Chemical burn wounds as a risk factor for gastric cancer: in-silico analyses-experimental research.

Introduction: The present study employs bioinformatics tools to identify shared upregulated genes between chemical burns and gastric cancer. Methods: Gene Expression Omnibus (GEO) retrieved gene sets for this investigation. GSEs with P value less than 0.05 and LOG fold change (FC) greater than 1 were valid and upregulated. Gastric cancer and chemical burn common elevated genes were found using Venn diagram online tools. In the second stage, the "string" visualized gastric cancer elevated genes network, and non-coding RNAs were deleted, and "interaction" greater than 1 was examined to choose important gene nodes. Next, they explored the String gene-interaction network for common genes. To determine the most interacting genes, Gephi (V 0.9.7) used "betweenness centrality" greater than "0" to evaluate the twenty-gene network. TISIDB and drug banks provide gene-related medications. Results: In the present study, two genes, including ALOX5AP and SERPINB2, were obtained, with the highest centrality among chemical burns and gastric cancer shared genes. Additionally, the current study presented five drugs, including Urokinase, Tenecteplase, DG031, AM103, and Fiboflapon, which can have predicted effects on gastric cancer following chemical burns. Conclusion: According to current in-silicon analyses, ALOX5AP and SERPINB2 are linked genetic keys between gastric chemical burn and cancer. Considering that burn is an environmental factor that leads to the upregulation of the two genes thus, the chemical burn can be related to the incidence of gastric cancer.

Esophageal chemical burns as a risk factor for esophageal malignancies: in-silico analyses - experimental research.

Introduction: Esophageal chemical burns often occur through accidental or intentional oral consumption of chemical agents and lead to severe complications such as esophageal stricture, acute perforation, and even death. Esophageal squamous cell carcinoma is a squamous epithelium tumor that lines the normal esophagus. Additionally, adenocarcinomas are tumors located at the interface between the distal esophagus and the proximal gastric and divided into esophageal adenocarcinoma and gastric-cardia adenocarcinoma. Various conditions, such as chemical burns, are considered risk factors in the disease's pathogenesis. In the in-silico study, the authors aim to present the relationship between chemical burns and esophageal cancer by analyzing bioinformatics genetic data. Methods: The proper gene set was extracted using the 'GEO' database. The string web tool was utilized to form the gene-interaction network. Gephi and Cytoscape software were applied to achieve network analysis. Results: According to in-silico data, 26 genes, including NCAPH, DLGAP5, CCNB1, KIF11, KIAA0101, CDCA5, BIRC5, NUF2, BUB1B, RRM2, TTK, CDC20, NUSAP1, CCNB2, CCNA2, MELK, TPX2, PRC1, KIF4A, CENPF, TOP2A, CDK1, ASPM, CEP55, BUB1, KIF20A were extracted that can be regarded as the most critical shared genes between chemical burns and esophageal cancer. Conclusion: In sum, esophageal chemical burns can be related to the occurrence of esophageal cancer. Moreover, esophageal chemical burn is an external factor that upregulates present genes and can be regarded as a worsening prognosis or risk factor for esophageal cancer.

Complement protein expression changes in various conditions of breast cancer: in-silico analyses-experimental research.

Introduction: Breast cancer is the most prevalent cancer diagnosed in females worldwide. The known biomarkers are insufficient to understand the actual prognosis of breast cancer, and identifying new biomarkers is desirable and valuable data to improve the patient's survival. Many inflammatory biomarkers, such as the complement system, can be regarded as prognostic values and as potent inflammatory mediators; complement proteins have a critical role in tumorigenesis. In the current study, the authors aim to investigate complement protein expression changes, particularly complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), in various conditions of breast cancer using in-silico tools. Methods: The intent data were extracted using webtools, including; Kaplan-Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr. To select valid data, a P greater than 0.05 was considered. Results: The current study clarified that 21 complement genes correlated to survival conditions. Also, down or upregulation of extracted genes and breast cancer statuses were identified. Additionally, expression level difference of complement genes in various breast cancer four stages was detected. Ultimately, co-expression genes with complement genes were extracted and networked. Conclusion: Changes in the expression of complement proteins can strongly correlate to breast cancer's prognosis, status, and survival. Furthermore, considering the vital role of CFD and CFB complement proteins in the alternative pathway in different stages of breast cancer, CFD and CFB can be regarded as reliable prognostic values for diagnosis.

From Multiple Sclerosis to Organ-Specific Autoimmune Disorders: Insights into the Molecular and Clinical Implications of Comorbidity.

Multiple sclerosis (MS) is a complex autoimmune disorder that affects the central nervous system. Although the pathological mechanisms of MS have been extensively studied, its association with other autoimmune diseases, known as comorbidities, remains unclear. In this comprehensive review article, we aim to clarify the cellular and molecular relationship between MS and the incidence of organ-specific autoimmune comorbidities by summarizing former studies. We will explore the commonalities and possible differences between the immune response mechanisms in MS and other autoimmune diseases and provide an overview of the current understanding of the pathophysiological processes involved in the co-occurrence of MS and other organ-specific autoimmune comorbidities. Through this review, we aim to contribute to the development of effective therapeutic strategies that can improve the quality of life of MS patients with comorbidities.

Prediction of immune molecules activity during burn wound healing among elderly patients: in-silico analyses: experimental research.

Introduction: Burn injuries lead to dysregulation of immune molecules, impacting cellular and humoral immune pathways. This study aims to determine the prediction of immune molecule activity during burn wound healing among elderly patients. Methods: The current study utilized the Gene Expression Omnibus (GEO) database to extract the proper gene set. Also, the literature review was conducted in the present study to find immune signatures. The study used the "enrich r" website to identify the biological functions of extracted genes. The critical gene modules related to mortality were identified using the weighted gene co-expression network analysis (WGCNA) R package. Results: The appreciated GSE was extracted. According to the data, the most upregulated signatures were related to natural killer (NK) cells, and the most downregulated signatures were associated with M1 macrophages. Also, the results of WGCNA have shown that the most related gene modules (P<107 and score 0.17) to mortality were investigated, and the modules 100 first genes were extracted. Additionally, the enrich r analysis has demonstrated related pathways, including the immune process, including regulation of histamine secreted from mast cell (P<0.05), T helper 17 cell differentiation (P<0.05), and autophagy (P<0.05) were obtained. Finally, by network analysis, the critical gene "B3GNT5" were obtained (degree>ten and "betweenness and centrality">30 were considered). Conclusion: The study identified significant changes in macrophage and NK cell expression patterns post-burn injury, linking them to potential improvements in clinical outcomes and wound healing. The gene B3GNT5, associated with mortality, was highlighted as a key marker for prognostic evaluation.

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.