Effects of grape seed proanthocyanidin extract on cholesterol metabolism and antioxidant status in finishing pigs.

Grape seed proanthocyanidin extract (GSPE) is a natural polyphenolic compound, which plays an important role in anti-inflammatory and antioxidant. The present study aimed to investigate the effects of GSPE supplementation on the cholesterol metabolism and antioxidant status of finishing pigs. In longissimus dorse (LD) muscle, the data showed that GSPE significantly decreased the contents of total cholesterol (T-CHO) and triglyceride (TG), and decreased the mRNA expression of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoAR) and Fatty acid synthase (FAS), while increased the mRNA expression of carnitine palmitoyl transferase-1b (CPT1b), peroxisome proliferator-activated receptors (PPARα) and peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α). GSPE also reduced the enzyme activities of HMG-CoAR and FAS, and meanwhile amplified the activity of CPT1b in LD muscle of finishing pigs. Furthermore, dietary GSPE supplementation increased the serum catalase (CAT) and total antioxidant capacity (T-AOC), serum and liver total superoxide dismutase (T-SOD) and glutathione peroxidase (GSH-Px) levels, while reduced serum and liver malondialdehyde (MDA) level in finishing pigs. In the liver, Superoxide Dismutase 1 (SOD1), catalase (CAT), glutathione peroxidase 1 (GPX1), Nuclear Factor erythroid 2-Related Factor 2 (NRF2) mRNA levels were increased by GSPE. In conclusion, this study showed that GSPE might be an effective dietary supplement for improving cholesterol metabolism and antioxidant status in finishing pigs.

Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)-a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. METHODS: Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. RESULTS: Our analyses revealed that eleven PyMGs-DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2-are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. CONCLUSIONS: This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma's molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes.

AIM: Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients. METHOD: Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan-Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages. RESULTS: A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax. CONCLUSION: By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

Development and validation of prognostic signatures of NAD+ metabolism and immune-related genes in colorectal cancer.

Background: Colorectal cancer (CRC) is a prevalent cause of death from malignant tumors. This study aimed to develop a nicotinamide adenine dinucleotide (NAD+) metabolism and immune-related prognostic signature, providing a theoretical foundation for prognosis and therapy in CRC patients. Methods: NAD + metabolism-related and immune-related subtypes of CRC patients were identified by consistent clustering. Differentially expressed genes (DEGs) between the two subtypes of CRC were identified by overlapping. A risk signature was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Independent prognostic predictors were authenticated by Cox analysis. Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were applied to investigate the connection between the prognostic signature and the immune microenvironment. Chemotherapy drug sensitivity and immunotherapy responsiveness were projected using the 'pRRophetic' package and Tumor Immune Dysfunction and Exclusion (TIDE) website. The Human Protein Atlas (HPA) database was used to assess the protein expression of prognostic genes in CRC and normal tissues. Results: Using bioinformatics methods, three prognostic genes related to immune-related NAD + metabolism were identified, and the results were used to establish and verify a prognostic signature related to immune-related NAD + metabolism in CRC patients. Cox regression analysis confirmed that the risk score was a reliable independent prognostic predictor. GSVA and ssGSEA indicated that the prognostic signature was associated with the immune microenvironment. TIDE analysis suggested that the signature might act as an immunotherapy predictor. Chemotherapy sensitivity analysis revealed that COMP was correlated with chemotherapy sensitivity in CRC patients and might be a potential therapeutic target. Conclusion: This study identified NAD + metabolism-immune-related prognostic genes (MOGAT2, COMP, and DNASE1L3) and developed a prognostic signature for CRC prognosis, which is significant for clinical prognosis prediction and treatment strategy decisions for CRC patients.

Expression of Iron Metabolism Genes Is Potentially Regulated by DOF Transcription Factors in Dendrocalamus latiflorus Leaves.

