The impact of lipocalin-type-prostaglandin-D-synthase as a predictor of kidney disease in patients with type 2 diabetes

Decoding the immune landscape: a comprehensive analysis of immune-associated biomarkers in cervical carcinoma and their implications for immunotherapy strategies

Background and aims

Cervical cancer, a prevalent gynecological malignant tumor, poses a significant threat to women's health and lives. Immune checkpoint inhibitor (ICI) therapy has emerged as a promising avenue for treating cervical cancer. For patients with persistent or recurrent metastatic cervical cancer, If the sequence of dead receptor ligand-1 (PD-L1) is positive, ICI show significant clinical efficacy. PD-L1 expression serves as a valuable biomarker for assessing ICI therapeutic efficacy. However, the complex tumor immune microenvironment (TIME), encompassing immune cell composition and tumor-infiltrating lymphocyte (TIL) status, also exerts a profound influence on tumor immunity and prognosis. Given the remarkable strides made by ICI treatments in improving the survival rates of cervical cancer patients, it becomes essential to identify a comprehensive biomarker that integrates various TIME aspects to enhance the effectiveness of ICI treatment. Therefore, the quest for biomarkers linked to multiple facets of TIME in cervical cancer is a vital pursuit.

Methods

In this study, we have developed an Immune-Associated Gene Prognostic Index (IRGPI) with remarkable prognostic value specifically for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). The Cancer Genome Atlas CESC dataset (n = 305) was meticulously analyzed to pinpoint key immune-related genes via weighted gene co-expression network analysis and differential gene expression assays. Subsequently, we employed Cox regression analysis to construct the IRGPI. Furthermore, the composition of immune cells and TIL status were examined using CIBERSORT and TIDE. Tumor expression of Epigen, LCN10, and P73 were determined with immunohistochemistry.

Results

The resulting IRGPI, composed of EPGN, LCN10, and TP73 genes, displayed a strong negative correlation with patient survival. The discovery was validated with a patient cohort from our hospital. The IRGPI not only predicts the composition of immune cell subtypes such as Macrophages M1, NK cells, Mast cells, Plasma cells, Neutrophils, Dendritic cells, T cells CD8, and T cells CD4 within CESC, but also indicates TIL exclusion, dysfunction, and PD-1 and PD-L1 expression. Therefore, the IRGPI emerges as a promising biomarker not only for prognostic assessment but also for characterizing multiple immune features in CESC. Additionally, our results underscored the significant associations between the IRGPI and immune cell composition, TIL exclusion, and dysfunction, along with PD-1 and PD-L1 expression in the TIME.

Conclusion

Consequently, the IRGPI stands out as a biomarker intimately connected to both the survival and TIME status of CESC patients, offering potential insights into immunotherapy strategies for CESC.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Lipocalin-Type Prostaglandin D2 Synthase Protein- A Central Player in Metabolism

Lipocalin-type prostaglandin D synthase was previously known as β-trace protein (BTP), a low-molecular-weight glycoprotein that is heavily expressed in human cerebrospinal fluid. Nevertheless, it is also seen to be expressed in numerous other tissues including the kidney, liver, lung, heart, adipose, muscle, and pancreas. Functionally, L-PGDS behaves like a lipocalin type protein where it helps in binding and transportation of small lipophilic substances, such as steroids, retinoids, and other lipophilic ligands. Enzymatically, L-PGDS functions as a prostaglandin synthase where it helps in the production of PGD₂ by catalyzing the isomerization of PGH₂, a common precursor of the two series of prostaglandins. PGD₂ regulates its physiological function through two individual receptors named DP1 and DP2. L-PGDS has been a central player in many diseases, its role in metabolism including diabetes, fatty liver disease, and obesity has gathered a large attention. In this review, we summarize the current state of knowledge about L-PGDS and it's signaling in adipose, hepatic, skeletal muscle, and pancreas tissues, which are core targets for metabolic studies. Modulation of L-PGDS signaling can be considered as a potential future therapeutic target for the treatment of insulin resistance as well as fatty liver disease.

Does Beta-Trace Protein (BTP) Outperform Cystatin C as a Diagnostic Marker of Acute Kidney Injury Complicating the Early Phase of Acute Pancreatitis?

