Weighted Gene Co-expression Network Research Articles

Gene co-expression networks unravel the molecular responses of freshwater hydrophytes to combined stress of salinity and cadmium

Salinization in freshwater lakes is becoming a serious global environmental problem, especially in lakes of plateaus such as south-western plateau of China. However, limited information is available about the molecular response of freshwater hydrophytes to salinity under multiple stress. In the present study, a weighted gene co-expression network (WGCNA) was used to identify the modules of co-expressed genes in the physiological and biochemical indicators of Pistia stratiotes to determine its molecular response to salinity (NaCl) alone and when combined with cadmium (Cd). The physiological and biochemical indicators showed that P. stratiotes improved its salt tolerance by enhancing photosynthetic abilities, reducing oxidative stress, and inducing osmoprotectant generation. Morever, addition of NaCl reduced the Cd accumulation in P. stratiotes. Transcriptome and WGCNA analysis revealed that the pathways of alpha-linolenic acid metabolism, ribosomal, flavonoid biosynthesis, and phenylpropanoid biosynthesis were significantly enriched in both treatments. Genes associated with photosynthesis-antenna proteins, nitrogen metabolism, and the acid cycle pathways were only expressed under salinity stress alone, while the proteasome pathway was only significantly enriched in the combined salinity and Cd treatment. Our findings provide novel insights into the effects of salinization on aquatic plants in freshwater ecosystems and the management of aquatic ecosystems under global change.

CircRNAs responsive to winter dormancy and spring flushing conditions of tea leaf buds

Circular RNAs (circRNAs) are important regulators of diverse biological processes of plants. However, the evolution and potential functions of circRNAs during winter dormancy and spring bud flushing of tea plant is largely unknown. Using RNA-seq data, a total of 1184 circRNAs were identified in the winter dormant and spring bud flushing leaf samples of tea plants in two different cultivars exhibiting different duration of winter dormancy. A total of 156 circRNAs are found to be differentially expressed and the weighted gene co-expression network (WGCNA) analysis revealed that 22 and 20 differentially expressed-circRNAs (DE-circRNAs) positively correlated with the flushing and dormant leaf traits, respectively, in both the tea cultivars used. Some transcription factors (TFs) viz. MYB, WRKY, ERF, bHLH and several genes related to secondary metabolite biosynthetic pathways are found to co-express with circRNAs. DE-circRNAs also predicted to interact with miRNAs and can regulate phytohormone biosynthesis and various signalling pathways in tea plant. This study uncovers the potential roles of circRNAs to determine winter dormancy and spring bud flushing conditions in tea plants.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicineand based on a 101-combination machine learning computational framework.

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear. Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR. We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified. Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches. The online version contains supplementary material available at 10.1007/s13167-023-00327-3.

Transcriptome analysis of novel macrophage M1-related biomarkers and potential therapeutic agents in ischemia-reperfusion injury after lung transplantation based on the WGCNA and CIBERSORT algorithms

Lung transplantation is the last effective treatment for end-stage respiratory failure, however, with ischemia–reperfusion injury (IRI) inevitably occurring in postoperative period. IRI is the major pathophysiologic mechanism of primary graft dysfunction, a severe complication that contributes to prolonged length of stay and overall mortality. The understanding of pathophysiology and etiology remain limited and the underlying molecular mechanism, as well as novel diagnostic biomarkers and therapeutic targets, urgently require exploration. Excessive uncontrolled inflammatory response is the core mechanism of IRI. In this research, a weighted gene co-expression network was established using the CIBERSORT and WGCNA algorithms in order to identify macrophage-related hub genes based on the data downloaded from the GEO database (GSE127003, GSE18995). 692 differentially expressed genes (DEGs) in reperfused lung allografts were identified, with three genes recognized as being related to M1 macrophages and validated as differentially expressed using GSE18995 dataset. Of these putative novel biomarker genes, TCRα subunit constant gene (TRAC) were downregulated, while Perforin-1 (PRF1) and Granzyme B (GZMB) were upregulated in reperfused vs. ischemic lung allografts. Furthermore, we obtained 189 potentially therapeutic small molecules for IRI after lung transplantation from the CMap database among which PD-98059 was the top molecule with the highest absolute correlated connectivity score (CS). Our study provides the novel insights into the impact of immune cells on the etiology of IRI and potential targets for therapeutic intervention. Nevertheless, further investigation of these key genes and therapeutic drugs is needed to validate their effects.

Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning.

Polycystic ovary syndrome (PCOS) is a complex endocrine metabolic disorder that affects 5-10% of women of reproductive age. The endometrium of women with PCOS has altered immune cells resulting in chronic low-grade inflammation, which attribute to recurrent implantation failure (RIF). In this study, we obtained three PCOS and RIF datasets respectively from the Gene Expression Omnibus (GEO) database. By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. PCOS and RIF validation datasets were assessed using the receiver operating characteristic (ROC) curve, and ideal area under the curve (AUC) values were obtained for each disease. Besides, we collected granulosa cells from healthy and PCOS infertile women, and endometrial tissues of healthy and RIF patients. RT-PCR was used to validate the reliability of GLIPR1 and MAMLD1. Furthermore, we performed gene set enrichment analysis (GSEA) and immune infiltration to explore the underlying mechanism of PCOS and RIF cooccurrence. Through the functional enrichment of twelve shared genes and two diagnostic genes, we found that both PCOS and RIF patients had disturbances in metabolites related to the TCA cycle, which eventually led to the massive activation of immune cells.

Insight on the hub gene associated signatures and potential therapeutic agents in epilepsy and glioma

ObjectiveThe relationship between epilepsy and glioma has long been widely recognized, but the mechanisms of interaction remain unclear. This study aimed to investigate the shared genetic signature and treatment strategies between epilepsy and glioma. MethodsWe subjected hippocampal tissue samples from patients with epilepsy and glioma to transcriptomic analysis to identify differential genes and associated pathways, respectively. Weight gene co-expression network (WGCNA) analysis was performed to identify conserved modules in epilepsy and glioma and to obtain differentially expressed conserved genes. Prognostic and diagnostic models were built using lasso regression. We also focused on building transcription factor-gene interaction networks and assessing the proportion of immune invading cells in epilepsy patients. Finally, drug compounds were inferred using a drug signature database (DSigDB) based on core targets. ResultsWe discovered 88 differently conserved genes, most of which are involved in synaptic signaling and calcium ion pathways. We used lasso regression model to reduce 88 characteristic genes, and finally screened out 14 genes (EIF4A2, CEP170B, SNPH, EPHA4, KLK7, GNG3, MYOP, ANKRD29, RASD2, PRRT3, EFR3A, SGIP1, RAB6B, CNNM1) as the features of glioma prognosis model whose ROC curve is 0.9. Then, we developed a diagnosis model for epilepsy patients using 8 genes (PRRT3, RASD2, MYPOP, CNNM1, ANKRD29, GNG3, SGIP1, KLK7) with area under ROC curve (AUC) values near 1. According to the ssGSEA method, we observed an increase in activated B cells, eosinophils, follicular helper T cells and type 2T helper cells, and a decrease in monocytes in patients with epilepsy. Notably, the great majority of these immune cells showed a negative correlation with hub genes. To reveal the transcriptional-level regulation mechanism, we also built a TF-gene network. In addition, we discovered that patients with glioma-related epilepsy may benefit more from gabapentin and pregabalin. ConclusionThis study reveals the modular conserved phenotypes of epilepsy and glioma and constructs effective diagnostic and prognostic markers. It provides new biological targets and ideas for the early diagnosis and effective treatment of epilepsy.

