Integrative Bioinformatics Approach Research Articles

Rhein suppresses esophageal cancer development by regulating cell cycle through DNMT3B gene.

The mechanism by which DNMT3B facilitates esophageal cancer (ESCA) progression is currently unknown, despite its association with adverse prognoses in several cancer types. To investigate the potential therapeutic effects of the Chinese herbal medicine rhubarb on esophageal cancer (ESCA), we adopted an integrated bioinformatics approach. Gene Set Enrichment Analysis (GSEA) was first utilized to screen active anti-ESCA components in rhubarb. We then employed Weighted Gene Co-expression Network Analysis (WGCNA) to identify key molecular modules and targets related to the active components and ESCA pathogenesis. This system-level strategy integrating multi-omics data provides a powerful means to unravel the molecular mechanisms underlying the anticancer activities of natural products, like rhubarb. To investigate module gene functional enrichment, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. In addition, we evaluated the predictive impact of DNMT3B expression on ESCA patients utilizing the Kaplan-Meier method. Finally, we conducted experiments on cell proliferation and the cell cycle to explore the biological roles of DNMT3B. In this study, we identified Rhein as the main active ingredient of rhubarb that exhibited significant anti-ESCA activity. Rhein markedly suppressed ESCA cell proliferation. Utilizing Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we determined that the blue module was associated with Rhein target genes and the cell cycle. Additionally, DNMT3B was identified as a Rhein target gene. Analysis of The Cancer Genome Atlas (TCGA) database revealed that higher DNMT3B levels were associated with poor prognosis in ESCA patients. Furthermore, Rhein partially reversed the overexpression of DNMT3B to inhibit ESCA cell proliferation. In vitro studies demonstrated that Rhein and DNMT3B inhibition disrupted the S phase of the cell cycle and affected the production of cell cycle-related proteins. In this study, we found that Rhein exerts its anti-proliferative effects in ESCA cells by targeting DNMT3B and regulating the cell cycle.

Altering biomolecular condensates as a potential mechanism that mediates cannabidiol effect on glioblastoma.

Glioblastoma (GBM) is an extremely aggressive primary brain tumor with poor prognosis, short survival time post-diagnosis and high recurrence. Currently, no cure for GBM exists. The identification of an effective therapeutic modality for GBM remains a high priority amongst medical professionals and researches. In recent studies, inhalant cannabidiol (CBD) has demonstrated promise in effectively inhibiting GBM tumor growth. However, exactly how CBD treatment affects the physiology of these tumor cells remains unclear. Stress granules (SG) (a sub-class of biomolecular condensates (BMC)) are dynamic, membrane-less intracellular microstructures which contain proteins and nucleic acids. The formation and signaling of SGs and BMCs plays a significant role in regulating malignancies. This study investigates whether inhaled CBD may play an intervening role towards SGs in GBM tumor cells. Integrated bioinformatics approaches were preformed to gain further insights. This includes use of Immunohistochemistry and flow cytometry to measure SGs, as well as expression and phosphorylation of eukaryotic initiation factor-2α (eIF2α). The findings of this study reveal that CBD receptors (and co-regulated genes) have the potential to play an important biological role in the formation of BMCs within GBM. In this experiment, CBD treatment significantly increased the volume of TIAR-1. This increase directly correlated with elevation in both eIF2α expression and p-eIF2α in CBD treated tissues in comparison to the placebo group (p < 0.05). These results suggest that inhalant CBD significantly up-regulated SGs in GBM, and thus support a theory of targeting BMCs as a potential therapeutic substrate for treating GBM.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Abstract LB370: Exploring SLC35B family genes in colorectal carcinoma: Bioinformatics and functional insights into prognosis and therapy

