Urinary exosomes as promising biomarkers for early kidney disease detection.

Introduction/Objective Glomerulonephritis is a serious kidney disease that can lead to kidney failure if not detected and treated early. Several potential biomarkers have been identified to aid in early detection of this condition. it is important to investigate and validate these novel biomarkers to improve patient outcomes in the management of glomerulonephritis. Methods/Case Report The various biomarkers such as Creatinine, urinary exosomes, Cystatin-C, interleukin-6, tumor necrosis factor-alpha (TNF-α), Fasting blood glucose, Albumin, MicroRNAs, and complement activation factors (C3, C4d) were measured and calculated in 98 infected cases with diabetic patients who had acute kidney diseases. The same biomarkers had been implemented for 87 cases with chronic renal disease on dialysis. Also, the same parameters were determined in 82 healthy persons as controls. Alternative markers' levels were determined by using Microscopic, Biochemical, ELISA, and RT-PCR techniques. The biopsy had been applied only to infected patients. Imaging diagnostic tools have been applied to all cases. Results (if a Case Study enter NA) The studied biomarkers have high sensitivity from 81.35% to 94.72% and accuracy from 88.76% to 96.83% as well as their cut off which approved the aim of this study. Thereafter, the studied markers' reports assisted in detecting glomerulonephritis diseases at early stages. Conclusion Alternative biomarkers (Cystatin-C, interleukin-6, tumor necrosis factor-alpha (TNF-α), urinary exosomes, MicroRNAs, and complement activation factors (C3, C4d)) are considered as prospective markers to differentiate healthy individuals from chronic kidney disease by their significant results. Hence, if the studied biomarker has been measured regularly for outpatients who complain of renal diseases, that can be assisted in the early detection of kidney diseases, including glomerulonephritis, and then it can be well managed.

Simple SummaryDetecting cancer early significantly increases the chances of successful (surgical) treatment. Pancreatic cancer is one of the deadliest cancer forms, since it is usually discovered at a late and already spread stage. Finding biomarkers showing pancreatic cancer at an early stage is a possible approach to early detection and improved treatment. The aim of our study was to assess the potential of tissue polypeptide specific antigen (TPS) as a biomarker for early pancreatic cancer detection. We studied TPS levels in blood plasma samples from a population-based biobank in Västerbotten, Sweden that were collected before individuals were diagnosed with pancreatic cancer. Although TPS levels are raised at diagnosis, this occurs late, and thus TPS does not seem to hold promise as an early detection marker for pancreatic cancer.Early detection of pancreatic ductal adenocarcinoma (PDAC) is challenging, and late diagnosis partly explains the low 5-year survival. Novel and sensitive biomarkers are needed to enable early PDAC detection and improve patient outcomes. Tissue polypeptide specific antigen (TPS) has been studied as a biomarker in PDAC diagnostics, and it has previously been shown to reflect clinical status better than the ‘golden standard’ biomarker carbohydrate antigen 19-9 (CA 19-9) that is most widely used in the clinical setting. In this cross-sectional case-control study using pre-diagnostic plasma samples, we aim to evaluate the potential of TPS as a biomarker for early PDAC detection. Furthermore, in a subset of individuals with multiple samples available at different time points before diagnosis, a longitudinal analysis was used. We assessed plasma TPS levels using enzyme-linked immunosorbent assay (ELISA) in 267 pre-diagnostic PDAC plasma samples taken up to 18.8 years before clinical PDAC diagnosis and in 320 matched healthy controls. TPS levels were also assessed in 25 samples at PDAC diagnosis. Circulating TPS levels were low both in pre-diagnostic samples of future PDAC patients and in healthy controls, whereas TPS levels at PDAC diagnosis were significantly increased (odds ratio 1.03; 95% confidence interval: 1.01–1.05) in a logistic regression model adjusted for age. In conclusion, TPS levels increase late in PDAC progression and hold no potential as a biomarker for early detection.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a poor prognosis, primarily due to a late diagnosis. Recent studies have focused on identifying non-invasive biomarkers for early detection, with microRNAs (miRNAs) emerging as promising candidates. This systematic review aims to evaluate the potential of circulating miRNAs as biomarkers for the early detection of PDAC, analyzing their diagnostic accuracy, specificity, and sensitivity. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search across PubMed, Embase, and the Cochrane Library was conducted. Studies published from January 2013 to October 2023 focusing on miRNA biomarkers for early PDAC detection were included. Data synthesis was performed through a narrative approach due to the heterogeneity of the studies. Nine studies met the inclusion criteria. Key findings include the elevated levels of specific miRNAs, such as miR-18a, miR-106a, and miR-25, in early-stage PDAC patients compared to controls. The integration of miRNA profiles with traditional biomarkers like CA19-9 showed improved diagnostic performance. However, challenges in the standardization of miRNA evaluation methodologies were noted. Circulating miRNAs demonstrate significant potential as non-invasive biomarkers for early PDAC detection. Despite promising results, further research and standardization are necessary for clinical application.

