Bioinformatics Studies Research Articles

Pharmacological targets and validation of remdesivir for the treatment of COVID-19-associated pulmonary fibrosis: A network-based pharmacology and bioinformatics study.

The objective of this study was to employ bioinformatics and network pharmacology methodologies to investigate the targets and molecular mechanisms of remdesivir in the treatment of coronavirus disease 2019 (COVID-19)-associated pulmonary fibrosis (PF). Several open-source databases were utilized to confirm the shared targets of remdesivir, COVID-19, and PF. Following this, a comprehensive analysis incorporating function enrichment, protein-protein interaction (PPI), transcription factor (TF), and molecular docking was conducted to investigate the potential mechanisms underlying the effectiveness of remdesivir in the treatment of COVID-19-associated PF. The initial validation of these findings was performed using publicly available histological and single-cell sequencing databases. The functional enrichment analysis revealed a strong association between remdesivir and viral defense, inflammatory response, and immune response. The key pathways identified in the study were transforming growth factor (TGF-β), PI3K-Akt, mTOR, MAPK, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, HIF-1, and Toll-like receptor signaling pathways. Additionally, the PPI analysis demonstrated the network relationships of 13 important targets, while the TF analysis provided valuable insights into the regulatory networks of these targets. Among the identified TFs, RELA was found to be the most significant. To validate our findings, we utilized publicly available histological and single-cell sequencing databases, successfully confirming the involvement of 8 key targets, including AKT1, EGFR, RHOA, MAPK1, PIK3R1, MAPK8, MAPK14, and MTOR. Furthermore, molecular docking studies were conducted to assess the interaction between remdesivir and the identified key targets, thus confirming its effective targeting effects. Remdesivir has the potential to exert antiviral, anti-inflammatory, and immunomodulatory effects in the context of COVID-19-associated PF.

Putative retina metal/metalloid-binding proteins: molecular functions, biological processes and retina disease associations.

The mammalian retina contains high amounts of metals/metalloid-selenium. Their dyshomeostases are associated with certain retinal diseases. We carried out this bioinformatics study to identify the relationships between putative retinal metal/selenium binding proteins, their molecular functions, and biological processes. Identification of putative mouse metal/selenium binding proteins was based on known binding motifs, domains, patterns, and profiles. Annotations were obtained from Uniprot keywords 'metal binding', 'metal ion co-factors', 'selenium proteins'. Protein functions were estimated by associative frequency with key words in UniProt annotations. The raw data of five mouse proteomics PRIDE datasets (available to date) were downloaded and processed with Mascot against the mouse taxa of Uniprot (SwissProt/Trembl) and MaxQuant (version 1.6.10.43) for qualitative and quantitative datasets, respectively. Clinically relevant variants were evaluated using archives and aggregated information in ClinVar. The 438 proteins common to all the retina proteomics datasets were used to identify over-represented Gene Ontology categories. The putative mouse retinal metal/metalloid binding proteins identified are mainly involved in: (1) metabolic processes (enzymes), (2) homeostasis, (3) transport (vesicle mediated, transmembrane, along microtubules), (4) cellular localization, (5) regulation of signalling and exocytosis, (6) organelle organization, (7) (de)phosphorylation, and (8) complex assembly. Twenty-one proteins were identified as involved in response to light stimulus and/or visual system development. An association of metal ion binding proteins rhodopsin, photoreceptor specific nuclear receptor, calcium binding protein 4 with disease-related mutations in inherited retinal conditions was identified, where the mutations affected an area within or in close proximity to the metal binding site or domain. These findings suggest a functional role for the putative metal/metalloid binding site in retinal proteins in certain retinal disorders.

Bioinformatic study of keratinases from the S01 family from Streptomyces microflavus strain Dsm 40593

Aim. Determination and study of the primary structures and domain organization of keratinase precursors (serine proteases from the S01 family) of S. microflavus DSM 40593 using bioinformatic methods. Methods. In the research information presented in databases on the NCBI server was analyzed. Computerized sequence analysis was performed using the BLAST program on the NCBI server. Results. BLAST analysis of the genome sequence of the strain S. microflavus DSM 40593 was carried out using the sequence of the precursor AGK81825 as a query. Thanks to bioinformative analysis, seven genes encoding precursors of serine proteases from the S01 family were found in the genome of the strain S. microflavus DSM 40593. Conclusions. In the genomes of streptomycetes, there may be more than one gene encoding precursors of keratinases from the S01 family. It was established that the amino acid sequences of the enzymatic domains of keratinase precursors from the S01 family are characterized by higher similarity indices than the sequences of foldase domains.

