Expression patterns of housekeeping genes and tissue-specific genes in black goats across multiple tissues.

Black goats are a significant meat breed in southern China. To investigate the expression patterns and biological functions of genes in various tissues of black goats, we analyzed housekeeping genes (HKGs), tissue-specific genes (TSGs), and hub genes (HUBGs) across 23 tissues. Additionally, we analyzed HKGs in 13 tissues under different feeding conditions. We identified 2968 HKGs, including six important ones. Interestingly, HKGs in grazing black goats demonstrated higher and more stable expression levels. We discovered a total of 9912 TSGs, including 134 newly identified ones. The number of TSGs for mRNA and lncRNA were nearly equal, with 127 mRNA TSGs expressed solely in one tissue. Additionally, the predicted functions of tissue-specific long non-coding RNAs (lncRNAs) targeting mRNAs corresponded with the physiological functions of the tissues.Weighted gene co-expression network analysis (WGCNA) constructed 30 modules, where the dark grey module consists almost entirely of HKGs, and TSGs are located in modules most correlated with their respective tissues. Additionally, we identified 289 HUBGs, which are involved in regulating the physiological functions of their respective tissues. Overall, these identified HKGs, TSGs, and HUBGs provide a foundation for studying the molecular mechanisms affecting the genetic and biological processes of complex traits in black goats.

Identification of key genes and pathways involved in T-DM1-resistance in OE-19 esophageal cancer cells through bioinformatics analysis.

Introduction: Esophageal Cancer (EC) ranks among the most common malignancies worldwide. Most EC patients acquire drug resistance to chemotherapy either intrinsically or acquired after T-DM1 treatment, which shows that increasing or decreasing the expression of particular genes might influence chemotherapeutic sensitivity or resistance. Therefore, gaining a deeper understanding of the altered expression of genes involved in EC drug resistance and developing new therapeutic methods are essential targets for continued advancement in EC therapy. Methods: The present study aimed to find critical regulatory genes/pathways in the progression of T-DM1 resistance in OE-19 EC cells. Expression datasets were extracted from GEO omnibus. Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then, enrichment analysis of the hub genes and network cluster analysis of the hub genes was performed. Finally, the genes were screened in the DrugBank database as therapeutic targets and molecular docking analysis was done on the selected targets. Results: In the current study, nine hub genes were identified in TDM-1-resistant EC cells (CTGF, CDH17, THBS1, CXCL8, NRP1, ITGB5, EDN1, FAT1, and PTGS2). The KEGG analysis highlighted the IL-17 signaling pathway and ECM-receptor interaction pathway as the most critical pathways; cluster analysis also showed the significance of these pathways. Therefore, the genes involved in these two pathways, including CXCL8, FSCN1, PTGS2, SERPINE2, LEF1, THBS1, CCN2, TAGLN, CDH11, and ITGA6, were searched in DrugBank as therapeutic targets. The DrugBank analysis suggests a potential role for Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) in reducing T-DM1 drug resistance in EC. The docking results revealed that NSAIDs, including Diclofenac, Mefenamic acid, Celecoxib, Naproxen, and Etoricoxib, significantly suppress resistant cancer cells. Conclusion: This comprehensive bioinformatics analysis deeply explains the molecular mechanisms governing TDM-1 resistance in EC. The identified hub genes and their associated pathways offer potential targets for therapeutic interventions. Moreover, the possible role of NSAIDs in mitigating T-DM1 resistance presents an intriguing avenue for further investigation. This research contributes significantly to the field and establishes a basis for further research to enhance treatment efficacy for EC patients.

Identification of neutrophil extracellular trap-related genes in Alzheimer's disease based on comprehensive bioinformatics analysis.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.

Investigating immune dysregulation and hub genes in septic cardiomyopathy development.

Septic cardiomyopathy is a life-threatening heart dysfunction caused by severe infection. Considering the complexity of pathogenesis and high mortality, the identification of efficient biomarkers are needed to guide clinical practice. Based on multimicroarray analysis, this study aimed to explore the pathogenesis of septic cardiomyopathy and the related immune landscape. The results showed that septic cardiomyopathy resulted in organ dysfunction due to extreme pro- and anti-inflammatory effects. In this process, KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were identified as the hub genes regulating the immune landscape of septic cardiomyopathy. Nine transcription factors regulated the expression of these genes: SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, SMAD3, and STAT3. The hub genes activated the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway. These pathways were mainly involved in regulating the inflammatory response, adaptive immune response, leukocyte-mediated immunity, cytokine-mediated immunity, immune effector processes, myeloid cell differentiation, and T-helper cell differentiation. These nine hub genes could be considered biomarkers for the early prediction of septic cardiomyopathy.

Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease.

OBJECTIVE: To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and further identify hub genes. MATERIALS AND METHODS: Gene Set Enrichment Analysis was used to identify the COPD-associated pathways and reversal pathways after ECC-BYF III + ER treatment. Protein-protein interaction network analysis and cytoHubba were used to identify the hub genes. These genes were verified using independent datasets, molecular docking and quantitative real-time polymerase chain reaction experiment. RESULTS: Using the high-throughput sequencing data of COPD rats from our laboratory, 49 significantly disturbed pathways were identified in COPD model compared with control group via gene set enrichment analysis (false discovery rate < 0.05). The 34 pathways were reversed after ECC-BYF III + ER treatment. In the 2306 genes of these 34 pathways, 121 of them were differentially expressed in COPD rats compared with control samples. A protein-protein interaction network comprising 111 nodes and 274 edges was created, and 34 candidate genes were identified. Finally, seven COPD hub genes (Il1b, Ccl2, Cxcl1, Apoe, Ccl7, Ccl12, and Ccl4) were well identified and verified in independent COPD rat data from our laboratory and the public dataset GSE178513. The area under the receiver operating characteristic curve values ranged from 0.86 to 1 and from 0.67 to 1, respectively. The reliability of the mentioned genes, which can bind to the active ingredients of ECC-BYF III through molecular docking, were further verified through qRT-PCR experiments. CONCLUSION: Thirty-four COPD-related pathways and seven hub genes that may be regulated by ECC-BYF III + ER were identified and well verified. The findings of this study may provide insights into the treatment and mechanism underlying COPD.

GSEA method can circumvent the limitations of the preacquisition of DEGs for ORA and is suitable for small sample data.34 COPD-related pathways that can be regulated by ECC-BYF III + ER were identified.Seven COPD hub genes were identified and well verified in independent RNA-seq data and PCR experiment, and they may play a crucial role in TCM treatment.

Network medicine based approach for identifying the type 2 diabetes, osteoarthritis and triple negative breast cancer interactome: Finding the hub of hub genes.

The increasing prevalence of multi-morbidities, particularly the incidence of breast cancer in diabetic/osteoarthritic patients emphasize on the need for exploring the underlying molecular mechanisms resulting in carcinogenesis. To address this, present study employed a systems biology approach to identify switch genes pivotal to the crosstalk between diseased states resulting in multi-morbid conditions. Hub genes previously reported for type 2 diabetes mellitus (T2DM), osteoarthritis (OA), and triple negative breast cancer (TNBC), were extracted from published literature and fed into an integrated bioinformatics analyses pipeline. Thirty-one hub genes common to all three diseases were identified. Functional enrichment analyses showed these were mainly enriched for immune and metabolism associated terms including advanced glycation end products (AGE) pathways, cancer pathways, particularly breast neoplasm, immune system signalling and adipose tissue. The T2DM-OA-TNBC interactome was subjected to protein-protein interaction network analyses to identify meta hub/clustered genes. These were prioritized and wired into a three disease signalling map presenting the enriched molecular crosstalk on T2DM-OA-TNBC axes to gain insight into the molecular mechanisms underlying disease-disease interactions. Deciphering the molecular bases for the intertwined metabolic and immune states may potentiate the discovery of biomarkers critical for identifying and targeting the immuno-metabolic origin of disease.

Identification and analysis correlation between hub genes and immune cell infiltration related to LPS-induced cognitive impairment.

