gene-miRNA Interactions Research Articles

Novel mutations found in genes involved in global developmental delay and intellectual disability by whole-exome sequencing, homology modeling, and systems biology

Background Genes associated with global developmental delay (GDD) and intellectual disability (ID) are increasingly being identified through next-generation sequencing (NGS) technologies. This study aimed to identify novel mutations in GDD/ID phenotypes through whole-exome sequencing (WES) and additional in silico analyses. Material and methods WES was performed on 27 subjects, among whom 18 were screened for potential novel mutations. In silico analyses included protein-protein interactions (PPIs), gene-miRNA interactions (GMIs), and enrichment analyses. The identified novel variants were further modelled using I-Tasser-MTD and SWISS-MODEL, with structural superimposition performed. Results Novel mutations were detected in 18 patients, with 10 variants reported for the first time. Among these, three were classified as pathogenic (DNMT1:c.856dup, KCNQ2:c.1635_1636insT, and TMEM94:c.2598_2599insC), and six were likely pathogenic. DNMT1 and MRE11 were highlighted as key players in PPIs and GMIs. GMIs analysis emphasised the roles of hsa-miR-30a-5p and hsa-miR-185-5p. The top-scoring pathways included the neuronal system (R-HSA-112316, p = 7.73E-04) and negative regulation of the smooth muscle cell apoptotic process (p = 3.37E-06). Homology modelling and superimposition revealed a significant functional loss in the mutated DNMT1 enzyme structure. Conclusion This study identified 10 novel pathogenic/likely pathogenic variants associated with GDD/ID, supported by clinical findings and in silico analyses focused on DNMT1 mutations.

A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy

Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP (https://www.ncbi.nlm.nih.gov/snp/), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein–protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene–miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein–Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, l-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein–Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.Graphical

Pharmacogenomics-based systematic review of coronary artery disease based on personalized medicine procedure

BackgroundCoronary artery disease (CAD) is the most common reason for mortality and disability-adjusted life years (DALYs) lost globally. This study aimed to suggest a new gene list for the treatment of CAD by a systematic review of bioinformatics analyses of pharmacogenomics impacts of potential genes and variants. MethodsPubMed search was filtered by the title including Coronary Artery Disease during 2020–2023. To find the genes with pharmacogenetic impact on the CAD, additional filtrations were considered according to the variant annotations. Protein-Protein Interactions (PPIs), Gene-miRNA Interactions (GMIs), Protein-Drug Interactions (PDIs), and variant annotation assessments (VAAs) performed by STRING-MODEL (ver. 12), Cytoscape (ver. 3.10), miRTargetLink.2., NetworkAnalyst (ver 0.3.0), and PharmGKB. ResultsResults revealed 5618 publications, 1290 papers were qualified, and finally, 650 papers were included. 4608 protein-coding genes were extracted, among them, 1432 unique genes were distinguished and 530 evidence-based repeated genes remained. 71 genes showed a pharmacogenetics-related variant annotation in at least (entirely 6331 annotations). Variant annotation assessment (VAA) showed 532 potential variants for the final report, and finally, the concluding PGs list represented 175 variants. Based on the function and MAF, 57 nonsynonymous variants of 29 Pharmacogenomics-related genes were associated with CAD. ConclusionConclusively, evaluating circulating miR33a in individuals’ plasma with CAD, and genotyping of rs2230806, rs2230808, rs2487032, rs12003906, rs2472507, rs2515629, and rs4149297 (ABCA1 variants) lead to precisely prescribing of well-known drugs. Also, the findings of this review can be used in both whole-genome sequencing (WGS) and whole-exome sequencing (WES) analysis in the prognosis and diagnosis of CAD.

Hub Genes, Possible Pathways and Predicted Drugs in Hereditary Gingival Fibromatosis by Bioinformatics Analysis.

