Identification Of Genes Research Articles

Identification of Key Genes and Pathways for Anaerobic Germination Tolerance in Rice Using Weighted Gene Co-Expression Network Analysis (WGCNA) in Association with Quantitative Trait Locus (QTL) Mapping

BackgroundRice is one of the most important food crops in the world, and with the development of direct seeding methods for rice, exposure to anaerobic stress has become a major factor limiting its growth.ResultsIn this experiment, we tested the tolerance to anaerobic germination of rice varieties NIP and HD84, and they were used as parents to construct a DH (doubled-haploid) population. The transcriptomes of NIP (highly tolerant) and HD86 (intolerant), and their progeny HR (highly tolerant) and NHR (intolerant) were sequenced from normal and anaerobic environments. The differentially-expressed genes (DEGs) were subjected to GO (Gene ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and WGCNA analyses. QTL mapping of the DH population identified tolerance to anaerobic germination-related chromosomal segments. The transcriptome results from 24 samples were combined with the anaerobic stress QTL results for 159 DH population lines to construct a metabolic network to identify key pathways and a gene interaction network to study the key genes. Essential genes were initially subjected to rigorous functional validation, followed by a comprehensive analysis aimed at elucidating their potential utility in domestication and breeding practices, particularly focusing on the exploitation of dominant haplotypes.ConclusionThe results show that pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH) are the starting signals of energy metabolism for coleoptile length growth, the auxin transporter EXPA is the determining signal for coleoptile length growth. The pivotal genes Os05g0498700 and Os01g0866100 exert a negative regulatory influence on coleoptile length, ultimately enhancing tolerance to anaerobic germination in rice. Analyses of breeding potential underscore the additional value of Os05g0498700-hyp2 and Os01g0866100-hyp2, highlighting their potential utility in further improving rice through breeding programs. The results of our study will provide a theoretical basis for breeding anaerobic-tolerant rice varieties.

Genome-wide identification of heavy-metal ATPases genes in Areca catechu: investigating their functionality under heavy metal exposure

Heavy-metal ATPases (HMAs) play a vital role in plants, helping to transport heavy metal ions across cell membranes.However, insufficient data exists concerning HMAs genes within the Arecaceae family.In this study, 12 AcHMA genes were identified within the genome of Areca catechu, grouped into two main clusters based on their phylogenetic relationships.Genomic distribution analysis reveals that the AcHMA genes were unevenly distributed across six chromosomes. We further analyzed their physicochemical properties, collinearity, and gene structure.Furthermore, RNA-seq data analysis exhibited varied expressions in different tissues of A. catechu and found that AcHMA1, AcHMA2, and AcHMA7 were highly expressed in roots, leaves, pericarp, and male/female flowers. A total of six AcHMA candidate genes were selected based on gene expression patterns, and their expression in the roots and leaves was determined using RT-qPCR under heavy metal stress. Results showed that the expression levels of AcHMA1 and AcHMA3 genes were significantly up-regulated under Cd2 + and Zn2 + stress. Similarly, in response to Cu2+, the AcHMA5 and AcHMA8 revealed the highest expression in roots and leaves, respectively. In conclusion, this study will offer a foundation for exploring the role of the HMAs gene family in dealing with heavy metal stress conditions in A. catechu.

Integrative genomic and transcriptomic analysis of Xanthomonas oryzae pv. oryzae pathotype IV, V, and IX in China reveals rice defense-responsive genes

Bacterial blight of rice is a devastating disease caused by the gram-negative bacteria Xanthomonas oryzae pv. oryzae (Xoo). Chinese Xoo strain pathotypes IV, V, and IX are the major virulent Xoo strain types in South China sequentially from the 1990s to the present. Here, we report the isolation of GD0201 and GD0202, which belong to pathotypes IV and IX, respectively, and the complete genome sequence and transcriptomic analysis of GD0201 (IV), GD1358 (V), and GD0202 (IX). We found that resistance genes xa5, Xa23, and Xa27 confer strong resistance to all three Xoo strains, indicating that they are currently good choices for resistance rice breeding. The genome analysis reveals fewer TAL and non-TAL effector coding genes in GD0202 than in the other two strains, potentially contributing to its strong virulence. Transcriptomic analysis of ZH11 inoculated with the three Xoo strains strongly suggests that three Xoo strains for better infection repress the ethylene response factor (ERF) gene family members. Furthermore, weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) analysis revealed 14 hub genes potentially associated with rice response to the three Xoo strains. The expression of several hub genes was validated to be induced by all three Xoo strains, suggesting its role in bacterial blight disease response to Xoo strains. Genomic analysis of the Xoo strains belonging to pathotypes IV, V, and IX, identification of effectors and genes related to Xoo virulence in rice plants will provide insights into understanding the molecular mechanism underlying rice-Xoo interaction and the gene expression pattern in response to Xoo infection.