Transcription factors (TFs) are crucial pre-transcriptional regulatory mechanisms that can modulate the expression of downstream genes by binding to their promoter regions. DOF (DNA binding with One Finger) proteins are a unique class of TFs with extensive roles in plant growth and development. Our previous research indicated that iron content varies among bamboo leaves of different colors. However, to our knowledge, genes related to iron metabolism pathways in bamboo species have not yet been studied. Therefore, in the current study, we identified iron metabolism related (IMR) genes in bamboo and determined the TFs that significantly influence them. Among these, DOFs were found to have widespread effects and potentially significant impacts on their expression. We identified specific DOF members in Dendrocalamus latiflorus with binding abilities through homology with Arabidopsis DOF proteins, and established connections between some of these members and IMR genes using RNA-seq data. Additionally, molecular docking confirmed the binding interactions between these DlDOFs and the DOF binding sites in the promoter regions of IMR genes. The co-expression relationship between the two gene sets was further validated using q-PCR experiments. This study paves the way for research into iron metabolism pathways in bamboo and lays the foundation for understanding the role of DOF TFs in D. latiflorus.

Gene signatures of copper metabolism related genes may predict prognosis and immunity status in Ewing's sarcoma.

Background: Cuproptosis is copper-induced cell death. Copper metabolism related genes (CMRGs) were demonstrated that used to assess the prognosis out of tumors. In the study, CMRGs were tested for their effect on TME cell infiltration in Ewing's sarcoma (ES). Methods: The GEO and ICGC databases provided the mRNA expression profiles and clinical features for downloading. In the GSE17674 dataset, 22prognostic-related copper metabolism related genes (PR-CMRGs) was identified by using univariate regression analysis. Subsequently, in order to compare the survival rates of groups with high and low expression of these PR-CMRGs,Kaplan-Meier analysis was implemented. Additionally, correlations among them were examined. The study employed functional enrichment analysis to investigate probable underlying pathways, while GSVA was applied to evaluate enriched pathways in the ES (Expression Set). Through an unsupervised clustering algorithm, samples were classified into two clusters, revealing significant differences in survival rates and levels of immune infiltration. Results: Using Lasso and step regression methods, five genes (TFRC, SORD, SLC11A2, FKBP4, and AANAT) were selected as risk signatures. According to the Kaplan-Meier survival analysis, the high-risk group had considerably lower survival rates than the low-risk group(p=6.013e-09). The area under the curve (AUC) values for the receiver operating characteristic (ROC) curve were 0.876, 0.883, and 0.979 for 1, 3, and 5 years, respectively. The risk model was further validated in additional datasets, namely GSE63155, GSE63156, and the ICGC datasets. To aid in outcome prediction, a nomogram was developed that incorporated risk levels and clinical features. This nomogram's performance was effectively validated through calibration curves.Additionally, the study evaluated the variations in immune infiltration across different risk groups, as well as high-expression and low-expression groups. Importantly, several drugs were identified that displayed sensitivity, offering potential therapeutic options for ES. Conclusion: The findings above strongly indicate that CMRGs play crucial roles in predicting prognosis and immune status in ES.

MICAL1 promotes the proliferation in acute myeloid leukemia and is associated with clinical prognosis and immune infiltration.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has a poor prognosis and a high rate of relapse. Dysregulated metabolism plays an important role in AML progression. This study aimed to conduct a comprehensive analysis of MRGs using TCGA and GEO datasets and further explore the potential function of critical MRGs in AML progression. In this study, we identified 17 survival-related differentially expressed MRGs in AML using TCGA and GEO datasets. The 150 AML samples were divided into three molecular subtypes using 17 MRGs, and we found that three molecular subtypes exhibited a different association with ferroptosis, cuproptosis and m6A related genes. Moreover, a prognostic signature that comprised nine MRGs and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. Among the 17 MRGs, our attention focused on MICAL1 which was highly expressed in many types of tumors, including AML and its overexpression was also confirmed in several AML cell lines. We also found that the expression of MICAL1 was associated with several immune cells. Moreover, functional experiments revealed that knockdown of MICAL1 distinctly suppressed the proliferation of AML cells. Overall, this study not only contributes to a deeper understanding of the molecular mechanisms underlying AML but also provides potential targets and prognostic markers for AML treatment. These findings offer robust support for further research into therapeutic strategies and mechanisms related to AML, with the potential to improve the prognosis and quality of life for AML patients. Nevertheless, further research is needed to validate these findings and explore more in-depth molecular mechanisms.