Abstract Colorectal carcinoma (CRC) poses a significant health challenge, with recent therapeutic advances yet to substantially improve long-term patient survival. Central to this study is the SLC35B gene family (SLC35B1-4), which encodes nucleotide sugar transporters essential to cellular membranes. Emerging research highlights a link between these transporters' dysfunction and the development and progression of tumors, yet the specific role of the SLC35B family in CRC remains unclear. Our investigation delves into the function of SLC35B genes in CRC, utilizing an integrated bioinformatics approach with databases like GENT2, TCGA, UALCAN, cBioportal, TIMER, and Kaplan-Meier plotter. Our analysis reveals a notable upregulation of SLC35B1/2/4 mRNA in CRC tissues compared to adjacent non-tumor tissues, with a concurrent downregulation of SLC35B3. Promoter methylation analysis shows increased methylation for SLC35B1/3/4, while SLC35B2 demonstrates decreased methylation. According to GEO and TCGA data, CRC patients exhibiting high SLC35B4 expression face lower survival rates. Multivariate Cox regression analysis confirms high SLC35B4 expression as an independent risk factor. Further, SLC35B4 expression correlates with increased infiltration of immune cells, particularly macrophages, and aligns positively with immune checkpoints such as CD274 and HAVCR2. In vitro assays link SLC35B4 to enhanced cancer cell proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT). In vivo, SLC35B4 knockdown leads to reduced tumor growth in mouse xenograft models. RNA sequencing identifies 241 genes differentially expressed in SLC35B4 knockdown compared to control cells, with Gene Set Enrichment Analysis (GSEA) associating SLC35B4 with the PI3K-Akt signaling pathway. In summary, our research sheds light on the SLC35B gene family's role in CRC, especially SLC35B4, highlighting its potential as both a prognostic marker and a therapeutic target in CRC, particularly through its involvement in the PI3K-Akt signaling pathway. Citation Format: Yu-Jia Chang, Chang-Chun Yang, Cheng-Chin Lee, Chien-Yu Huang. Exploring SLC35B family genes in colorectal carcinoma: Bioinformatics and functional insights into prognosis and therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB370.

In silico investigation of potential COVID-19-associated microRNA signatures

Purpose: The global pandemic COVID-19, caused by the coronavirus SARS-CoV-2, is persistent despite the increasing vaccination rates, with new cases being reported per week. MicroRNAs, that is, non-coding RNA species that regulate gene expression at the post-transcriptional level, play a pivotal role in the SARS-CoV-2 life cycle, pathophysiology and host’s anticoronaviral responses. The objective of this study was the in silico discovery of functionally associated miRNAs that likely co-regulate COVID-19-related genes &#x0D; Materials and Methods: In the present study, an integrative bioinformatics approach was employed, including database searching, gene set enrichment analysis, network-based and microRNA target prediction methods, towards the discovery of epigenetic determinants of COVID-19.&#x0D; Results: An intricate microRNA-target gene network was constructed, and a set of 8 highly interacting microRNAs, that potentially co-target and co-regulate key COVID-19-related genes, was detected. These miRNAs and their corresponding genes are likely involved in the host’s response to SARS-CoV-2 infection.&#x0D; Conclusion: The 8 functionally associated miRNAs could constitute a signature for COVID-19 diagnosis.

Retracted: Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach.

[This retracts the article DOI: 10.1155/2022/1776082.].

Molecular analysis to identify novel potential biomarkers as drug targets in colorectal cancer therapy: an integrated bioinformatics analysis

ABSTRACT Colorectal cancer (CRC) is a heterogeneous disease that requires new diagnostic and prognostic markers. Integrated bioinformatics approach to identify novel therapeutic targets associated with CRC. Using GEO2R identified DEGs in CRC, and Funrich software facilitated the visualization of DEGs through Venn diagrams. From a total of 114 enhanced DEGs, potential hub genes were further filtered based on their nodal strength and edges using STRING database. To gain insights into the functional roles of these hub genes, gene ontology and pathway enrichment were conducted thorough g: profiler web server. Subsequently, overall survival plots from GEPIA and oncogenic predictive functions like mRNA expressions for stages and nodal metastasis were employed to identify hub genes in CRC patient samples. Additionally, the cBioPortal and HPA databases also revealed genetic alterations and expression levels in these hub genes in CRC patients, further supporting their involvement in colorectal cancer. Gene expression by RT-PCR shows upregulation of hub genes in HT-29 cells. Finally, our integrated bioinformatic analysis revealed that ABCE1, AURKA, HSPD1, PHKA1, CDK4, and YWHAE as hub genes with potential oncogenic roles in CRC. These genes hold promise as diagnostic and prognostic markers for colorectal tumorigenesis, providing insights into targeted therapies for improved patient outcomes.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the “limma” package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein–protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Single-cell RNA sequencing reveals peripheral blood leukocyte responses to spinal cord injury in mice with humanised immune systems