Background: The occurrence of immunity and inflammation outside the central nervous system frequently results in acute cognitive impairment among elderly patients. However, there is currently a lack of standardized methods for diagnosing acute cognitive impairment. The objective of our study was to identify potential mRNA biomarkers and investigate the pathogenesis of acute cognitive impairment in mice brains. Methods: To analyze changes in hub genes associated with acute cognitive impairment, bioinformatics analysis was performed on the mouse brain injury data of sterile saline control group and lipopolysaccharide (LPS) induced experimental group in Gene Expression Omnibus (GEO). Functional analysis was conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), which facilitated to identify some potential mRNA biomarkers for hub gene expression in mice brains. Additionally, the "CIBERSORT X″ R kit was employed to examine immune cell infiltrations of mice brains in LPS group and saline group. Results: In the LPS and saline group, 102 significantly upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were identified. The pathway enrichment analysis using GO and KEGG revealed that these DEGs were mainly related to the regulation of cytokine, cytokine-cytokine receptor interaction, as well as protein interaction with cytokine and cytokine receptor. Immune cell infiltration analysis indicated potential involvement of M1 macrophages, NK cells resting, T cells CD4 memory, and T cells CD8 naive in the process of cognitive impairment. By constructing a protein-protein interaction (PPI) network, five hub genes (Cxcl10, Cxcl12, Cxcr3, Gbp2, and Ifih1) showed significant associations with immune cell types by using a threshold Spearman's rank correlation coefficient of R > 0.50 and p < 0.05. Conclusion: The mRNA expression profile of the mice brain tissues in the LPS group differed from that in the normal saline group. These significantly expressed mRNAs may act an importance in the pathogenesis of acute cognitive impairment through mechanisms involving immunity and neuroinflammation.

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.

Introduction: Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. Methods: BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD. Results: Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes. Discussion: This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

On discovery of novel hub genes for ER+ and TN breast cancer types through RNA seq data analyses and classification models.

Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG's exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics.

Identifying hub genes for chemo-radiotherapy sensitivity in cervical cancer: a bi-dataset in silico analysis.

PURPOSE: To identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer. METHODS: The gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database. RESULTS: A total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value < 0.05). A nomogram and calibration curve were generated using the six hub genes to predict prognosis with high accuracy. A regulatory network comprising TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and several competitive endogenous RNA (ceRNA) networks comprising mRNAs, miRNAs, and lncRNAs were constructed. Data from HPA indicated that the protein expression of the six hub genes differed significantly between patients with CESC and healthy individuals. CONCLUSION: Upregulation of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 is associated with radiotherapy sensitivity and is involved in various cellular processes in CESC. These six genes may serve as biomarkers for predicting the radiotherapy response and prognosis in patients with CESC.

Identification of RTP4 facilitating ovarian cancer by bioinformatics analysis and experimental validation.

Ovarian cancer (OV) is the most malignant gynecological tumor in women, with poor prognosis and high mortality rate. This study aims to identify hub genes in OV and explore the role of Receptor transporter 4 (RTP4) in OV progression. Common differentially expressed genes (DEGs) were screened from two microarray datasets. GO and KEGG enrichment analysis were performed. Protein-protein interaction (PPI) network was constructed by STING. Kaplan-Meier plotter was used to analyze prognosis. The effect of target gene on immune infiltration was analyzed by TIMER. The proliferation, migration, and invasion of OV cells were measured by CCK-8, wound healing assay, and trans-well assay, respectively. A total of 293 common DEGs were selected from GSE12470 and GSE16709 datasets. Hub genes, EPCAM, KIFC1, RTP4, TAGLN, and ZFP36 were selected by PPI network. Kaplan-Meier plotter demonstrated that high expression of RTP4 was related to low overall survival in OV patients. The TIMER result showed that high expression of RTP4 promoted immune infiltration of CD8+ T cells, B cells, neutrophils, and dendritic cells in OV. Moreover, silencing RTP4 significantly inhibited the proliferation, migration, and invasion of OV cells. RTP4 was associated with the poor prognosis in OV. In summary, silencing RTP4 inhibited the proliferation, migration, and invasion of OV cells, having the potential to be a novel therapeutic target for OV.

Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

FN1, a reliable prognostic biomarker for thyroid cancer, is associated with tumor immunity and an unfavorable prognosis.