Transcriptome-based identification and validation of reference genes for corm growth stages, different tissues, and drought stress in Taro (Colocasia esculenta)

Taro is a widely utilized starch resource plant. It is essential to quantify the expression levels of functional genes associated with taro growth using real-time quantitative polymerase chain reaction (RT-qPCR). However, to obtain reliable RT-qPCR results, appropriate reference genes (RGs) are required for data normalization. In this study, we screened seven novel candidate RGs using transcriptome datasets from taro, encompassing data from growth corms and various tissues. The expression stability of these seven new RGs, along with the commonly used RGs Actin, EF1-α, and β-tubulin, was assessed using Delta Ct, BestKeeper, geNorm, and NormFinder algorithms. Furthermore, we conducted a comprehensive analysis using the RefFinder program and validated the results using the target gene, CeAGPL1. The findings revealed that ACY-1 and PIA2 were the optimal multiple RGs for normalization during corm growth, while COX10 and Armc8 were suitable for samples including various types of tissues. Furthermore, we found three RGs, Armc8, COX10 and CCX4L, were the optimal RGs for drought stress. This study assessed the suitability of RGs in taro for the first time. The identified RGs provide valuable resources for studying corm growth, diverse tissues, and drought stress. This study contributes to the advancement of our understanding of the underlying mechanisms that govern the growth of taro.

Identification, Classification, and Expression Analysis of Leucine-Rich Repeat Extensin Genes from Brassica rapa Reveals Salt and Osmosis Stress Response Genes

Identification, Classification, and Expression Analysis of Leucine-Rich Repeat Extensin Genes from Brassica rapa Reveals Salt and Osmosis Stress Response Genes

Identification and Functional Analysis of KH Family Genes Associated with Salt Stress in Rice.

Salinity stress has a great impact on crop growth and productivity and is one of the major factors responsible for crop yield losses. The K-homologous (KH) family proteins play vital roles in regulating plant development and responding to abiotic stress in plants. However, the systematic characterization of the KH family in rice is still lacking. In this study, we performed genome-wide identification and functional analysis of KH family genes and identified a total of 31 KH genes in rice. According to the homologs of KH genes in Arabidopsis thaliana, we constructed a phylogenetic tree with 61 KH genes containing 31 KH genes in Oryza sativa and 30 KH genes in Arabidopsis thaliana and separated them into three major groups. In silico tissue expression analysis showed that the OsKH genes are constitutively expressed. The qRT-PCR results revealed that eight OsKH genes responded strongly to salt stresses, and OsKH12 exhibited the strongest decrease in expression level, which was selected for further study. We generated the Oskh12-knockout mutant via the CRISPR/Cas9 genome-editing method. Further stress treatment and biochemical assays confirmed that Oskh12 mutant was more salt-sensitive than Nip and the expression of several key salt-tolerant genes in Oskh12 was significantly reduced. Taken together, our results shed light on the understanding of the KH family and provide a theoretical basis for future abiotic stress studies in rice.

Haplotype-resolved gapless genome and chromosome segment substitution lines facilitate gene identification in wild rice

The abundant genetic variation harbored by wild rice (Oryza rufipogon) has provided a reservoir of useful genes for rice breeding. However, the genome of wild rice has not yet been comprehensively assessed. Here, we report the haplotype-resolved gapless genome assembly and annotation of wild rice Y476. In addition, we develop two sets of chromosome segment substitution lines (CSSLs) using Y476 as the donor parent and cultivated rice as the recurrent parents. By analyzing the gapless reference genome and CSSL population, we identify 254 QTLs associated with agronomic traits, biotic and abiotic stresses. We clone a receptor-like kinase gene associated with rice blast resistance and confirm its wild rice allele improves rice blast resistance. Collectively, our study provides a haplotype-resolved gapless reference genome and demonstrates a highly efficient platform for gene identification from wild rice.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Integrative Bioinformatics Approaches to Uncover Hub Genes and Pathways Involved in Cardiovascular Diseases.