Pancreatic cancer is one of the most fatal human cancers, with an overall 5-year survival rate of 10.8%. Early detection is critical for improving pancreatic cancer prognosis, but biomarkers for early detection are lacking. We conducted a two-stage study to identify circulating miRNAs as biomarkers for pancreatic cancer early detection using pre-diagnostic plasma samples, collected within 5 years prior to cancer diagnosis, from case-control studies nested in five prospective studies. The discovery stage included 185 case-control pairs from the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial. Replication stage samples comprised 277 case-control pairs from diverse cohorts: Shanghai Women’s and Men’s Health Studies, Southern Community Cohort Study (SCCS), and Multiethnic Cohort. Controls were individually matched on age at enrollment, sex, recruitment site (SCCS), race/ethnicity, and date of blood draw in each cohort. Cell-free small RNAs were extracted from plasma samples and miRNAs were measured by the NanoString nCounter Analysis System using the Human v3 miRNA Expression panel (a total of 798 miRNAs). Normalized miRNAs were categorized by decile. For miRNAs that have ≥10% samples with an undetectable level (0), the non-zero level was categorized by approximately 10% increment. Associations of circulating miRNAs with pancreatic cancer risk, measured in odds ratios (ORs) and 95% confidence intervals (CIs) per decile change, were calculated using conditional logistic regression analyses in discovery and replication studies, separately within each cohort, and results meta-analyzed. We identified three miRNAs, hsa-miR-199a-3p+/hsa-miR-199b-3p, hsa-miR-191-5p, hsa-miR-767-5p, being consistently associated with pancreatic risk in both discovery and replication sets, with combined ORs (95% CIs) of 0.89 (0.84-0.95), 0.90 (0.85-0.95), and 1.08 (1.02-1.13), and P of 9.09E-05, 6.95E-05 and 4.03E-03, respectively. Adjustment for age, BMI, smoking, diabetes and family history of pancreatic cancer did not change the associations. Stratified analyses by age at diagnosis found five additional replicated miRNAs: hsa-miR-640, hsa-miR-1299, hsa-miR-22-3p, hsa-miR-874-5p, and hsa-miR-449b-5p among those 65 years or older, with combined ORs (95% CIs) of 1.33 (1.16-1.52), 1.28 (1.12-1.46), 0.76 (0.65-0.89), 1.25 (1.09-1.43), and 1.22 (1.07-1.39), and P-value ranging from 4.75E-05 to 0.003. These results suggest that circulating miRNA biomarkers may be useful in identifying individuals with high risk of developing pancreatic cancer for close surveillance and/or a screening test. Citation Format: Cong Wang, Hui Cai, Qiuyin Cai, Jie Wu, Rachael Stolzenberg-Solomon, Clair Zhu, Yu-Tang Gao, Jordan Berlin, Fei Ye, Wei Zheng, Veronica W. Setiawan, Xiao-Ou Shu. Circulating miRNA as biomarkers for pancreatic cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB109.

Urinary Exosomes (UE) are involved in several metabolic processes, including hypertension and are considered to be vehicles for eradicating cell waste products. UE have important functions in human diseases as carriers of information including, proteins, lipids and miRNA (mi‐R). Because of their high stability, UE mi‐Rs can be useful predictors of the progression of diabetes and hypertension. UE mi‐Rs 451, 638, 362 and 16 are potential biomarkers for early diabetic nephropathy, salt‐sensitive hypertension and cardiovascular disease, respectively. We hypothesized that mi‐Rs 451, 638, 362 and 16 are increased in diabetic + hypertensive patients when compared to controls. To address this hypothesis, human urine samples were collected from control, diabetic and hypertensive patients. UE were isolated in each group and mi‐R 451, 638, 362 and 16 expression was quantified by RT‐PCR. mi‐R expression was expressed as log2, normalized to U6 small nuclear RNA as mean and standard error. UE mi‐R 451 expression in diabetic + hypertensive patients was 2.4 ±1.8 and 0.5 ±0.8 in controls. UE mi‐R 638 expression in diabetic + hypertensive patients was 1.0 ±2.0 and 0.1 ±1.7 in controls. UE mi‐R 362 expression in diabetic + hypertensive patients was 1.9 ±1.8 and 0.2 ±0.8 in controls. UE mi‐R 16 expression in diabetic + hypertensive patients was similar to the controls (0.1 ±10 and −9 ±1, respectively). Our results suggest that both mi‐R 451, 638 and 362 expressions are increased in diabetic + hypertensive patients. There was not an increase in UE mi‐R 16 in diabetic + hypertensive patients when compared to controls. In conclusion, mi‐R 451, 638 and 362 expressions may serve as non‐invasive biomarkers for the early detection for diabetes and hypertension.Support or Funding InformationR25AG047843‐NIHThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

