Comprehensive Analysis of Fatty Acid Metabolism in Diabetic Nephropathy from the Perspective of Immune Landscapes, Diagnosis and Precise Therapy.

Objective: Diabetic nephropathy (DN) represents the principal cause of end-stage renal diseases worldwide, lacking effective therapies. Fatty acid (FA) serves as the primary energy source in the kidney and its dysregulation is frequently observed in DN. Nevertheless, the roles of FA metabolism in the occurrence and progression of DN have not been fully elucidated. Methods: Three DN datasets (GSE96804/GSE30528/GSE104948) were obtained and combined. Differentially expressed FA metabolism-related genes were identified and subjected to DN classification using "ConsensusClusterPlus". DN subtypes-associated modules were discovered by "WGCNA", and module genes underwent functional enrichment analysis. The immune landscapes and potential drugs were analyzed using "CIBERSORT" and "CMAP", respectively. Candidate diagnostic biomarkers of DN were screened using machine learning algorithms. A prediction model was constructed, and the performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The online tool "Nephroseq v5" was conducted to reveal the clinical significance of the candidate diagnostic biomarkers in patients with DN. A DN mouse model was established to verify the biomarkers' expression. Results: According to 39 dysregulated FA metabolism-related genes, DN samples were divided into two molecular subtypes. Patients in Cluster B exhibited worse outcomes with a different immune landscape compared with those in Cluster A. Ten potential small-molecular drugs were predicted to treat DN in Cluster B. The diagnostic model based on PRKAR2B/ANXA1 was created with ideal predictive values in early and advanced stages of DN. The correlation analysis revealed significant association between PRKAR2B/ANXA1 and clinical characteristics. The DN mouse model validated the expression patterns of PRKAR2B/ANXA1. Conclusion: Our study provides new insights into the role of FA metabolism in the classification, immunological pathogenesis, early diagnosis, and precise therapy of DN.

Telbivudine-induced rhabdomyolysis in a patient undergoing haemodialysis: A case report and review of literature.

Herein, we describe a case of acute rhabdomyolysis in a man in his early 50s undergoing haemodialysis and receiving the antiviral drug, telbivudine, for chronic hepatitis B virus (HBV) infection. Following diagnosis by electromyography (EMG), magnetic resonance image (MRI) scans and laboratory data (i.e., elevated serum creatinine kinase (CK) and myoglobin) telbivudine was discontinued and the patient was treated with methylprednisolone. While his CK and myoglobin levels decreased rapidly, his muscle weakness and pain improved slowly. Learning points include: patients undergoing haemodialysis and concomitantly receiving antiviral treatment for HBV, should have their serum levels of CK and myoglobin monitored regularly; treatment with corticosteroids maybe required; relief from rhabdomyolysis-induced muscle weakness and pain may be slow due to nerve fibre damage.

Diagnostic Values of METTL1-Related Genes and Immune Characteristics in Systemic Lupus Erythematosus.

Purpose: Methyltransferase like 1 (METTL1) regulates epitranscriptomes via the m7G modification in mammalian mRNA and microRNA. Systemic lupus erythematosus (SLE) is caused by abnormal immune reactivity and has diverse clinical manifestations. RNA methylation as a mechanism to regulate gene expression is widely implicated in immune regulation. However, the role of m7G in immune response of SLE has not been extensively studied. Patients and Methods: Expression of METTL1 was identified in the public dataset GSE122459 and validated in an independent cohort of SLE patients. We investigated the association between METTL1-expression and clinical manifestations of SLE. Subsequently, differentially expressed genes (DEG) that were correlated with METTL1-expression in GSE122459 were used for functional enrichment analysis. The correlation between infiltrating immune cells and METTL1, as well as candidate biomarkers identified to be correlated with either METTL1 or immune cell infiltration were assessed by single-sample GSEA. Potential mechanisms were explored with Gene ontology and KEGG pathway enrichment. Diagnostic performances of candidate biomarkers in SLE were analyzed. Results: The mRNA and protein expression of METTL1 in SLE patients were significantly decreased in both datasets. METTL1-coexpressed DEGs were enriched in several key immune-related pathways. Activated CD8 T cells, activated CD4 T cells, memory B cells and type 2 helper T cells were different between patients with high and low METTL1 expression. Further, activated CD8 T-cells, activated CD4 T-cells, memory B-cells were correlated with METTL1. The genes of LAMP3, CD83, PDCD1LG2, IGKVD3D-20, IGKV5-2, IGKV2D-30, IGLV3-19 and IGLV4-60 were identified as candidate targets that were correlated with immune cell proportion. Moreover, LAMP3, CD83, and PDCD1LG2 expression were of diagnostic value in SLE as indicated by ROC analysis. Conclusion: Our findings suggested that METTL1 and its candidate targets LAMP3, CD83, PDCD1LG2 may be used for diagnosing SLE and could be explored for developing targeted molecular therapy for SLE.

