Bioinformatics Perspective Research Articles

Building a Human Ovarian Antioxidant ceRNA Network "OvAnOx": A Bioinformatic Perspective for Research on Redox-Related Ovarian Functions and Dysfunctions.

The ovary is a major determinant of female reproductive health. Ovarian functions are mainly related to the primordial follicle pool, which is gradually lost with aging. Ovarian aging and reproductive dysfunctions share oxidative stress as a common underlying mechanism. ROS signaling is essential for normal ovarian processes, yet it can contribute to various ovarian disorders when disrupted. Therefore, balance in the redox system is crucial for proper ovarian functions. In the present study, by focusing on mRNAs and ncRNAs described in the ovary and taking into account only validated ncRNA interactions, we built an ovarian antioxidant ceRNA network, named OvAnOx ceRNA, composed of 5 mRNAs (SOD1, SOD2, CAT, PRDX3, GR), 10 miRNAs and 5 lncRNAs (XIST, FGD5-AS1, MALAT1, NEAT1, SNHG1). Our bioinformatic analysis indicated that the components of OvAnOx ceRNA not only contribute to antioxidant defense but are also involved in other ovarian functions. Indeed, antioxidant enzymes encoded by mRNAs of OvAnOx ceRNA operate within a regulatory network that impacts ovarian reserve, follicular dynamics, and oocyte maturation in normal and pathological conditions. The OvAnOx ceRNA network represents a promising tool to unravel the complex dialog between redox potential and ovarian signaling pathways involved in reproductive health, aging, and diseases.

Pan-cancer analysis identifies venous thromboembolism-related genes F3, PLAT, and C1S as potential prognostic biomarkers for glioblastoma and lower grade glioma

Venous thromboembolism (VTE) is a prevalent complication among patients with cancer, contributing significantly to morbidity and mortality. However, the relationship between VTE-related genes (VRGs) and their potential impact on prognosis, immune response, and therapeutic targets in various cancer types remains unclear. Based on the coagulation and complement pathways, we identified hub VRGs that play a role in regulating the immune response in cancer. Specifically, coagulation factor III (F3), plasminogen activator (PLAT) and complement C1s (C1S) were identified as genes that exhibit high expression levels, positively correlating with tumor stemness and copy number variations, while inversely correlating with methylation levels, in particular cancer types. Pan-cancer survival analysis revealed detrimental effects of these VRGs in several cancer types, notably in glioblastoma and lower grade glioma (GMBLGG). Further analysis using receiver operating characteristic (ROC) curves demonstrated a high accuracy of F3, PLAT and C1S in predicting outcomes in GBMLGG, with area under the curve (AUC) values ranging from 0.78 to 0.9. Validation of the prognostic value of these three genes in GMBLGG was conducted using an independent Gene Expression Omnibus (GEO) dataset. Additionally, gene–drug association analysis identified ciclosporin, ouabain and 6- mercaptopurine, which all exhibit immunosuppressive properties, as potential therapeutic options for tumor patients exhibiting high F3, PLAT or C1S expression, respectively. In summary, our findings provide a bioinformatics perspective on VRGs in pan-cancer, highlighting the pivotal roles of F3, PLAT and C1S, which could potentially be therapeutically exploited and targeted in several cancers, especially in GBMLGG.

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease

Premature ovarian failure (POF), which is often comorbid with dry eye disease (DED) is a key issue affecting female health. Here, we explored the mechanism underlying comorbid POF and DED to further elucidate disease mechanisms and improve treatment. Datasets related to POF (GSE39501) and DED (GSE44101) were identified from the Gene Expression Omnibus (GEO) database and subjected to weighted gene coexpression network (WGCNA) and differentially expressed genes (DEGs) analyses, respectively, with the intersection used to obtain 158 genes comorbid in POF and DED. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of comorbid genes revealed that identified genes were primarily related to DNA replication and Cell cycle, respectively. Protein–Protein interaction (PPI) network analysis of comorbid genes obtained the 15 hub genes: CDC20, BIRC5, PLK1, TOP2A, MCM5, MCM6, MCM7, MCM2, CENPA, FOXM1, GINS1, TIPIN, MAD2L1, and CDCA3. To validate the analysis results, additional POF- and DED-related datasets (GSE48873 and GSE171043, respectively) were selected. miRNAs-lncRNAs-genes network and machine learning methods were used to further analysis comorbid genes. The DGIdb database identified valdecoxib, amorfrutin A, and kaempferitrin as potential drugs. Herein, the comorbid genes of POF and DED were identified from a bioinformatics perspective, providing a new strategy to explore the comorbidity mechanism, opening up a new direction for the diagnosis and treatment of comorbid POF and DED.