Cardiovascular diseases (CVD) represent a significant global health challenge resulting from a complex interplay of genetic, environmental, and lifestyle factors. However, the molecular pathways and genetic factors involved in the onset and progression of CVDs remain incompletely understood. Here, we performed an integrative bioinformatic analysis to highlight specific genes and signaling pathways implicated in the pathogenesis of 80 CVDs. Differentially expressed genes (DEGs) were identified through the integrated analysis of microarray and GWAS datasets. Then, hub genes were identified after gene ontology functional annotation analysis and protein-protein internet (PPI) analysis. In addition, pathways were identified through KEGG and gene ontology enrichment analyses. A total of 821 hub genes related to 80 CVDs were identified, including 135 common and frequent CVD-associated genes. TNF, IL6, VEGFA, and TGFB.1 genes were the central core genes expressed in 50% or more of CVDs, confirming that the inflammation is a key pathological feature of CVDs. Analysis of hub genes by KEGG enrichment revealed predominant enrichment in 201 KEGG pathways, of which the AGE-RAGE signaling pathway in diabetic complications was identified as the common key KEGG implicated in 62 CVDs. In addition, the outcomes showed an overrepresentation in pathways categorized under human diseases, particularly in the subcategories of infectious diseases and cancers, which may be common risk factors for CVDs. In conclusion, this powerful approach for in silico fine-mapping of genes and pathways allowed the identification of determinant hubs genes and pathways implicated in the pathogenesis of CVDs which could be employed in developing more targeted and effective interventions for preventing, diagnosing, and treating CVDs. The function of these hub genes in CVDs needs further exploration to elucidate their biological characteristics.

Identification of RNAi-Related Genes and Transcriptome Assembly of Loblolly Pine (Pinus taeda, L.) Seedlings Exposed to Insect-Specific dsRNA

Considerable research has focused on gene silencing in tree-feeding insects, but how trees recognize and process double-stranded RNA (dsRNA) engineered to target plant pests is unknown. We performed transcriptomic assembly, preliminary differential expression analysis, and in silico annotation on loblolly pine (Pinus taeda, L.) seedlings exposed to southern pine beetle-specific dsRNA. This pilot study sought to elucidate the baseline response of seedlings challenged with insect-specific dsRNA. Treated and untreated seedlings were sequenced and following transcriptome assembly 20 RNAi-related proteins (RRPs) were annotated. Differential gene expression analysis conducted using DESeq2 followed by pathway enrichment revealed 7131 differentially expressed transcripts, of which 33% were upregulated and 67% were downregulated. Only two RRPs selected for analysis were upregulated in treated seedlings, showing a lack of detectable RNAi response with our methodology. Beyond RNAi-related proteins, pathway enrichment mapped to immune response systems and genetic and cellular processing. Upregulated transcripts included autophagy, amino sugar and nucleotide sugar metabolism, and plant hormone signal transduction. Downregulated transcripts included RNA degradation and fatty acid metabolism pathways. Multiple DICER-LIKE and ARGONAUTE proteins were also annotated in five other North American pines, revealing diversity among these crucial proteins. Understanding host plant response to RNAi-mediated pest control is essential to further develop this technology against tree pests.

The potential role of RNA sequencing in diagnosing unexplained insensitivity to conventional chemotherapy in pediatric patients with B-cell acute lymphoblastic leukemia

Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is a highly heterogeneous disease. According to large-scale RNA sequencing (RNA-seq) data, B-ALL patients can be divided into more than 10 subgroups. However, many genomic defects associated with resistance mechanisms have not yet been identified. As an individual clinical tool for molecular diagnostic risk classification, RNA-seq and gene expression pattern-based therapy could be potential upcoming strategies. In this study, we retrospectively analyzed the RNA-seq gene expression profiles of 45 children whose molecular diagnostic classifications were inconsistent with the response to chemotherapy. The relationship between the transcriptome and chemotherapy response was analyzed. Fusion gene identification was conducted for the included patients who did not have known high-risk associated fusion genes or gene mutations. The most frequently detected fusion gene pair in the high-risk group was the DHRSX duplication, which is a novel finding. Fusions involving ABL1, LMNB2, NFATC1, PAX5, and TTYH3 at onset were more frequently detected in the high-risk group, while fusions involving LFNG, TTYH3, and NFATC1 were frequently detected in the relapse group. According to the pathways involved, the underlying drug resistance mechanism is related to DNA methylation, autophagy, and protein metabolism. Overall, the implementation of an RNA-seq diagnostic system will identify activated markers associated with chemotherapy response, and guide future treatment adjustments.

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.

Identification of key genes and immune infiltration modulated in insomnia by integrated bioinformatics analysis and mendelian randomization study

Insomnia is a prevalent sleep disorder in modern times, often linked to various factors. Its occurrence, in turn, can lead to the development of several diseases, such as common cardiovascular conditions like coronary heart disease and hypertension. Moreover, insomnia can significantly impact the strength of the immune system. Traditional Chinese medicine (TCM) classifies sleep deprivation as a form of sleepiness, attributing prolonged sleepiness to Yin-Yang imbalance, resulting in Qi consumption and impairment of the five Zang-organs. Both Qi consumption and damage to the five Zang-organs mentioned in TCM research are correlated with the immune system in medical studies. Consequently, current research indicates that sleep deprivation escalates the risk of immune-related diseases. However, more research and analysis concerning immune infiltration related explicitly to insomnia are needed. Through gene differential analysis of sample files obtained from the Gene Expression Omnibus (GEO) dataset GSE208668 , we identified differentially expressed genes (DEGs) between the Insomnia and control groups. After screening these DEGs, we conducted GO and KEGG pathway analyses to identify insomnia-related gene pathways. Subsequently, I identified ten hub genes (IL2, CALML3, GATA3, EOMES, PTGS2, ESCO2, CXCL2, GPT, CRT, PRF1), and using a logistic regression model, I predicted insomnia based on the expression patterns of these ten genes. The analysis revealed that CALML3 and IL2, which showed the best performance in ROC curve analysis and had the highest degree, respectively, were selected for further investigation. Continuing the research, we further employed CIBERSORT to study immune infiltration in patients with insomnia, discovering an association between mast cells and insomnia partially correlated with the identified hub genes. In conclusion, these essential genes and immune infiltrations are relevant for treating and alleviating insomnia. Understanding these genes and immune cells could offer crucial insights for future therapeutic approaches to insomnia.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis.

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis. The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes. We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle. Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

Computational Analysis of Differentially Expressed Circulating MicroRNA and Identification of Key Genes in Prostate Cancer

Computational Analysis of Differentially Expressed Circulating MicroRNA and Identification of Key Genes in Prostate Cancer

Organic matter degradation in the deep, sulfidic waters of the Black Sea: insights into the ecophysiology of novel anaerobic bacteria

BackgroundRecent studies have reported the identity and functions of key anaerobes involved in the degradation of organic matter (OM) in deep (> 1000 m) sulfidic marine habitats. However, due to the lack of available isolates, detailed investigation of their physiology has been precluded. In this study, we cultivated and characterized the ecophysiology of a wide range of novel anaerobes potentially involved in OM degradation in deep (2000 m depth) sulfidic waters of the Black Sea.ResultsWe have successfully cultivated a diverse group of novel anaerobes belonging to various phyla, including Fusobacteriota (strain S5), Bacillota (strains A1T and A2), Spirochaetota (strains M1T, M2, and S2), Bacteroidota (strains B1T, B2, S6, L6, SYP, and M2P), Cloacimonadota (Cloa-SY6), Planctomycetota (Plnct-SY6), Mycoplasmatota (Izemo-BS), Chloroflexota (Chflx-SY6), and Desulfobacterota (strains S3T and S3-i). These microorganisms were able to grow at an elevated hydrostatic pressure of up to 50 MPa.Moreover, this study revealed that different anaerobes were specialized in degrading specific types of OM. Strains affiliated with the phyla Fusobacteriota, Bacillota, Planctomycetota, and Mycoplasmatota were found to be specialized in the degradation of cellulose, cellobiose, chitin, and DNA, respectively, while strains affiliated with Spirochaetota, Bacteroidota, Cloacimonadota, and Chloroflexota preferred to ferment less complex forms of OM. We also identified members of the phylum Desulfobacterota as terminal oxidizers, potentially involved in the consumption of hydrogen produced during fermentation. These results were supported by the identification of genes in the (meta)genomes of the cultivated microbial taxa which encode proteins of specific metabolic pathways. Additionally, we analyzed the composition of membrane lipids of selected taxa, which could be critical for their survival in the harsh environment of the deep sulfidic waters and could potentially be used as biosignatures for these strains in the sulfidic waters of the Black Sea.ConclusionsThis is the first report that demonstrates the cultivation and ecophysiology of such a diverse group of microorganisms from any sulfidic marine habitat. Collectively, this study provides a step forward in our understanding of the microbes thriving in the extreme conditions of the deep sulfidic waters of the Black Sea.Exyq52VguUrmavAnxe2qURVideo

