Genealyzer: web application for the analysis and comparison of gene expression data

Background Compared to all other racial groups, black Americans are most likely to be diagnosed with colorectal cancer (CRC) and are most likely to die from the disease. These racial disparities have been largely attributed to social and economic conditions that impact the prevalence of co-morbidities and access to quality health care. However, the molecular and cellular differences in CRC tumors between black and white subjects remain largely uncharacterized. Methods We analyzed paired tumor and adjacent normal RNA sequencing reads collected from 15 black and 18 white subjects at Downstate Medical Center and Stony Brook University Hospital (DSSBU). 273 additional RNA sequencing and clinicopathologic data from The Cancer Genome Atlas (TCGA) were used as a validation cohort. In the TCGA dataset, 54 black subjects were propensity score matched to 108 white subjects by age, sex, tumor location and tumor stage. Differential expression analysis was performed with EdgeR and Wilcoxon rank-sum test, which was recently recommended for human population samples. Gene Ontology (GO) enrichment analysis of DEGs (absolute log 2-fold change > 1, adjust p value < 0.05) was performed using enrichR. Gene set enrichment analysis (GSEA) of hallmark gene sets was applied to the gene list ranked by fold changes. The TPM (transcript per million) data was used to estimate relative cell abundance via xCell. Antitumor immune response likelihood scores were generated via the Estimate Systems Immune Response (EaSIeR) R package. Results EdgeR analysis identified 15 and 80 DEGs in the DSSBU and TCGA datasets, respectively. Two DEGs, CXCL10 and RPPH1 have decreased expression while EEF1A1P13 has higher expression in black samples from both datasets. CXCL10 remained a DEG under the Wilcoxon test in the TCGA dataset and in propensity score matched samples. GO enrichment analysis identified “Chemokine-mediated signaling pathway” as a significantly different biological process between tumor groups with a FDR of 0.043 and 0.0004 in TCGA and DSSBU respectively. GSEA computed a negative enrichment score for IL6 JAK STAT3 signaling, Interferon alpha response, Interferon gamma response, Allograft rejection and Myc targets V1 in the black group from both datasets. xCell analysis of the DSSBU data revealed significant enrichment for endothelial cells, adipocytes and hematopoietic stem cells in black tumor samples, while white samples were significantly enriched for basophils, T helper 2 cells and common lymphoid progenitor cells. No significant differences in cell type enrichment were identified in the TCGA dataset. Predicted immunotherapy response scores were not significantly different between races in both cohorts. Conclusion Our results show differences in genes and pathways associated with inflammation and immune cell recruitment which may impact anti-tumor activity. Our future work will include experimental validation of interesting genes accounting for the differences. Citation Format: Dimitri F. Joseph, Ellen Li, Bin Chen, Joseph F. LaComb, Alexandra Guillaume. Deciphering molecular and cellular differences between black and white colorectal cancers through in-depth transcriptomic analyses [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A010.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Synaptic loss and dysfunction are one of the earliest signs of neurodegeneration associated with cognitive decline in Alzheimer’s disease (AD) and other neurodegenerative diseases. This study aimed to assess the relationships between biological processes of the synaptic pathology underlying AD, molecular functions, and dynamics of the change concentrations of selected proteins reflecting synaptic and axonal pathology in dementia stages. Neurogranin (Ng), neuronal pentraxin receptor (NPTXR), and Visinin-like protein 1 (VILIP1) concentrations were measured in the cerebrospinal fluid (CSF) of MCI, AD, and non-demented controls (CTRL) using quantitative immunological methods. Gene ontology (GO) enrichment analysis was used for the functional analysis of tested proteins. The CSF Aβ42/Ng ratio was significantly different between all the compared groups. The CSF NPTXR/Ng ratio was significantly different between MCI compared to CTRL and AD compared to CTRL. The GO enrichment analysis revealed that two terms (the Biological Process (BP) and Cellular Component (CC) levels) are significantly enriched for NPTXR and Ng but not for VILIP1. Both Ng and NPTXR concentrations in CSF are promising synaptic dysfunction biomarkers for the early diagnosis of the disease. Moreover, both proteins are biochemically associated with classical biomarkers and VILIP-1. Mapping shared molecular and biological functions for the tested proteins by GO enrichment analysis may be beneficial in screening and setting new research targets.

BackgroundDifferential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms.MethodsTranscriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis.ResultsIn this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene.ConclusionsWe demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses.