Weighted gene coexpression network analysis and machine learning for the determination of tfh cell and B cell infiltrating biomarkers in thymoma-associated myasthenia gravis

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

Understanding the bacteria in Mycobacterium avium complex (MAC) from a bioinformatic perspective – a review

Mycobacterium avium complex (MAC) houses a group of non-tuberculous mycobacteria causing pulmonary and disseminated infections. They are accountable for nodular bronchiectatic and fibrocavitary lung diseases in humans, Johne’s disease in ruminants, and respiratory diseases in birds. MAC infections pose challenges, owing to antibiotic resistance, prolonged therapy with antibiotic combinations, side effects, and risk of reinfections. Our objective was to summarize the outcome of computational research on the bacteria in MAC. This aimed to advance our understanding of characteristics, pathogenicity, and transmission dynamics to control infections. We incorporated information from the research on genomes, microbiomes, phylogeny, transcriptomes, proteomes, antibiotic resistance, and vaccine/drug target development to enhance our knowledge. It illuminated the significance of computational studies in distinguishing MAC species/subspecies and recognizing: virulence factors, lineage-specific markers, and transmission clusters. Moreover, it assisted in understanding: genomic diversity, resistance patterns, impact of polymorphisms in disease susceptibility, and taxa-induced dysbiosis in microbiomes. Additionally, this work highlighted the outcome of bioinformatic studies in predicting suitable vaccine epitopes, and novel drug targets to combat MAC infections. Bioinformatic research on bacteria within MAC has contributed to a deeper insight into the pathogens. These would facilitate better diagnosis, improved: therapeutic strategies, patient-specific surveillance, and community-level awareness.

Amentoflavone reverses epithelial-mesenchymal transition in hepatocellular carcinoma cells by targeting p53 signalling pathway axis.

Epithelial-mesenchymal transition (EMT) and its reversal process are important potential mechanisms in the development of HCC. Selaginella doederleinii Hieron is widely used in Traditional Chinese Medicine for the treatment of various tumours and Amentoflavone is its main active ingredient. This study investigates the mechanism of action of Amentoflavone on EMT in hepatocellular carcinoma from the perspective of bioinformatics and network pharmacology. Bioinformatics was used to screen Amentoflavone-regulated EMT genes that are closely related to the prognosis of HCC, and a molecular prediction model was established to assess the prognosis of HCC. The network pharmacology was used to predict the pathway axis regulated by Amentoflavone. Molecular docking of Amentoflavone with corresponding targets was performed. Detection and evaluation of the effects of Amentoflavone on cell proliferation, migration, invasion and apoptosis by CCK-8 kit, wound healing assay, Transwell assay and annexin V-FITC/propidium iodide staining. Eventually three core genes were screened, inculding NR1I2, CDK1 and CHEK1. A total of 590 GO enrichment entries were obtained, and five enrichment results were obtained by KEGG pathway analysis. Genes were mainly enriched in the p53 signalling pathway. The outcomes derived from both the wound healing assay and Transwell assay demonstrated significant inhibition of migration and invasion in HCC cells upon exposure to different concentrations of Amentoflavone. The results of Annexin V-FITC/PI staining assay showed that different concentrations of Amentoflavone induces apoptosis in HCC cells. This study revealed that the mechanism of Amentoflavone reverses EMT in hepatocellular carcinoma, possibly by inhibiting the expression of core genes and blocking the p53 signalling pathway axis to inhibit the migration and invasion of HCC cells.

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics

BackgroundRheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. MethodsIn this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. ResultsThe results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. ConclusionThis study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis.

