Identification and Validation of New Biomarkers to Help the Creation of Clinical Diagnostics in Autoimmune and Cancer Diseases

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Immunology and Immunotherapy".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 8128

Special Issue Editor

Exagen Inc., 1261 Liberty Way, Vista, CA 92081, USA
Interests: diagnostics; immunology; biomarkers; autoimmune diseases; infectious diseases; immunotherapy; validation; assay development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The identification of biomarkers in patients with immunological diseases, including cancer and autoimmune diseases, is critical to prevent disease progression (stopping disease evolution), predicting the prognosis (correlation between the biomarker and clinical outcomes), targeting the appropriate therapeutic drugs and personalized medicine (response to treatment), and clinical decision-making processes involved in diagnosis and assessment of disease activity (ability to reflect changes in clinical status). Measurement of biomarker proteins and signature protein expression specific to the disease and biological samples (blood and/or tissue) help to understand the mechanisms involved in the pathogenesis of these disorders. For cancer, immune-monitoring assays for immunosuppressive factors that prevent an effective antitumor immune response in tumor tissues and peripheral blood are necessary to improve immunotherapy strategies. New assays should be optimized to obtain the optimal accuracy of analyte in various samples (matrix effect and inhibitory agents), as well as an appropriate balance of sensitivity and specificity before being implanted in a clinical diagnostic laboratory. Additional prerequisites of biomarker validation are reliability, accuracy, and reproducibility. However, the validation of an assay measuring biomarker(s) specific to a disease or clinical outcomes is often lacking in products and reagents for biomedical research.

This Special Issue aims to concentrate on and describe the current development of research on new assays to identify and validate new biomarkers to enhance the creation of clinical diagnostics in autoimmune and cancer diseases.

Dr. Veronique Demers-Mathieu
Guest Editor

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Keywords

  • diagnostics
  • biomarkers
  • autoimmune diseases
  • cancers
  • immunotherapy
  • assay development
  • prognostic
  • immune-Monitoring
  • signature protein expression

Published Papers (5 papers)