Acute pancreatitis (AP) belongs to the commonest acute gastrointestinal conditions requiring hospitalization. Acute kidney injury (AKI) often complicates moderately severe and severe AP, leading to increased mortality. Among the laboratory markers proposed for early diagnosis of AKI, few have been studied in AP, including cystatin C and neutrophil gelatinase-associated lipocalin (NGAL). Beta-trace protein (BTP), a low-molecular-weight glycoprotein proposed as an early marker of decreased glomerular filtration, has never been studied in AP. We investigated the diagnostic usefulness of serum BTP for early diagnosis of AKI complicating AP in comparison to previously studied markers. BTP was measured in serum samples collected over the first three days of hospital stay from 73 adult patients admitted within 24 h of mild to severe AP. Thirteen patients (18%) developed AKI in the early phase of AP. Serum BTP was higher in patients who developed AKI, starting from the first day of hospitalization. Strong correlations were observed between BTP and serum cystatin C but not serum or urine NGAL. On admission, BTP positively correlated with endothelial dysfunction. The diagnostic usefulness of BTP for AKI was similar to cystatin C and lower than NGAL. Increased BTP is an early predictor of AKI complicating AP. However, it does not outperform cystatin C or NGAL.

Potential urine biomarkers for gestational hypertension and preeclampsia

Differential proteomic technology was used to identify urine proteomic profile of gestational hypertension and preeclampsia. Urine samples were collected from 10 patients with gestational hypertension, 10 patients with mild preeclampsia, 10 patients with severe preeclampsia and 10 normal pregnancies and analyzed by 2‑D difference gel electrophoresis, then matrix assisted laser desorption ionization mass spectrometry was used to identify differential proteins. Subsequently, ELISA was used to verify the content variation of the identified proteins in 200 urine samples. In total, 30 differential proteins were identified. For prostaglandin‑H2 D‑isomerase (L‑PGDS), perlecan and other 15 proteins, the contents in patients with gestational hypertension were higher than that of normal pregnancies, but lower in mild and severe preeclampsia. By contrast, serum albumin and α‑1‑antitrypsin was lower in samples from patients with gestational hypertension and higher in patients with mild and severe preeclampsia compared with normal pregnancies. ELISA verified that the urinary concentration of L‑PGDS and perlecan were significantly lower in patients with preeclampsia than in normal pregnancies (P<0.05). Urine proteomics is a useful tool to identify potential biomarkers to distinguish between different types of hypertensive disorders in pregnancy. L‑PGDS and perlecan could potentially be used as markers to reflect the state of renal function, and may participate in the genesis and development of renal injury during preeclampsia.

Urinary beta-trace protein gene expression analysis in type 2 diabetes mellitus patients

Objective

To evaluate the gene expression of beta-trace protein in urine of diabetic patients, with no reduction in glomerular filtration rate, which was defined as below 60mL/min/1.73m².

Methods

Type 2 diabetes mellitus patients were recruited, and a group of non-diabetic individuals served as control. Beta-trace protein gene expression was analyzed by quantitative PCR. Blood samples were collected to establish glucose levels and baseline kidney function. Accuracy was analyzed using ROC curves.

Results

Ninety type 2 diabetes mellitus patients and 20 non-diabetic individuals were recruited. The area under the curve was 0.601, sensitivity of 20% and specificity of 89.47%. Among diabetic participants, 18% showed an expression above the cutoff point.

Conclusion

These results of accuracy of beta-trace protein gene expression in urine of diabetic patients are promising, although they did not achieve a higher area under the curve level.

Serum NGAL and Cystatin C Comparison With Urinary Albumin-to-Creatinine Ratio and Inflammatory Biomarkers as Early Predictors of Renal Dysfunction in Patients With Type 2 Diabetes

Introduction

Diabetic nephropathy is associated with specific histological changes. Early detection of poor glomerular and tubular function can be achieved with biomarkers of diabetes. The aim of this study was to evaluate the accuracy of kidney dysfunction biomarkers in type 2 diabetes (T2D).

Methods

Patients with T2D were grouped according to their glycated hemoglobin level. Patients' urine and blood samples were taken to measure cystatin C (CysC), neutrophil gelatinase-associated lipocalin, beta-trace protein levels, and the first morning void albumin-to-creatinine ratio. Patients in the end stage of renal disease or receiving dialysis were not included. Receiver operating characteristic curves were generated, and the areas under the curve were compared with the performance of the biomarkers used to evaluate kidney dysfunction in T2D.

Results

Ninety patients with T2D were chosen. CysC was positively correlated with creatinine (P < 0.001), estimated glomerular filtration rate (P < 0.001), and urinary beta-trace protein (P = 0.01). The area under the curve was 0.635 for CysC, 0.621 for serum neutrophil gelatinase-associated lipocalin, and 0.660 for the albumin-to-creatinine ratio. A crude logistic regression model showed a positive association between serum CysC (P = 0.01) and serum neutrophil gelatinase-associated lipocalin (P < 0.001). A linear regression model showed a positive association between serum CysC, creatinine, and estimated glomerular filtration rate (P < 0.001) but did not show a positive association with glycated hemoglobin (P = 0.892).

Discussion

Neutrophil gelatinase-associated lipocalin and serum CysC were positively associated with the presence of renal dysfunction and had better performance on receiver operating characteristic analysis than the other markers evaluated in patients with T2D without kidney dysfunction.