Although several studies have reported a link between chronic atrophic gastritis (CAG) and atherosclerosis, the underlying mechanisms have not been elucidated. The present study aimed to investigate the molecular mechanisms common to both diseases from a bioinformatics perspective. Gene expression profiles were obtained from the Gene Expression Omnibus database. Data on atherosclerosis and CAG were downloaded from the GSE28829 and GSE60662 datasets, respectively. We identified the differentially expressed genes co-expressed in CAG and atherosclerosis before subsequent analyses. We constructed and identified the hub genes and performed functional annotation. Finally, the transcription factor (TF)-target genes regulatory network was constructed. In addition, we validated core genes and certain TFs. We identified 116 common differentially expressed genes after analyzing the 2 datasets (GSE60662 and GSE28829). Functional analysis highlighted the significant contribution of immune responses and the positive regulation of tumor necrosis factor production and T cells. In addition, phagosomes, leukocyte transendothelial migration, and cell adhesion molecules strongly correlated with both diseases. Furthermore, 16 essential hub genes were selected with cytoHubba, including PTPRC, TYROBP, ITGB2, LCP2, ITGAM, FCGR3A, CSF1R, IRF8, C1QB, TLR2, IL10RA, ITGAX, CYBB, LAPTM5, CD53, CCL4, and LY86. Finally, we searched for key gene-related TFs, especially SPI1. Our findings reveal a shared pathogenesis between CAG and atherosclerosis. Such joint pathways and hub genes provide new insights for further studies.

Dickkopf-1 is an immune infiltration-related prognostic biomarker of head and neck squamous cell carcinoma.

Immunotherapy is currently one of the most viable therapies for head and neck squamous cell carcinoma (HNSCC), characterized by high immune cell infiltration. The Wnt-signaling inhibitor and immune activation mediator, Dickkopf-1 (DKK1), has a strong correlation with tumor growth, tumor microenvironment, and, consequently, disease prognosis. Nevertheless, it is still unclear how DKK1 expression, HNSCC prognosis, and tumor-infiltrating lymphocytes are related. To better understand these associations, we examined how DKK1 expression varies across different tumor and normal tissues. In our study, we investigated the association between DKK1 mRNA expression and clinical outcomes. Next, we assessed the link between DKK1 expression and tumor immune cell infiltration. Additionally, using immunohistochemistry, we evaluated the expression of DKK1 in 15 healthy head and neck tissue samples, and the expression of CD3, CD4, and DKK1 in 27 HNSCC samples. We also explored aberrant DKK1 expression during tumorigenesis. DKK1 expression was remarkably higher in HNSCC tissues than in healthy tissues, and was shown to be associated with tumor stage, grade, lymph node metastasis, histology, and a dismal clinical prognosis in HNSCC. DKK1 expression in HNSCC tissues was inversely correlated with CD3+ (P < 0.0001) and CD4+ (P < 0.0001) immune cell infiltration, while that in immune cells was inversely associated with HNSCC prognosis. These findings offer a bioinformatics perspective on the function of DKK1 in HNSCC immunotherapy and provide justification for clinical research on DKK1-targeted HNSCC treatments. DKK1 is a central target for improving the efficacy of HNSCC immunotherapy.

Comprehensive Analysis of the Role of Forkhead Box J3 (FOXJ3) in Human Cancers

Forkhead box J3 (FOXJ3) is a member of the forkhead box (Fox) family. Recently, increasing evidence has revealed the relationship between Fox family members and cancer. FOXJ3 is involved in various types of cancer, including lung cancer, tongue squamous carcinoma, and prostate cancer; however, a comprehensive pan-cancer analysis of FOXJ3 remains lacking. Here, we explored the function of FOXJ3 across cancers using online websites and databases including TIMER2.0, SangerBox, UALCAN, GEPIA2.0, cBioPortal, CancerSEA,STRING, BioGRID and Metascape to analyze the role of FOXJ3 in cancers. Abnormal expression of FOXJ3 was found in various tumors. The genetic alteration percentage in tumors was determined, and elevated FOXJ3 expression was found to be associated with worse overall survival in brain lower grade glioma(LGG), liver hepatocellular carcinoma (LIHC), sarcoma (SARC) and thyroid carcinoma. Elevated FOXJ3 expression was related to worse prognosis with disease-free survival in adrenocortical carcinoma, LGG and LIHC. FOXJ3 expression was related to immune infiltration in several cancers. Enrichment analysis showed that histone modification, the TGF-β signaling pathway, and chromatin organization were the top three enriched ontology clusters of the top 100 similar genes of FOXJ3. Our pan-cancer analysis provides comprehensive insights into FOXJ3 from the perspective of bioinformatics in different cancers, where it serves as a potential biomarker for prognosis, especially in LGG and LIHC. FOXJ3 is also correlated with immune infiltrates in certain human tumors.