Thyroid cancer (THCA) is a malignant tumor that affects the endocrine system. At present, an effective treatment for THCA remains elusive, particularly for medullary carcinoma and undifferentiated carcinoma, due to the lack of suitable medications and prognostic markers. Patient RNA-sequencing and clinical data were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Protein-protein interaction analyses were performed for differentially expressed genes related to THCA. Moreover, the associations between fibronectin 1 (FN1), clinical data, immune checkpoint genes and immune cell infiltration was assessed. The potential functional role of the FN1 gene was evaluated through gene set enrichment analysis. Immunohistochemistry was used to assess FN1 expression in 103 cases of THCA, comprising 32 with papillary carcinoma, 30 with follicular carcinoma, 35 with medullary carcinoma and 6 with undifferentiated carcinoma. Finally, 11 co-expression modules were constructed and the expression of five identified hub genes (FN1, mucin-1, keratin 19, intracellular adhesion molecule 1 and neural cell adhesion molecule) were evaluated. The results demonstrated that higher FN1 gene expression levels were strongly associated with a higher pathologic stage and tumor stage, and were significantly associated with immune cell infiltration in THCA. Significant increases in FN1 protein expression levels were noted among patients diagnosed with four types of THCA, comprising papillary carcinoma, follicular carcinoma, medullary carcinoma and undifferentiated carcinoma. Patients diagnosed with medullary carcinoma and undifferentiated carcinoma, and with low FN1 expression levels, exhibited a significant survival advantage compared with those with high FN1 expression levels. In conclusion, the present study identified five hub genes involved in the onset and progression of THCA. Furthermore, FN1 could serve as a candidate biomarker and a therapeutic target for THCA and may be a key gene mediating THCA immune infiltration.

Identification of key genes in diabetic nephropathy based on lipid metabolism.

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes with a high incidence rate. Notably, the disturbance of lipid metabolism is associated with DN progression. The present study aimed to identify lipid metabolism-related hub genes associated with DN for improved diagnosis of DN. The gene expression profile data of DN and healthy samples (GSE142153) were obtained from the Gene Expression Omnibus database, and the lipid metabolism-related genes were obtained from the Molecular Signatures Database. Differentially expressed genes (DEGs) between DN and healthy samples were analyzed. The weighted gene co-expression network analysis (WGCNA) was performed to examine the relationship between genes and clinical traits to identify the key module genes associated with DN. Next, the Venn Diagram R package was used to identify the lipid metabolism-related genes associated with DN and their protein-protein interaction (PPI) network was constructed. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The hub genes were identified using machine-learning algorithms. The Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. The present study also investigated the immune infiltration discrepancies between DN and healthy samples, and assessed the correlation between the immune cells and hub genes. Finally, the expression levels of key genes were verified by reverse transcription-quantitative (RT-q)PCR. The present study determined 1,445 DEGs in DN samples. In addition, 694 DN-related genes in MEyellow and MEturquoise modules were identified by WGCNA. Next, the Venn Diagram R package was used to identify 17 lipid metabolism-related genes and to construct a PPI network. GO analysis revealed that these 17 genes were markedly associated with 'phospholipid biosynthetic process' and 'cholesterol biosynthetic process', while the KEGG analysis showed that they were enriched in 'glycerophospholipid metabolism' and 'fatty acid degradation'. In addition, SAMD8 and CYP51A1 were identified through the intersections of two machine-learning algorithms. The results of GSEA revealed that the 'mitochondrial matrix' and 'GTPase activity' were the markedly enriched GO terms in both SAMD8 and CYP51A1. Their KEGG pathways were mainly concentrated in the 'pathways of neurodegeneration-multiple diseases'. Immune infiltration analysis showed that nine types of immune cells had different expression levels in DN (diseased) and healthy samples. Notably, SAMD8 and CYP51A1 were both markedly associated with activated B cells and effector memory CD8 T cells. Finally, RT-qPCR confirmed the high expression of SAMD8 and CYP51A1 in DN. In conclusion, lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN. The present study provides fundamental information on lipid metabolism that may aid the diagnosis and treatment of DN.

Investigation of Common Pathways and Putative Biomarker Candidates of Colorectal Cancer and Insomnia by Using Integrative In-Silico Approaches.

