Weighted Gene Co-expression Network Research Articles

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 < 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 < 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.

SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast cells showed significant differences in penetration in the high and low SPP1 expression groups. Next, we used the Weighted Gene Co-Expression Network and Least Absolute Shrinkage Sum Selection Operator algorithms to construct a risk score for the 9-immune-related genes signature. The risk score showed a good ability to identify high and low-risk patients and improved survival prediction. We also used multivariate Cox regression to validate that risk score was significantly correlated with SPP1 and overall survival. Lastly, the Back-Propagation Neural Network confirmed the reliability of the results of multiple algorithms. In conclusion, the findings suggest that SPP1 is an independent marker of HCC survival and immunotherapy.

Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network

Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses.

Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated. The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes. CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation. These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection.

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

Full-length transcriptome and metabolite analysis reveal reticuline epimerase-independent pathways for benzylisoquinoline alkaloids biosynthesis in Sinomenium acutum.

Benzylisoquinoline alkaloids (BIAs) are a large family of plant natural products with important pharmaceutical applications. Sinomenium acutum is a medicinal plant from the Menispermaceae family and has been used to treat rheumatoid arthritis for hundreds of years. Sinomenium acutum contains more than 50 BIAs, and sinomenine is a representative BIA from this plant. Sinomenine was found to have preventive and curative effects on opioid dependence. Despite the broad applications of S. acutum, investigation on the biosynthetic pathways of BIAs from S. acutum is limited. In this study, we comprehensively analyzed the transcriptome data and BIAs in the root, stem, leaf, and seed of S. acutum. Metabolic analysis showed a noticeable difference in BIA contents in different tissues. Based on the study of the full-length transcriptome, differentially expressed genes, and weighted gene co-expression network, we proposed the biosynthetic pathways for a few BIAs from S. acutum, such as sinomenine, magnoflorine, and tetrahydropalmatine, and screened candidate genes involved in these biosynthesis processes. Notably, the reticuline epimerase (REPI/STORR), which converts (S)-reticuline to (R)-reticuline and plays an essential role in morphine and codeine biosynthesis, was not found in the transcriptome data of S. acutum. Our results shed light on the biogenesis of the BIAs in S. acutum and may pave the way for the future development of this important medicinal plant.

Global transcriptomic analysis reveals candidate genes associated with different phosphorus acquisition strategies among soybean varieties.

Soybean adapts to phosphorus-deficient soils through three important phosphorus acquisition strategies, namely altered root conformation, exudation of carboxylic acids, and symbiosis with clumping mycorrhizal fungi. However, the trade-offs and regulatory mechanisms of these three phosphorus acquisition strategies in soybean have not been researched. In this study, we investigated the responses of ten different soybean varieties to low soil phosphorus availability by determining biomass, phosphorus accumulation, root morphology, exudation, and mycorrhizal colonization rate. Furthermore, the molecular regulatory mechanisms underlying root phosphorus acquisition strategies were examined among varieties with different low-phosphorus tolerance using transcriptome sequencing and weighted gene co-expression network analysis. The results showed that two types of phosphorus acquisition strategies-"outsourcing" and "do-it-yourself"-were employed by soybean varieties under low phosphorus availability. The "do-it-yourself" varieties, represented by QD11, Zh30, and Sd, obtained sufficient phosphorus by increasing their root surface area and secreting carboxylic acids. In contrast, the "outsourcing" varieties, represented by Zh301, Zh13, and Hc6, used increased symbiosis with mycorrhizae to obtain phosphorus owing to their large root diameters. Transcriptome analysis showed that the direction of acetyl-CoA metabolism could be the dividing line between the two strategies of soybean selection. ERF1 and WRKY1 may be involved in the regulation of phosphorus acquisition strategies for soybeans grown under low P environments. These findings will enhance our understanding of phosphorus acquisition strategies in soybeans. In addition, they will facilitate the development of breeding strategies that are more flexible to accommodate a variety of production scenarios in agriculture under low phosphorus environments.

Prediction of immune subtypes and overall survival in lung squamous cell carcinoma

Objective Lung squamous cell carcinoma (LUSC), one of the most common subtypes of lung cancer, is a leading cause of cancer-caused deaths in the world. It has been well demonstrated that a deep understanding of the tumor environment in cancer would be helpful to predict the prognosis of patients. This study aimed to evaluate the tumor environment in LUSC, and to construct an efficient prognosis model involved in specific subtypes. Methods Four expression files were downloaded from the Gene Expression Omnibus (GEO) database. Three datasets (GSE19188, GSE2088, GSE6044) were considered as the testing group and the other dataset (GSE11969) was used as the validation group. By performing LUSC immune subtype consensus clustering (CC), LUSC patients were separated into two immune subtypes comprising subtype 1 (S1) and subtype 2 (S2). Weighted gene co-expression network (WGCNA) and least absolute shrinkage and selection operator (LASSO) were performed to identify and narrow down the key genes among subtype 1 related genes that were closely related to the overall survival (OS) of LUSC patients. Using immune subtype related genes, a prognostic model was also constructed to predict the OS of LUSC patients. Results It showed that LUSC patients in the S1 immune subtype exhibited a better OS than in the S2 immune subtype. WGCNA and LASSO analyses screened out important immune subtype related genes in specific modules that were closely associated with LUSC prognosis, followed by construction of the prognostic model. Both the testing datasets and validation dataset confirmed that the prognostic model could be efficiently used to predict the OS of LUSC patients in subtype 1. Conclusion We explored the tumor environment in LUSC and established a risk prognostic model that might have the potential to be applied in clinical practice.

