Decoding common genetic alterations between Barrett's esophagus and esophageal adenocarcinoma: A bioinformatics analysis.

Background: Esophageal adenocarcinoma (EAC) is a common cancer with a poor prognosis in advanced stages. Therefore, early EAC diagnosis and treatment have gained attention in recent decades. It has been found that various pathological changes, particularly Barrett's Esophagus (BE), can occur in the esophageal tissue before the development of EAC. In this study, we aimed to identify the molecular contributor in BE to EAC progression by detecting the essential regulatory genes that are differentially expressed in both BE and EAC. Materials and methods: We conducted a comprehensive bioinformatics analysis to detect BE and EAC-associated genes. The common differentially expressed genes (DEGs) and common single nucleotide polymorphisms (SNPs) were detected using the GEO and DisGeNET databases, respectively. Then, hub genes and the top modules within the protein-protein interaction network were identified. Moreover, the co-expression network of the top module by the HIPPIE database was constructed. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Lastly, we inspected the DGIdb database for possible interacted drugs. Results: Our microarray dataset analysis identified 92 common DEGs between BE and EAC with significant enrichment in skin and epidermis development genes. The study also identified 22 common SNPs between BE and EAC. The top module of PPI network analysis included SCEL, KRT6A, SPRR1A, SPRR1B, SPRR3, PPL, SPRR2B, EVPL, and CSTA. We constructed a ceRNA network involving three specific mRNAs, 23 miRNAs, and 101 selected circRNAs. According to the results from the DGIdb database, TD101 was found to interact with the KRT6A gene. Conclusion: The present study provides novel potential candidate genes that may be involved in the molecular association between Esophageal adenocarcinoma and Barrett's Esophagus, resulting in developing the diagnostic tools and therapeutic targets to prevent progression of BE to EAC.

Identification of a valuable gene network for the diagnosis and treatment of non-obstructive azoospermia: in-silico analyses - experimental research.

Introduction: Non-obstructive azoospermia (NOA) is an etiology of infertility in men. NOA may have various classifications; however, hypogonadotropic hypogonadism can be regarded as a class of NOA associated with genetic factors. Former studies have shown that noncoding RNA (ncRNA) plays an essential role in NOA incidence, but few studies have been performed on the NOA-related ncRNA interaction network. In the current study, genes, NOA-related microRNA (miRNA), and circular RNA (circRNA) were found by bioinformatics methods to offer a new perspective on NOA treatment. Methods: The gonadotropin-releasing hormone receptor (GnRHR)-related protein-protein interaction (PPI) network was extracted by searching in 'string-database'. GO, KEGG, and Enrichr databases were used to identify pathways, molecular function, and biological processing. Four databases, including TargetScan, mirDIP, miRmap, and miRWalk, were used to extract miRNAs. At last, the circ2GO, circBase, and literature were used to identify circRNAs and their genes. Results: The current study identified the four proteins associated with the GnRHR signaling; eight shared miRNAs that affect the expression of found proteins and 25 circRNAs and their origin genes that regulate the miRNAs' function. Conclusion: The two miRNAs, hsa-miR-134-3p and hsa-miR-513C-3p, the three genes, VCAN, NFATC3, and PRDM5, and their associated circRNAs can perform as a valuable gene network in the diagnosis and treatment of NOA pathogenesis.

SARS-CoV-2 helicase might interfere with cellular nonsense-mediated RNA decay: insights from a bioinformatics study.

BACKGROUND: Viruses employ diverse strategies to interfere with host defense mechanisms, including the production of proteins that mimic or resemble host proteins. This study aimed to analyze the similarities between SARS-CoV-2 and human proteins, investigate their impact on virus-host interactions, and elucidate underlying mechanisms. RESULTS: Comparing the proteins of SARS-CoV-2 with human and mammalian proteins revealed sequence and structural similarities between viral helicase with human UPF1. The latter is a protein that is involved in nonsense-mediated RNA decay (NMD), an mRNA surveillance pathway which also acts as a cellular defense mechanism against viruses. Protein sequence similarities were also observed between viral nsp3 and human Poly ADP-ribose polymerase (PARP) family of proteins. Gene set enrichment analysis on transcriptomic data derived from SARS-CoV-2 positive samples illustrated the enrichment of genes belonging to the NMD pathway compared with control samples. Moreover, comparing transcriptomic data from SARS-CoV-2-infected samples with transcriptomic data derived from UPF1 knockdown cells demonstrated a significant overlap between datasets. CONCLUSIONS: These findings suggest that helicase/UPF1 sequence and structural similarity might have the ability to interfere with the NMD pathway with pathogenic and immunological implications.

Identification of LncPVT1 and CircPVT1 as prognostic biomarkers in human colorectal polyps.