ABSTRACTPurpose: The aim of this study was to investigate the molecular mechanism of lung cancer among nonsmoking Taiwan females. Materials and methods: By using the GSE19804 microarray data accessible from Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs) between nonsmoking female lung cancer patients and healthy controls (!logFC! >1.5 and p-value < 0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) tool was utilized to build a protein–protein interaction (PPI) network, followed by the construction of a transcriptional regulatory network based on Transcription factor (TRANSFAC) database. Results: As a result, 320 DEGs were identified between nonsmoking female patients with lung cancer and healthy controls. Pathway enrichment analysis showed significantly enriched pathways such as extracellular matrix (ECM)-receptor interaction and peroxisome proliferator-activated receptor (PPAR) signaling pathway, both of which were enriched with genes COL11A1 (encoding collagen XI alpha-1 chain protein), COL1A1, cluster of differentiation 36(CD36). GO enrichment analysis found that DEGs were significantly related to chemotaxis, vasculature development and cell adhesion GO terms. IL-6 was the node of the PPI network. Critical transcription factors (TFs) including CCAAT/enhancer-binding protein delta (CEBPD) and Rel/NF-κB were also identified. Conclusions: Our study revealed that ECM-receptor interaction, PPAR signaling pathways, and important biomolecules including COL11A1, COL1A1, CD36, IL-6, CEBPD, and Rel/NF-κB might be involved in lung cancer. This study might pave the way for the development and application of targeted therapeutics of lung cancer irrelevant to smoking.

Study the principle and possible mechanism of Ginkgo biloba in the treatment of Alzheimer's disease (AD) which is based on network pharmacology. The potential targets of active ingredients of Ginkgo biloba were collected by Traditional Chinese Medicine Integrated Database platform (TCMSP). TCMSP is a pharmacological system for drug discovery from Chinese herbal medicine. The disease targets of AD were searched and collected by the database of gene-disease associations (DisGeNET) and literature. The obtained targets were standardized by the UniProt database. STING network platform and Cytoscape were used to construct protein-protein interaction network (PPI) of the key targets. According to Materscape, we clarify the possible mechanism of action including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis. The compound-target network contains 27 active ingredients, 191 related targets, 18 key targets, including PLAU, HMOX1, TNF, INSR, MPO, MAOB, IGF2, IL1B, ESR1, BCL2, ACHE, BAX, GSK3B, PPARG, SLC2A4, NOS3, CASP3, VEGFA. GO enrichment analysis has got a total of 640 GO items, including 609 biological process (BP) items (95.1%), 16 molecular function (MF) items (2.5%) and 15 cellular component (CC) items (2.4%). After KEGG enrichment, 44 pathways were obtained. Through the construction of "component-target-pathway", GO biological function and KEGG pathway enrichment analysis were performed on core targets, and the possibility of Ginkgo biloba for the treatment of AD was explored from multiple targets and pathways, which provided a new approach for multi-target treatment.

Background:Cyperus esculentus L. is a unique tuber oil crop, in which tuber size directly determines both yield and oil storage capacity. It is crucial to clarify the tuber expansion pattern and explore the key genes associated with tuber expansion in Cyperus esculentus for crop improvement. Methods: This study conducted comprehensive morphological and cytological observations as well as transcriptomic analysis of tubers at multiple developmental stages. Tubers at 1, 5, 10, and 15 d were collected for transcriptome sequencing to identify differentially expressed genes (DEGs) and differentially expressed transcription factors. Gene ontology (GO) enrichment analysis was used to determine key functional categories. RT-qPCR was employed to verify the expression patterns of key genes. Results:Cyperus esculentus tubers expanded rapidly from 1 d to 15 d after initial tuber formation, and the expansion rate exhibited a trend of increasing first (1~5 d) and then decreasing (5~15 d). Cell expansion, rather than number, mainly contributed to tuber expansion. By combining the analysis of differential expression and the variation pattern of tuber expansion rate, 822 DEGs were identified to be associated with tuber expansion. GO enrichment analysis revealed that 20 genes were significantly enriched in GO:0043231 (cell wall), especially five remarkable genes encoding expansin, which exercise the function of cell wall loosening and have been proven to be associated with cell expansion. In addition, 57 differentially expressed TFs were further identified to be associated with tuber expansion. Conclusions: This study revealed the tuber expansion pattern of Cyperus esculentus and identified several key genes and TFs, which will facilitate the construction of the regulatory network and the analysis of the mechanism of tuber expansion in Cyperus esculentus.

Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.

BackgroundSeveral tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied.ResultsHere we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes into account the relative frequency of certain GO terms and their specificity (hierarchical position) within the GO graph. We evaluated the correlation between our representation score R and a standard measure of enrichment, namely p-values to convey the versatility of our approach to other methods and point out differences between our method and commonly used enrichment analyses. Although p values and the R score formally measure different quantities they should be correlated, because relative frequencies of GO terms occurrences within a dataset are an indirect measure of protein numbers related to this term. Therefore they are also related to enrichment. We showed that our score enables us to identify more specific GO-terms i.e. those positioned further down the GO-graph than other common tools used for this purpose. PCA2GO allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. Our method is intended for the analysis of several experimental sets, not for one set, like standard enrichment tools. To demonstrate the usefulness of our approach we performed a PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS). The analysis enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions.ConclusionsWe conclude that PCA2GO is an alternative efficient GO analysis tool with unique features for detection and visualization of multidimensional dependencies within the dataset under study. PCA2GO reveals strongly correlated GO terms within the experimental setting (in this case different fractions) by PCA group formation and improves detection of more specific GO terms within experiment dependent GO term groups than standard p value calculations.

Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development. Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development.

BackgroundMetabolic reprogramming is one of hallmarks of cancer progression and is of great importance for the tumor microenvironment (TME). As an abundant metabolite, lactate has been found to play a critical role in cancer development and immunosuppression of TME. However, the potential role of lactate metabolism-related genes in endometrial cancer (EC) remains obscure.MethodsRNA sequencing data and clinical information of EC were obtained from The Cancer Genome Atlas (TCGA) database. Lactate metabolism-related genes (LMRGs) WERE from Molecular Signature Database v7.4 and then compared the candidate genes from TCGA to obtain final genes. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to screen prognostic genes. A lactate metabolism-related risk profile was constructed using multivariate Cox regression analysis. The signature was validated by time-dependent ROC curve analysis and Kaplan-Meier analysis. The relationship between the risk score and age, grade, stage, tumor microenvironmental characteristics, and drug sensitivity was as well explored by correlation analyses. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional analysis between the high and low-risk groups were performed. CCK8, EdU, and clone formation assays were applied to detect the proliferation ability of EC cells, Transwell assay was performed to detect the migration ability of EC cells, and intracellular lactate and glucose content was used to asses lactate metabolism.ResultsWe constructed a risk signature based on 18 LMRGs. Kaplan-Meier curves confirmed that the high-risk group had poorer prognosis compared to the low-risk group. A nomogram was then constructed to predict the probability of EC survival. We also performed GO enrichment analysis and KEGG pathway functional analysis between the high and low-risk groups, and the outcome revealed that the features were significantly associated with energy metabolism. There was a significant correspondence between LMRGs and tumor mutational load, checkpoints and immune cell infiltration. C1, C2, and C4 were the most infiltrated in the high-risk group. The high-risk group showed increased dendritic cell activation, while the low-risk group showed increased plasma cells and Treg cells. Drug sensitivity analysis showed LMRGs risk was more resistant to Scr kinase inhibitors. We further proved that one of the lactate metabolism related genes, TIMM50 could promote EC cell proliferation, migration and lactate metabolism.ConclusionIn conclusion, we have established an effective prognostic signature based on LMRG expression patterns, which may greatly facilitate the assessment of prognosis, molecular features and treatment modalities in EC patients and may be useful in the future translation to clinical applications. TIMM50 was identified as a novel molecule that mediates lactate metabolism in vitro and in vivo, maybe a promising target for EC prognosis.

BackgroundBreast cancer (BC) is a common tumor that seriously affects women’s physical/mental health and even life. BC invasion and metastasis are still the main causes of mortality in BC patients. Exosomal long non-coding RNAs (exo-lncRNA) play an important role in cell communication and can help to understand better the physiological and pathological conditions that result from BC. This study investigates new potential targets and functions of the expression profiles of exo-lncRNAs in BC patients through high-throughput screening and bioinformatics.MethodsSamples were collected from two BC patients and one healthy subject. The serum exosomal RNAs were subsequently purified, and a library was established for quality inspection and sequencing. The resultant data was compared with the reference data to obtain the differential expression of exo-lncRNAs, and predict the target genes. To obtain the final results, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to annotate the function and pathway of the differentially expressed genes.ResultsAfter a comprehensive comparison of the BC patients and healthy subjects, we discovered five up-regulated exo-lncRNAs and six down-regulated exo-lncRNAs of interest. Combining our results with a literature review and screening, we found that VIM-AS1, SNHG8, and ELDR play a role in the progression of BC, with VIM-AS1 predicting 35 target miRNAs; SNHG8 predicting 12 target miRNAs, and ELDR predicting 24 target miRNAs. Target prediction considered that the target gene of VIM-AS1 was VIM and that the target gene of SNHG8 was PRSS12. GO enrichment analysis showed that VIM mainly played a role in cell processes, biological regulation, metabolic regulation, and molecular adhesion, while PRSS12 was enriched through cell metabolism, catalytic activity, and hydrolase activity. KEGG pathway enrichment results also indicated how the VIM protein functions in cancer development through the viral infection signaling pathway and miRNA signaling pathway.ConclusionsThere is a significant difference in the expression profiles of serum exo-lncRNAs between BC patients and healthy individuals. This may be closely related to BC’s occurrence, development, and metastasis, and therefore provides a theoretical basis for more in-depth studies into exo-lncRNA.