DNA computing for gastric cancer analysis and functional classification.

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis

BackgroundUlcerative colitis (UC) refers to an intractable intestinal inflammatory disease. Its increasing incidence rate imposes a huge burden on patients and society. The UC etiology has not been determined, so screening potential biomarkers is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively.MethodsThe microarray datasets of intestinal mucosal biopsy of UC patients were selected from the GEO database, and integrated with R language to screen differentially expressed genes and draw proteins interaction network diagrams. GO, KEGG, DO and GSEA enrichment analyses were performed to explore their biological functions. Through machine learning and WGCNA analysis, targets that can be used as UC potential biomarkers are screened out. ROC curves were drawn to verify the reliability of the results and predicted the mechanism of marker genes from the aspects of immune cell infiltration, co-expression analysis, and competitive endogenous network (ceRNA).ResultsTwo datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained. They were mainly related to biological functions such as humoral immune response and inflammatory response. Further screened out five marker genes, and found that they were associated with M0 macrophages, quiescent mast cells, M2 macrophages, and activated NK cells in terms of immune cell infiltration. The co-expression network found significant co-expression relationships between 54 miRNAs and 5 marker genes. According to the ceRNA hypothesis, NEAT1-miR-342-3p/miR-650-SLC6A14, NEAT1-miR-650-IRAK3, and XIST-miR-342-3p-IRAK3 axes were found as potential regulatory pathways in UC.ConclusionThis study screened out five biomarkers that can be used for the diagnosis and treatment of UC, namely SLC6A14, TIMP1, IRAK3, HMGCS2, and APOBEC3B. Confirmed that they play a role in the occurrence and development of UC at the level of immune infiltration, and proposed a potential RNA regulatory pathway that controls the progression of UC.

Pantranscriptome combined with phenotypic quantification reveals germplasm kinship and regulation network of bract color variation in Bougainvillea.

Bracts are the metamorphic non-flower organ in angiosperm plants. The variation of the color and shape of bracts was found to be neo-functionalized (i.e., similar to petals), garnering research interest as a pollinator attractor. Bougainvillea is known for its specialized, large, and colorful bracts, which contrast with its tiny colorless flowers. As a plant whose bracts vary greatly in terms of coloration, the molecular mechanisms for Bougainvillea bract coloration and polychroism are largely unknown. The lack of genomic information for Bougainvillea largely hinders studies into the evolution and genetic basis of bract color variation. In this study, a pan-transcriptome of bracts obtained from 18 Bougainvillea glabra accessions was employed to investigate the global population-level germplasm kinship and the gene regulation network for bract color variation. Our results showed that the bracts of B. glabra accessions have largely differentiated International Commission on Illumination (CIE) L-a-b values. Moreover, germplasm kinship detected using principal component analysis, phylogeny, and admixture analysis showed three optimal subgroups, two of them distinctly clustered, which were not directly correlated with bract color variation at the population level. Differentially expressed genes (DEGs) between accessions of high vs. low L-a-b values revealed several considerable upregulated genes related to bract color L-a-b variation. A weighted gene co-expression network was constructed, and eight co-expressed regulation modules were identified that were highly correlated with variation in bract CIE L-a-b color values. Several candidate DEGs and co-expressed hub genes (e.g., GERD, SGR, ABCA3, GST, CYP76AD1, CYP76C, and JAZ) that were tightly associated with bract color variation were eventually determined responsible for L-a-b colorations, which might be the core regulation factors contributing to the B. glabra bract color variation. This study provides valuable insights into the research on germplasm kinship, population-level pan-transcriptome expression profiles, and the molecular basis of color variation of key innovative bracts in horticultural Bougainvillea.

Role of ferroptosis-associated genes in ankylosing spondylitis and immune cell infiltration

Ankylosing spondylitis (AS) is a chronic progressive autoimmune disease with insidious onset, high rates of disability among patients, unknown pathogenesis, and no effective treatment. Ferroptosis is a novel type of regulated cell death that is associated with various cancers and diseases. However, its relation to AS is not clear. In the present study, we identified two potential therapeutic targets for AS based on genes associated with ferroptosis and explored their association with immune cells and immune cell infiltration (ICI). We studied gene expression profiles of two cohorts of patients with AS (GSE25101 and GSE41038) derived from the gene expression omnibus database, and ferroptosis-associated genes (FRGs) were obtained from the FerrDb database. LASSO regression analysis was performed to build predictive models for AS based on FRGs, and the ferroptosis level in each sample was assessed via single-sample gene set enrichment analysis. Weighted gene co-expression network and protein-protein interaction network analyses were performed for screening; two key genes, DDIT3 and HSPB1, were identified in patients with AS. The relationship between key genes and ICI levels was assessed using the CIBERSORT algorithm, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, DDIT3 and HSPB1 were identified as diagnostic markers and potential therapeutic targets for AS. DDIT3 was highly positively correlated with the infiltration levels of various immune cells, while HSPB1 was negatively correlated with the infiltration levels of several different types of immune cells. In conclusion, DDIT3 and HSPB1 may induce ferroptosis in the cells of patients with AS via changes in the inflammatory response in the immune microenvironment, and these genes could serve as molecular targets for AS therapy.