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology.

Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to people's health, which is a high-risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. We employed bioinformatics and systems biology to explore these links between GC and COVID-19. Gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were obtained from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. We identified 209 shared DEGs between COVID-19 and GC. Functional analyses highlighted immune-related pathways as key players in both diseases. Ten hub genes (CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, IFIT2) were identified. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This research offer valuable insights into the molecular interplay between COVID-19 and GC, potentially guiding future therapeutic strategies.

Identification of Immunogenic Cell Death-Associated Long Non-Coding RNA in the Prognosis of Cervical Cancer

Cervical cancer (CC) is a malignant tumor with a high incidence in women. It is urgent to develop new diagnostic and predictive markers of CC to improve the prognosis of CC. Programmed cell death (PCD) of cells has been proven to play an essential role in the occurrence and development of tumors. From the perspective of bioinformatics, we discussed the role of 12 PCD-related genes in the CC radiation-sensitive group and radiation-resistant group and establishes the CC risk model accordingly. At first, we identified two hub genes related to prognosis: PRKAA2 and MLLT11. There was an indirect correlation between MLLT11 and CC, and the effect of PRKAA2 on cervical cancer needs further study. Subsequently, we constructed the risk model about CC and confirmed that there was a significant difference in prognosis between the two groups, and the Top pathway involved was closely related to CC. Finally, by discussing the immune landscape of prognosis-related genes in CC, it was found that there were significant differences between the two groups in the abundance of immune cell infiltration, the expression value of immune inspection sites, and the expression values of PD-L1 and PD-L2. In addition, we performed RT-qPCR to validate the levels of hub genes. This study is helpful for the individualized treatment of CC patients.

Immune-Related Genes and Immune Cell Infiltration Characterize the Maturation Status of Arteriovenous Fistulas: An Integrative Bioinformatics Study and Experimental Validation Based on Transcriptome Sequencing.

Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, but the low maturation rate is concerning. Immune cells' impact on AVF maturation lacks bioinformatics research. The study aims to investigate the potential predictive role of immune-related genes and immune cell infiltration characteristics in AVF maturation. We analyzed the high-throughput sequencing dataset to identify differentially expressed genes (DEGs). Then, we performed enrichment analyses (GO, KEGG, GSEA) on immune-related genes and pathways in mature AVF. We focused on differentially expressed immune-related genes (DEIRGs) and constructed a PPI network to identify hub genes. These hub genes were validated in other databases and experiments, including qPCR and immunohistochemistry (IHC). The immune cell infiltration characteristics in native veins, failed AVFs, and matured AVFs were analyzed by cibersortX. Partial experimental validation was conducted using clinical samples. Our results showed that immune-related genes and signaling pathways are significantly enriched in mature AVF. We validated this in other databases and ultimately identified three hub genes (IL1B, IL6, CXCR4) in combination with experiments. Significant differences in immune cell infiltration characteristics were observed among native veins, failed AVFs, and matured AVFs. Immune cell infiltration analysis revealed that accumulation of CD4+ T cells, dendritic cells, mast cells and M2 macrophages contribute to AVF maturation. These immune-related genes and immune cells have the potential to serve as predictive factors for AVF maturation. We partially validated this experimentally. From a bioinformatics perspective, our results have identified, for the first time, a set of immune-related genes and immune cell infiltration features that can characterize the maturation of AVF and significantly impact AVF maturation. These features hold potential as predictive indicators for AVF maturation outcomes.

Tryptophan Metabolism in Alzheimer's Disease with the Involvement of Microglia and Astrocyte Crosstalk and Gut-Brain Axis.