Next-generation humanised mouse models and single-cell RNA sequencing (scRNAseq) approaches enable in-depth studies into human immune cell biology. Here we used NSG-SGM3 mice engrafted with human umbilical cord haematopoietic stem cells to investigate how human immune cells respond to and/or are changed by traumatic spinal cord injury (SCI). We hypothesised that the use of such mice could help advance our understanding of spinal cord injury-induced immune depression syndrome (SCI-IDS), and also how human leukocytes change as they migrate from the circulation into the lesion site. Our scRNAseq experiments, supplemented by flow cytometry, demonstrate the existence of up to 11 human immune cell (sub-) types and/or states across the blood and injured spinal cord (7 days post-SCI) of humanised NSG-SGM3 mice. Further comparisons of human immune cell transcriptomes between naïve, sham-operated and SCI mice identified a total of 579 differentially expressed genes, 190 of which were ‘SCI-specific’ (that is, genes regulated only in response to SCI but not sham surgery). Gene ontology analysis showed a prominent downregulation of immune cell function under SCI conditions, including for T cell receptor signalling and antigen presentation, confirming the presence of SCI-IDS and the transcriptional signature of human leukocytes in association with this phenomenon. We also highlight the activating influence of the local spinal cord lesion microenvironment by comparing the transcriptomes of circulating versus infiltrated human immune cells; those isolated from the lesion site were enriched for genes relating to both immune cell activity and function (e.g., oxidative phosphorylation, T cell proliferation and antigen presentation). We lastly applied an integrated bioinformatics approach to determine where immune responses in humanised NSG-SGM3 mice appear congruent to the native responses of human SCI patients, and where they diverge. Collectively, our study provides a valuable resource and methodological framework for the use of these mice in translational research.

ITGAM-mediated macrophages contribute to basement membrane damage in diabetic nephropathy and atherosclerosis

BackgroundDiabetic nephropathy (DN) and atherosclerosis (AS) are prevalent and severe complications associated with diabetes, exhibiting lesions in the basement membrane, an essential component found within the glomerulus, tubules, and arteries. These lesions contribute significantly to the progression of both diseases, however, the precise underlying mechanisms, as well as any potential shared pathogenic processes between them, remain elusive.MethodsOur study analyzed transcriptomic profiles from DN and AS patients, sourced from the Gene Expression Omnibus database. A combination of integrated bioinformatics approaches and machine learning models were deployed to identify crucial genes connected to basement membrane lesions in both conditions. The role of integrin subunit alpha M (ITGAM) was further explored using immune infiltration analysis and genetic correlation studies. Single-cell sequencing analysis was employed to delineate the expression of ITGAM across different cell types within DN and AS tissues.ResultsOur analyses identified ITGAM as a key gene involved in basement membrane alterations and revealed its primary expression within macrophages in both DN and AS. ITGAM was significantly correlated with tissue immune infiltration within these diseases. Furthermore, the expression of genes encoding core components of the basement membrane was influenced by the expression level of ITGAM.ConclusionOur findings suggest that macrophages may contribute to basement membrane lesions in DN and AS through the action of ITGAM. Moreover, therapeutic strategies that target ITGAM may offer potential avenues to mitigate basement membrane lesions in these two diabetes-related complications.

Integrated bioinformatics approaches and expression assays identified new markers in pituitary adenomas

Pituitary adenomas (PA) include about one third of primary central nervous tumors in adolescent and young adult. Despite extensive research, the underlying mechanism of PA tumorigenesis is still unknown. In the present study, through bioinformatics analysis of a PA-related dataset downloaded from GEO database, we attempted to identify pair(s) of lncRNA/target mRNA whose expression changes may be involved in the tumorigenesis of PAs. For this end, we evaluated expression of a set of bioinformatically obtained genes in 46 PA tissues against adjacent non-tumor pituitary tissues. The bioinformatics step led to selection of four genes for validation through expression assays. Expression levels of HIF1A and MAPK1 were increased in NFPA tissues (P < 0.0001 and =0.0042, respectively). Expression level of BANCR was significantly decreased in tumor tissues (P < 0.0001). However, expression of STAT3 was not meaningfully different between the two tissue types (P = 0.56). Since there was no significant correlation between MAPK1 and BANCR expressions in either tumor or adjacent normal tissues, the regulatory effect of BANCR on MAPK1 was not confirmed. In conclusion, this study offers information about deregulation of bioinformatically identified genes in PA tumors and indicates that further studies in this field is needed to understand the involved molecular mechanisms.

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process. In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis. Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues. The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes. Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

Autophagic Regulation of Adipogenesis Through TP53INP2: Insights from In Silico and In Vitro Analysis.