Identification of lipid metabolism-related gene signature in the bone marrow microenvironment of multiple myelomas through deep analysis of transcriptomic data.

Dysregulated lipid metabolism in the bone marrow microenvironment (BMM) plays a vital role in multiple myeloma (MM) development, progression, and drug resistance. However, the exact mechanism by which lipid metabolism impacts the BMM, promotes tumorigenesis, and triggers drug resistance remains to be fully elucidated.By analyzing the bulk sequencing and single-cell sequencing data of MM patients, we identified lipid metabolism-related genes differential expression significantly associated with MM prognosis, referred to as LMRPgenes. Using a cohort of ten machine learning algorithms and 117 combinations, LMRPgenes predictive models were constructed. Further exploration of the effects of the model risk score (RS) on the survival status, immune status of patients with BMM, and response to immunotherapy was conducted. The study also facilitated the identification of personalized therapeutic strategies targeting specified risk categories within patient cohorts.Analysis of the scRNA-seq data revealed increased lipid metabolism-related gene enrichment scores (LMESs) in erythroblasts and progenitor, malignant, and Tprolif cells but decreased LMESs in lymphocytes. LMESs were also strongly correlated with most of the 50 hallmark pathways within these cell populations. An elevated malignant cell ratio and reduced lymphocytes were observed in the high LMES group. Moreover, the LMRPgenes predictive model, consisting of 14 genes, showed great predictive power. The risk score emerged as an independent indicator of poor outcomes. Inverse relationships between the RS and immune status were noted, and a high RS was associated with impaired immunotherapy responses. Drug sensitivity assays indicated the effectiveness of bortezomib, buparlisib, dinaciclib, staurosporine, rapamycin, and MST-312 in the high-RS group, suggesting their potential for treating patients with high-RS values and poor response to immunotherapy. Ultimately, upon verification via qRT-PCR, we observed a significant upregulation of ACBD6 in NDMM group compared to the control group.Our research enhances the knowledge base regarding the association between lipid metabolism-related genes (LMRGs) and the BMM in MM patients, offering substantive insights into the mechanistic effects of the BMM mediated by LMRGs.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics.

BACKGROUND: Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. RESULTS: Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. CONCLUSION: A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Identification and analysis of immunogenicity and immunotherapy efficacy by fatty acid genes: a novel prognostic features of lumbar disc herniation and Mendelian randomization analysis.

BACKGROUND: Sciatica is a phrase used to describe radiating leg discomfort. The most common cause is lumbar disc herniation (LDH), which is considered to start in the nucleus pulposus. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in LDH, remain largely unexplored and are a subject of ongoing research. METHODS: The junction of the weighted correlation network analysis (WGCNA) test with 6 FAMGs enabled the finding of FAMGs. Gene set variation analysis (GSVA) was used to identify the possible biological activities and pathways of FAMGs. LASSO was used to determine diagnostic effectiveness of the four FAMGs in diagnosing LDH. GSE124272, GSE147383, GSE150408, and GSE153761 were utilized to confirm the levels of expression of four FAMGs. RESULTS: Four FAMGs were discovered [Acyl-CoA Thioesterase 4 (ACOT4), Cytochrome P450 Family 4 Subfamily A Member 11 (CYP4A11), Acyl-CoA Dehydrogenase Long Chain (ACADL), Enoyl-CoA Hydratase and 3-Hydroxyacyl CoA Dehydrogenase (EHHADH)] For biological function analysis, mhc class ib receptor activity, response to thyroxine, response to l phenylalanine derivative were emphasized. CONCLUSIONS: FAMGs can help with prognosis and immunology, and provide evidence for fatty acid metabolism-related targeted therapeutics. In LDH, FAMGs and their interactions with immune cells might be therapeutic targets.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis.

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes.

Background and Aims: Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC. Methods: Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes. Results: HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified. Conclusions: This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes.

BACKGROUND: Osteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes. METHODS: We obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website. RESULTS: We identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified. CONCLUSION: 4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

A prognostic metabolism-related gene signature associated with the tumor immune microenvironment in neuroblastoma.