To explore potential pathogenic processes and possible treatments using unbiased and reliable bioinformatic tools. Gene expression profiles of control and hepatocyte growth factor (HGF) samples were downloaded from CNP0000995. Analysis of differentially expressed genes (DEGs) was conducted using R software (version 4.2.1, R Foundation, Vienna, Austria). Functional enrichment analyses were performed using the Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) databases, then the proteinprotein interaction (PPI) network was constructed to screen the top 10 hub genes. Finally, five genes related to cell junctions were selected to build gene-miRNA interactions and predict small-molecule drugs. A total of 342 downregulated genes and 188 upregulated genes were detected. Candidate pathways include the extracellular matrix (ECM) receptor interaction pathway, the TGF-β signalling pathway and the cell adhesion molecule (CAM) pathway, which were discovered through KEGG and GSEA enrichment studies. GO analyses revealed that these DEGs were significantly enriched in cell adhesion, the adherens junction and focal adhesion. Five hub genes (CDH1, SNAP25, RAC2, APOE and ITGB4) associated with cell adhesion were identified through PPI analysis. Finally, the gene-miRNA regulatory network identified three target miRNAs: hsa-miR-7110-5p, hsa-miR-149-3p and hsa-miR-1207-5p. Based on the gene expression profile, the small-molecule drugs zebularine, ecuronium and prostratin were selected for their demonstrated binding activity when docked with the mentioned molecules. This study offered some novel insights into molecular pathways and identified five hub genes associated with cell adhesion. Based on these hub genes, three potential therapeutic miRNAs and small-molecule drugs were predicted, which are expected to provide guidance for the treatment of patients with HGF.

Comparative analysis of gene expression between mice and humans in acetaminophen-induced liver injury by integrating bioinformatics analysis

ObjectiveMice are routinely utilized as animal models of drug-induced liver injury (DILI), however, there are significant differences in the pathogenesis between mice and humans. This study aimed to compare gene expression between humans and mice in acetaminophen (APAP)-induced liver injury (AILI), and investigate the similarities and differences in biological processes between the two species.MethodsA pair of public datasets (GSE218879 and GSE120652) obtained from GEO were analyzed using “Limma” package in R language, and differentially expressed genes (DEGs) were identified, including co-expressed DEGs (co-DEGs) and specific-expressed DEGS (specific-DEGs). Analysis of Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed analyses for specific-DEGs and co-DEGs. The co-DEGs were also used to construct transcription factor (TF)-gene network, gene-miRNA interactions network and protein-protein interaction (PPI) network for analyzing hub genes.ResultsMouse samples contained 1052 up-regulated genes and 1064 down-regulated genes, while human samples contained 1156 up-regulated genes and 1557 down-regulated genes. After taking the intersection between the DEGs, only 154 co-down-regulated and 89 co-up-regulated DEGs were identified, with a proportion of less than 10%. It was suggested that significant differences in gene expression between mice and humans in drug-induced liver injury. Mouse-specific-DEGs predominantly engaged in processes related to apoptosis and endoplasmic reticulum stress, while human-specific-DEGs were concentrated around catabolic process. Analysis of co-regulated genes reveals showed that they were mainly enriched in biosynthetic and metabolism-related processes. Then a PPI network which contains 189 nodes and 380 edges was constructed from the co-DEGs and two modules were obtained by Mcode. We screened out 10 hub genes by three algorithms of Degree, MCC and MNC, including CYP7A1, LSS, SREBF1, FASN, CD44, SPP1, ITGAV, ANXA5, LGALS3 and PDGFRA. Besides, TFs such as FOXC1, HINFP, NFKB1, miRNAs like mir-744-5p, mir-335-5p, mir-149-3p, mir-218-5p, mir-10a-5p may be the key regulatory factors of hub genes.ConclusionsThe DEGs of AILI mice models and those of patients were compared, and common biological processes were identified. The signaling pathways and hub genes in co-expression were identified between mice and humans through a series of bioinformatics analyses, which may be more valuable to reveal molecular mechanisms of AILI.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets

BackgroundMicroRNAs (miRNAs) are promising therapeutic agents for nonalcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression. MethodsGene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300 and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes. ResultsThe most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2 and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2 and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes. ConclusionIn silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

Identification of hub genes significantly linked to temporal lobe epilepsy and apoptosis via bioinformatics analysis.