LncPVT1 and CircPVT1 are isoforms for the PVT1 gene and are associated with cancer progression and carcinogenesis. Our study investigated the expression of LncPVT1 and CircPVT1 in colon adenoma polyps. 40 tissues of colorectal polyps and 40 normal-adjacent tissues (NATs) were taken. The expression of LncPVT1 and CircPVT1 was evaluated through qRael-Time PCR. The relation between expression and features of clinicopathological was explored. The ceRNA network was constructed by LncPVT1 and CircPVT1 and predicted miRNAs and miRNAs targets. Further, hub nodes in this network were determined using the cytoHubba package. Over-expressed LncPVT1 and CircPVT1 were differentiated in polyp and NATs. The expression level of LncPVT1 and CircPVT1 were significantly higher in adenoma polyps than in hyperplastic polyps. The area under the curve of the ROC estimate for the LncPVT1 and CircPVT1 was 0.74 and 0.77, respectively. A positive correlation was observed between the LncPVT1 expression and CircPVT1. Three miRNAs, including hsa-miR-484, hsa-miR-24-3p, hsa-miR-423-5p, and CircPVT1, were detected as ceRNA hub nodes. In this study, expression profiles of LncPVT1 and CircPVT1 were significantly higher in precancerous polyps. In addition, based on our in silico analysis, LncPVT1, CircPVT1/miR-484, miR-24-3p, miR-423-5p/PLAGL2 axis might be involved in colon cancer development. LncPVT1 and CircPVT1 can be prescribed as warning problems as potential prognostic biomarkers in patients with pre-CRC colon polyps.

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.

Long non­coding RNA LINC00460 contributes as a potential prognostic biomarker through its oncogenic role with ANXA2 in colorectal polyps.

BACKGROUND: Long intergenic non-coding RNA 460 (LINC00460) as a potential oncogene and Annexin A2 (ANXA2) as a promoter in different cancer progression processes was considered. A significant relationship between the LINC00460 and ANXA2 has been recently discovered in colorectal cancer (CRC). Therefore, defining molecular biomarkers accompanied by lesion histopathologic features can be a suggestive prognostic biomarker in precancerous polyps. This study aimed to investigate the elusive expression pattern of ANXA2 and LINC00460 in polyps. MATERIALS AND METHODS: The construction of the co-expression and correlation network of LINC00460 and ANXA2 was plotted. LINC00460 and ANXA2 expression in 40 colon polyps was quantified by reverse transcription-real-time polymerase chain reaction. The receiver operating characteristic (ROC) curve was designed for distinguishing the high-risk precancerous lesion from the low-risk. Further, bioinformatics analysis was applied to find the shared MicroRNA-Interaction-Targets (MITs) between ANXA2 and LINC00460, and the associated pathways. RESULTS: ANXA2 has a high co-expression rank with LINC00460 in the lncHUB database. Overexpression of ANXA2 and LINC00460 was distinguished in advanced adenoma polyps compared to the adjacent normal samples. The estimated AUC for ANXA2 and LINC00460 was 0.88 - 0.85 with 93%-90% sensitivity and 81%-70% specificity. In addition, eight MITs were shared between ANXA2 and LINC00460. Enrichment analysis detected several GO terms and pathways, including HIF-1α associated with cancer development. CONCLUSION: In conclusion, the expression of the ANXA2 and LINC00460 were significantly elevated in pre-cancerous polyps, especially in high-risk adenomas. Collectively, ANXA2 and LINC00460 may be administered as potential prognostic biomarkers in patients with a precancerous large intestine lesion as an alarming issue.

SARS-CoV-2 spike protein displays sequence similarities with paramyxovirus surface proteins; a bioinformatics study.

Recent emergence of SARS-CoV-2 and associated COVID-19 pandemic have posed a great challenge for the scientific community. In this study, we performed bioinformatic analyses on SARS-CoV-2 protein sequences, trying to unravel potential molecular similarities between this newly emerged pathogen with non-coronavirus ssRNA viruses. Comparing the proteins of SARS-CoV-2 with non-coronavirus positive and negative strand ssRNA viruses revealed multiple sequence similarities between SARS-CoV-2 and non-coronaviruses, including similarities between RNA-dependent RNA-polymerases and helicases (two highly-conserved proteins). We also observed similarities between SARS-CoV-2 surface (i.e. spike) protein with paramyxovirus fusion proteins. This similarity was restricted to a segment of spike protein S2 subunit which is involved in cell fusion. We next analyzed spike proteins from SARS-CoV-2 "variants of concern" (VOCs) and "variants of interests" (VOIs) and found that some of these variants show considerably higher spike-fusion similarity with paramyxoviruses. The 'spike-fusion' similarity was also observed for some pathogenic coronaviruses other than SARS-CoV-2. Epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that several B cell epitopes as well as T cell and MHC binding epitopes map within the spike-fusion similarity region. These data indicate that there might be a degree of convergent evolution between SARS-CoV-2 and paramyxovirus surface proteins which could be of pathogenic and immunological importance.