The relationship between plasma serglycin levels and the diagnosis of diabetic retinopathy

Identification of the immune-associated characteristics and predictive biomarkers of keratoconus based on single-cell RNA-sequencing and bulk RNA-sequencing

Background

Whether keratoconus (KC) is an inflammatory disease is currently debated. Hence, we aimed to investigate the immune-related features of KC based on single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

Methods

scRNA-seq data were obtained from the Genome Sequence Archive (GSA), bulk RNA-seq data were obtained from the Gene Expression Omnibus (GEO), and immune-associated genes(IAGs) were obtained from the ImmPort database. Cell clusters of KC were annotated, and different cell clusters were then selected. The IAG score of each cell was calculated using the AUCell package. Three bulk RNA-seq datasets were merged and used to identify the differentially expressed genes (DEGs), biological functions, and immune characteristics. Weighted gene coexpression network analysis (WGCNA) was used to select the IAG score-related hub genes. Based on scRNA-seq and bulk RNA-seq analyses, three machine learning algorithms, including random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) regression analysis, were used to identify potential prognostic markers for KC. A predictive nomogram was developed based on prognostic markers.

Results

Six cell clusters were identified in KC, and decreased corneal stromal cell-5 (CSC-5) and increased CSC-6 were found in KC. CSC and immune cell clusters had the highest IAG scores. The bulk RNA-seq analysis identified 1362 DEGs (553 upregulated and 809 downregulated) in KC. We found different immune cell populations and differentially expressed cytokines in KC. More than three key IAG score-related modules and 367 genes were identified. By integrating the scRNA-seq and bulk RNA-seq analyses, 250 IAGs were selected and then incorporated into three machine learning models, and 10 IAGs (CEP112, FYN, IFITM1, IGFBP5, LPIN2, MAP1B, RNASE1, RUNX3, SMIM10, and SRGN) were identified as potential prognostic genes that were significantly associated with cytokine and matrix metalloproteinase(MMP)1-14 expression. Finally, a predictive nomogram was constructed and validated.

Conclusion

Taken together, our results identified CSCs and immune cell clusters that may play a key role during KC progression by regulating immunological features and maintaining cell stability.

Identification of Candidate Biomarkers of Alzheimer's Disease via Multiplex Cerebrospinal Fluid and Serum Proteomics

Alzheimer's disease (AD) is the most prevalent form of dementia among elderly people worldwide. Cerebrospinal fluid (CSF) is the optimal fluid source for AD biomarkers, while serum biomarkers are much more achievable. To search for novel diagnostic AD biomarkers, we performed a quantitative proteomic analysis of CSF and serum samples from AD and normal cognitive controls (NC). CSF and serum proteomes were analyzed via data-independent acquisition quantitative mass spectrometry. Our bioinformatic analysis was based on Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. In comparison to the controls, 8 proteins were more abundant in AD CSF, and 60 were less abundant in AD CSF, whereas 55 proteins were more and 10 were less abundant in the serum samples. ATPase-associated activity for CSF and mitochondrial functions for CSF and serum were the most enriched GO terms of the DEPs. KEGG enrichment analysis showed that the most significant pathways for the differentially expressed proteins were the N-glycan biosynthesis pathways. The area under the curve (AUC) values for CSF sodium-/potassium-transporting ATPase subunit beta-1 (AT1B1), serglycin (SRGN), and thioredoxin-dependent peroxide reductase, mitochondrial (PRDX3) were 0.867 (p = 0.004), 0.833 (p = 0.008), and 0.783 (p = 0.025), respectively. A panel of the above three CSF proteins accurately differentiated AD (AUC = 0.933, p = 0.001) from NC. The AUC values for serum probable phospholipid-transporting ATPase IM (AT8B4) and SRGN were moderate. The AUC of the CSF SRGN + serum SRGN was 0.842 (p = 0.007). These novel AD biomarker candidates are mainly associated with inflammation, ATPase activity, oxidative stress, and mitochondrial dysfunction. Further studies are needed to investigate the molecular mechanisms by which these potential biomarkers are involved in AD.

The relationship between plasma serglycin levels and the diagnosis of diabetic retinopathy

Background

Diabetic retinopathy (DR), a microvascular complication which is closely related to diabetes, remains the leading cause of vision loss around the world among older adults. Serglycin (SRGN) was known as a hematopoietic cell granule proteoglycan, exerting its function in the formation of mast cell secretory granules and mediates the storage of various compounds in secretory vesicles. The present study illustrates the potential clinical value and experimental mechanisms of SRGN in the DR.

Methods

Firstly, the mRNA expression and protein expression of SRGN in plasma samples from NPDR, PDR patients, type 2 diabetes mellitus (T2Dm) cases, and healthy controls were measured by qPCR and Western blotting assays, respectively. Then, the potentials of SRGN functioning as a diagnostic indicator in DR were verified by the receiver operating characteristic (ROC) analysis. We established in vitro DR model of human retinal endothelial cells through high‐glucose treatment. The expression of SRGN and its mechanisms of regulating cellular processes were illustrated subsequently.

Results

Our data revealed that SRGN was dramatically upregulated in NPDR and PDR cases compared with healthy controls and T2DM patients; meanwhile, the expression of SRGN was further increased in the PDR group with regard to the NPDR group. Furthermore, the ROC analysis demonstrated that SRGN could distinguish the DR cases from type 2 diabetes mellitus (T2DM) patients and healthy controls with the area under the curve (AUC) of 0.7680 (95% CI = 0.6780 ~ 0.8576, sensitivity = 47.27%, specificity = 100%, cutoff value = 1.4727) and 0.8753 (95% CI = 0.8261 ~ 0.9244, sensitivity = 69.23%, specificity = 100%, cutoff value = 1.6923), respectively. In vitro high‐glucose treatment showed that the SRGN expressions were dramatically increased. The loss of SRGN could partially counteract the inhibition of HREC proliferation caused by high‐glucose stimulation. Meanwhile, SRGN knockdown could reverse the promotion of HREC apoptosis induced by high glucose as well.

Conclusions

Consequently, our study implied that SRGN might serve as a promising biomarker with high specificity and sensitivity in the DR diagnosis and progression.