Mechanism-based biomarker discovery

Unveiling heterogeneity in MSCs: exploring marker-based strategies for defining MSC subpopulations

Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.

Combination of an Autoantibody Panel and Alpha-Fetoprotein for Early Detection of Hepatitis B Virus-Associated Hepatocellular Carcinoma

The purpose of this study was to identify biomarkers associated with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and to develop a new combination with good diagnostic performance. This study was divided into four phases: discovery, verification, validation, and modeling. A total of four candidate tumor-associated autoantibodies (TAAb; anti-ZIC2, anti-PCNA, anti-CDC37L1, and anti-DUSP6) were identified by human proteome microarray (52 samples) and bioinformatics analysis. Subsequently, these candidate TAAbs were further confirmed by indirect ELISA with two testing cohorts (120 samples for verification and 663 samples for validation). The AUC for these four TAAbs to identify patients with HBV-HCC from chronic hepatitis B (CHB) patients ranged from 0.693 to 0.739. Finally, a diagnostic panel with three TAAbs (anti-ZIC2, anti-CDC37L1, and anti-DUSP6) was developed. This panel showed superior diagnostic efficiency in identifying early HBV-HCC compared with alpha-fetoprotein (AFP), with an AUC of 0.834 [95% confidence interval (CI), 0.772-0.897] for this panel and 0.727 (95% CI, 0.642-0.812) for AFP (P = 0.0359). In addition, the AUC for this panel to identify AFP-negative patients with HBV-HCC was 0.796 (95% CI, 0.734-0.858), with a sensitivity of 52.4% and a specificity of 89.0%. Importantly, the panel in combination with AFP significantly increased the positive rate for early HBV-HCC to 84.1% (P = 0.005) and for late HBV-HCC to 96.3% (P < 0.001). Our findings suggest that AFP and the autoantibody panel may be independent but complementary serologic biomarkers for HBV-HCC detection.

PREVENTION RELEVANCE

We developed a robust diagnostic panel for identifying patients with HBV-HCC from patients with CHB. This autoantibody panel provided superior diagnostic performance for HBV-HCC at an early stage and/or with negative AFP results. Our findings suggest that AFP and the autoantibody panel may be independent but complementary biomarkers for HBV-HCC detection.

Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Blood-Based Biomarkers of Cancer-Related Cognitive Impairment in Non-Central Nervous System Cancer: A Scoping Review

This scoping review was designed to synthesize the extant literature on associations between subjective and/or objective measures of cancer-related cognitive impairment (CRCI) and blood-based biomarkers in adults with non-central nervous system cancers. The literature search was done for studies published from the start of each database searched (i.e., MEDLINE, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, grey literature) through to October 20, 2021. A total of 95 studies are included in this review. Of note, a wide variety of biomarkers were evaluated. Most studies evaluated patients with breast cancer. A variety of cognitive assessment measures were used. The most consistent significant findings were with various subjective and objective measures of CRCI and levels of interleukin-6 and tumor necrosis factor. Overall, biomarker research is in an exploratory phase. However, this review synthesizes findings and proposes directions for future research.

LBD: a manually curated database of experimentally validated lymphoma biomarkers

Lymphoma is a heterogeneous disease caused by malignant proliferation of lymphocytes, resulting in significant mortality worldwide. While more and more lymphoma biomarkers have been identified with the advent and development of precision medicine, there are currently no databases dedicated to systematically gathering these scattered treasures. Therefore, we developed a lymphoma biomarker database (LBD) to curate experimentally validated lymphoma biomarkers in this study. LBD consists of 793 biomarkers extracted from 978 articles covering diverse subtypes of lymphomas, including 715 single and 78 combined biomarkers. These biomarkers can be categorized into molecular, cellular, image, histopathological, physiological and other biomarkers with various functions such as prognosis, diagnosis and treatment. As a manually curated database that provides comprehensive information about lymphoma biomarkers, LBD is helpful for personalized diagnosis and treatment of lymphoma.