Preparation, Evaluation, and Bioinformatics Study of Hyaluronic Acid-Modified Ginsenoside Rb1 Self-Assembled Nanoparticles for Treating Cardiovascular Diseases.

(1) Objective: To optimize the preparation process of hyaluronic acid-modified ginsenoside Rb1 self-assembled nanoparticles (HA@GRb1@CS NPs), characterize and evaluate them in vitro, and investigate the mechanism of action of HA@GRb1@CS NPs in treating cardiovascular diseases (CVDs) associated with inflammation and oxidative stress. (2) Methods: The optimal preparation process was screened through Plackett-Burman and Box-Behnken designs. Physical characterization of HA@GRb1@CS NPs was conducted using transmission electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and differential scanning calorimetry. Stability experiments, in vitro drug release studies, and lyophilisate selection were performed to evaluate the in vitro performance of HA@GRb1@CS NPs. The anti-inflammatory and antioxidant capabilities of HA@GRb1@CS NPs were assessed using H9c2 and RAW264.7 cells. Additionally, bioinformatics tools were employed to explore the mechanism of action of HA@GRb1@CS NPs in the treatment of CVDs associated with inflammation and oxidative stress. (3) Results: The optimal preparation process for HA@GRb1@CS NPs was achieved with a CS concentration of 2 mg/mL, a TPP concentration of 2.3 mg/mL, and a CS to TPP mass concentration ratio of 1.5:1, resulting in a particle size of 126.4 nm, a zeta potential of 36.8 mV, and a PDI of 0.243. Characterization studies confirmed successful encapsulation of the drug within the carrier, indicating successful preparation of HA@GRb1@CS NPs. In vitro evaluations demonstrated that HA@GRb1@CS NPs exhibited sustained-release effects, leading to reduced MDA (Malondialdehyde) content and increased SOD (Superoxide Dismutase) content in oxidatively damaged H9c2 cells. Furthermore, it showed enhanced DPPH (2,2-Diphenyl-1-picrylhydrazyl) and ABTS+ [2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)] free radical scavenging rates and inhibited the release of inflammatory factors NO (Nitric Oxide) and IL-6 (Interleukin-6) from RAW264.7 cells. (4) Conclusions: The HA@GRb1@CS NPs prepared in this study exhibit favorable properties with stable quality and significant anti-inflammatory and antioxidant capabilities. The mechanisms underlying their therapeutic effects on CVDs may involve targeting STAT3, JUN, EGFR, CASP3, and other pathways regulating cell apoptosis, autophagy, anti-lipid, and arterial sclerosis signaling pathways.

1-L Transcription in Prion Diseases.

Understanding the pathogenesis and mechanisms of prion diseases can significantly expand our knowledge in the field of neurodegenerative diseases. Prion biology is increasingly recognized as being relevant to the pathophysiology of Alzheimer's disease and Parkinson's disease, both of which affect millions of people each year. This bioinformatics study used a theoretical protein-RNA recognition code (1-L transcription) to reveal the post-transcriptional regulation of the prion protein (PrPC). The principle for this method is directly elucidated on PrPC, in which an octa-repeat can be 1-L transcribed into a GGA triplet repeat RNA aptamer known to reduce the misfolding of normal PrPC into abnormal PrPSc. The identified genes/proteins are associated with mitochondria, cancer, COVID-19 and ER-stress, and approximately half are directly or indirectly associated with prion diseases. For example, the octa-repeat supports CD44, and regions of the brain with astrocytic prion accumulation also display high levels of CD44.