Background: Colorectal cancer (CRC) is one of the leading causes of cancer-related mortalities across the globe. Accumulating evidence shows that individuals having sleep disorders such as insomnia are at high risk of developing CRC, yet the association of sleep disorders with CRC risk is still unclear. Here, we investigated the potential molecular connections between CRC and insomnia using integrative in silico approaches. Objective: This study aims to explore the potential molecular connections between CRC and insomnia utilizing integrative in-silico methodologies. Methods and Methods: Gene expression microarray datasets for CRC and insomnia samples were retrieved from the NCBI-GEO database and analyzed using R. Functional enrichment analysis of common differentially expressed genes (DEGs) was performed by the g: Profiler tool. Cytoscape software was used to construct a protein-protein interaction network and hub gene identification. Expression profiles of hub genes in TCGA datasets were also determined, and predicted miRNAs targeting hub genes were analyzed by miRNA target prediction tools. Results: Our results revealed a total of 113 shared DEGs between the CRC and insomnia datasets. Six genes (HSP8A, GAPDH, HSP90AA1, EEF1G, RPS6, and RPLP0), which were also differently expressed in TCGA datasets, were prioritized as hub genes and were found to be enriched in pathways related to protein synthesis. hsa-miR-324-3p, hsa-miR-769-3p, and hsa-miR-16-5p were identified as promising miRNA biomarkers for two diseases. Conclusions: Our in-silico analysis provides promising evidence of the molecular link between CRC and insomnia and highlights multiple potential molecular biomarkers and pathways. Validation of the results by wet lab work can be utilized for novel translational and precision medicine applications to alleviate the public health burden of CRC.

Transcriptome analysis unravels key pathways and hub genes related to immature fruit abscission in Camellia oleifera.

Immature fruit abscission of Camellia oleifera (C. oleifera) is a common problem limiting yield increases. However, the regulatory mechanisms underlying immature fruit abscission in C. oleifera are unclear. In this study, we systematically investigated changes in the morphological, physiological, and gene expression of fruit abscission zones (FAZs) of soon-to-abscise fruits (M2). We found that fruit abscission before ripening mainly occurs during the August abscission stage of 'Huashuo'. At the beginning of this stage, the FAZs of M2 have a marked dent, and the separation layer structures are preliminarily formed. Phytohormone analysis showed that the contents of indole-3-acetic acid (IAA) and jasmonic acid (JA) in the FAZs of M2 were significantly decreased compared with the non-abscised fruits, while the content of trans-zeatin (TZR) was increased. Transcriptome analysis identified differentially expressed genes (DEGs) mainly involved in phytohormone metabolism, including ethylene, auxin, JA, and the cis-zeatin signal transduction pathway. There were also many DEGs involved in cell wall catabolism. Weighted gene co-expression network analysis (WGCNA) further suggested that the transcription factors NAC100 and ERF114 participate in the immature fruit abscission of C. oleifera. This study provides insights into the fruit abscission mechanism of C. oleifera.

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection.

Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A total of 349 upregulated and 260 downregulated differentially expressed genes (DEGs) and eight hub genes (ISG15, HELZ2, HNRNPK, TIAL1, SKIV2L2, PABPC1, SIRT1, and PPARA) were identified. Notably, HNRNPK, TIAL1, and PABPC1 exhibited the highest predictive potential for ACR with AUCs of 0.706, 0.798, and 0.801, respectively. KEGG analysis of hub genes revealed that ACR following LT was predominately associated with ferroptosis, protein processing in the endoplasmic reticulum, complement and coagulation pathways, and RIG-I/NOD/Toll-like receptor signaling pathway. According to the immune cell infiltration analysis, γδT cells, NK cells, Tregs, and M1/M2-like macrophages had the highest levels of infiltration. Compared to SIRT1, ISG15 was positively correlated with γδT cells and M1-like macrophages but negatively correlated with NK cells, CD4+ memory T cells, and Tregs. In conclusion, this study identified eight hub genes and their potential pathways, as well as the immune cells involved in ACR following LT with the greatest levels of infiltration. These findings provide a new direction for future research on the underlying mechanism of ACR following LT.

Transcriptomic analysis reveals cross-talk genes between type 2 diabetes and recurrent benign paroxysmal positional vertigo.

