Systematic identification of key extracellular proteins as the potential biomarkers in lupus nephritis

TGFBI promotes proliferation and epithelial-mesenchymal transition in renal cell carcinoma through PI3K/AKT/mTOR/HIF-1α pathway

BACKGROUND

Renal cell carcinoma (RCC) is presently recognized as the most prevalent kidney tumor. However, the role and underlying mechanism of action of the conversion factor-inducible protein (TGFBI), an extracellular matrix protein, in RCC remain poorly understood.

METHODS

In this study, we employed Western blot, quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry techniques to assess the expression of TGFBI in RCC tissues or cells. Furthermore, we analyzed the proliferation and migration of RCC cells using CCK8, cloning, scratching, and migration assays. Additionally, we examined apoptosis and cell cycle progression through flow cytometry, analysis. Lastly, we employed gene set enrichment analysis (GSEA) to investigate the biological processes associated with TGFBI, which were subsequently validated.

RESULTS

The findings indicate that TGFBI exhibits significantly elevated expression levels in both renal cell carcinoma (RCC) tissues and cells. Furthermore, the knockdown of TGFBI in SiRNA transfected cells resulted in the inhibition of RCC cell proliferation, migration, invasiveness, apoptosis, and alteration of the cell cycle. Additionally, TGFBI was found to impede the epithelial-mesenchymal transition (EMT) process in RCC cells. Bioinformatics analysis suggests that TGFBI may exert its influence on various biological processes in RCC through the tumor immune microenvironment. Moreover, our study demonstrates that TGFBI promotes RCC progression by activating the PI3K/AKT/mTOR/HIF-1α.

CONCLUSIONS

Our research indicates that TGFBI exhibits high expression in RCC and facilitate RCC progression and metastasis through various molecular mechanisms. Hence, TGFBI has the potential to be a novel therapeutic target for the diagnosis and treatment of RCC in the future.

Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer

Background

Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified.

Methods

Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC).

Results

The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor.

Conclusion

Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.

Noninvasive biomarkers for lupus nephritis

Lupus nephritis (LN) is one of the most severe clinical manifestations of systemic lupus erythematosus (SLE). Notably, the clinical manifestations of LN are not always consistent with the histopathological findings. Therefore, the diagnosis and activity monitoring of this disease are challenging and largely depend on invasive renal biopsy. Renal biopsy has side effects and is associated with the risk of bleeding and infection. There is a growing interest in the development of novel noninvasive biomarkers for LN. In this review, we summarize most of the LN biomarkers discovered so far by correlating current knowledge with future perspectives. These biomarkers fundamentally reflect the biological processes of kidney damage and repair during disease. Furthermore, this review highlights the role of urinary cell phenotype detection in the diagnosis, monitoring, and treatment of LN and summarizes the limitations and countermeasures of this test.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Urinary SPP1 has potential as a non-invasive diagnostic marker for focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non-invasive diagnostic tools. We downloaded scRNA-seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP-Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP-Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP-Genes might be involved in the immune response and extracellular components. Six key EP-Genes were identified and correlated with renal function. IMC showed that key EP-Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non-invasive diagnosis of FSGS.

Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is identified as the cause of coronavirus disease 2019 (COVID-19) pandemic. Acute kidney injury (AKI), one of serious complications of COVID-19 infection, is the leading contributor to renal failure, associating with high mortality of the patients. This study aimed to identify the shared gene signatures and construct the gene regulatory network between COVID-19 and AKI, contributing to exploring the potential pathogenesis.

METHODS

Utilizing the machine learning approach, the candidate gene signatures were derived from the common differentially expressed genes (DEGs) obtained from COVID-19 and AKI. Subsequently, receiver operating characteristic (ROC), consensus clustering and functional enrichment analyses were performed. Finally, protein-protein interaction (PPI) network, transcription factor (TF)-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory network were systematically undertaken.