Alzheimer's disease (AD) is an age-dependent neurodegenerative disease characterized by extracellular Amyloid Aβ peptide (Aβ) deposition and intracellular Tau protein aggregation. Glia, especially microglia and astrocytes are core participants during the progression of AD and these cells are the mediators of Aβ clearance and degradation. The microbiota-gut-brain axis (MGBA) is a complex interactive network between the gut and brain involved in neurodegeneration. MGBA affects the function of glia in the central nervous system (CNS), and microbial metabolites regulate the communication between astrocytes and microglia; however, whether such communication is part of AD pathophysiology remains unknown. One of the potential links in bilateral gut-brain communication is tryptophan (Trp) metabolism. The microbiota-originated Trp and its metabolites enter the CNS to control microglial activation, and the activated microglia subsequently affect astrocyte functions. The present review highlights the role of MGBA in AD pathology, especially the roles of Trp per se and its metabolism as a part of the gut microbiota and brain communications. We (i) discuss the roles of Trp derivatives in microglia-astrocyte crosstalk from a bioinformatics perspective, (ii) describe the role of glia polarization in the microglia-astrocyte crosstalk and AD pathology, and (iii) summarize the potential of Trp metabolism as a therapeutic target. Finally, we review the role of Trp in AD from the perspective of the gut-brain axis and microglia, as well as astrocyte crosstalk, to inspire the discovery of novel AD therapeutics.

Prognostic significance and immune characteristics of APOE in gastric cancer.

Gastric cancer (GC) is a prevalent malignancy affecting the digestive system, and it is the second leading cause of cancer-related mortality worldwide. Immunotherapy presents a potential lifeline for patients with advanced gastric cancer, emphasizing the need to find new molecular targets that improve the response to immunotherapy. In our research, we conducted a comprehensive bioinformatic analysis to investigate the expression profiles of apolipoprotein E (APOE) transcription. Subsequently, we examined the correlation between APOE transcription and the prognosis of GC patients. Additionally, we evaluated the connection between APOE transcription and immune cells abundance. To validate our findings, we conducted immunohistochemistry experiment to ascertain the level of APOE protein in GC patients and assessed its prognostic role in a cohort of 97 GC individuals. Our results revealed that APOE is increased in GC tissues, and APOE displays diagnostic potential in distinguishing GC from normal tissues. Notably, upregulated APOE expression in GC patients is associated with unfavorable overall survival. Differential APOE expression was further observed across different immune subtypes of GC, indicating its involvement in immune cell activation and infiltration. Moreover, we detected increased APOE protein expression in GC tissues, which exhibited a strong correlation with poor survival outcomes. In light of these findings, APOE has become a crucial prognostic molecular with immunomodulatory function in GC. These results underscore the significance of APOE across various cancer types, including GC, and provide valuable insights into its role from both a bioinformatics and clinical perspective.

The MT1 receptor as the target of ramelteon neuroprotection in ischemic stroke.

Stroke is the leading cause of death and disability worldwide. Novel and effective therapies for ischemic stroke are urgently needed. Here, we report that melatonin receptor 1A (MT1) agonist ramelteon is a neuroprotective drug candidate as demonstrated by comprehensive experimental models of ischemic stroke, including a middle cerebral artery occlusion (MCAO) mouse model of cerebral ischemia in vivo, organotypic hippocampal slice cultures ex vivo, and cultured neurons in vitro; the neuroprotective effects of ramelteon are diminished in MT1-knockout (KO) mice and MT1-KO cultured neurons. For the first time, we report that the MT1 receptor is significantly depleted in the brain of MCAO mice, and ramelteon treatment significantly recovers the brain MT1 losses in MCAO mice, which is further explained by the Connectivity Map L1000 bioinformatic analysis that shows gene-expression signatures of MCAO mice are negatively connected to melatonin receptor agonist like Ramelteon. We demonstrate that ramelteon improves the cerebral blood flow signals in ischemic stroke that is potentially mediated, at least, partly by mechanisms of activating endothelial nitric oxide synthase. Our results also show that the neuroprotection of ramelteon counteracts reactive oxygen species-induced oxidative stress and activates the nuclear factor erythroid 2-related factor 2/heme oxygenase-1 pathway. Ramelteon inhibits the mitochondrial and autophagic death pathways in MCAO mice and cultured neurons, consistent with gene set enrichment analysis from a bioinformatics perspective angle. Our data suggest that Ramelteon is a potential neuroprotective drug candidate, and MT1 is the neuroprotective target for ischemic stroke, which provides new insights into stroke therapy. MT1-KO mice and cultured neurons may provide animal and cellular models of accelerated ischemic damage and neuronal cell death.