Pan-cancer analysis of kinesin family members with potential implications in prognosis and immunological role in human cancer.

Background: Kinesin is a molecular motor for transporting "goods" within cells and plays a key role in many types of tumors. The multi-angle study of kinesin at the pan-cancer level is conducive to understanding its role in tumorigenesis and development and clinical treatment potential. Methods: We evaluated the expression of KIF genes, performed differential analysis by using the R package limma, and explored the pan-cancer prognosis of KIF genes by univariate Cox regression analysis. To evaluate the pan-cancer role of KIF genes as a whole, we defined the KIFscore with the help of gene set variation analysis (GSVA) and explored the KIFscores across normal tissues, tumor cell lines, and 33 tumor types in TCGA. Next, we used spearman correlation analysis to extensively study the correlation between the KIFscore and tumor prognosis and be-tween the KIFscore and clinical indicators. We also identified the relationship between the KIFscore and genomic variation and immune molecular signatures by multiplatform analysis. Finally, we identified the key genes in clear cell renal cell carcinoma (ccRCC) through machine learning algorithms and verified the candidate genes by CCK8, wound healing assay, Transwell assay, and flow cytometry. Results: In most cancers, KIFscores are high and they act as a risk factor for cancer. The KIFscore was significantly associated with copy number variation (CNV), tumor mutation burden (TMB), immune subtypes, DNA repair deficiency, and tumor stemness indexes. Moreover, in almost all cancer species, the KIFscore was positively correlated with T cell CD4+ TH2, the common lymphoid pro-genitor, and the T cell follicular helper. In addition, it was negatively correlated with CXCL16, CCL14, TNFSF13, and TNFRSF14 and positively correlated with ULBP1, MICB, and CD276. Machine learning helped us to identify four hub-genes in ccRCC. The suitable gene, KIF14, is highly expressed in ccRCC and promotes tumor cell proliferation, migration, and invasion. Conclusion: Our study shows that the KIF genes play an important pan-cancer role and may become a potential new target for a variety of tumor treatments in the future. Furthermore, KIF14, a key molecule in the KIF genes, can provide a new idea for the ccRCC treatment.