Identification of novel likely pathogenic variant in CDH23 causing non-syndromic hearing loss, and a novel variant in OTOGL in an extended Iranian family

BackgroundSensorineural hearing loss (SNHL) is a clinically and genetically heterogeneous group of disorders of the auditory system. SNHL can occur as a symptom in more than 400 syndromes, and mutations in more than 150 genes can lead to SNHL. Mutations in the GJB2 and GJB6 genes are among the most common causes of SNHL worldwide. Mutations in Cadherin 23 (CDH23) can cause Usher syndrome and/or non-syndromic hearing loss (NSHL).Material and methodsIn this study, the Whole Exome Sequencing (WES) was used to detect the cause of hearing loss in a large consanguineous Iranian family with two patients. All family members underwent a thorough Genotype–phenotype correlation assessment and co-segregation analysis to understand the inheritance pattern within the family. The candidate variants were further confirmed by Sanger sequencing. In addition, in silico analysis was performed to predict the functional impact of the variants; the interpretation of the variants was performed in accordance with the American College of Medical Genetics (ACMG) guidelines.ResultsWES results identified two novel variants, a homozygous missense variant in CDH23 (c.2961T > G) and a heterozygous splice site variant in OTOGL that was compatible with the autosomal recessive pattern of inheritance. Bioinformatics studies confirmed the pathogenic effects of novel variants. The c.2961T > G variant was classified as likely pathogenic.ConclusionsThe novel identified variant in the CDH23 was the cause of congenital profound progressive form of HL. Samples were not available from the second family to distinguish which variant is responsible for the molecular pathology of the disease. Further studies and functional examinations are suggested for investigating the role of OTOGL: c. 1863-1G > T in deafness.

The role of IL-17 and Th17 cells in keloid pathogenesis.

Keloids are characterized histologically by excessive fibroblast proliferation and connective tissue deposition, and clinically by scar tissue extending beyond the original site of skin injury. These scars can cause pruritus, pain, physical disfigurement, anxiety, and depression. As a result, keloid patients often have a diminished quality of life with a disproportionate burden on ethnic minorities. Despite advances in understanding keloid pathology, there is no effective Food and Drug Administration (FDA)-approved pharmacotherapy. Recent studies have highlighted the possible pathologic role of T helper (Th)17 cells and interleukin (IL)-17 in keloid formation, as well as their implication in other inflammatory disorders. This systematic review characterizes the role of Th17 cells and IL-17 in keloid pathogenesis, highlighting this pathway as a potential therapeutic target. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search on PubMed, Embase, MEDLINE, and Web of Science databases on June 5, 2024. The search included terms related to Th17 cells, IL-17, and keloids. Thirteen studies met the inclusion criteria, comprising basic science and bioinformatic studies focusing on Th17 cells and IL-17. Key findings include increased Th17 cell infiltration and IL-17 expression in keloids, IL-17's role in amplifying the inflammatory and fibrotic response via the promotion of IL-6 expression, and IL-17's involvement in upregulating fibrotic markers via SDF-1 and HIF-1α pathways. IL-17 also activates the transforming growth factor beta (TGF-β)/Smad pathway in keloid fibroblasts. Th17 cells and IL-17 significantly contribute to the inflammatory and fibrotic processes in keloid pathogenesis. Therefore, targeting the IL-17 pathway offers a potential new therapeutic target to improve keloid patients' outcomes. Future research could further elucidate the role of Th17 cells and IL-17 in keloid pathogenesis and assess the safety and efficacy of targeting this pathway in human studies.

The group of hypermethylated long noncoding RNA genes is associated with different types of metastasis in ovarian cancer