The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer.

The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.

The Expression Characteristics and Function of the RECQ Family in Pan-Cancer.

The genes of the RECQ DNA helicase family play a part in preserving the stability of the genome and controlling different disease mechanisms. However, the expression features of RECQs in relation to pan-cancer, their correlation with the immune microenvironment of tumors, and the landscape of prognostic power are still undisclosed. Various sequence and clinical data extracted from 33 cancers were utilized to generate a comprehensive overview of RECQs in the landscape. Afterward, we discovered variations in gene expression, potential enrichment of functions, genetic alterations, and analysis related to the immune response in tumors. Additionally, we explored the clinical characteristics and diagnostic significance of RECQs. And the important association of RECQL4 with liver hepatocellular carcinoma (LIHC) was investigated. RECQs exhibited extensive mutations in different types of cancers. The expression of RECQ may be influenced by an oncogenic mutation in certain types of cancer, resulting in the observed genomic and epigenetic changes in diverse tumor formations. Furthermore, RECQs originating from tumors exhibited a significant association with the immune microenvironment of the tumor, indicating their potential as promising targets for therapy. Patient prognosis was significantly associated with the majority of genes in the RECQ family. In LIHC, RECQL4 eventually emerged as a separate prognostic determinant. To summarize, RECQs are essential for the regulation of the immune system in tumors, and RECQL4 serves as a prognostic indicator in LIHC. The results of our study offer fresh perspectives on RECQs from a bioinformatics perspective and emphasize the importance of RECQs in the diagnosis and treatment of cancer.

COVID Variants, Villain and Victory: A Bioinformatics Perspective.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Elucidating the role of T-cell exhaustion-related genes in colorectal cancer: a single-cell bioinformatics perspective.

Colorectal cancer (CRC) remains a significant global health issue. In this study, the role of T-cell exhaustion-related genes (TEXs) in CRC was investigated using single-cell and bulk RNA-seq analysis. This research involved extensive data analysis using multiple databases, including the TCGA-COAD cohort, GSE14333, and GSE39582. Through single-cell analysis, distinct cell populations within CRC samples were identified and classified T-cells into four subgroups: regulatory T-cells (Tregs), conventional CD4+ T-cells (CD4+ T conv), CD8+ T, and CD8+ T exhausted cells. Intercellular communication networks and signaling pathways associated with TEXs using computational tools such as CellChat and PROGENy. Additionally, TEX-related alterations in tumor gene pathways were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Prognostic models were developed, and their correlation with immune infiltration was assessed. The study revealed the presence of distinct cell populations within CRC, with TEXs playing a crucial role in the tumor microenvironment. CD8+ T exhausted cells exhibited expression of specific markers, indicating their involvement in tumor immune evasion. CellChat and PROGENy analyses revealed intricate communication networks and signaling pathways associated with TEXs, including RNA splicing and viral carcinogenesis. Furthermore, the prognostic risk model developed on the basis of TEXs demonstrated its efficacy in stratifying CRC patients. This risk model exhibited strong correlations with immune infiltration by various effector immune cells, highlighting the influence of TEXs on the tumor immune response. The complex interactions and signaling pathways underlying TEX-associated immune dysregulation in CRC were revealed by employing advanced analytical approaches. The development of a prognostic risk model based on TEXs offers a promising tool for prognostic stratification in patients with CRC. Furthermore, the correlations observed between TEXs and immune infiltration provide valuable insights into the potential of TEXs as therapeutic targets and highlight the need for further investigation into TEX-mediated immune evasion mechanisms. This study thus provides valuable insights into the role of TEXs in CRC.