Screening of immune-related secretory proteins linking chronic kidney disease with calcific aortic valve disease based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Chronic kidney disease (CKD) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular diseases such as calcific aortic valve disease (CAVD). We aim to explore the CKD-associated genes potentially involving CAVD pathogenesis, and to discover candidate biomarkers for the diagnosis of CKD with CAVD. METHODS: Three CAVD, one CKD-PBMC and one CKD-Kidney datasets of expression profiles were obtained from the GEO database. Firstly, to detect CAVD key genes and CKD-associated secretory proteins, differentially expressed analysis and WGCNA were carried out. Protein-protein interaction (PPI), functional enrichment and cMAP analyses were employed to reveal CKD-related pathogenic genes and underlying mechanisms in CKD-related CAVD as well as the potential drugs for CAVD treatment. Then, machine learning algorithms including LASSO regression and random forest were adopted for screening candidate biomarkers and constructing diagnostic nomogram for predicting CKD-related CAVD. Moreover, ROC curve, calibration curve and decision curve analyses were applied to evaluate the diagnostic performance of nomogram. Finally, the CIBERSORT algorithm was used to explore immune cell infiltration in CAVD. RESULTS: The integrated CAVD dataset identified 124 CAVD key genes by intersecting differential expression and WGCNA analyses. Totally 983 CKD-associated secretory proteins were screened by differential expression analysis of CKD-PBMC/Kidney datasets. PPI analysis identified two key modules containing 76 nodes, regarded as CKD-related pathogenic genes in CAVD, which were mostly enriched in inflammatory and immune regulation by enrichment analysis. The cMAP analysis exposed metyrapone as a more potential drug for CAVD treatment. 17 genes were overlapped between CAVD key genes and CKD-associated secretory proteins, and two hub genes were chosen as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning. Furthermore, SLPI/MMP9 expression patterns were confirmed in our external cohort and the nomogram could serve as novel diagnosis models for distinguishing CAVD. Finally, immune cell infiltration results uncovered immune dysregulation in CAVD, and SLPI/MMP9 were significantly associated with invasive immune cells. CONCLUSIONS: We revealed the inflammatory-immune pathways underlying CKD-related CAVD, and developed SLPI/MMP9-based CAVD diagnostic nomogram, which offered novel insights into future serum-based diagnosis and therapeutic intervention of CKD with CAVD.

Targeting NK-1R attenuates renal fibrosis via modulating inflammatory responses and cell fate in chronic kidney disease.

Background: Renal fibrosis is the final common pathway of chronic kidney disease (CKD), which is clinically irreversible and without effective therapy. Renal tubules are vulnerable to various insults, and tubular injury is involving in the initiation and evolution of renal inflammation and fibrosis. Neurokinin-1 receptor (NK-1R) functions by interacting with proinflammatory neuropeptide substance P (SP), exerting crucial roles in various neurological and non-neurological diseases. However, its roles in renal inflammation and fibrosis are still unknown. Methods: We collected renal biopsy specimens and serum samples of individuals with or without CKD. Additionally, knockout mice lacking NK-1R expression, SP addition and NK-1R pharmacological antagonist treatment in the unilateral ureteral obstruction (UUO) model, and NK-1R-overexpressed HK-2 cells were employed. Results: Renal SP/NK-1R and serum SP were increased in patients with CKD and mice experiencing UUO and correlated with renal fibrosis and function. SP addition enhanced UUO-induced progressive inflammatory responses and renal fibrosis, whereas genetically or pharmacologically targeting NK-1R attenuated these effects. Mechanistically, TFAP4 promoted NK-1R transcription by binding to its promoter, which was abolished by mutation of the binding site between TFAP4 and NK-1R promoter. Furthermore, SP acted through the NK-1R to activate the JNK/p38 pathways to modulate cell fate of tubular epithelial cells including growth arrest, apoptosis, and expression of profibrogenic genes. Conclusion: Our data reveals that SP/NK-1R signaling promotes renal inflammatory responses and fibrosis, suggesting NK-1R could be a potential therapeutic target for the patients with CKD.

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments.

Introduction: Diabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD. Methods: We downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA. Results: Biological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers. Conclusion: In conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.

The Comprehensive Role of High Mobility Group Box 1 (HMGB1) Protein in Different Tumors: A Pan-Cancer Analysis.

