Pre- and Post-analytical Factors in Biomarker Discovery

Application of Proteomics in Maternal and Neonatal Health: Advancements and Future Directions

Maternal and neonatal health (women during pregnancy, childbirth, and the postnatal period) presents a spectrum of healthcare challenges, including preterm birth, preeclampsia, intrauterine growth restriction, polycystic ovarian syndrome, and gestational diabetes mellitus. While genomic investigations have shed light on many of these topics, protein biomarker discovery, a pivotal aspect of such research, holds promise in offering insights into disease diagnosis, progression, and prognosis. This review paper aims to explore the landscape of proteomics research pertaining to the aforementioned disorders. In the search for viable biomarkers, existing ones are either outdated or lack specificity and new ones being investigated do not commonly make it to the validation stage. In this review, the reasons for the gap between the biomarker discovery stage and the clinical validation stage are evaluated, in addition to what steps are being taken to mitigate the unexpectedly slow scientific and clinical progress. Notably, this paper also delves into the ethnic disparities found in maternal and neonatal health research, as well as how AI is currently being used to alleviate socioeconomic and ethnic disparities, as well as its advantages for the analysis of large "omics" datasets. We anticipate this investigation will provide critical, invaluable information for researchers, medical professionals, and policy decision-makers in this field to improve overall maternal and neonatal health outcomes.

Strategies for discovering novel hepatocellular carcinoma biomarkers

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Interlaboratory evaluation of LC-MS-based biomarker assays

Validation of biomarker assays is crucial for effective drug development and clinical applications. Interlaboratory reproducibility is vital for reliable comparison and combination of data from different centers. This review summarizes interlaboratory studies of quantitative LC-MS-based biomarker assays using reference standards for calibration curves. The following points are discussed: trends in reports, reference and internal standards, evaluation of analytical validation parameters, study sample analysis and normalization of biomarker assay data. Full evaluation of these parameters in interlaboratory studies is limited, necessitating further research. Some reports suggest methods to address variations in biomarker assay data among laboratories, facilitating organized studies and data combination. Method validation across laboratories is crucial for reducing interlaboratory differences and reflecting target biomarker responses.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Comparative sera proteomics analysis of differentially expressed proteins in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) has increased in incidence from 1990 to 2017, especially in South and Southeast Asia. It is often diagnosed at an advanced stage with a poor prognosis. Therefore, early detection of OSCC is essential to improve the prognosis of OSCC. This study aims to identify the differentially expressed serum proteins as potential biomarkers for oral squamous cell carcinoma (OSCC).

Methods

Comparative proteomics profiling of serum samples from OSCC patients, oral potentially malignant disorder (OPMD) patients, and healthy individuals were performed using two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry (MS) (n = 60) and bioinformatics analysis. The enzyme-linked immunosorbent assay (ELISA) (n = 120) and immunohistochemistry (IHC) (n = 70) were used to confirm our findings.

Results

The 2-DE analysis revealed that 20 differentially expressed proteins were detected in OPMD and OSCC (p < 0.05). Bioinformatics analysis indicated that the activation of classical complement, liver X receptor/retinoid X receptor (LXR/RXR) activation, and acute phase response signaling pathway are associated with the development and progression of OSCC. Most of the detected proteins are acute-phase proteins and were related to inflammation and immune responses, including apolipoprotein A-I (APOA1), complement C3 (C3), clusterin (CLU), and haptoglobin (HP). The expression levels of CLU and HP in ELISA are consistent with the findings from the 2-DE analysis, except for the mean serum level of HP in OPMD, whereby it was slightly higher than that in control. IHC results demonstrated that CLU and HP are significantly decreased in OSCC tissues.

Conclusion

Decreased expression of CLU and HP could serve as complementary biomarkers of OSCC. These proteins may assist in predicting the outcomes of OSCC patients. However, a larger cohort is needed for further investigation.

Comparison of Circulating Biomarkers in Predicting Diabetic Kidney Disease Progression With Autoantibodies to Erythropoietin Receptor

Introduction

Several circulating markers, including autoantibodies to erythropoietin receptor (anti-EPOR antibodies), have been identified as useful biomarkers in predicting diabetic kidney disease progression. However, a direct comparison of their utility is lacking. We aimed to validate and to compare the prognostic value of anti-EPOR antibodies with that of other known biomarkers, using the ADVANCE trial and its long-term follow-up, ADVANCE-ON, cohorts.

Methods

In this nested case-control study from the ADVANCE trial cohort, we included 165 case participants who had the composite kidney outcome (renal replacement therapy, renal death, or doubling of serum creatinine to ≥200 μmol/l) and 330 matched controls. We compared the associations of baseline plasma levels of anti-EPOR antibodies, tumor necrosis factor receptor (TNFR)-1 and -2, and bone morphogenetic protein (BMP)-7 with kidney outcomes.

Results

Cases had higher baseline plasma levels of anti-EPOR antibodies than controls (median 1.7 vs. 0.6 enzyme-linked immunosorbent assay unit, P < 0.001). Higher levels of anti-EPOR antibodies were associated with an increased risk of kidney outcome (odds ratio 2.16 [95% confidence interval 1.51, 3.08], per 1 SD of log-transformed levels) after adjusting for conventional markers. Elevated circulating TNFR1 and TNFR2 levels, and lower BMP-7 levels at baseline, were associated with poor kidney outcome (odds ratios 2.06 [1.29, 3.30], 1.66 [1.13, 2.43], and 0.45 [0.32, 0.65], respectively). The addition of anti-EPOR antibodies into the model improved the prediction of kidney outcome, regardless of other biomarkers.

Conclusion

Anti-EPOR antibodies provide a promising biomarker, as with TNFR1, TNFR2, and BMP-7, in predicting kidney disease progression in people with type 2 diabetes mellitus.

Prognostic Biomarkers in Endometrial Cancer: A Systematic Review and Meta-Analysis

Endometrial cancer (EC) is the sixth most common cancer in women worldwide and its mortality is directly associated with the presence of poor prognostic factors driving tumor recurrence. Stratification systems are based on few molecular, and mostly clinical and pathological parameters, but these systems remain inaccurate. Therefore, identifying prognostic EC biomarkers is crucial for improving risk assessment pre- and postoperatively and to guide treatment decisions. This systematic review gathers all protein biomarkers associated with clinical prognostic factors of EC, recurrence and survival. Relevant studies were identified by searching the PubMed database from 1991 to February 2020. A total number of 398 studies matched our criteria, which compiled 255 proteins associated with the prognosis of EC. MUC16, ESR1, PGR, TP53, WFDC2, MKI67, ERBB2, L1CAM, CDH1, PTEN and MMR proteins are the most validated biomarkers. On the basis of our meta-analysis ESR1, TP53 and WFDC2 showed potential usefulness for predicting overall survival in EC. Limitations of the published studies in terms of appropriate study design, lack of high-throughput measurements, and statistical deficiencies are highlighted, and new approaches and perspectives for the identification and validation of clinically valuable EC prognostic biomarkers are discussed.