Obesity is an epidemic disease associated with multimorbidity resulting in higher mortality risk. The imbalance between energy storage and expenditure is the prime factor in the prognosis of the disease. Specifically, excessive lipid storage through adipogenesis leads to obesity. Adipogenesis is the process that converts preadipocytes into mature adipocytes by regulating major transcription factors like PPARγ and C/EBPα, contributes to lipid storage in adipose tissue. On the contrary, autophagy is a self-degradative process that maintains homeostasis in adipose tissue by regulating adipogenesis and lipolysis. TP53INP2 is a key player that regulates the autophagy process, and it negatively regulates adipogenesis and lipid storage. The gene expression profile GSE93637 was retrieved from the GEO database and analyzed using an integrated bioinformatics approach. The differentially expressed genes (DEGs) were analyzed using R-Bioconductor for TP53INP2 knockdown microarray dataset of 3T3L1 cells, and the DEGs were analyzed for the functional enrichment analysis. Further, the genes involved in the potential biological and molecular functions were evaluated for pathway enrichment analysis by KEGG (Kyoto Encyclopedia of Genes and Genomes). A total of 726 DEGs were found including 391 upregulated and 335 downregulated genes. Further, the functional and pathway enrichment analysis was employed to identify the highly interacting genes, and we identified a total of 56 genes that are highly interacting through a protein-protein interaction network. The DEGs mainly regulate the Peroxisome proliferator-activated receptor (PPAR) signaling pathway, lipolysis, and autophagy. Further, we investigated the associated Hub genes for enriched pathway genes and found the involvement of two autophagic genes ATG7 and sequestosome 1 (p62). In addition, in vitro studies of qRT-PCR (Quantitative real-time polymerase chain reaction) and Western blot analysis revealed that increased autophagy resulted in reduced lipid storage through down-regulation of the adipogenic gene. Moreover, increased expression of autophagic gene TP53INP2 and ATG7 facilitates the down-regulation of p62 and PPARγ gene resulting in lipolysis in mature adipocytes through autophagy. There is no specific treatment to reduce obesity other than a caloric diet and exercise. Hence, this study provides sufficient evidence to conclude that TP53INP2 negatively regulates adipogenesis and increases the degradation of lipids in mature adipocytes which is crucial for reducing obesity. Therefore, it is plausible to consider TP53INP2 as a promising therapeutic target for managing adipogenesis and obesity. However, further studies are necessary to validate their functional and molecular pathway analysis in the regulation of adipogenesis and obesity.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach.

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.

Identification of differentially expressed genes, signaling pathways and immune infiltration in postmenopausal osteoporosis by integrated bioinformatics analysis

BackgroundPostmenopausal osteoporosis is a systemic metabolic disorder typified by an imbalance in bone turnover, where bone resorption supersedes bone formation. This imbalance primarily arises from a decline in bone mass induced by estrogen deficiency, and an elevated risk of fractures resulting from degradation of bone microstructure. Despite recognizing these changes, the precise causative factors and potential molecular pathways remain elusive. In this study, we aimed to identify differentially expressed genes (DEGs), associated pathways, and the role of immune infiltration in osteoporosis, leveraging an integrated bioinformatics approach to shed light on potential underlying molecular mechanisms. MethodsWe retrieved the expression profiles of GSE230665 from the Gene Expression Omnibus database, comprising 15 femur samples, including 12 postmenopausal osteoporosis samples and 3 normal controls. From the aggregated microarray datasets, we derived differentially expressed genes (DEGs) for further bioinformatics analysis. We used WGCNA, analyzed DEGs, PPI, and conducted GO analysis to identify pivotal genes. We then used the CIBERSORT method to explore the degree of immune cell infiltration within femur specimens affected by postmenopausal osteoporosis. To probe into the relationship between pivotal genes and infiltrating immune cells, we conducted correlation analysis. ResultsWe identified a total of 12,204 DEGs. Among these, 12,157 were up-regulated, and 47 were down-regulated. GO and KEGG pathway analyses indicated that these DEGs predominantly targeted cellular protein localization activity and associated signaling pathways. The protein-protein interaction network highlighted four central hub-genes: RPL31, RPL34, EEF1G, and BPTF. Principal component analysis indicated a positive correlation between the expression of these genes and resting NK cells (as per CIBERSORT). In contrast, the expression of RPL31, RPL34, and EEF1G showed a negative correlation with T cells (gamma delta per CIBERSORT). ConclusionsImmune infiltration plays a role in the development of osteoporosis.