Background: HMGB1 is a highly conserved nuclear protein widely expressed in mammalian cells. This study aimed to comprehensively investigate the roles and mechanisms of HMGB1 in different tumors. Methods: Original data on HMGB1 expression, localization, potential interacting proteins, genetics were obtained from The Cancer Genome Atlas, Genotype-Tissue Expression, Cancer Cell Line Encyclopedia, Human Protein Atlas, Compartmentalized Protein-Protein Interaction and cBioPortal databases. Then, correlation between HMGB1 expression levels and tumor stage, prognosis, potential pathways, tumor microenvironment, ESTIMATE score, immune-related genes, immune cell infiltration, microsatellite instability, tumor mutation burden, or anti-tumor drug resistance was investigated. The above results consistently indicated that high expression of HMGB1 protein may be related to clinical prognosis of HCC patients. Therefore, clinical tissues of HCC patients were selected to verify the differential expression of HMGB1 protein in HCC. The sensitivity of HMGB1-siRNA transfected HepG2 cells to sorafenib was assessed. Results: HMGB1 was found to be differentially expressed in many tumors and normal tissues. HMGB1 was mainly located in the nucleus and might interact with proteins such as TLR2 and TLR4. Furthermore, HMGB1 expression was closely related to tumor stage, prognosis, tumor microenvironment, immune-related genes, immune cell infiltration, microsatellite instability, tumor mutation burden, and anti-tumor drug resistance and might be involved in different pathways of various tumors. Immunohistochemistry results further verified the differential expression of HMGB1 in HCC and paracancerous tissues. HMGB1-siRNA transfected HepG2 cells had a tendency to be more insensitive to sorafenib treatment compared to the control group. Conclusions: HMGB1 was differentially expressed in most tumors and normal tissues, and was closely related to the clinical stage, prognosis, immune infiltration, tumor microenvironment, and drug resistance of tumors. Therefore, HMGB1 may serve as a novel biomarker for predicting tumor prognosis, efficacy of immune checkpoint inhibitors, and a potential target for anti-tumor therapy.

Analysis of Pyroptosis-Related Immune Signatures and Identification of Pyroptosis-Related LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a common urinary system malignant tumor with a high incidence and recurrence rate. Pyroptosis is a kind of programmed cell death caused by inflammasomes. More and more evidence had confirmed that pyroptosis plays a very significant part in cancer, and it is controversial whether pyroptosis promotes or inhibits tumors. Consistently, its potential role in ccRCC treatment efficacy and prognosis remains unclear. In this study, we systematically investigated the role of pyroptosis in the ccRCC samples from The Cancer Genome Atlas (TCGA) database. Based on the differentially expressed pyroptosis-related genes (DEPRGs), we identified three pyroptosis subtypes with different clinical outcomes, immune signatures, and responses to immunotherapy. Gene set variation analysis (GSVA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pyroptosis activation meant infiltration of more immune cells that is conducive to tumor progression. To further investigate the immunomodulatory effect of pyroptosis in ccRCC, we constructed a pyroptosis-score based on the common differential prognostic genes of the three pyroptosis subtypes. It was found that patients with high pyroptosis-score were in an unfavorable immune environment and the prognosis was worse. Gene set enrichment analysis suggested that immune-related biological processes were activated in the high pyroptosis-score group. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression was implemented for constructing a prognostic model of eight pyroptosis-related long noncoding RNAs (PRlncRNAs) in the TCGA dataset, and the outcomes revealed that, compared with the low-risk group, the model-based high-risk group was intently associated with poor overall survival (OS). We further explored the relationship between high- and low-risk groups with tumor microenvironment (TME), immune infiltration, and drug therapy. Finally, we constructed and confirmed a robust and reliable PRlncRNA pairs prediction model of ccRCC, identified PRlncRNA, and verified it by experiments. Our findings suggested the potential role of pyroptosis in ccRCC, offering new insights into the prognosis of ccRCC and guiding effectual targeted therapy and immunotherapy.

ZIP14 is involved in iron deposition and triggers ferroptosis in diabetic nephropathy.

Ferroptosis is caused by lipid peroxidation and iron accumulation and can cause cell death. Abnormally expressed iron transporters are involved in ferroptosis in a variety of diseases. ZRT/IRT-like protein 14 (ZIP14) is a transport protein that can mediate cellular uptake of iron, zinc, and manganese. Herein, we have tested the hypothesis that the divalent metal transporter ZIP14 is involved in the initiation of ferroptosis in diabetic nephropathy (DN). DN was induced in 8-week-old male rats by streptozotocin before analysis of the degree of renal tubular injury. In addition, an in vitro model of DN in human kidney proximal tubular cell line was used. We showed that ZIP14 was up-regulated and ferrous iron (Fe2+) levels increased both in vivo and in vitro. Expression of glutathione peroxidase 4 and the level of glutathione were reduced, whereas that of malondialdehyde (MDA) increased. Ferrostatin-1 (Fer-1) treatment reduced the expression of ZIP14 and the levels of Fe2+ and MDA, which is consistent with ferroptosis. Fer-1 improved kidney function in DN rats. This was characterized by urine levels of protein-to-creatinine ratio, α1-microglobulin, and N-acetyl-ß-D-glucosaminidase. Our study demonstrates a novel role for ZIP14 in diabetic kidney injury mediated by ferroptosis, and suggests a potential new therapeutic approach for the treatment of diabetic nephropathy.