Epilepsy stands as an intricate disorder of the central nervous system, subject to the influence of diverse risk factors and a significant genetic predisposition. Within the pathogenesis of temporal lobe epilepsy (TLE), the apoptosis of neurons and glial cells in the brain assumes pivotal importance. The identification of differentially expressed apoptosis-related genes (DEARGs) emerges as a critical imperative, providing essential guidance for informed treatment decisions. We obtained datasets related to epilepsy, specifically GSE168375 and GSE186334. Utilizing differential expression analysis, we identified a set of 249 genes exhibiting significant variations. Subsequently, through an intersection with apoptosis-related genes, we pinpointed 16 genes designated as differentially expressed apoptosis-related genes (DEARGs). These DEARGs underwent a comprehensive array of analyses, including enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, prediction of miRNA and transcription factors, and molecular docking analysis. In the epilepsy datasets examined, we successfully identified 16 differentially expressed apoptosis-related genes (DEARGs). Subsequent validation in the external dataset GSE140393 revealed the diagnostic potential of five biomarkers (CD38, FAIM2, IL1B, PAWR, S100A8) with remarkable accuracy, exhibiting an impressive area under curve (AUC) (The overall AUC of the model constructed by the five key genes was 0.916, and the validation set was 0.722). Furthermore, a statistically significant variance (p < 0.05) was observed in T cell CD4 naive and eosinophil cells across different diagnostic groups. Exploring interaction networks uncovered intricate connections, including gene-miRNA interactions (164 interactions involving 148 miRNAs), gene-transcription factor (TF) interactions (22 interactions with 20 TFs), and gene-drug small molecule interactions (15 interactions involving 15 drugs). Notably, IL1B and S100A8 demonstrated interactions with specific drugs. In the realm of TLE, we have successfully pinpointed noteworthy differentially expressed apoptosis-related genes (DEARGs), including CD38, FAIM2, IL1B, PAWR, and S100A8. A comprehensive understanding of the implications associated with these identified genes not only opens avenues for advancing our comprehension of the underlying pathophysiology but also bears considerable potential in guiding the development of innovative diagnostic methodologies and therapeutic interventions for the effective management of epilepsy in the future.

Exploring Shared Genetic Signatures of Alzheimer’s Disease and Multiple Sclerosis: A Bioinformatic Analysis Study

Introduction: Many clinical studies reported the coexistence of Alzheimer’s disease (AD) and multiple sclerosis (MS), but the common molecular signature between AD and MS remains elusive. The purpose of our study was to explore the genetic linkage between AD and MS through bioinformatic analysis, providing new insights into the shared signatures and possible pathogenesis of two diseases. Methods: The common differentially expressed genes (DEGs) were determined between AD and MS from datasets obtained from Gene Expression Omnibus (GEO) database. Further, functional and pathway enrichment analysis, protein-protein interaction network construction, and identification of hub genes were carried out. The expression level of hub genes was validated in two other external AD and MS datasets. Transcription factor (TF)-gene interactions and gene-miRNA interactions were performed in NetworkAnalyst. Finally, receiver operating characteristic (ROC) curve analysis was applied to evaluate the predictive value of hub genes. Results: A total of 75 common DEGs were identified between AD and MS. Functional and pathway enrichment analysis emphasized the importance of exocytosis and synaptic vesicle cycle, respectively. Six significant hub genes, including CCL2, CD44, GFAP, NEFM, STXBP1, and TCEAL6, were identified and verified as common hub genes shared by AD and MS. FOXC1 and hsa-mir-16-5p are the most common TF and miRNA in regulating hub genes, respectively. In the ROC curve analysis, all hub genes showed good efficiency in helping distinguish patients from controls. Conclusion: Our study first identified a common genetic signature between AD and MS, paving the road for investigating shared mechanism of AD and MS.

Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network

BackgroundThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is identified as the cause of coronavirus disease 2019 (COVID-19) pandemic. Acute kidney injury (AKI), one of serious complications of COVID-19 infection, is the leading contributor to renal failure, associating with high mortality of the patients. This study aimed to identify the shared gene signatures and construct the gene regulatory network between COVID-19 and AKI, contributing to exploring the potential pathogenesis.MethodsUtilizing the machine learning approach, the candidate gene signatures were derived from the common differentially expressed genes (DEGs) obtained from COVID-19 and AKI. Subsequently, receiver operating characteristic (ROC), consensus clustering and functional enrichment analyses were performed. Finally, protein-protein interaction (PPI) network, transcription factor (TF)-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory network were systematically undertaken.ResultsWe successfully identified the shared 6 candidate gene signatures (RRM2, EGF, TMEM252, RARRES1, COL6A3, CUBN) between COVID-19 and AKI. ROC analysis showed that the model constructed by 6 gene signatures had a high predictive efficacy in COVID-19 (AUC = 0.965) and AKI (AUC = 0.962) cohorts, which had the potential to be the shared diagnostic biomarkers for COVID-19 and AKI. Additionally, the comprehensive gene regulatory networks, including PPI, TF-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory networks were displayed utilizing NetworkAnalyst platform.ConclusionsThis study successfully identified the shared gene signatures and constructed the comprehensive gene regulatory network between COVID-19 and AKI, which contributed to predicting patients’ prognosis and providing new ideas for developing therapeutic targets for COVID-19 and AKI.