International Liver Cancer Association (ILCA) White Paper on Biomarker Development for Hepatocellular Carcinoma

Circulating tumor DNA as an early cancer detection tool.

Methods and biomarkers for early detection, prediction, and diagnosis of colorectal cancer

Urinary extracellular vesicles (EVs), including microvesicles and exosomes, play several important roles in cell biology and serve as potential biomarkers in various kidney diseases. Although they have differential biophysical properties, specific biomarkers are required to discriminate these EVs during isolation/purification. The present study aimed to define differential lipidome profiles of urinary microvesicles vs. exosomes. Urine samples collected from eight healthy individuals were pooled and underwent lipid extraction using 2:1(v/v) chloroform/methanol. The recovered lipids were resolved by thin layer liquid chromatography (TLC) and analyzed by MALDI-TOF MS. From three and five TLC bands observed in microvesicles and exosomes, respectively, several fatty acids, glycerolipids and phospholipids were identified from both EVs without clear differential patterns. However, their sphingolipid profiles were unique. Ceramide phosphates (CerP), hexosyl sphingoid bases (HexSph), lactosyl ceramides (LacCer), mannosyl di-PI-ceramides (M(IP)2 C), sulfatides hexosyl ceramide (SHexCer) and sulfatides hexoxyl sphingoid bases (SHexSph) were detectable only in urinary exosomes, whereas phosphatidylinositol ceramides (PI-Cer) were detectable only in urinary microvesicles. The presence of CerP only in urinary exosomes was successfully validated by dot blot analysis. Our extensive lipidome analyses of urinary microvesicles vs. exosomes provide potential lipidome markers to discriminate exosomes from microvesicles and may lead to better understanding of EVs biogenesis.

Introduction: Though early detection of NSCLC greatly improves prognosis, we lack useful clinical tests. Genomics approaches utilizing cell-free DNA provide suitable specificity but moderate sensitivity for early cancer detection. Plasma proteins have the potential to deliver robust panels of biomarkers for early cancer detection that may be complimentary to genomics markers. Complex workflows, which enable deep and unbiased interrogation of plasma proteins that span 10 orders of magnitude, have made it impractical to efficiently perform robust studies, and consequently, comprehensive proteomic data vastly lags other “omics”. Herein, we report a multi-NP Proteograph platform that rapidly, reproducibly, deeply, and scalably interrogates proteins from biofluids. In a study of 268 subjects, comparing on average 1779 plasma proteins of NSCLC subjects to healthy and pulmonary co-morbid controls, we identified classification panels comprising proteins with known and unknown roles in NSCLC, offering the promise of new biomarkers for early disease detection. Methods: Subject plasma samples were grouped into NSCLC stages 1,2,3 (early), NSCLC stage 4 (late), or healthy and pulmonary co-morbid controls, for a randomly selected cohort of 288 age- and gender-matched subjects, and interrogated with a panel of NPs in an efficient automated work-flow. Peptides from NP-bound proteins underwent data-independent-acquisition mass spectrometry. Subject samples were also interrogated using conventional Agilent MARS-14 immunodepletion column, which has historically yielded limited clinical value, to determine differences in depth and types protein coverage achieved as compared with panel of NPs. Results: On average 1,779 proteins were detected from each of the 268 subject samples vs. 413 from depleted plasma. The healthy vs early NSCLC random classification after depleted plasma protein removal achieved an average AUC of 0.90. Classification of healthy subjects to late NSCLC had an average AUC of 0.98. Comparison of the top features of the NSCLC classifiers to the co-morbid classifier indicated clinically significant differences. Among the former were proteins with both known and unknown roles in NSCLC (OpenTargets), underscoring the value of unbiased proteomic analysis. Conclusions: We demonstrate the utility of the multi-NP Proteograph platform to deeply profile plasma proteins as novel biomarkers. The performance of the healthy vs. early NSCLC classifier confirms the potential of proteins in early disease detection. Our platform enables deep unbiased plasma protein biomarker profiling that matches genomics workflow throughput and suggests feasibility of parallel large-scale complementary studies of proteins and nucleic acids. Citation Format: John E. Blume, William C. Manning, Gregory Troiano, Asim Siddiqui, Philip Ma, Robert Langer, Vivek Farias, Omid C. Farokhzad. Efficient and scalable profiling of an average of 1779 plasma proteins in 268 subjects with multi-nanoparticle (NP) Proteograph platform enables robust detection of early-stage non-small cell lung cancer (NSCLC) and classification vs. healthy and co-morbid subjects [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-147.