GPX2 predicts recurrence-free survival and triggers the Wnt/β-catenin/EMT pathway in prostate cancer.

ObjectiveThis study aimed to establish a prognostic model related to prostate cancer (PCa) recurrence-free survival (RFS) and identify biomarkers.MethodsThe RFS prognostic model and key genes associated with PCa were established using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression from the Cancer Genome Atlas (TCGA)-PRAD and the Gene Expression Omnibus (GEO) GSE46602 datasets. The weighted gene co-expression network (WGCNA) was used to analyze the obtained key modules and genes, and gene set enrichment analysis (GSEA) was performed. The phenotype and mechanism were verified in vitro.ResultsA total of 18 genes were obtained by LASSO regression, and an RFS model was established and verified (TCGA, AUC: 0.774; GSE70768, AUC: 0.759). Three key genes were obtained using multivariate Cox regression. WGCNA analysis obtained the blue module closely related to the Gleason score (cor = –0.22, P = 3.3e − 05) and the unique gene glutathione peroxidase 2 (GPX2). Immunohistochemical analysis showed that the expression of GPX2 was significantly higher in patients with PCa than in patients with benign prostatic hyperplasia (P < 0.05), but there was no significant correlation with the Gleason score (GSE46602 and GSE6919 verified), which was also verified in the GSE46602 and GSE6919 datasets. The GSEA results showed that GPX2 expression was mainly related to the epithelial–mesenchymal transition (EMT) and Wnt pathways. Additionally, GPX2 expression significantly correlated with eight kinds of immune cells. In human PCa cell lines LNCaP and 22RV1, si-GPX2 inhibited proliferation and invasion, and induced apoptosis when compared with si-NC. The protein expression of Wnt3a, glycogen synthase kinase 3β (GSK3β), phosphorylated (p)-GSK3β, β-catenin, p-β-catenin, c-myc, cyclin D1, and vimentin decreased; the expression of E-cadherin increased; and the results for over-GPX2 were opposite to those for over-NC. The protein expression of GPX2 decreased, and β-catenin was unchanged in the si-GPX2+ SKL2001 group compared with the si-NC group.ConclusionWe successfully constructed the PCa RFS prognostic model, obtained RFS-related biomarker GPX2, and found that GPX2 regulated PCa progression and triggered Wnt/β-catenin/EMT pathway molecular changes.

Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications

BackgroundPreeclampsia is a pregnancy-related condition that causes high blood pressure and proteinuria after 20 weeks of pregnancy. It is linked to increased maternal mortality, organ malfunction, and foetal development limitation. In this view, there is a need critical to identify biomarkers for the early detection of preeclampsia. The objective of this study is to discover critical genes and explore medications for preeclampsia treatment that may influence these genes.MethodsFour datasets, including GSE10588, GSE25906, GSE48424 and GSE60438 were retrieved from the Gene Expression Omnibus database. The GSE10588, GSE25906, and GSE48424 datasets were then removed the batch effect using the “sva” R package and merged into a complete dataset. The differentially expressed genes (DEGs) were identified using the “limma” R package. The potential small-molecule agents for the treatment of PE was further screened using the Connective Map (CMAP) drug database based on the DEGs. Further, Weight gene Co-expression network (WGNCA) analysis was performed to identified gene module associated with preeclampsia, hub genes were then identified using the logistic regression analysis. Finally, the immune cell infiltration level of genes was evaluated through the single sample gene set enrichment analysis (ssGSEA).ResultsA total of 681 DEGs (376 down-regulated and 305 up-regulated genes) were identified between normal and preeclampsia samples. Then, Dexamethasone, Prednisone, Rimexolone, Piretanide, Trazodone, Buflomedil, Scoulerin, Irinotecan, and Camptothecin drugs were screened based on these DEGs through the CMAP database. Two modules including yellow and brown modules were the most associated with disease through the WGCNA analysis. KEGG analysis revealed that the chemokine signaling pathway, Th1 and Th2 cell differentiation, B cell receptor signalling pathway and oxytocin signalling pathway were significantly enriched in these modules. Moreover, two key genes, PLEK and LEP were evaluated using the univariate and multivariate logistic regression analysis from the hub modules. These two genes were further validated in the external validation cohort GSE60438 and qRT-PCR experiment. Finally, we evaluated the relationship between immune cell and two genes.ConclusionIn conclusion, the present study investigated key genes associated with PE pathogenesis that may contribute to identifying potential biomarkers, therapeutic agents and developing personalized treatment for PE.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis.

BackgroundAs a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.MethodsWe obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.ResultsWe identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.ConclusionIn summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.