Multi-omics reveals that the host-microbiome metabolism crosstalk of differential rumen bacterial enterotypes can regulate the milk protein synthesis of dairy cows

BackgroundDairy cows’ lactation performance is the outcome of the crosstalk between ruminal microbial metabolism and host metabolism. However, it is still unclear to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to regulating the milk protein yield (MPY).MethodsThe rumen fluid, serum and milk of 12 Holstein cows with the same diet (45% coarseness ratio), parity (2–3 fetuses) and lactation days (120–150 d) were used for the microbiome and metabolome analysis. Rumen metabolism (rumen metabolome) and host metabolism (blood and milk metabolome) were connected using a weighted gene co-expression network (WGCNA) and the structural equation model (SEM) analyses.ResultsTwo different ruminal enterotypes, with abundant Prevotella and Ruminococcus, were identified as type1 and type2. Of these, a higher MPY was found in cows with ruminal type2. Interestingly, [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae (the differential bacteria) were the hub genera of the network. In addition, differential ruminal, serum and milk metabolome between enterotypes were identified, where the cows with type2 had higher L-tyrosine of rumen, ornithine and L-tryptophan of serum, and tetrahydroneopterin, palmitoyl-L-carnitine, S-lactoylglutathione of milk, which could provide more energy and substrate for MPY. Further, based on the identified modules of ruminal microbiome, as well as ruminal serum and milk metabolome using WGCNA, the SEM analysis indicated that the key ruminal microbial module1, which contains the hub genera of the network ([Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae) and high abundance of bacteria (Prevotella and Ruminococcus), could regulate the MPY by module7 of rumen, module2 of blood, and module7 of milk, which contained L-tyrosine and L-tryptophan. Therefore, in order to more clearly reveal the process of rumen bacterial regulation of MPY, we established the path of SEM based on the L-tyrosine, L-tryptophan and related components. The SEM based on the metabolites suggested that [Ruminococcus] gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione, which could enhance pyruvate metabolism. Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine, which could provide the substrate for MPY.ConclusionOur results indicated that the represented enterotype genera of Prevotella and Ruminococcus, and the hub genera of [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan. Moreover, the combined analysis of enterotype, WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism, which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric sepsis by integrating bioinformatics and machine learning.

Pediatric sepsis is a complicated condition characterized by life-threatening organ failure resulting from a dysregulated host response to infection in children. It is associated with high rates of morbidity and mortality, and rapid detection and administration of antimicrobials have been emphasized. The objective of this study was to evaluate the diagnostic biomarkers of pediatric sepsis and the function of immune cell infiltration in the development of this illness. Three gene expression datasets were available from the Gene Expression Omnibus collection. First, the differentially expressed genes (DEGs) were found with the use of the R program, and then gene set enrichment analysis was carried out. Subsequently, the DEGs were combined with the major module genes chosen using the weighted gene co-expression network. The hub genes were identified by the use of three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. The receiver operating characteristic curve and nomogram model were used to verify the discrimination and efficacy of the hub genes. In addition, the inflammatory and immune status of pediatric sepsis was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The relationship between the diagnostic markers and infiltrating immune cells was further studied. Overall, after overlapping key module genes and DEGs, we detected 402 overlapping genes. As pediatric sepsis diagnostic indicators, CYSTM1 (AUC = 0.988), MMP8 (AUC = 0.973), and CD177 (AUC = 0.986) were investigated and demonstrated statistically significant differences (P < 0.05) and diagnostic efficacy in the validation set. As indicated by the immune cell infiltration analysis, multiple immune cells may be involved in the development of pediatric sepsis. Additionally, all diagnostic characteristics may correlate with immune cells to varying degrees. The candidate hub genes (CD177, CYSTM1, and MMP8) were identified, and the nomogram was constructed for pediatric sepsis diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for pediatric sepsis patients.

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Alzheimer’s disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations “Alzheimer’s disease pathway,” “Parkinson’s Disease,” “Ribosome,” and “Chronic myeloid leukemia.” In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.

