied. In this study, we aimed to identify cooperative prognostic biomarker signature conducted by GGT and KRT genes for overall survival prediction and discrimination in patients with low-grade glioma (LGG) and glioblastoma multiforme (GBM). To this end, we employed a differential expression network analysis on LGG-NORMAL, GBM-NORMAL, and LGG-GBM datasets. Then, all the differentially expressed genes related to a GO term "GGT activity" were excluded. After that, for obtained potential biomarkers genes, differentially expressed lncRNAs were used to detect cis-regulatory elements (CREs) and trans-regulatory elements (TREs). To scrutinize the regulation on the cytoplasm, potential interactions between these biomarker genes and DElncRNAs were predicted. Our analysis, for the first time, revealed that GGT6, KRT33B, and KRT75 in LGG, GGT2, and KRT75 in GBM and KRT75 for LGG to GBM transformation tumors can be novel cooperative prognostic biomarkers that may be applicable for early detection of LGG, GBM, and LGG to GBM transformation tumors. Consequently, KRT75 was the most important gene being regulated at both transcriptional and post-transcriptional levels significantly. Furthermore, CREs and their relative genes were coordinative up-regulated or down-regulated suggesting CREs as regulation points of these genes. In the end, up-regulation of most DElncRNAs that had physical interaction with target genes pints out that the transcripted genes may have obstacles for translation process.

Interplay between SARS-CoV-2 and human long non-coding RNAs.

The long non-coding RNAs (lncRNAs) play a critical regulatory role in the host response to the viral infection. However, little is understood about the transcriptome architecture, especially lncRNAs pattern during the SARS-CoV-2 infection. In the present study, using publicly available RNA sequencing data of bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy individuals, three interesting findings highlighted: (a) More than half of the interactions between lncRNAs-PCGs of BALF samples established by three trans-acting lncRNAs (HOTAIRM1, PVT1 and AL392172.1), which also exhibited the high affinity for binding to the SARS-CoV-2 genome, suggesting the major regulatory role of these lncRNAs during the SARS-CoV-2 infection. (b) lncRNAs of MALAT1 and NEAT1 are possibly contributed to the inflammation development in the SARS-CoV-2 infected cells. (c) In contrast to the 3' part of the SARS-CoV-2 genome, the 5' part can interact with many human lncRNAs. Therefore, the mRNA-based vaccines will not show any side effects because of the off-label interactions with the human lncRNAs. Overall, the putative functionalities of lncRNAs can be promising to design the non-coding RNA-based drugs and to inspect the efficiency of vaccines to overcome the current pandemic.

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.

RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions.

Feature extraction is one of the most important preprocessing steps in predicting the interactions between RNAs and proteins by applying machine learning approaches. Despite many efforts in this area, still, no suitable structural feature extraction tool has been designed. Therefore, an online toolbox, named RPINBASE which can be applied to different scopes of biological applications, is introduced in this paper. This toolbox employs efficient nested queries that enhance the speed of the requests and produces desired features in the form of positive and negative samples. To show the capabilities of the proposed toolbox, the developed toolbox was investigated in the aptamer design problem, and the obtained results are discussed. RPINBASE is an online toolbox and is accessible at http://rpinbase.com.

Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis.

To compare the pattern of brain waves in video game addicts and normal individuals, a case-control study was carried out on both. Thirty participants were recruited from 14 to 20 years old males from two gaming centers. Twenty healthy participants were gathered from different schools in Tehran using the available sampling method. The QEEG data collection was performed in three states: closed-eye and open-eye states, and during a working memory task. As expected, the power ratios did not show a significant difference between the two groups. Regarding our interest in the complexity of signals, we used the Higuchi algorithm as the feature extractor to provide the input materials for the multilayer perceptron classifier. The results showed that the model had at least a 95% precision rate in classifying the addicts and healthy controls in all three types of tasks. Moreover, significant differences in the Higuchi Fractal Dimension of a few EEG channels have been observed. This study confirms the importance of brain wave complexity in QEEG data analysis and assesses the correlation between EEG-complexity and gaming disorder. Moreover, feature extraction by Higuchi algorithm can render support vector machine classification of the brain waves of addicts and healthy controls more accurate.