Background: Ovarian tumors are characterized by asymptomatic progression until their late stages, when at the time of diagnosis the patient already has extensive metastatic disease. In addition to lymphogenous and hematogenous metastasis in ovarian cancer, there are peritoneal dissemination and metastasis to the greater omentum with ascites; moreover, peritoneal carcinomatosis is the predominant route of metastasizing of ovarian cancer. Epigenetic factors, such as gene methylation, regulatory microRNAs and long non-coding RNAs (lncRNAs), contribute to progression of this cancer. Our previous bioinformatic and experimental studies have identified 13 genes of lncRNAs (GAS5, HOTAIR, LINC00472, LINC00886, MAFG-DT, PLUT/PDX1-AS1, SNHG1, SNHG6, SNHG12, SNHG17, TINCR, TP53TG1, TUG1) hypermethylated in the ovarian neoplasms. Aim: To evaluate the clinical significance of methylation levels of 13 lncRNA genes (GAS5, HOTAIR, LINC00472, LINC00886, MAFG-DT, PLUT/PDX1-AS1, SNHG1, SNHG6, SNHG12, SNHG17, TINCR, TP53TG1, TUG1) associated with various types of ovarian cancer metastasis, including lymphogenous, peritoneal, omental, and distant metastases. Methods: The methylation levels of lncRNA genes GAS5, HOTAIR, LINC00472, LINC00886, MAFG-DT, PLUT/PDX1-AS1, SNHG1, SNHG6, SNHG12, SNHG17, TINCR, TP53TG1, TUG1 were analyzed by quantitative real-time methylation-specific polymerase chain reaction. We tested 122 duplicate samples of ovarian neoplasms, including 104 malignancies and 18 borderline tumors, as well as 45 peritoneal macro metastases, collected in the N.N. Blokhin National Medical Research Center of Oncology in 2020 to 2023. The study included 21 samples of primary tumor from patients with lymphogenous metastases, 45 samples from patients with peritoneal dissemination, 61 from those with omental metastases, 49 from patients with ascites, and 9 with distant metastases. Results: The tumor samples from the patients with lymphatic nodes metastases showed a significant increase in the methylation level of two lncRNA genes: SNHG6 (p = 0.044) and SNHG12 (p = 0.006). Peritoneal dissemination was associated with hypermethylation of four lncRNA genes: GAS5, HOTAIR, LINC00472 (p 0.05), and most significantly TINCR (p = 0.001). GAS5, HOTAIR, LINC00886 (p 0.05) and most significantly LINC00472 (p 0.001) hypermethylation was typical for omental metastasis, and that of LINC00472 and LINC00886, with ascites (p 0.05). Peritoneal macroscopic metastases demonstrated increased methylation of MAFG-DT (p 0.001) and TP53TG1 (p 0.001) and desmethylation of LINC00886 (p = 0.003) and SNHG12 (p = 0.002), compared to their primary tumors. Conclusion: We performed the analysis of clinical significance of 13 hypermethylated lncRNA genes in ovarian cancer and were the first to show that 10 genes (GAS5, HOTAIR, LINC00472, LINC00886, MAFG-DT, SNHG6, SNHG12, TINCR, TP53TG1, TUG1) were associated with various types of ovarian tumor metastasis. Also, we were able to determine certain panels of lncRNA, which, if demonstrate abnormal methylation, were specific for lymphogenous and peritoneal metastasis of ovarian tumors.

Isospongian Diterpenoids from the Leaves of Amomum tsao-ko Promote GLP-1 Secretion via Ca2+/CaMKII and PKA Pathways and Inhibit DPP-4 Enzyme.

Three uncommon isospongian diterpenoids including a new one, 3-epi-kravanhin A (2), were isolated from the leaves of Amomum tsao-ko. Compounds 2 and 3 dose-dependently promoted GLP-1 secretion on STC-1 cells with promotion ratios of 109.7% and 186.1% (60 μM). Mechanism study demonstrated that the GLP-1 stimulative effects of 2 and 3 were closely related with Ca2+/CaMKII and PKA pathways, but irrelevant to GPBAR1 and GPR119 receptors. Moreover, compound 1 showed moderate DPP-4 inhibitory activity with an IC50 value of 311.0 μM. Molecular docking verified the binding affinity of 1 with DPP-4 by hydrogen bonds between the γ-lactone carbonyl (C-15) and Arg61 residue. Bioinformatics study indicated that compound 1 exerted antidiabetic effects by improving inflammation, oxidative stress and insulin resistance. This study first disclosed the presence of isospongian diterpenoids in A. tsao-ko, which showed antidiabetic potency by promoting GLP-1 secretion and inhibiting DPP-4 activity.