RESULTS

We successfully identified the shared 6 candidate gene signatures (RRM2, EGF, TMEM252, RARRES1, COL6A3, CUBN) between COVID-19 and AKI. ROC analysis showed that the model constructed by 6 gene signatures had a high predictive efficacy in COVID-19 (AUC = 0.965) and AKI (AUC = 0.962) cohorts, which had the potential to be the shared diagnostic biomarkers for COVID-19 and AKI. Additionally, the comprehensive gene regulatory networks, including PPI, TF-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory networks were displayed utilizing NetworkAnalyst platform.

CONCLUSIONS

This study successfully identified the shared gene signatures and constructed the comprehensive gene regulatory network between COVID-19 and AKI, which contributed to predicting patients' prognosis and providing new ideas for developing therapeutic targets for COVID-19 and AKI.

Bioinformatics-based analysis of the roles of basement membrane-related gene AGRN in systemic lupus erythematosus and pan-cancer development

Introduction

Systemic lupus erythematosus (SLE) is an autoimmune disease involving many systems and organs, and individuals with SLE exhibit unique cancer risk characteristics. The significance of the basement membrane (BM) in the occurrence and progression of human autoimmune diseases and tumors has been established through research. However, the roles of BM-related genes and their protein expression mechanisms in the pathogenesis of SLE and pan-cancer development has not been elucidated.

Methods

In this study, we applied bioinformatics methods to perform differential expression analysis of BM-related genes in datasets from SLE patients. We utilized LASSO logistic regression, SVM-RFE, and RandomForest to screen for feature genes and construct a diagnosis model for SLE. In order to attain a comprehensive comprehension of the biological functionalities of the feature genes, we conducted GSEA analysis, ROC analysis, and computed levels of immune cell infiltration. Finally, we sourced pan-cancer expression profiles from the TCGA and GTEx databases and performed pan-cancer analysis.

Results

We screened six feature genes (AGRN, PHF13, SPOCK2, TGFBI, COL4A3, and COLQ) to construct an SLE diagnostic model. Immune infiltration analysis showed a significant correlation between AGRN and immune cell functions such as parainflammation and type I IFN response. After further gene expression validation, we finally selected AGRN for pan-cancer analysis. The results showed that AGRN's expression level varied according to distinct tumor types and was closely correlated with some tumor patients' prognosis, immune cell infiltration, and other indicators.

Discussion

In conclusion, BM-related genes play a pivotal role in the pathogenesis of SLE, and AGRN shows immense promise as a target in SLE and the progression of multiple tumors.

Extracellular Vesicles as Source of Biomarkers in Glomerulonephritis

Kidney disease is a global health and healthcare burden. Glomerulonephritis (Gn), both primary and secondary, is generally characterized by an inflammatory glomerular injury and may lead to end-stage renal disease. Kidney biopsy is fundamental to the diagnosis; however, kidney biopsy presents some concerns that may partly hamper the clinical process. Therefore, more accurate diagnostic tools are needed. Extracellular vesicles (EVs) are membranous vesicles released by cells and found in bodily fluids, including urine. EVs mediate intercellular signaling both in health and disease. EVs can have both harmful and cytoprotective effects in kidney diseases, especially Gn. Previous findings reported that the specific cargo of urinary EV contains an aerobic metabolic ability that may either restore the recipient cell metabolism or cause oxidative stress production. Here, we provide an overview of the most recent proteomic findings on the role of EVs in several aspects of glomerulopathies, with a focus on this metabolic and redox potential. Future studies may elucidate how the ability of EVs to interfere with aerobic metabolism and redox status can shed light on aspects of Gn etiology which have remained elusive so far.

Integrative analysis of the association between circadian rhythm and lupus nephritis

Background

Lupus nephritis (LN) is an autoimmune nephropathy associated with systemic lupus erythematosus. Circadian rhythms are involved in the development of several diseases, especially inflammation-related diseases, but their relationship with LN is unclear.

Methods

This was an integrative bioinformatics study. The expression profile from glomeruli, tubular interstitium and renal whole tissue samples was used to assess the expression levels and relevance of circadian rhythm-related genes. To screen for circadian rhythm-related signatures, we employed the LASSO and SVM-RFE algorithms. A consensus clustering algorithm was used to classify LN patients into two circadian rhythm patterns (cluster A and cluster B). We made immune cell infiltration analysis. We used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific differentially expressed genes. Nephroseq data were used to observe the relationship between genes and renal function.