Objective To explore the network pharmacological mechanism of Wugedan Pill in improving insulin resistance (IR). Methods The main active ingredients and corresponding target genes of Wugedan Pill were retrieved from the Traditional Chinese Medicine System Pharmacology Technology Platform (TCMSP), and the related targets of insulin resistance were obtained through the Human Gene Database (GeneCards) and the Human Online Mendelian Inheritance Database (OMIM). Gene, the active ingredient target of the drug is mapped with the insulin resistance target, and the intersection target is obtained, which is the predicted target of Wugedan Pill acting on insulin resistance. The "pharmaceutical active ingredient-target" network model was constructed by Cytoscape 3.7.0 software, and the key active ingredients were selected. The STRING database was used to construct an intersecting target-protein interaction network (PPI), and key proteins were selected. Intersection targets were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Metascape platform. Results There are 94 active ingredients and 163 related targets of Wugedan Pill acting on insulin resistance. The key active ingredients are quercetin, β-sitosterol and kaempferol, and the key proteins are estrogen receptor 1 (ESR1), aldose reductase (AR), nuclear receptor coactivator protein 1 (NCOA1), Fos protein et al.; GO enrichment analysis showed that Wugedan Pill could affect the activities of oxidoreductase, tyrosine kinase-binding protein, steroid-binding protein and other proteins in improving insulin resistance; KEGG pathway enrichment analysis showed that the effect was the most Notably lipid and atherosclerotic pathways, also significantly affected diabetes complications AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, neuroactive ligand-receptor interactions, alcoholic liver disease, prostate cancer, cancer miRNAs expression, pancreatic cancer, thyroid hormone signaling and other pathways. Conclusion Wugedan Pill may play a role in improving insulin resistance, regulating lipid metabolism, reducing blood glucose and preventing complications by regulating the above targets and pathways with the effective ingredients quercetin, β-sitosterol and kaempferol.

Background/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this study was to provide an unbiased catalog of common gene-expression changes of CKD and reveal the underlying molecular mechanism using an integrative bioinformatics approach. Methods: We systematically collected over 250 Affymetrix microarray datasets from the glomerular and tubulointerstitial compartments of healthy renal tissues and those with various types of established CKD (diabetic kidney disease, hypertensive nephropathy, and glomerular nephropathy). Then, using stringent bioinformatics analysis, shared differentially expressed genes (DEGs) of CKD were obtained. These shared DEGs were further analyzed by the gene ontology (GO) and pathway enrichment analysis. Finally, the protein-protein interaction networks(PINs) were constructed to further refine our results. Results: Our analysis identified 176 and 50 shared DEGs in diseased glomeruli and tubules, respectively, including many transcripts that have not been previously reported to be involved in kidney disease. Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. By constructing PINs, we identified several hub genes (e.g. OAS1, JUN, and FOS) and clusters that might play key roles in regulating the development of CKD. Conclusion: Our study not only further reveals the unifying molecular mechanism of CKD pathogenesis but also provides a valuable resource of potential biomarkers and therapeutic targets.

Gene Ontology (GO) analysis can provide a comprehensive function analysis for investigating genes, allowing us to identify the potential biological roles of genes. The present study conducted GO analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in disease progression and mediating tumor response to RT. Methods: We performed a GO enrichment analysis on the RNA-seq data to validate radiation-induced gene expression. A total of 172 I-IVB specimens from oral squamous cell carcinoma patients were collected for clinical analysis, from which IRAK2 expression was analyzed by immunohistochemistry. This was a retrospective study conducted between IRAK2 expression and the outcomes of oral squamous cell carcinoma patients after radiotherapy treatment. We conducted Gene Ontology (GO) analysis to explore the biological function of IRAK2 and performed a case analysis to define its clinical role in mediating tumor response to radiotherapy. GO enrichment analysis to validate radiation-induced gene expression was performed. Clinically, 172 stage I-IVB resected oral cancer patients were used to validate IRAK2 expression in predicting clinical outcomes. GO enrichment analysis showed that IRAK2 is involved in 10 of the 14 most enriched GO categories for post-irradiation biological processes, focusing on stress response and immune modulation. Clinically, high IRAK2 expression was correlated with adverse disease features, including pT3-4 status (p = 0.01), advanced overall stage (p = 0.02), and positive bone invasion (p = 0.01). In patients who underwent radiotherapy, the IRAK2-high group was associated with reduced post-irradiation local recurrence (p = 0.025) compared to the IRAK2-low group. IRAK2 plays a crucial role in the radiation-induced response. Patients with high IRAK2 expression demonstrated more advanced disease features but predicted higher post-irradiation local control in a clinical setting. These findings support IRAK2 as a potential predictive biomarker for radiotherapy response in non-metastatic and resected oral cancer patients.