Intermittent concurrent use of clopidogrel and proton pump inhibitors did not increase risk of adverse clinical outcomes in Chinese patients with coronary artery disease.

BACKGROUND: Proton pump inhibitors (PPIs) are frequently prescribed to patients with coronary heart disease (CHD) under antiplatelet therapy to prevent gastrointestinal (GI) bleeding. However, its clinical impact is still under debate, especially in Asian population. This study was undertaken to explore the effects of concurrent use of clopidogrel and PPIs on the clinical outcomes in Chinese patients with CHD in secondary prevention. METHODS: A single-center retrospective study was conducted in 638 patients with CHD on consecutive clopidogrel therapy for at least 1 year. After 18-month follow-up, adverse clinical events were collected. Cox regression was used to calculate hazard ratios (HR) and 95% confidence interval (CI) for the effect of PPI use on the outcomes. A total of 638 patients were recruited from 2014 to 2015 in this study, among whom 201 were sustained PPI users, 188 were intermittent PPI users and the remaining 249 were non-PPI users. RESULTS: Compared with sustained PPI users, intermittent use of PPIs was associated with a lower risk of stroke, major adverse cardiac events (MACE) and net adverse clinical event (NACE) (stroke: adjusted HR: 0.109, 95% CI 0.014-0.878, p = 0.037; MACE: adjusted HR: 0.293, 95% CI 0.119-0.722; p = 0.008; NACE: adjusted HR: 0.357, 95% CI 0.162-0.786, p = 0.011). Subgroup analysis further revealed the benefit of intermittent PPI use was significant in male CHD patients over 60 years old, with hypertension or chronic kidney disease, and undergoing percutaneous coronary intervention during hospitalization. CONCLUSION: The current findings suggest that the intermittent concurrent use of PPIs and clopidogrel is not associated with an increased risk of 18-month adverse clinical outcomes, and intermittent use of PPIs is associated with a lower rate of MACE and NACE.

CC chemokine receptor 2 functions in osteoblastic transformation of valvular interstitial cells.

AIMS: Calcific aortic valve disease (CAVD) emerges as a challenging clinical issue, which is associated with high cardiovascular mortality. It has been demonstrated that osteoblastic transformation of AVICs is a key mechanism of CAVD and C-C motif chemokine receptors (CCRs) may favor this process. Thus, we aimed to investigate whether CCRs were involved in osteoblastic transformation of AVICs during the development CAVD. MAIN METHODS: We first analyzed microarray data (GSE51472 and GSE12644) to identify differentially expressed genes between CAVD aortic valve tissue and normal samples, followed by verification of immunohistochemistry, qPCR and western blotting. Primary aortic valvular interstitial cells (AVICs) were incubated with specific inhibitors and/or siRNA of CCR2 and CCL2 under pro-calcifying medium. The levels of CCL2 in the medium were measured by ELISA. In addition, we used recombinant CCL2 to activate CCR2 in calcifying AVICs. Alizarin red S staining and calcium deposition were used to evaluate the degree of calcification. Western blotting was used to determine osteoblastic transformation of AVIC and total Akt and phosphorylated Akt expression. KEY FINDING: CCR2 levels were remarkably up-regulated in calcified aortic valve and calcifying AVICs. Silencing CCR2 inhibited the osteoblastic transformation and calcification of AVICs. In addition, recombinant CCL2 activated CCR2 and accelerated AVICs calcification through PI3K/Akt pathway. SIGNIFICANCE: We characterize abnormal activation of CCL2/CCR2 axis as a promoter of AVICs osteoblastic transformation and calcification. Our findings implicate the CCL2/CCR2-PI3K/Akt pathway as a potential target for treatment of CAVD.