Analysis of the regulatory role of miR-34a-5p/PLCD3 in the progression of osteoarthritis

Osteoarthritis is a heterogeneous disease with a complex etiology. However, there is no effective treatment strategy at present. The purpose of this study was to explore the miRNA‒mRNA regulatory network and molecular mechanism that regulate the progression of osteoarthritis. In this article, we downloaded datasets (GSE55457, GSE82107, GSE143514 and GSE55235) from Gene Expression Omnibus (GEO) to screen differentially expressed mRNAs in osteoarthritis. Then, through weighted gene coexpression network (WGCNA), functional enrichment, protein‒protein interaction (PPI) network, miRNA‒mRNA coexpression network, ROC curve, and immune infiltration analyses and qPCR, the mRNA PLCD3, which was highly expressed in osteoarthritis and had clinical predictive value, was screened. We found that PLCD3 directly targets miR-34a-5p through DIANA and dual-luciferase experiments. The expression levels of PLCD3 and miR-34a-5p were negatively correlated. In addition, CCK-8 and wound healing assays showed that the miR-34a-5p mimic inhibited hFLS-OA cell proliferation and promoted hFLS-OA cell migration. PLCD3 overexpression showed the opposite trend. Western blotting further found that overexpression of miR-34a-5p reduced the protein expression levels of p-PI3K and p-AKT, while overexpression of PLCD3 showed the opposite trend. In addition, combined with the effect of the PI3K/AKT pathway inhibitor BIO (IC50 = 5.95μM), the results showed that overexpression of miR-34a-5p increased the inhibitory effects of BIO on p-PI3K and p-AKT protein expression, while overexpression of PLCD3 significantly reversed these inhibitory effects. Overall, the miR-34a-5p/PLCD3 axis may mediate the PI3K/AKT pathway in regulating cartilage homeostasis in synovial osteoarthritis. These data indicate that miR-34a-5p/PLCD3 may be a new prognostic factor in the pathology of synovial osteoarthritis.

Gene network analyses of larvae under different egg-protecting behaviors provide novel insights into immune response mechanisms of Amphioctopus fangsiao

Amphioctopus fangsiao was a representative economic species in cephalopods, which was vulnerable to marine bacteria. Vibrio anguillarum was a highly infectious pathogen that have recently been found to infect A. fangsiao and inhibit its growth and development. There were significant differences in the immune response mechanisms between egg-protected and egg-unprotected larvae. To explore larval immunity under different egg-protecting behaviors, we infected A. fangsiao larvae with V. anguillarum for 24 h and analyzed the transcriptome data about egg-protected and egg-unprotected larvae infected with 0, 4, 12, and 24 h using weighted gene co-expression networks (WGCNA) and protein-protein interaction (PPI) networks. Network analyses revealed a series of immune response processes after infection, and identified six key modules and multiple immune-related hub genes. Meanwhile, we found that ZNF family, such as ZNF32, ZNF160, ZNF271, ZNF479, and ZNF493 might play significant roles in A. fangsiao immune response processes. We first creatively combined WGCNA and PPI network analysis to deeply explore the immune response mechanisms of A. fangsiao larvae with different egg-protecting behaviors. Our results provided further insights into the immunity of V. anguillarum infected invertebrates, and laid the foundation for exploring the immune differences among cephalopods with different egg protecting behaviors.