Results

We found more significant differences in circadian rhythm-related gene expression in LN glomeruli compared with tubulointerstitial and whole-kidney tissues. We established a circadian rhythm-related signature consisting of eight genes that can easily distinguish LN from healthy individuals. Patients in cluster A were associated with B-cell-dominated immunity, whereas patients in cluster B were associated with T-cell-dominated immunity. As most of the patients with proliferative LN combined with membranous LN belonged to cluster B, patients in cluster B may have more severe renal pathology compared with patients in cluster A. Fifteen circadian rhythm-related genes associated with LN and LN typing were screened using the WGCNA algorithm, with COL1A2 and DOCK2 associated with renal prognosis.

Conclusions

This study found that circadian rhythms are associated with the occurrence of LN, providing new ideas for the development of new LN treatment options from the perspective of circadian rhythms.

Biochemical Targets and Molecular Mechanism of Matrine against Aging

The aim of this study is to explore the potential targets and molecular mechanism of matrine (MAT) against aging. Bioinformatic-based network pharmacology was used to investigate the aging-related targets and MAT-treated targets. A total of 193 potential genes of MAT against aging were obtained and then the top 10 key genes (cyclin D1, cyclin-dependent kinase 1, Cyclin A2, androgen receptor, Poly [ADP-ribose] polymerase-1 (PARP1), histone-lysine N-methyltransferase, albumin, mammalian target of rapamycin, histone deacetylase 2, and matrix metalloproteinase 9) were filtered by the molecular complex detection, maximal clique centrality (MMC) algorithm, and degree. The Metascape tool was used for analyzing biological processes and pathways of the top 10 key genes. The main biological processes were response to an inorganic substance and cellular response to chemical stress (including cellular response to oxidative stress). The major pathways were involved in cellular senescence and the cell cycle. After an analysis of major biological processes and pathways, it appears that PARP1/nicotinamide adenine dinucleotide (NAD⁺)-mediated cellular senescence may play an important role in MAT against aging. Molecular docking, molecular dynamics simulation, and in vivo study were used for further investigation. MAT could interact with the cavity of the PARP1 protein with the binding energy at -8.5 kcal/mol. Results from molecular dynamics simulations showed that the PARP1-MAT complex was more stable than PARP1 alone and that the binding-free energy of the PARP1-MAT complex was -15.962 kcal/mol. The in vivo study showed that MAT could significantly increase the NAD⁺ level of the liver of d-gal-induced aging mice. Therefore, MAT could interfere with aging through the PARP1/NAD⁺-mediated cellular senescence signaling pathway.

Periostin as a Biomarker in the Setting of Glomerular Diseases-A Review of the Current Literature

Chronic kidney disease (CKD) is a highly prevalent and potential progressive condition with life-threatening consequences. Glomerular diseases (glomerulopathies) are causes of CKD that are potentially amenable by specific therapies. Significant resources have been invested in the identification of novel biomarkers of CKD progression and new targets for treatment. By using experimental models of kidney diseases, periostin has been identified amongst the most represented matricellular proteins that are commonly involved in the inflammation and fibrosis that characterize progressive kidney diseases. Periostin is highly expressed during organogenesis, with scarce expression in mature healthy tissues, but it is upregulated in multiple disease settings characterized by tissue injury and remodeling. Periostin was the most highly expressed matriceal protein in both animal models and in patients with glomerulopathies. Given that periostin is readily secreted from injury sites, and the variations in its humoral levels compared to the normal state were easily detectable, its potential role as a biomarker is suggested. Moreover, periostin expression was correlated with the degree of histological damage and with kidney function decline in patients with CKD secondary to both inflammatory (IgA nephropathy) and non-inflammatory (membranous nephropathy) glomerulopathies, while also displaying variability secondary to treatment response. The scope of this review is to summarize the existing evidence that supports the role of periostin as a novel biomarker in glomerulopathies.