Database URL

http://bis.zju.edu.cn/LBD

Characterization of miR-200 family members as blood biomarkers for human and laying hen ovarian cancer

MicroRNA-200 (miR-200) family is highly expressed in ovarian cancer. We evaluated the levels of family members relative to the internal control miR-103a in ovarian cancer and control blood specimens collected from American and Hong Kong Chinese institutions, as well as from a laying hen spontaneous ovarian cancer model. The levels of miR-200a, miR-200b and miR-200c were significantly elevated in all human cancer versus all control blood samples. Further analyses showed significantly higher miR-200 levels in Chinese control (except miR-429) and cancer (except miR-200a and miR141) samples than their respective American counterparts. Subtype-specific analysis showed that miR-200b had an overall elevated level in serous cancer compared with controls, whereas miR-429 was significantly elevated in clear cell and endometrioid cancer versus controls. MiR-429 was also significantly elevated in cancer versus control in laying hen plasma samples, consistent with the fact that endometrioid tumor is the prevalent type in this species. A neural network model consisting of miR-200a/200b/429/141 showed an area under the curve (AUC) value of 0.904 for American ovarian cancer prediction, whereas a model consisting of miR-200b/200c/429/141 showed an AUC value of 0.901 for Chinese women. Hence, miR-200 is informative as blood biomarkers for both human and laying hen ovarian cancer.

A novel Trypanosoma cruzi secreted antigen as a potential biomarker of Chagas disease

Chagas drug discovery has been hampered by a lack of validated assays to establish treatment efficacy in pre-clinical animal models and in patients infected with T. cruzi. Reduced levels of parasite secreted antigens in the blood of infected hosts could be used to demonstrate treatment efficacy. A published proteomic study of parasite secreted antigens identified the hypothetical protein Tc_5171 as a secreted antigen. In this report, we developed Tc_5171 specific antibodies and showed that the native protein was expressed by the three life cycle stages of the parasite. Anti-peptide antibodies were able to detect the parasite antigen in blood of infected mice during the acute and the chronic phase of infection. Benznidazole treatment of infected mice significantly reduced their blood antigen levels. Of clinical significance, patients diagnosed with Chagas disease, either asymptomatic or with cardiac clinical symptoms had significantly higher Tc_5171 antigen levels compared to endemic controls. Pair-wise analysis, before and after Benznidazole treatment, of patients with asymptomatic Chagas disease showed a significant reduction in antigen levels post treatment. Taken together, our results indicate that Tc_5171 could be used as a novel biomarker of Chagas disease for diagnosis and to assess treatment efficacy.

Identification of potential markers for internal exposure to ambient ozone in oral cavity of healthy adults

BACKGROUND

Ozone is a highly oxidative gaseous pollutant associated with adverse health outcomes, but markers for internal exposure to ambient ozone are not well-established.

METHODS

We aimed to evaluate the feasibility and suitability of the markers in oral microbiome for ambient ozone exposure. Between March and May in 2018, 97 healthy adults were examined on 2 or 3 occasions for oral swab sampling. Hourly concentrations of ambient ozone 1-7 days preceding sampling were collected. Mixed-effect models were fitted to examine the associations between ambient ozone and the diversity and taxon abundances of oral microbiome. Receiver operating characteristic (ROC) curves estimated the accuracies of markers to delineate between samples exposed to different concentrations of ambient ozone. The associations between the makers and lung function were further examined by linear mixed effect models.