Differentially Expressed Genes, miRNAs and Network Models: A Strategy to Shed Light on Molecular Interactions Driving HNSCC Tumorigenesis.

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene-miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein-protein interaction (PPI) and bipartite miRNA-target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC.

Identification of Chemokines-Related miRNAs as Potential Biomarkers in Psoriasis Based on Integrated Bioinformatics Analysis.

Psoriasis is an immune-mediated skin disorder caused by the proliferation of keratinocytes. Although psoriasis is generally diagnosed based on clinical manifestations, sensitive biomarkers are needed to help diagnose psoriasis early with atypical presentations. MicroRNAs play a functional role in the development of psoriasis, and they are stable and suitable as biomarkers in psoriasis. The GSE50790 and GSE53552 datasets from the Gene Expression Omnibus (GEO) database were used to identify Differentially Expressed Genes (DEGs) between the control group and the lesional group. DEGs were processed for enrichment analysis to explore the functions, and a Protein-Protein Interaction (PPI) network was constructed to obtain gene clusters. The signalling pathway associated with gene cluster 1 was processed to further identify related genes. Hub genes were obtained through the intersection of cluster 1 and the related genes. Hub genes were used to predict the miRNAs through a gene-miRNA interaction network. The relative expression of miRNAs was measured by qRT-PCR to identify the suitability of miRNAs as biomarkers. Bioinformatics analysis revealed that the chemokine signalling pathway is involved in the development of psoriasis. Five related miRNAs were mined from the datasets, and qRT-PCR showed that hsa-miR-612 (p=0.0015), hsa-miR-3194-5p (p=0.0078) and hsa-miR-4316 (p<0.0001) may be potential biomarkers in psoriasis.

GADD45B predicts lung squamous cell carcinoma survival and impacts immune infiltration, and T cell exhaustion

Background: This study focussed on exploring the prognostic prediction performance of the growth arrest and DNA damage-inducible 45 beta (GADD45B) and its associations with T-cell activity and immune soakage in different malignancies, especially lung squamous cell carcinoma (LUSC). Methods: We applied TIMER database for comparing the expressions of GADD45B among different cancers. OncoLnc, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Kaplan-Meier Plotter were utilised to evaluate the prognostic prediction performance of GADD45B. Besides, the associations of GADD45B with clinical stage, associated gene markers, and immune infiltration were examined through TISIDB, GEPIA2, and Tumour Immune Estimation Resource (TIMER). Biological processes (BPs) and KEGG enrichment analyses were performed to illustrate the possible role of GADD45B in LUSC. The miRWalk database was adopted to analyse the gene miRNA interaction network of GADD45B in LUSC. Results: GADD45B expression was decreased in most of the malignancies, with relation to the poor prognosis in LUSC. GADD45B also significantly affected the survival of LUSC subgroups divided by clinic data. GADD45B significantly correlates with and may stimulate T cell exhaustion in LUSC. Conclusions: GADD45B is a prognostic indicator in multiple tumours, especially in LUSC. Moreover, modulating GADD45B expression may improve immunotherapy efficacy in LUSC.

Identification of the potential ferroptosis key genes in lung cancer with bone metastasis.