Effects of prenatal pesticide exposure on the fetal brain and placenta transcriptomes in a rodent model

Organophosphate and pyrethroid pesticides are among the most extensively used insecticides worldwide. Prenatal exposures to both classes of pesticides have been linked to a wide range of neurobehavioral deficits in the offspring. The placenta is a neuroendocrine organ and the crucial regulator of the intrauterine environment; early-life toxicant exposures could impact neurobehavior by disrupting placental processes. Female C57BL/6 J mice were exposed via oral gavage to an organophosphate, chlorpyrifos (CPF) at 5 mg/kg, a pyrethroid, deltamethrin (DM), at 3 mg/kg, or vehicle only control (CTL). Exposure began two weeks before breeding and continued every three days until euthanasia at gestational day 17. The transcriptomes of fetal brain (CTL n = 18, CPF n = 6, DM n = 8) and placenta (CTL n = 19, CPF n = 16, DM n = 12) were obtained through RNA sequencing, and resulting data was evaluated using weighted gene co-expression networks, differential expression, and pathway analyses. Fourteen brain gene co-expression modules were identified; CPF exposure disrupted the module related to ribosome and oxidative phosphorylation, whereas DM disrupted the modules related to extracellular matrix and calcium signaling. In the placenta, network analyses revealed 12 gene co-expression modules. While CPF exposure disrupted modules related to endocytosis, Notch and Mapk signaling, DM exposure dysregulated modules linked to spliceosome, lysosome and Mapk signaling pathways. Overall, in both tissues, CPF exposure impacted oxidative phosphorylation, while DM was linked to genes involved in spliceosome and cell cycle. The transcription factor Max involved in cell proliferation was overexpressed by both pesticides in both tissues. In summary, gestational exposure to two different classes of pesticide can induce similar pathway-level transcriptome changes in the placenta and the brain; further studies should investigate if these changes are linked to neurobehavioral impairments.

Screening of key pathogenic genes of type 1 diabetes in children.

The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

PhebZIP47, a bZIP transcription factor from moso bamboo (Phyllostachys edulis), positively regulates the drought tolerance of transgenic plants

The bZIP transcription factor (TF) family plays indispensable roles in plant response to environmental stresses. However, little is known about the functional roles of bZIP genes in moso bamboo (Phyllostachys edulis). Here, transcriptome data from public databases was used to analyse the expression patterns of 154 bZIP genes, and the results exhibited that the expression of many bZIP genes were strongly induced by drought. Combined with weighted gene co-expression network and qRT-PCR analysis, we screened a key candidate gene PhebZIP47 in response to drought and ABA. Next, the lines of transgenic PhebZIP47-overexpressing Arabidopsis and rice were generated. Transgenic plants showed enhanced resistance to drought at the seed germination and adult stage. Measurements of physiological indicators including chlorophyll content, relative water content, electrical conductivity, malondialdehyde and proline content were used to support this conclusion. Transcriptome analysis of transgenic rice showed that there were a large number of differentially expressed genes (DEGs) regulated by PhebZIP47, including genes of drought tolerance regulatory pathway and ABA signalling pathway. Meanwhile, a G-box element was significantly enriched in the promoters of the DEGs annotated as ‘response to stress’, and EMSA experiment suggested that this element could be bound by PhebZIP47. In addition, transgenic plants were less sensitive to ABA compared with wild-type plants under exogenous ABA treatment. Overall, our study provided a key candidate gene for the molecular breeding of moso bamboo and laid a foundation for the study of drought-resistance mechanism of bZIP genes.

Abstract P5-02-41: UBE2E3 promotes the progression of HER2-positive breast cancer and influences the efficacy of targeted therapy via EGFR stabilization