Retracted: Implications of Human Antimicrobial Peptide Defensin Beta-1 in Clinical Oral Squamous Cell Carcinoma Patients via an Integrated Bioinformatics Approach

Retracted: Implications of Human Antimicrobial Peptide Defensin Beta-1 in Clinical Oral Squamous Cell Carcinoma Patients via an Integrated Bioinformatics Approach

An integrated bioinformatics approach to identify key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and construction of mRNA–miRNA-lncRNA/circRNA networks

Objective The purpose of this study was to identify potential biomarkers in the tubulointerstitium of focal segmental glomerulosclerosis (FSGS) and comprehensively analyze its mRNA–miRNA-lncRNA/circRNA network. Methods The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT–PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA–miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database. Results A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha (PPARGC1A) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT–PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A. The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs. Conclusions PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS.

Identification of key genes and their correlation with immune infiltration in osteoarthritis using integrative bioinformatics approaches and machine-learning strategies.

Osteoarthritis (OA) is a common degenerative joint disease and is closely associated with chronic, low-grade inflammation. Regulating ferroptosis by targeting ferroptosis-related genes may be a fast and effective way to delay the degeneration of OA. However, the molecular mechanisms and gene targets related to ferroptosis in OA are still unclear. Data of OA samples from 3 gene expression omnibus (GEO) datasets were combined to identify differentially expressed genes (DEGs). Ferroptosis-related genes (FRGs) retrieved by the Ferroptosis database were intersected with DEGs, and the intersected hub genes were used for functional enrichment analysis. The feature genes were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm, support vector machine recursive feature elimination (SVM-RFE) algorithm, and random forest (RF) algorithm. Single sample gene set enrichment analysis (ssGSEA) was used to compare immune infiltration between OA patients and normal controls, and the correlation between feature genes and immune cells was analyzed. The expression levels of feature genes were confirmed by RT-PCR. In addition, to explore the applicability of these genes, we extended the bioinformatics analysis of these feature genes to cancer. Finally, 4 feature genes, GABARAPL1, TNFAIP3, ARNTL, and JUN, were confirmed in OA. Theirs expression level were validated by RT-PCR. ROC curves of the 4 genes exhibit excellent diagnostic efficiency for OA, suggesting that the 4 genes were associated with the pathogenesis of OA. Another GEO dataset validated this result. Further analysis revealed that the 4 feature genes were all closely related to the immune infiltration cells in OA. Additionally, results of prognosis analysis indicated that JUN might be a promising therapeutic target for cancer. GABARAPL1, TNFAIP3, ARNTL, and JUN may be predicted biomarkers for OA. The feature genes and association between feature genes and immune infiltration may provide potential biomarkers for OA prediction along with the better assessment of the disease.

Genome-wide identification, classification, and characterization of lectin gene superfamily in sweet orange (Citrus sinensis L.).

Lectins are sugar-binding proteins found abundantly in plants. Lectin superfamily members have diverse roles, including plant growth, development, cellular processes, stress responses, and defense against microbes. However, the genome-wide identification and functional analysis of lectin genes in sweet orange (Citrus sinensis L.) remain unexplored. Therefore, we used integrated bioinformatics approaches (IBA) for in-depth genome-wide identification, characterization, and regulatory factor analysis of sweet orange lectin genes. Through genome-wide comparative analysis, we identified a total of 141 lectin genes distributed across 10 distinct gene families such as 68 CsB-Lectin, 13 CsLysin Motif (LysM), 4 CsChitin-Bind1, 1 CsLec-C, 3 CsGal-B, 1 CsCalreticulin, 3 CsJacalin, 13 CsPhloem, 11 CsGal-Lec, and 24 CsLectinlegB.This classification relied on characteristic domain and phylogenetic analysis, showing significant homology with Arabidopsis thaliana's lectin gene families. A thorough analysis unveiled common similarities within specific groups and notable variations across different protein groups. Gene Ontology (GO) enrichment analysis highlighted the predicted genes' roles in diverse cellular components, metabolic processes, and stress-related regulation. Additionally, network analysis of lectin genes with transcription factors (TFs) identified pivotal regulators like ERF, MYB, NAC, WRKY, bHLH, bZIP, and TCP. The cis-acting regulatory elements (CAREs) found in sweet orange lectin genes showed their roles in crucial pathways, including light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and more. These findings will aid in the in-depth molecular examination of these potential genes and their regulatory elements, contributing to targeted enhancements of sweet orange species in breeding programs.