Minning of Immuno-mitotic and GABAergic genes as potential biomarkers of glioblastoma: An integrated transcriptomic analysis

Glioblastoma (GBM) is a highly lethal Central Nervous System (CNS) tumor prevalent in both adults and children, exhibiting elevated rates of mortality and morbidity. Due to the heterogenous nature of GBM, coupled with its nonspecific symptoms underscore the imperative for innovative biomarkers to enhance prognosis and the development of efficacious therapeutic interventions. This bioinformatics study seeks to elucidate the culprit genes, both up-regulated and down-regulated, within the context of their functional relevance, through a comparative analysis of gene expression profiles in GBM and normal brain tissues. Deregulated genes were identified from two Gene Expression Omnibus (GEO) datasets, employing the GEO2R tool to analyze expression data from normal and GBM tissues. Subsequently, differences in expression of genes (DEGs) through functional enrichment analysis were conducted by DAVID to discern their functional implications. Further, Protein-Protein Interaction (PPI) networks were constructed to identify hub genes among the selected up-regulated and down-regulated genes, employing various bioinformatics tools. The impact of the selected hub genes on patient overall survival was investigated using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Notably, the up-regulated hub genes KIF2C and TTK exhibited significant correlations with overall survival, implicating their potential as immuno-mitotic biomarkers. Conversely, GAD2, the sole down-regulated hub gene, emerged as a promising molecular target for GBM, given its association with GABAergic signaling and amino acid metabolism. Consequently, these findings suggest that KIF2C and TTK may serve as immune-mitotic biomarkers, while GAD2 could be explored as a molecular target for GBM therapy. Nevertheless, additional research is essential to unravel the precise mechanistic underpinnings of GBM.

Molecular insights into the hedgehog signaling pathway correlated non-coding RNAs in acute lymphoblastic leukemia, a bioinformatics study.

Acute lymphoblastic leukemia (ALL) is a common hematologic cancer with unique incidence and prognosis patterns in people of all ages. Recent molecular biology advances have illuminated ALL's complex molecular pathways, notably the Hedgehog (Hh) signaling system and non-coding RNAs (ncRNAs). This work aimed to unravel the molecular complexities of the link between Hh signaling and ALL by concentrating on long non-coding RNAs (lncRNAs) and their interactions with significant Hh pathway genes. To analyze differentially expressed lncRNAs and genes in ALL, microarray data from the Gene Expression Omnibus (GEO) was reanalyzed using a systems biology approach. Hh signaling pathway-related genes were identified and their relationship with differentially expressed long non-coding RNAs (DElncRNAs) was analyzed using Pearson's correlation analysis. A regulatory network was built by identifying miRNAs that target Hh signaling pathway-related mRNAs. 193 DEGs and 226 DElncRNAs were found between ALL and normal bone marrow samples. Notably, DEGs associated with the Hh signaling pathway were correlated to 26 DElncRNAs. Later studies showed interesting links between DElncRNAs and biological processes and pathways, including drug resistance, immune system control, and carcinogenic characteristics. DEGs associated with the Hh signaling pathway have miRNAs in common with miRNAs already known to be involved in ALL, including miR-155-5p, and miR-211, highlighting the complexity of the regulatory landscape in this disease. The complex connections between Hh signaling, lncRNAs, and miRNAs in ALL have been unveiled in this study, indicating that DElncRNAs linked to Hh signaling pathway genes could potentially serve as therapeutic targets and diagnostic biomarkers for ALL.

Early alterations of thalami- and hippocampi-cortical functional connectivity as biomarkers of seizures after traumatic brain injury

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx, project 3) is a prospective multicenter clinical observational study to identify early biomarkers of epileptogenesis after moderate-to-severe traumatic brain injury (TBI). We used a seed-based approach applied to acute (i.e., ≤14 days) fMRI imaging data, directly testing the hypothesis that the presence of seizures up to two years following brain trauma is associated with functional changes within hippocampi and thalami-cortical networks. Additionally, we hypothesized that the network connectivity involving thalami and hippocampi circuits underlying early and late-onset seizures would differ. Approximately 30% of the initial dataset was deemed unusable due to MRI issues. Approximately 50% of the enrolled sample was lost to a 2-year follow-up. After preprocessing the fMRI data, approximately 40% of the follow-up sample had to be excluded from the analysis due to excessive in-scanner movements, as assessed by state-of-the-art quality control protocols. Only 37 patients provided data that was suitable for the seed-based analysis. Despite these challenges, the remaining, high-quality data returned noteworthy findings. We identified specific hippocampi and thalami biomarkers associated with both early and late seizures following TBI (p < .05, FWE-corrected at the cluster level). The predictive capability for the development of late seizures after TBI, when adding fMRI data to demographic and clinical data, provided 88% accuracy — an additional 8% improvement compared to using demographic and clinical data alone. Our findings highlight the potential of fMRI for uncovering, in hippocampal and thalamic cortical networks, biomarkers of early and late seizures following TBI. However, they also highlight the important challenges that need to be overcome in order for fMRI to become an effective biomarker and prognostic tool in the intensive care context.