Abstract Background: In the past 20 years, the efficacy and prognosis of HER2-positive breast cancer have significantly improved. However, nearly 50% of patients still have residual invasive tumors after chemotherapy combined with dual-targeted neoadjuvant therapy, especially for those with disease progression during treatment. A lack of effective therapeutic regimens results from the failure of targeted therapy, whose heterogeneity is especially worthy of our attention. The aim of this study was to look for efficacy markers and investigate new drug-resistance mechanisms. Methods: Firstly, the high-throughput sequencing data from 81 patients who received neoadjuvant chemotherapy TCbH (paclitaxel + carboplatin + trastuzumab) was analyzed by the efficacy outcomes. They were divided into 8 patients with stable or progressive disease (SD/PD), 35 with partial response (PR), and 38 with pathological complete remission (pCR). Then, UBE2E3 was chosen from the different expression genes between SD/PD and pCR based on efficacy results and the weighted gene co-expression network (WGCNA). UBE2E3 clinical correlations were investigated using publicly available data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and UBE2E3 was validated using immunohistochemistry (IHC) on 200 HER2-positive breast cancer tissue chips. Further, the UBE2E3 knockdown and overexpression stable transfer cell lines were constructed, and the effects of UBE2E3 on cell proliferation, clone formation, and drug sensitivity were verified by live cell imaging, the CCK8 assay, plate cloning, and IC50 assays, respectively. The tumor growth of UBE2E3 in vivo was investigated by an in situ transplantation tumor assay in nude mice. Meanwhile, the p-RB assay of mouse tissues by IHC was used to explore the effect of UBE2E3 on cell proliferation. RNA-seq was used to screen the downstream molecules of UBE2E3. Western blotting was used to verify the results of bioinformatics analysis and to explore the downstream key molecules. The protease inhibitor MG132 and actinomycin CHX were used to look at the effect on the stability of the target protein. Immunoprecipitation and silver staining assays were used to find interacting proteins with the UBE2E3. Results: Ten hub-genes which were efficacy-related were identified by WGCNA analysis, in which UBE2E3 was highly expressed in the SD/PD group (p &amp;lt; 0.05). In HER2-positive breast cancer, high expression of UBE2E3 was associated with poor prognosis and decreased disease-free survival both in public data and Fudan University Shanghai Cancer Center (FUSCC) data [HR 2. 36, (1.25–4.47), p &amp;lt; 0.05]. The experimental results demonstrated that UBE2E3 promoted the proliferation of HER2-positive breast cancer cells, enhanced clone formation, and resisted lapatinib’s treatment in cellular phenotype; and that UBE2E3 promoted tumor growth in vivo and upregulated the expression of p-RB. The differentially expressed genes’ sets of the RNA-seq between overexpressed cell lines and control showed that overexpressing UBE2E3 activated the EGFR pathway. Further, an immunoblot assay confirmed that UBE2E3 positively regulated EGFR levels and activated the downstream MAPK pathway. The proteasome inhibitor MG132 and CHX assays showed that UBE2E3 could stabilize EGFR proteins. The co-immunoprecipitation and silver staining assays showed that UBE2E3 stabilized EGFR proteins by interacting with c-Cbl. Conclusion: UBE2E3 could negatively affect the efficacy of HER2-positive breast cancer therapy and is significantly associated with poor prognosis. UBE2E3 may serve as a potential marker of efficacy and prognosis for HER2-positive breast cancer in the future. Therapeutic efficacy is affected by UBE2E3, which binds to c-Cbl and causes upregulation of EGFR expression in vivo, which in turn causes the MAPK pathway to be activated and tumor growth to be pushed up. Citation Format: Pei Li, Wei-Ru Chi, Bingqiu Xiu, Qi Zhang, Liyi Zhang, Ming Chen, Jingyan Xue, Xiaoyan Huang, Yayun Chi, Jiong Wu. UBE2E3 promotes the progression of HER2-positive breast cancer and influences the efficacy of targeted therapy via EGFR stabilization [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-41.

Clinical Significance and Tumor Microenvironment Characterization of a Novel Immune-Related Gene Signature in Bladder Cancer

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.

APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment.