To identify the potential key genes of ferroptosis in the pathogenesis of lung cancer with bone metastasis (LCBM) by bioinformatics analysis to provide new targets for treating LCBM and an indicator for early monitoring. We first obtained differentially expressed genes (DEGs) associated with ferroptosis from the Gene Expression Omnibus (GEO) database. MiRWalk 2.0 was used to predict the key microRNAs (miRNAs) and construct related gene-miRNA interaction networks. The functional enrichment analysis of key miRNAs was performed using the miEAA database. Finally, the clinical data of 105 lung cancer patients were retrospectively analyzed, and logistic regression analysis was conducted to assess the relationship between serum alkaline phosphatase (ALP), neuron-specific enolase (NSE), and bone metastasis in lung cancer patients, and a receiver operating characteristic (ROC) curve was drawn. We identified 15 ferroptosis-related genes that were differentially expressed in lung cancer bone metastasis. GO and KEGG enrichment analyses suggested that these genes may affect the oxidative stress response, hypoxia response, rough endoplasmic reticulum, mitochondrial outer membrane, iron-sulfur cluster binding, virus receptor activity, central carbon metabolism in cancer, the interleukin-17 (IL-17) signaling pathway, and other aspects to participate in the occurrence and development of lung cancer bone metastasis. Among the 105 lung cancer patients included in the study, 39 cases had bone metastasis, and the incidence rate was 37.14%. A high Eastern Cooperative Oncology Group (ECOG) score and serum ALP and NSE overexpression were associated with bone metastasis in patients with lung cancer. By assessing the risk of bone metastasis in patients with lung cancer, we found that the Area Under Curves (AUCs) of serum ALP and NSE alone and combined were >0.70. The differentially expressed ferroptosis-related genes and predicted miRNA regulatory network in lung cancer bone metastasis and the related functional enrichment analysis provide new targets for the treatment of lung cancer bone metastasis. At the same time, from a serological perspective, it was found that early monitoring of serum ALP and NSE expression in patients with lung cancer could be considered to assess the risk of bone metastasis in the future.

Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach

The fields of data mining, computational biology, and statistics have been combined to form the massive research area of bioinformatics. In the areas of genetics, education, and healthcare, bioinformatics integrates the tools available in different fields such as computing, inventorying, performing statistical analyses, and collecting and processing genomic data. Stress and depression are two of the most severe mental disorders that affect people of all ages, including children and adults. The goal of this study was to look into the relationship between genetic alterations and the two diseases mentioned above as well as to develop a PPI network or related channel. The first step is to determine whether or not there is a biological relationship between them. This would assist us in connecting both of them as well as building therapeutic drugs that are effective against stress and depression disorders. Using R programs, the genes that are responsible for different diseases are acquired, pre-processed, analyzed, and mined in order to better understand them. During the study, a novel pathway was discovered. Based on common genes between the two diseases studied, the PPI network, gene-miRNA interaction, TF-gene interaction, and PDI network were established. This data can help us better understand how the PPI network binds to its ligands. We anticipate that our study will contribute to the development of new drugs for stress and depression.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

Transposable Elements-Derived MicroRNA Expression Patterns in TCGA Dataset for 10 Species.

MicroRNAs (miRNAs) are a class of non-coding RNAs that act as regulators of disease. An evolutionary approach to the disease could reveal factors such as diagnosis, treatment, and prognosis prediction. The expression patterns of transposable element (TEs)-derived miRNAs could help elucidate diseases, and their evolutionary patterns are also valuable. The 34 miRNAs were compared in terms of stage survival and tumor status in 33 carcinomas from TCGA. Expression levels were compared using a t-test and presented as differentially expressed miRNAs (DEMs). For DEMs showing statistically specific expression patterns for 3 conditions (normal and cancer, early and advanced stage, and survival), interactions with related genes in 10 species, including humans, were compared. The enrichment term was discovered for the gene-miRNA interactions. In 18 out of the 33 carcinomas, at least one miRNA was retrieved with P < .05 and |fold change| >.05. A total of 128 DEMs for the 9 miRNAs were identified. Based on the TargetScan database, interactions between miRNAs and genes in 10 species, including humans, were confirmed. The evolutionarily conserved miR-130a was observed in all 10 species, whereas miR-151a was only observed in humans. GO terms of related genes were selected for the miRNAs commonly found in each species. Evolutionary analysis of TE-derived disease-associated miRNAs was performed, and the evolutionarily conserved miR-130a-related carcinomas included renal and thyroid cancers. Human and rhesus monkey-specific miR-625 is associated with various carcinomas.

Molecular Classification of Colorectal Cancer by microRNA Profiling: Correlation with the Consensus Molecular Subtypes (CMS) and Validation of miR-30b Targets.

Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p &lt; 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p &lt; 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3'-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3'-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response.

Identification of Potential Ferroptosis Key Genes in the Pathogenesis of Lumbosacral Spinal Root Avulsion by RNA Sequencing and Bioinformatics Analysis.