Plants such as herbs are widely used in the medical and cosmetic industry. Recognizing a species and detecting an early disease of a plant are quite challenging and difficult to implement as an automated device. The manual identification process is a lengthy process and requires a prior understanding about the plant itself, such as shape, odour, and texture. Thus, this research aimed to realize the computerized method to recognize the species and detect early disease of the herbs by referring to these characteristics. This research has been developed a system for recognizing the species and detecting the early disease of the herbs using computer vision and electronic nose, which focus on odour, shape, colour and texture extraction of herb leaves, together with a hybrid intelligent system that are involved fuzzy inference system, naive Bayes (NB), probabilistic neural network (PNN) and support vector machine (SVM) classifier. These techniques were used to perform a convenient and effective herb species recognition and early disease detection on ten different herb species samples. The species recognition accuracy rate among ten different species using computer vision and electronic nose is archived 97% and 96%, respectively, in SVM, 98% and 98%, respectively, in PNN and both 94% in NB. In the early disease detection, the detection rate among ten different herb’s species using computer vision and electronic nose are 98% and 97%, respectively, in SVM, both 98% in PNN, 95% and 94%, respectively, in NB. Integrated three machine learning approaches have successfully achieved almost 99% for recognition and detection rate.

Secreted in Xylem (SIX) are small effector proteins released by Fusarium oxysporum f.sp. cubense (Foc) into the plant's xylem sap disrupting the host's defence responses causing Fusarium wilt disease resulting in a significant decline in banana crop yields and economic losses. Notably, different races of Foc possess unique sets of SIX genes responsible for their virulence, however, these genes remain underutilized, despite their potential as biomarkers for early disease detection. Herein, we identified seven SIX genes i.e. SIX1, SIX2, SIX4, SIX6, SIX8a, SIX9a and SIX13 present in Foc Tropical Race 4 (FocTR4), while only SIX9b in Foc Race 1 (Foc1). Analysis of SIX gene expression in infected banana roots revealed differential patterns during infection providing valuable insights into host-pathogen interactions, virulence level, and early detection time points. Additionally, a comprehensive analysis of virulent Foc1_C2HIR and FocTR4_C1HIR isolates yielded informative genomic insights. Hence, these discoveries contribute to our comprehension of potential disease control targets in these plants, as well as enhancing plant diagnostics and breeding programs.

Volatile organic compounds (VOCs) produced by plants during plant-pathogen interactions can be highly informative for early disease detection. The real-time capability of field asymmetric ion mobility spectrometry (FAIMS) offers a valuable opportunity to monitor plant VOCs nondestructively and dynamically. This study evaluated the FAIMS system reliability in measuring VOC profiles for an early diagnosis of Aphanomyces root rot (ARR) in pea (Pisum sativum L.). This evaluation utilized pea lines with a major quantitative trait locus (QTL Ae-Ps7.6) and lines without QTL, identified to provide partial resistance against ARR. For the first time, a VOC biomarker associated with ARR was detected as early as 2 days after inoculation (DAI). Furthermore, at 7 DAI, one of the biomarkers showed significant differences between lines with and without QTL Ae-Ps7.6 in the noninoculated samples. These findings demonstrate the potential applicability of the FAIMS system as a valuable tool for detecting volatile biomarkers for early plant disease detection.

Metabolomics insights into activated redox signaling and lipid metabolism dysfunction in chronic kidney disease progression

DETECT: Development of Technologies for Early HCC Detection