The Stabilizing Mechanism of Immobilized Metagenomic Xylanases on Bio-Based Hydrogels to Improve Utilization Performance: Computational and Functional Perspectives.

While polysaccharide-based superabsorbent hydrogels (SHs) have attracted increasing interest as proficient carriers in the enzyme immobilization, the nature of the favored interactions between the SHs and enzymes is still unclear. Herein, a combined experimental and computational study was employed to investigate the dominant parameters affecting on the stabilization of two metagenomic xylanases on the SHs. The thermostable enzymes (PersiXyn3 and PersiXyn4) with similar domains were screened, cloned, expressed, and purified from cattle rumen metagenome. Then, the enzymes were immobilized on the carboxymethyl cellulose-g-poly(acrylic acid-co-acrylamide) hydrogel which resulted in increasing their activity and stability. The carboxymethyl cellulose (CMC)-based characteristic of the hydrogel provided high numbers of H-bondings/ionic bridges, causing an improvement in the stability, hydrolysis performance, and reusability of the immobilized enzymes. More specifically, enzyme immobilization resulted in ∼40% increase in the content of the reducing sugars released after treatment of paper pulp. After 16 reuse cycles, the immobilized PersiXyn4 displayed 35.9% activity, but the immobilized PersiXyn3 retained just 8.2% of its initial activity. The comparative investigations illustrated that a higher number of positively charged amino acids in the binding site of the enzyme provided stronger electrostatic attractions between it and negative functionalities of the hydrogel. This was suggested as the main reason for the higher affinity of PersiXyn4 toward hydrogel and explained the better hydrolysis performance and reusability of the immobilized PersiXyn4 on the SH. These findings are essential for designing novel innovative SH carriers and the successful engineering of optimal enzyme assemblies through the prediction of the immobilized enzyme's stabilities.

Site Directed Disulfide PEGylation of Interferon-ß-1b with Fork Peptide Linker.

The attachment of PEG to biopharmaceuticals has been applied for enhancement of bioavailability and improved stability. The PEG polymer is highly hydrated; thus effective attachment to inaccessible sites could be hindered. We have devised a scheme to address this issue by introducing a considerable distance between PEG and protein by addition of a linear peptide, appended to long chained reactive linkers. Second, the position of PEG conjugation directly affects biological activity. Accordingly, a disulfide bond could be considered as an ideal choice for site directed PEGylation; but reactivity of both thiol moieties to bridging reagent is critical for maintenance of protein structure. In our design, a forked structure with two arms provides essential flexibility to account for dissociation of reduced cysteines. An efficient yield for disulfide PEGylation of IFN-ß1b was attained and specificity, biophysical characterization, biological activity, and pharmacokinetics were surveyed.

Block alignment: New representation and comparison method to study evolution of genomes.

Genomes are not random sequences because natural selection has injected information in biological sequences for billions of years. Inspired by this idea, we developed a simple method to compare genomes considering nucleotide counts in subsequences (blocks) instead of their exact sequences. We introduce the Block Alignment method for comparing two genomes and based on this comparison method, define a similarity score and a distance. The presented model ignores nucleotide order in the sequence. On the other hand, in this block comparison method, due to exclusion of point mutations and small size variations, there is no need for high coverage sequencing which is responsible for the high costs of data production and storage; moreover, the sequence comparisons could be performed with higher speed. Phylogenetic trees of two sets of bacterial genomes were constructed and the results were in full agreement with their already constructed phylogenetic trees. Furthermore, a weighted and directed similarity network of each set of bacterial genomes was inferred ab initio by this model. Remarkably, the communities of these networks are in agreement with the clades of the corresponding phylogenetic trees which means these similarity networks also contain phylogenetic information about the genomes. Moreover, the block comparison method was used to distinguish rob(15;21)c-associated iAMP21 and sporadic iAMP21 rearrangements in subgroups of chromosome 21 in acute lymphoblastic leukemia. Our results show a meaningful difference between the number of contigs that mapped to chromosomes 15 and 21 in these cases. Furthermore, the presented block alignment model can select the candidate blocks to perform more accurate analysis and it is capable to find conserved blocks on a set of genomes.

Candidate novel long noncoding RNAs, MicroRNAs and putative drugs for Parkinson's disease using a robust and efficient genome-wide association study.