Objective: Ferroptosis is a type of cell death involved in various human diseases, including nerve injury. However, the role of ferroptosis in lumbosacral spinal root avulsion (LSRA) remains unknown. This study aims to investigate whether ferroptosis is induced after LSRA and the key ferroptosis-related genes and their potential function in LSRA. Methods: The biochemical and morphological changes of ferroptosis were determined by detection of iron accumulation and by transmission electron microscopy in a rat LSRA model. The transcriptional expression profile following LSRA was investigated by RNA sequencing and ferroptosis-related genes were downloaded from FerrDb and used to identify ferroptosis differentially expressed genes (DEGs). The differential expressions of ferroptosis DEGs were confirmed by qRT-PCR analysis. The potential functions of ferroptosis DEGs were revealed by DAVID 6.8 and WebGestalt. A protein–protein interaction (PPI) network and gene–miRNA interaction network were further constructed to identify key modules in ferroptosis DEGs, and the results were verified by qRT-PCR and western blot analysis. Results: LSRA was followed by ferroptosis-specific changes, such as shrunken mitochondria and increased iron accumulation, that can be alleviated by ferroptosis inhibitor deferoxamine (DFO). A total of 2,446 DEGs and 46 ferroptosis DEGs were identified after LSRA, and over 90% of the ferroptosis DEGs were confirmed to be differentially expressed following LSRA, which can also be eliminated by DFO treatment. Functional analysis demonstrated significant enrichment of the ferroptosis DEGs in pathways related to the oxidative stress response, the HIF-1 signaling pathway, and the tumor necrosis factor signaling pathway. PPI network analysis demonstrated that a set of key modules in ferroptosis DEGs were related to the HIF-1 signaling pathway: Il6, Nos2, Stat3, Hif1a, Vegfa, Cdkn1a, and Rela. Construction of a gene–miRNA network predicted miRNAs targeting four key ferroptosis DEGs—Stat3, Hif1a, Vegfa, and Rela, and further western blot analysis confirmed their upregulation after LSRA, which can be alleviated by DFO pretreatment. Conclusion: The data revealed the induction of ferroptosis in a rat LSRA model and identified possible regulatory roles for ferroptosis-related genes in the molecular mechanisms of LSRA, which provides new insights into the pathogenesis and helps to find new molecular targets for the treatment of LSRA.

Correlation of MKI67 with prognosis, immune infiltration, and T cell exhaustion in hepatocellular carcinoma

BackgroundMKI67 plays a vital role in the tumour microenvironment (TME) and congenital immunity. The present work focuses on exploring the prognosis prediction performance of MKI67 and its associations with T cell activity and immune infiltration within numerous cancers, especially hepatocellular liver carcinoma (LIHC).MethodsOncomine, GEPIA2, and HPA were adopted to analyse MKI67 levels in different types of cancers. The prognostic prediction performance of MKI67 was evaluated through the TCGA portal, GEPIA2, LOGpc, and Kaplan–Meier Plotter databases. The associations of MKI67 with related gene marker sets and immune infiltration were inspected through TISIDB, GEPIA2, and TIMER. We chose MKI67 to analyse biological processes (BPs) and KEGG pathways related to the coexpressed genes. Furthermore, the gene–miRNA interaction network for MKI67 in liver cancer was also examined based on the miRWalk database.ResultsMKI67 expression decreased in many cancers related to the dismal prognostic outcome of LIHC. We found that MKI67 significantly affected the prognosis of LIHC in terms of histology and grade. Increased MKI67 levels were directly proportional to the increased immune infiltration degrees of numerous immune cells and functional T cells, such as exhausted T cells. In addition, several critical genes related to exhausted T cells, including TIM-3, TIGIT, PD-1, LAG3, and CXCL13, were strongly related to MKI67. Further analyses showed that MKI67 was associated with adaptive immunity, cell adhesion molecules (CAMs), and chemokine/immune response signal transduction pathways.ConclusionMKI67 acts as a prognostic prediction biomarker in several cancers, particularly LIHC. Upregulation of MKI67 elevates the degree of immune infiltration of many immune cell subtypes, including functional T cells, CD4+ T cells, and CD8+ T cells. Furthermore, MKI67 shows a close correlation with T cell exhaustion, which plays a vital role in promoting T cell exhaustion within LIHC. Detection of the MKI67 level contributes to prognosis prediction and MKI67 modulation within exhausted T cells, thus providing a new method to optimize the efficacy of anti-LIHC immunotherapy.