Emergence of Virulent Extensively Drug-Resistant Vancomycin-Resistant Enterococci Among Diarrheic Pet Animals: A Possible Public Health Threat on the Move.

Background: Vancomycin-resistant enterococci (VRE) have become an increasing public health concern in the past few decades, being associated with serious multidrug-resistant (MDR) infections. This study was conducted to investigate the role of diarrheic pet animals as potential reservoirs for virulent extensively drug-resistant (XDR) VRE and their threat on human health. Materials and Methods: Rectal swabs were collected from 153 diarrheic pet animals (80 dogs and 73 cats). The collected swabs were cultured on CHROMagarTMVRE for the isolation of vancomycin-resistant Enterococcus faecalis and Enterococcus faecium, and then suspected colonies were identified as enterococci after Gram staining, conventional biochemical tests, and molecular techniques. VRE were basically identified using the disk diffusion method; however, molecular identification of vanA and vanB genes was carried out among confirmed VRE isolates. Moreover, three virulence genes (cytolysin A, cylA; enterococcal surface protein, esp; and hyaluronidase, hyl) were investigated in VRE isolates. Thereafter, VRE strains that harbored virulence genes were tested for antimicrobial susceptibility. Results: Eighteen out of 153 animals (11.8%) were positive for VRE, which were obtained from 15% and 8.2% of the examined dogs and cats, respectively. None of the obtained isolates carried the vanA gene, whereas the vanB gene was detected in E. faecalis (4/10) with a prevalence rate (40%). Of the obtained VRE isolates, five possessed esp and/or cylA, while all strains were negative for the hyl gene. Furthermore, four virulent VRE isolates exhibited an XDR pattern, and one isolate was MDR. Conclusion: Diarrheic pet animals could represent a potential zoonotic reservoir for virulent XDR vancomycin-resistant E. faecalis, which may have serious public health implications.

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma.

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to elucidate key genes and pathways underlying vascular aging in diabetes using integrated bioinformatics and machine learning approaches. Gene expression datasets related to vascular smooth muscle cell (VSMC) senescence and diabetic vascular aging were analyzed. Differential expression analysis identified 428 genes associated with VSMC senescence. Functional enrichment revealed their involvement in cellular senescence, ECM-receptor interaction, PI3K-Akt and AGE-RAGE signaling pathways. Further analysis of diabetic vascular aging datasets revealed 52 differentially expressed genes, enriched in AMPK signaling, AGE-RAGE signaling, cellular senescence, and VEGF signaling pathways. Machine learning algorithms, including LASSO regression and SVM-RFE, pinpointed six key genes: TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1. Immune cell infiltration analysis demonstrated correlations between diabetic vascular aging, the identified key genes, and infiltration levels of plasma cells, M1 macrophages, CD8+ T cells, eosinophils, and regulatory T cells. In conclusion, this study identified six pivotal genes (TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1) closely associated with diabetic vascular aging through integrative bioinformatics and machine learning approaches. These genes are linked to alterations in the immune microenvironment during diabetic vascular aging. This study provides a reference and basis for molecular mechanism research, biomarker mining, and diagnosis and treatment evaluation of diabetes-related vascular aging.