Neuroblastoma (NB) is the most prevalent malignant solid tumor in children. Tumor metabolism, including lipid, amino acid, and glucose metabolism, is intricately linked to the genesis and progression of tumors. This study aimed to establish a prognostic gene signature for NB patients, based on metabolism-related genes, and to investigate a treatment approach that could enhance the survival rate of high-risk NB patients. From the NB dataset GSE49710, we identified metabolism-related gene markers utilizing the "limma" R package and univariate Cox analysis combined with least absolute shrinkage and selection operator (LASSO) regression analysis. We explored the correlation between these gene markers and the overall survival of NB patients. Gene set enrichment analysis (GSEA) and single-sample GSEA algorithms were used to assess the differences in metabolism and immune status. Furthermore, we examined the association between metabolic subgroups and drug responsiveness. Concurrently, data downloaded from TARGET and MTAB were used for external verification. Using multicolor immunofluorescence and immunohistochemistry, we investigated the relationship between the lipid metabolism-related gene ELOVL6 with both the International Neuroblastoma Staging System classification of NB and survival rate. Finally, we explored the effect of high ELOVL6 expression on the immune microenvironment in NB using flow cytometry. We identified an eight-gene signature comprising metabolism-related genes in NB: ELOVL6, OSBPL9, RPL27A, HSD17B3, ACHE, AKR1C1, PIK3R1, and EPHX2. This panel effectively predicted disease-free survival, and was validated using an internal dataset from GSE49710 and two external datasets from the TARGET and MTAB databases. Moreover, our findings confirmed that ELOVL6 fosters an immunosuppressive microenvironment and contributes to the malignant progression in NB. The eight-gene signature is significant in predicting the prognosis of NB, effectively classifying patients into high- and low-risk groups. This classification may guide the development of innovative treatment strategies for these patients. Notably, the signature gene ELOVL6 markedly encourages an immunosuppressive microenvironment and malignant progression in NB.

Genome analysis of Shewanella putrefaciens 4H revealing the potential mechanisms for the chromium remediation.

Microbial remediation of heavy metal polluted environment is ecofriendly and cost effective. Therefore, in the present study, Shewanella putrefaciens stain 4H was previously isolated by our group from the activated sludge of secondary sedimentation tank in a dyeing wastewater treatment plant. The bacterium was able to reduce chromate effectively. The strains showed significant ability to reduce Cr(VI) in the pH range of 8.0 to 10.0 (optimum pH 9.0) and 25-42 ℃ (optimum 30 ℃) and were able to reduce 300 mg/L of Cr(VI) in 72 h under parthenogenetic anaerobic conditions. In this paper, the complete genome sequence was obtained by Nanopore sequencing technology and analyzed chromium metabolism-related genes by comparative genomics The genomic sequence of S. putrefaciens 4H has a length of 4,631,110 bp with a G + C content of 44.66% and contains 4015 protein-coding genes and 3223,  2414, 2343 genes were correspondingly annotated into the COG, KEGG, and GO databases. The qRT-PCR analysis showed that the expression of chrA, mtrC, and undA genes was up-regulated under Cr(VI) stress. This study explores the Chromium Metabolism-Related Genes of S. putrefaciens 4H and will help to deepen our understanding of the mechanisms of Cr(VI) tolerance and reduction in this strain, thus contributing to the better application of S. putrefaciens 4H in the field of remediation of chromium-contaminated environments.

Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma.

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution.

Transcriptomic analysis reveals the lipid metabolism-related gene regulatory characteristics and potential therapeutic agents for myocardial ischemia-reperfusion injury.