RESULTS

The averages of daily mean concentrations of ambient ozone (O_3-daily), maximum 8-h means (O_3-8hmax) and 1-h maximums (O_3-1hmax) were respectively 72 μg/m³, 123 μg/m³ and 144 μg/m³. O_3-daily was positively associated with α-diversity of oral microbiome, but the exposure-response curves only yielded positive associations in the range of O_3-daily from 60 μg/m³ to 75 μg/m³. Results of O_3-8hmax and O_3-1hmax were consistent with these of O_3-daily. With an interquartile range increase in O_3-daily at lag04, the abundance of Proteobacteria decreased by 3.1% (95% CI: -4.0%, -2.2%) and Firmicutes increased by 3.3% (95% CI: 2.3%, 4.3%), whilst the Proteobacteria:Firmicutes ratio (P/F) decreased by 0.9 (95% CI: -1.5, -0.4). The areas under ROC curves for Proteobacteria, Firmicutes and P/F were 0.8535, 0.7569 and 0.8929, respectively. Proteobacteria and P/F were associated with forced expiratory volume in the first second and fractional exhaled nitric oxide significantly.

CONCLUSION

Ambient ozone disturbs oral microbial homeostasis. Proteobacteria, Firmicutes and their ratio may be potential markers for short-term ambient ozone exposure, and indicators of airway inflammation or lung function decline.

[Laboratory diagnostics in medicine]

The development of clinical laboratory diagnostics is in line with the evidence-based medicine, which requires that clinical decisions have to be based on diagnostic methods with proven informativity. This creates a request for the scientific validity of the use of laboratory researches and application of probabilistic interpretation tools corresponding to the tasks. The concept of indefiniteness (analytical, biological and clinical) is at the heart of interpretation of laboratory results. The inclusion of laboratory research in clinical guidelines, the choice and appointment of this research to the patient should not be made from the position of ideas about increasing or decreasing the laboratory index in the disease, but on the basis of its scientifically proven characteristics as a laboratory biomarker sensitivity, specificity, predictive value, as well as the relationship with certain clinical events, outcomes, risks. These characteristics are probabilistic and can be defined.

Advances in biomarker detection: Alternative approaches for blood-based biomarker detection

In the clinical setting, a blood sample is typically the starting point for biomarker search and discovery. Mass spectrometry (MS) is a highly sensitive and informative method for characterizing a very wide range of metabolites and proteins and is therefore a potentially powerful tool for biomarker discovery. However, the physicochemical characteristics of blood coupled with very large ranges of protein and metabolite concentrations present a significant technical obstacle for resolving and quantifying putative biomarkers by MS. Blood fractionation procedures are being developed to reduce the proteome/metabolome complexity and concentration ranges, allowing a greater diversity of analytes, including those at very low concentrations, to be quantified. In this chapter, several strategies for enriching and/or isolating specific blood components are summarized, including methods for the analysis of low and high molecular weight compounds, usually neglected in this type of assays, extracellular vesicles, and peripheral blood mononuclear cells (PBMCs). For each method, relevant practical information is presented for effective implementation.

Unbiased data analytic strategies to improve biomarker discovery in precision medicine

Omics technologies promised improved biomarker discovery for precision medicine. The foremost problem of discovered biomarkers is irreproducibility between patient cohorts. From a data analytics perspective, the main reason for these failures is bias in statistical approaches and overfitting resulting from batch effects and confounding factors. The keys to reproducible biomarker discovery are: proper study design, unbiased data preprocessing and quality control analyses, and a knowledgeable application of statistics and machine learning algorithms. In this review, we discuss study design and analysis considerations and suggest standards from an expert point-of-view to promote unbiased decision-making in biomarker discovery in precision medicine.

Network-based technologies for early drug discovery

Although the traditional drug discovery approach has led to the development of many successful drugs, the attrition rates remain high. Recent advances in systems-oriented approaches (systems-biology and/or pharmacology) and 'omics technologies has led to a plethora of new computational tools that promise to enable a more-informed and successful implementation of the reductionist, one drug for one target for one disease, approach. These tools, based on biomolecular pathways and interaction networks, offer a systematic approach to unravel the mechanism(s) of a disease and link them to the chemical space and network footprint of a drug. Drug discovery can draw upon this holistic approach to identify the most-promising targets and compounds during the early phases of development.