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases

BackgroundNeurodegenerative diseases are one of the main causes of physical or behavioral complications which is considered as one of the main health concerns of the elderly population. However, treatment options for neurological diseases are still limited. Recent advances in bioinformatics studies provide an opportunity to understand the mechanisms of these diseases to identify therapeutic targets. In this research, the mRNAs involved in Alzheimer's, Multiple sclerosis, Parkinson's, and Huntington's neurological diseases, which are regulated by upstream factors, have been investigated. Only 2% of all transcripts of a gene are translated into protein and the rest are converted into miRNAs, lncRNAs or circRNAs. miRNAs have crucial role in regulating mRNAs and in a similar sequence lncRNAs or circRNAs are crucial in regulating miRNAs, which disrupts gene expression. ResultsTo discover above relations in neurodegenerative disease, miRNA-mRNA and lncRNA-miRNA bipartite networks were constructed and then were integrated to construct lncRNA-miRNA-mRNA tripartite networks. Constructing these networks leads to understand deeply about the structure of this mechanism and introducing new biomarkers for the studied diseases. In the next step, enrichment analysis was performed to recognize the genes involved in crucial pathways. Finally, the obtained biomarkers were investigated over the previous studies to prove the accuracy of proposed method. ConclusionsIn conclusion, for all four diseases, several numbers of mRNAs, miRNAs and lncRNAs were identified, which are introduced as biomarkers extracted by this study.

Bioinformatics study of bortezomib resistance-related proteins and signaling pathways in mantle cell lymphoma.

The bortezomib (BTZ) resistance mechanisms in mantle cell lymphoma (MCL) are complex, involving various genes and signaling pathways. This study used bioinformatical tools to identify and analyze differentially expressed genes (DEGs) associated with BTZ resistance. Gene chip datasets containing MCL BTZ-resistant and normal control cohorts (GSE20915 and GSE51371) were selected from the Gene Expression Omnibus (GEO) database. GEO2R was used to identify the upregulated DEGs in the microarray datasets, using a significance threshold of P<0.05. Subsequently, these DEGs were subjected to a Gene Ontology (GO) functional analysis, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and a protein-protein interaction (PPI) network assessment. Additionally, 40 MCL patients who underwent second-line BTZ treatment were included in this study. The patients were categorized into resistant and sensitive groups based on treatment response. The enzyme-linked immunosorbent assay (ELISA) technique was employed to evaluate the expression levels of specific DEGs in the serum of the patients in both groups. In the GSE20915 dataset, 144 upregulated genes were identified as DEGs. Similarly, in the GSE51371 dataset, 219 upregulated genes were identified as DEGs. By employing a Venn diagram to compare the upregulated DEGs from both datasets, we identified 11 DEGs linked to BTZ resistance in MCL. The enrichment analysis of the KEGG signaling pathways revealed that the DEGs were predominantly enriched in key biological processes (BP), including the cell cycle, cellular senescence, the p53 signaling pathway, the interleukin 17 (IL-17) signaling pathway, and the nuclear factor kappa-B (NF-κB) signaling pathway. A distinct cluster was revealed by creating a PPI network and performing a module analysis of a set of typical DEGs. This cluster comprised four candidate genes; that is, cyclin-dependent kinase inhibitor 1A (CDKN1A), CDKN1C, midkine (MDK), and TNF alpha induced protein 3 (TNFAIP3). Among these genes, MDK was found to be the key gene. The serum concentration of MDK in the resistant group [1,539 (1,212, 2,023) ng/L] was significantly higher than that in the sensitive group [1,175 (786, 1,502) ng/L] (P<0.05). Identifying the key gene MDK and its associated signaling pathways extends our understanding of the molecular processes that underlie resistance to BTZ in MCL. This discovery establishes a theoretical framework for future investigations of targeted therapy in clinical settings.