Background: Impaired energy balance caused by lipid metabolism dysregulation is an essential mechanism of myocardial ischemia-reperfusion injury (MI/RI). This study aims to explore the lipid metabolism-related gene (LMRG) expression patterns in MI/RI and to find potential therapeutic agents. Methods: Differential expression analysis was performed to screen the differentially expressed genes (DEGs) and LMRGs in the MI/RI-related dataset GSE61592. Enrichment and protein-protein interaction (PPI) analyses were performed to identify the key signaling pathways and genes. The expression trends of key LMRGs were validated by external datasets GSE160516 and GSE4105. The corresponding online databases predicted miRNAs, transcription factors (TFs), and potential therapeutic agents targeting key LMRGs. Finally, the identified LMRGs were confirmed in the H9C2 cell hypoxia-reoxygenation (H/R) model and the mouse MI/RI model. Results: Enrichment analysis suggested that the "lipid metabolic process" was one of the critical pathways in MI/RI. Further differential expression analysis and PPI analysis identified 120 differentially expressed LMRGs and 15 key LMRGs. 126 miRNAs, 55 TFs, and 51 therapeutic agents were identified targeting these key LMRGs. Lastly, the expression trends of Acadm, Acadvl, and Suclg1 were confirmed by the external datasets, the H/R model and the MI/RI model. Conclusion: Acadm, Acadvl, and Suclg1 may be the key genes involved in the MI/RI-related lipid metabolism dysregulation; and acting upon these factors may serve as a potential therapeutic strategy.

Prognostic Value of Fatty Acid Metabolism-related Genes in Patients with Bladder Cancer.

INTRODUCTION: This study aimed to explore the expression profiles of fatty acid metabolism- related genes (FAMRGs) in patients with bladder cancer (BLCA). METHODS: The corresponding clinicopathological features of BLCA patients and RNA sequencing data were downloaded from TCGA and GSE13507. Univariate Cox regression was used to determine the prognostic value of FRGS in BLCA patients. LASSO regression analysis was then performed to select potential risk genes and eliminate genes that might overfit the model. Based on the independent prognostication-related FRGs, the nomogram survival model was established using the root mean square value of the R packet to predict the 1-year, 3-year, and 5-year survival rates of BLCA patients. By determining the area under the curve (AUC) value, the time-dependent receiver operating characteristic curve (ROC) was used to evaluate the prognostic efficiency of our model. RESULTS: A total of 243 DEFRGs were identified. Twenty FRGs were found to be related to the prognosis of BLCA in the TCGA database. Survival curves showed that high-risk patients had significantly shorter OS than low-risk cases (p < 0.001). The AUC of risk was 0.784, which was superior to age, sex, and stage, suggesting that the risk score was more favorable in predicting OS than traditional pathologic prognostic factors. The AUC was 0.757 at 1 year, 0.732 at 3 years, and 0.733 at 5 year-OS. CONCLUSION: In this study, we demonstrated that a FAMRG prognosis biomarker is associated with the tumor immune microenvironment in patients with BLCA.

Tumor markers for lipid metabolism-related genes: Based on small cell lung cancer and bronchial asthma dual analysis.

Numerous studies have elucidated the intricate relationship between bronchial asthma and small cell lung cancer (SCLC), as well as the role lipid metabolism genes play in transitioning from bronchial asthma to SCLC. Despite this, the predictive power of single gene biomarkers remains insufficient and necessitates the development of more accurate prognostic models. In our study, we downloaded and preprocessed scRNA-seq of SCLC from the GEO database GSE164404 and severe asthma scRNA-seq from GSE145013 using the Seurat package. Using the MSigDB database and geneCard database, we selected lipid metabolism-related genes and performed scRNA-seq data analysis from the gene expression GEO database, aiming to uncover potential links between immune signaling pathways in bronchial asthma and SCLC. Our investigations yielded differentially expressed genes based on the scRNA-seq dataset related to lipid metabolism. We executed differential gene analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. In-depth GSEA pathway activation analysis, crucial target gene predictions via protein-protein interactions, and key cluster gene evaluations for differential and diagnostic ROC values correlation analysis confirmed that key cluster genes are significant predictors for the progression of bronchial asthma to SCLC. To validate our findings, we performed wet laboratory experiments using real-time quantitative PCR to assess the expression of these relevant genes in SCLC cell lines. In conclusion, this research proposes a novel lipid metabolism-related gene marker that can offer comprehensive insights into the pathogenesis of bronchial asthma leading to SCLC. Although this study does not directly focus on senescence-associated molecular alterations, our findings in the lipid metabolism genes associated with inflammation and cancer progression offer valuable insights for further research targeting senescence-related changes in treating inflammatory diseases.

Development of a Novel Lipid Metabolism-related Gene Prognostic Signature for Patients with Colorectal Cancer.

BACKGROUND: The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC). METHODS: The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis. RESULTS: A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score. CONCLUSION: In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.