Parkinson's disease (PD) is a neurodegenerative disorder with serious symptoms of which, are not clearly demonstrated at the beginning stages of the disease, making treatment challenging. Understanding the genetic causes of PD can be useful for determining its mechanisms and proposing treatments and preventive methods. For different populations with different genetic backgrounds and lifestyles, genome-wide association studies (GWASs) represent a crucial approach for genetic analysis. In this study, a robust and efficient GWAS without dimensionality reduction applied to evaluate heritability and genetic causes of PD in the German and US populations. The results show higher rate of PD heritability in the German population. Moreover, 25 significant SNPs have been determined, as well as five newly identified candidate genes associated with PD and some potential drug candidates. Analysis also reveals various long noncoding RNAs (lncRNAs), microRNAs and transcription-factor binding sites (TFBSs) with potential in the prevention and treatment of PD.

Deciphering the impact of microbial interactions on COPD exacerbation: An in-depth analysis of the lung microbiome.

In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients.

Heterogeneity and molecular landscape of melanoma: implications for targeted therapy.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Examining the clinical and genetic spectrum of maturity-onset diabetes of the young (MODY) in Iran.

Maturity-onset diabetes of the young (MODY) is an uncommon monogenic type of diabetes mellitus. Detecting genetic variants for MODY is a necessity for precise diagnosis and treatment. The majority of MODY genetic predisposition has been documented in European populations and a lack of information is present in Iranians which leads to misdiagnosis as a consequence of defects in unknown variants. In this study, using genetic variant information of 20,002 participants from the family-based TCGS (Tehran Cardiometabolic Genetic Study) cohort, we evaluated the genetic spectrum of MODY in Iran. We concentrated on previously discovered MODY-causing genes. Genetic variants were evaluated for their pathogenicity. We discovered 6 variants that were previously reported in the ClinVar as pathogenic/likely pathogenic (P/LP) for MODY in 45 participants from 24 families (INS in 21 cases, GCK in 13, HNF1B in 8, HNF4A, HNF1A, and CEL in 1 case). One potential MODY variant with Uncertain Risk Allele in ClinVar classification was also identified, which showed complete disease penetrance (100%) in four subjects from one family. This is the first family-based study to define the genetic spectrum and estimate the prevalence of MODY in Iran. The discovered variants need to be investigated by additional studies.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition.

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.

Parallel molecular alteration between Alzheimer's disease and major depressive disorder in the human brain dorsolateral prefrontal cortex: an insight from gene expression and methylation profile analyses.

Alzheimer's disease (AD) and major depressive disorder (MDD) are comorbid neuropsychiatric disorders that are among the leading causes of long-term disability worldwide. Recent research has indicated the existence of parallel molecular mechanisms between AD and MDD in the dorsolateral prefrontal cortex (DLPFC). However, the premorbid history and molecular mechanisms have not yet been well characterized. In this study, differentially expressed gene (DEG), differentially co-expressed gene and protein-protein interaction (PPI) network propagation analyses were applied to gene expression data of postmortem DLPFC samples from human individuals diagnosed with and without AD or MDD (AD: cases = 310, control = 157; MDD: cases = 75, control = 161) to identify the main genes in the two disorders' specific and shared biological pathways. Subsequently, the results were evaluated using another four assessment datasets (n1 = 230, n2 = 65, n3 = 58, n4 = 48). Moreover, the postmortem DLPFC methylation status of human subjects with AD or MDD was compared using 68 and 608 samples for AD and MDD, respectively. Eight genes (XIST, RPS4Y1, DDX3Y, USP9Y, DDX3X, TMSB4Y, ZFY and E1FAY) were common DEGs in DLPFC of subjects with AD or MDD. These genes play important roles in the nervous system and the innate immune system. Furthermore, we found HSPG2, DAB2IP, ARHGAP22, TXNRD1, MYO10, SDK1 and KRT82 as common differentially methylated genes in the DLPFC of cases with AD or MDD. Finally, as evidence of shared molecular mechanisms behind this comorbidity, we propose some genes as candidate biomarkers for both AD and MDD. However, more research is required to clarify the molecular mechanisms underlying the co-existence of these two important neuropsychiatric disorders.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers.

BACKGROUND: Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset. RESULTS: The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes. CONCLUSION: The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.