Background: Benign paroxysmal positional vertigo (BPPV) is a common neurological disorder with a high recurrence rate. Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for BPPV recurrence. However, the genomic association between T2DM and BPPV recurrence remains understudied. Methods: Differential gene expression analysis and weighted gene co-expression network analysis were used to identify shared genes between BPPV recurrence and T2DM. The MCC algorithm was employed to select hub genes from the protein-protein interaction network of the shared genes. The predictive efficacy of hub genes for BPPV recurrence and T2DM was assessed using ROC curve analysis. Genemania database was used to identify downstream targets of hub genes. The immune infiltration landscape of BPPV and T2DM was characterized using the CIBERSORT algorithm. Correlation analysis was performed to explore the relationship between hub genes and immune cells. The expression levels of hub genes in patient blood samples were validated using qPCR. Results: Thirteen shared genes were identified and a protein-protein interaction network was constructed for BPPV recurrence and T2DM. Subsequently, four hub genes were selected, and their expression levels effectively predicted the occurrence of BPPV recurrence and T2DM. These hub genes were highly correlated with immune cell infiltration, indicating a common mechanism underlying recurrent BPPV and T2DM. Finally, the upregulation of hub genes in patients with T2DM comorbid with BPPV recurrence was confirmed in blood samples. These hub genes may serve as predictive biomarkers for assessing the recurrence rate in BPPV patients with comorbid T2DM. Conclusion: We proposed shared gene characteristics between BPPV recurrence and T2DM, revealing an immune-mediated inflammatory regulation as a common pathway and identifying four immune-related biomarkers and potential therapeutic targets for T2DM comorbid with recurrent BPPV.

Identification of prevention marker associated with DNA replication in ovarian cancer: Expression of MCM2 protein and bioinformatics analysis.

Ovarian cancer is one of the types of gynecological cancers that is considered to be particularly dangerous. Ovarian cancer treatment has come a long way in recent years, but the disease is still quite likely to spread to other parts of the body. In this line of research, our goal is to pinpoint the shifts in gene expression profiles that are responsible for the avoidance of ovarian cancer. The dataset GSE54388 which was deposited in the Gene Expression Omnibus (GEO) database was processed in order to find differentially expressed genes (DEGs) that were present between human ovarian surface epithelium samples and tumor epithelial component samples. The weighted gene correlation network analysis, also known as WGCNA, was performed on the modules that were associated with the ovarian cancer group. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) were used to compile a summary of the DEGs that were found in the Venn analysis of the Royalbule module. This analysis found 186 genes that overlapped in the royal blue module. Using the cytohubba plug-in that is included in the Cytoscape software, the Protein-protein Interaction (PPI) network was created and then searched to identify hub genes. Based on these findings, it seems that 10 genes have a role as hub genes in the prevention of ovarian cancer.

Molecular insights into epoxyazadiradione induced death in triple-negative breast cancer cells: A system biology approach.

Epoxyazadiradione is an important limonoid with immense pharmacological potential. We have reported previously that epoxyazadiradione (EAD) induces apoptosis in triple negative breast cancer cells (MDA-MB 231) by modulating diverse cellular targets. Here, we identify the key genes/pathways responsible for this effect through next-generation sequencing of the transcriptome from EAD treated cells and integrated molecular data analysis using bioinformatics. In silico analysis indicated that EAD displayed favourable drug-like properties and could target multiple macromolecules relevant to TNBC. RNA sequencing revealed that EAD treatment results in the differential expression of 1838 genes in MDA-MB 231 cells, with 752 downregulated and 1086 upregulated. Gene set enrichment analysis of these genes suggested that EAD disrupts protein folding in the endoplasmic reticulum, triggering the unfolded protein response (UPR) and potentially leading to cell death. EAD also induced oxidative stress and DNA damage, downregulated pathways linked to metabolism, cell cycle progression, pro-survival signalling, cell adhesion, motility and inflammatory response. The identification of protein cluster and hub genes were also done. The validation of the identified hub genes gave an inverse correlation between their expression in EAD treated cells and TNBC patient samples. Thus, the identified hub genes could be explored as therapeutic or diagnostic markers for TNBC. Hence, EAD appears to be a promising therapeutic candidate for TNBC by targeting various hallmarks of cancer, including cell death resistance, uncontrolled proliferation and metastasis. To conclude, the identified pathways and validated targets for EAD will provide a roadmap for further in vivo studies and preclinical/clinical validation required for potential drug development.