Bioinformatics and computational studies of chabamide F and chabamide G for breast cancer and their probable mechanisms of action

Globally, the prevalence of breast cancer (BC) is increasing at an alarming level, despite early detection and technological improvements. Alkaloids are diverse chemical groups, and many within this class have been reported as potential anticancer compounds. Chabamide F (F) and chabamide G (G) are two dimeric amide alkaloids found in a traditional medicinal plant, Piper chaba, and possess significant cytotoxic effects. However, their scientific rationalization in BC remains unknown. Here, we aimed to investigate their potential and molecular mechanisms for BC through in silico approaches. From network pharmacology, we identified 64 BC-related genes as targets. GO and KEGG studies showed that they were involved in various biological processes and mostly expressed in BC-related pathways such as RAS, PI3K-AKT, estrogen, MAPK, and FoxO pathways. However, PPI analysis revealed SRC and AKT1 as hub genes, which play key roles in BC tumorigenesis and metastasis. Molecular docking revealed the strong binding affinity of F (− 10.7 kcal/mol) and G (− 9.4 and − 11.7 kcal/mol) for SRC and AKT1, respectively, as well as the acquisition of vital residues to inhibit them. Their long-term stability was evaluated using 200 ns molecular dynamics simulation. The RMSD, RMSF, Rg, and SASA analyses showed that the G-SRC and G-AKT1 complexes were excellently stable compared to the control, dasatinib, and capivasertib, respectively. Additionally, the PCA and DCCM analyses revealed a significant reduction in the residual correlation and motions. By contrast, the stability of the F-SRC complex was greater than that of the control, whereas it was moderately stable in complex with AKT1. The MMPBSA analysis demonstrated higher binding energies for both compounds than the controls. In particular, the binding energy of G for SRC and AKT1 was − 120.671 ± 16.997 and − 130.437 ± 19.111 kJ/mol, respectively, which was approximately twice as high as the control molecules. Van der Waal and polar solvation energies significantly contributed to this energy. Furthermore, both of them exhibited significant interactions with the binding site residues of both proteins. In summary, this study indicates that these two molecules could be a potential ATP-competitive inhibitor of SRC and an allosteric inhibitor of AKT1.

Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis

Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis.

Anticancer activity of naringenin on human liposarcoma: An experimental and bioinformatic study

Naringenin (NAR) has shown potential as a cancer treatment, reducing cell proliferation and invasion in soft tissue sarcomas like liposarcoma (LPS). This study investigates NAR's role and molecular mechanism. Bioinformatic analysis was performed to assess the expression level of genes in LPS based on the GEO dataset. The heat map and PPI of genes were also analyzed. MTT, wound healing, DAPI staining, and flow cytometry evaluated the cell viability, migration, and apoptosis. Besides, real-time PCR was used to measure the NAR's impact on the expression levels of EMT, apoptosis, inflammation, and metastasis-related genes. The results showed that NAR reduces cell viability, proliferation, and migration but induces apoptosis in LPS cells. RT-PCR results revealed that NAR is capable of regulating the expression level of the apoptosis, EMT, migration, and Inflammation-related genes. This study demonstrated that NAR may play a crucial role in reducing cell viability, inducing apoptosis, and attenuating migration in Sw872 LPS cells. Consequently, NAR might be a promising and efficient factor in the treatment of LPS.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Construction of CeRNA Regulatory Network Based on WGCNA to Reveal Potential Prognostic Biomarkers for AML Cancer