Diabetic nephropathy is the leading cause of end-stage renal disease, which imposes a huge economic burden on individuals and society, but effective and reliable diagnostic markers are still not available. Differentially expressed genes (DEGs) were characterized and functional enrichment analysis was performed in DN patients. Meanwhile, a weighted gene co-expression network (WGCNA) was also constructed. For further, algorithms Lasso and SVM-RFE were applied to screening the DN core secreted genes. Lastly, WB, IHC, IF, and Elias experiments were applied to demonstrate the hub gene expression in DN, and the research results were confirmed in mouse models and clinical specimens. 17 hub secretion genes were identified in this research by analyzing the DEGs, the important module genes in WGCNA, and the secretion genes. 6 hub secretory genes (APOC1, CCL21, INHBA, RNASE6, TGFBI, VEGFC) were obtained by Lasso and SVM-RFE algorithms. APOC1 was discovered to exhibit elevated expression in renal tissue of a DN mouse model, and APOC1 is probably a core secretory gene in DN. Clinical data demonstrate that APOC1 expression is associated significantly with proteinuria and GFR in DN patients. APOC1 expression in the serum of DN patients was 1.358±0.1292μg/ml, compared to 0.3683±0.08119μg/ml in the healthy population. APOC1 was significantly elevated in the sera of DN patients and the difference was statistical significant (P > 0.001). The ROC curve of APOC1 in DN gave an AUC = 92.5%, sensitivity = 95%, and specificity = 97% (P < 0.001). Our research indicates that APOC1 might be a novel diagnostic biomarker for diabetic nephropathy for the first time and suggest that APOC1 may be available as a candidate intervention target for DN.

Targeted inhibition of RBPJ transcription complex alleviates the exhaustion of CD8+ T cells in hepatocellular carcinoma

Impaired function of CD8+ T cells in hepatocellular carcinoma (HCC) is an important reason for acquired resistance. Compared with single-target inhibitors, small-molecule compounds that could both inhibit tumor cells and alleviate T cell exhaustion are more promising to reduce resistance. In this study, we screened immunosuppressive targets in HCC by combining cancer–immunity cycle score with weighted gene co-expression network and system analysis. Through in vitro and in vivo validation experiments, we found that one of the screened molecules, recombination signal binding protein for immunoglobulin kappa J region (RBPJ), was negatively correlated with CD8+ T cell mediated killing function. More importantly, its transcription complex inhibitor RIN1 not only inhibited the malignant biological behaviors of HCC cells by inhibiting mTOR pathway, but also reduced the expression of PD-L1 and L-kynurenine synthesis in HCC cells, thus alleviating T cell exhaustion. Meanwhile, the combination of RIN1 and anti-PD-1/PD-L1 antibodies could further activate CD8+ T cells. In short, RBPJ is an important factor regulating the function of T cells. Target inhibition of RBPJ transcription complex by small molecule compound may be a new strategy for immunotherapy of HCC.

How does SARS-CoV-2 infection impact on immunity, processionand treatment of pan cancers.

We identified 14 immune-related differentially Expressed Genes (DEGs) between COVID-19 patients and normal controls and the receiver operator characteristic curve results showed that they could be used to discriminate COVID-19 patients from healthy controls. Single-sample gene set enrichment analysis and CIBERSORT analysis displayed immune landscape of COVID-19 patients that the fraction of immune cells (like B cell subtypes and T cell subtypes) decreased distinctly in the first SARS-CoV-2 infection which may further weaken immunity of cancer patients and increasing inflammatory cells (Neutrophils and Macrophages) may further promote inflammatory response of cancer patients. Based on expression levels of 14 DEGs we found that first SARS-CoV-2 infection may accelerate progression of cancer patients by Kaplan-Meier survival, immune subtypes and tumor microenvironment analyses, and may weaken anti-PD-1 monoclonal antibody treatment effect of cancer patients by weighted gene co-expression network, tumor mutation burden and microsatellite instability analysis. The second SARS-CoV-2 infection was beneficial to control development of tumor seemingly, but it may be difficult for cancer patients to experience destroy successfully from first SARS-CoV-2 infection, let alone benefits from second SARS-CoV-2 infection. In addition, this study also emphasized significance of multi-factor analysis when analyzing impacts of SARS-CoV-2 infection on cancer patients.