Background: Acute myeloid leukemia (AML) is a type of blood cancer with diverse genetic pathogenesis. The identification of potential molecular biomarkers could improve the AML diagnosis and targeted therapy. Recently, competing endogenous RNAs (ceRNAs) became an active area in cancer research to determine molecular mechanisms underlying tumor development. Objectives: The aim of the present study was to investigate the key molecular biomarkers that are closely related to AML through bioinformatics analysis. Methods: In this research, the RNA-seq data of 151 AML patients and 151 corresponding health samples were retrieved from The Cancer Genome Atlas (TCGA) database and GTEx Portal (genotype-tissue expression), respectively. After that we screened the differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs by “limma” package in R to construct the co-expression network ceRNA (miRNA-lncRNA-mRNA) with weighted gene co-expression network analysis (WGCNA) package in R and Cytoscape (version 3.7.2) software, respectively. Then the relevant modules were identified and functional enrichment analysis was used to uncover the modules that are biologically related to AML cancer. Results: Based on our bioinformatics studies, we constructed a significant module which contain 333 genes. Among them, five up-regulated miRNAs with the highest GS (gene significant), include hsa-miR-374B, hsa-miR-553, hsa-miR-3679, hsa-miR-548L, hsa-miR-597 and five down-regulated miRNAs with the lowest GS including hsa-miR-3934, hsa-miR-4746, hsa-miR-466, hsa-miR-6722, hsa-miR-4490. For protein-coding genes (PCGs), the top-five up-regulated PCGs with the highest GS were AMIGO3, H2AC17, SPACA5, GPR21, and OR13C3. The top-five down-regulated PCGs with the lowest GS were XBP1, TOMM6, TREX1, TNFRSF6B, and BOLA2. Among the lncRNAs, the five up-regulated lncRNAs with the highest level of GS were GREP1, ZBTB20-AS2, LINC01596, LINC00345, and RMRP. The five down-regulated lncRNAs with the lowest GS were LINC01609, LINC01707, LINC02270, LINC02309, and LINC02243. These lncRNAs can serve as potential biomarkers to distinguish between AML and normal samples. Conclusions: Finally, considering the role of ceRNA network in various biological processes, examining the role of ceRNAs in AML may provide a deeper insight into molecular mechanisms underlying the pathogenesis of cancer. This understanding may propose potential biomarkers for diagnosis and therapeutic interventions.

Elucidating the pan-oncologic landscape of S100A9: prognostic and therapeutic corollaries from an integrative bioinformatics and Mendelian randomization analysis

The calcium-binding protein S100A9 has emerged as a pivotal biomolecular actor in oncology, implicated in numerous malignancies. This comprehensive bioinformatics study transcends traditional boundaries, investigating the prognostic and therapeutic potential of S100A9 across diverse neoplastic entities. Leveraging a wide array of bioinformatics tools and publicly available cancer genomics databases, such as TCGA, we systematically examined the S100A9 gene. Our approach included differential expression analysis, mutational burden assessment, protein interaction networks, and survival analysis. This robust computational framework provided a high-resolution view of S100A9’s role in cancer biology. The study meticulously explored S100A9’s oncogenic facets, incorporating comprehensive analyses of its relationship with prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), DNA methylation, and immune cell infiltration across various tumor types. This study presents a panoramic view of S100A9 expression across a spectrum of human cancers, revealing a heterogeneous expression landscape. Elevated S100A9 expression was detected in malignancies such as BLCA (Bladder Urothelial Carcinoma), CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma), COAD (Colon adenocarcinoma), ESCA (Esophageal carcinoma), and GBM (Glioblastoma multiforme), while reduced expression was noted in BRCA (Breast invasive carcinoma), HNSC (Head and Neck squamous cell carcinoma), and KICH (Kidney Chromophobe). This disparate expression pattern suggests that S100A9’s role in cancer biology is multifaceted and context-dependent. Prognostically, S100A9 expression correlates variably with patient outcomes across different cancer types. Furthermore, its expression is intricately associated with TMB and MSI in nine cancer types. Detailed examination of six selected tumors—BRCA, CESC, KIRC (Kidney renal clear cell carcinoma), LUSC (Lung squamous cell carcinoma), SKCM (Skin Cutaneous Melanoma); STAD (Stomach adenocarcinoma)—revealed a negative correlation of S100A9 expression with the infiltration of most immune cells, but a positive correlation with neutrophils, M1 macrophages, and activated NK cells, highlighting the complex interplay between S100A9 and the tumor immune environment. This bioinformatics synthesis posits S100A9 as a significant player in cancer progression, offering valuable prognostic insights. The data underscore the utility of S100A9 as a prognostic biomarker and its potential as a therapeutic target. The therapeutic implications are profound, suggesting that modulation of S100A9 activity could significantly impact cancer management strategies.