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Research

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19 pages, 7272 KiB  
Article
The Gastric Cancer Immune Prognostic Score (GCIPS) Shows Potential in Predicting an Unfavorable Prognosis for Gastric Cancer Patients Undergoing Immune Checkpoint Inhibitor Treatment
by Yanjiao Zuo, Hao Sun, Hongming Pan, Ruihu Zhao, Yingwei Xue and Hongjiang Song
Biomedicines 2024, 12(3), 491; https://doi.org/10.3390/biomedicines12030491 - 22 Feb 2024
Viewed by 440
Abstract
(1) Background: This study aims to explore the predictive capability of the Gastric Cancer Immune Prognostic Score (GCIPS) for an unfavorable prognosis in gastric cancer patients undergoing immune checkpoint inhibitor (ICI) treatment. (2) Methods: This study included 302 gastric cancer patients who underwent [...] Read more.
(1) Background: This study aims to explore the predictive capability of the Gastric Cancer Immune Prognostic Score (GCIPS) for an unfavorable prognosis in gastric cancer patients undergoing immune checkpoint inhibitor (ICI) treatment. (2) Methods: This study included 302 gastric cancer patients who underwent treatment with ICIs at our institution from January 2017 to December 2022. The patients were randomly divided into a test set (201 cases) and a validation set (101 cases) using a random number table. Kaplan–Meier survival analysis and the log-rank test were used to investigate survival differences. Cox regression analysis and Lasso regression analysis were employed to establish the GCIPS and identify independent prognostic indicators. ROC curves, time–ROC curves, and nomograms were utilized to further explore the predictive performance of GCIPS. (3) Results: The test set and validation set showed no statistical differences in clinical and pathological features, as well as blood parameters (all p > 0.05). Cox regression analysis revealed that white blood cells (WBC), lymphocytes (LYM), and the international normalized ratio (INR) emerged as independent prognostic blood indicators after eliminating collinearity through Lasso analysis. The GCIPS was established using β coefficients with the following formula: GCIPS = WBC (109/L) × 0.071 − LYM (109/L) × 0.375 + INR × 2.986. ROC curves based on death and time–ROC curves demonstrated that the GCIPS had higher AUCs than other classical markers at most time points. Survival analyses of all subgroups also revealed a significant correlation between the GCIPS and patients’ progression-free survival (PFS) and overall survival (OS) (all p < 0.05). Furthermore, the GCIPS was identified as an independent prognostic factor for both PFS and OS. Analyses in the validation set further confirmed the reliability and stability of the GCIPS in predicting patient prognosis. Finally, nomograms incorporating the GCIPS exhibited high accuracy in both the test and validation sets. Additionally, the nomograms revealed that the GCIPS had a higher prognostic value than any other factor, including the TNM stage. (4) Conclusions: The GCIPS demonstrated its ability to predict adverse outcomes in gastric cancer patients undergoing ICIs treatment and had a high prognostic value. As a readily accessible and simple novel biomarker, it effectively identified high-risk patients. Full article
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12 pages, 1594 KiB  
Article
Textural Analysis of Magnetic Resonance Images as an Additional Evaluation Tool of Parotid Glands in Sjögren—Primarily Findings
by Małgorzata Grzywińska, Magdalena Karwecka, Anna Pomorska, Ninela Irga-Jaworska and Dominik Świętoń
Biomedicines 2023, 11(12), 3132; https://doi.org/10.3390/biomedicines11123132 - 24 Nov 2023
Viewed by 679
Abstract
Magnetic Resonance Imaging (MRI) plays a leading role in diagnosing soft tissue pathologies, especially in the head and neck. It is increasingly popular for evaluating salivary gland issues like neoplasms and Sjogren’s Syndrome. Advanced MRI methods, including MRI sialography and texture analysis, offer [...] Read more.
Magnetic Resonance Imaging (MRI) plays a leading role in diagnosing soft tissue pathologies, especially in the head and neck. It is increasingly popular for evaluating salivary gland issues like neoplasms and Sjogren’s Syndrome. Advanced MRI methods, including MRI sialography and texture analysis, offer non-invasive alternatives, enhancing MRI’s role. This study focused on the relationship between the apparent diffusion coefficient (ADC) and T2-weighted MRI sialography and texture analysis (TA) of parotid glands in children with and without Sjogren’s Syndrome (SS). Using 3.0 Tesla MRI with DWI and T2-weighted imaging, expended texture analysis, first-order statistics (FSOs), second-order, and higher-order statistics were conducted. The results showed significant differences in parotid ADC values, with lower values in the SS group, particularly in cases of higher disease activity. Lower kurtosis values were associated with more severe Tonami Scale grades. FSO parameters correlated well with the texture analysis from T2-weighted images, indicating promise in grading parotid gland inflammation. However, further research is needed to understand the impact of variables like binning and region of interest (ROI) size. This study highlights the potential of texture analysis for assessing parotid gland inflammation and emphasizes the need for more investigations in this area. Full article
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8 pages, 367 KiB  
Communication
High-Throughput and Automated Detection of HLA-B*27 Using the LabTurboTM AIO System
by Yung-Che Chou and Tze-Kiong Er
Biomedicines 2023, 11(3), 986; https://doi.org/10.3390/biomedicines11030986 - 22 Mar 2023
Cited by 1 | Viewed by 1204
Abstract
The adoption of an automated system can decrease the hands-on time requirements in a clinical laboratory setting. For the detection of HLA-B*27, implementing a high-throughput and fully automated system has several advantages over using manual methods. Therefore, this study aimed to evaluate automation [...] Read more.
The adoption of an automated system can decrease the hands-on time requirements in a clinical laboratory setting. For the detection of HLA-B*27, implementing a high-throughput and fully automated system has several advantages over using manual methods. Therefore, this study aimed to evaluate automation efficiency for the detection of HLA-B*27. Peripheral blood samples were obtained from 50 participants, and DNA was isolated from these samples. A Pharmigene PG27 detection kit was used for the qualitative detection of HLA-B*27. The performances of the semi-automated and fully automated LabTurboTM AIO systems in the detection of HLA-B*27 were compared. The mean absorbance (optical density) values for the MaelstromTM 8 and LabTurboTM AIO systems were found to be 1.88 and 1.9, respectively. The housekeeping gene was amplified and quantified using a real-time PCR assay across all DNA extracts to check the quality of the extracted human DNA. The results were expressed as the cycle threshold (Ct) values for all DNA extracts from both platforms. The mean Ct values for the Roche Cobas z480 and LabTurboTM AIO systems were found to be 22.7 and 20.4, respectively. This study demonstrated that the semi-automated method and the LabTurboTM AIO system yield consistent results for the detection of HLA-B*27. However, compared to the semi-automated method, the LabTurboTM AIO system provides standardized procedures, avoids manual handling, and improves turnaround time. Full article
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Review

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19 pages, 1026 KiB  
Review
Small Cell Lung Carcinoma: Current Diagnosis, Biomarkers, and Treatment Options with Future Perspectives
by Kristina Krpina, Semir Vranić, Krešimir Tomić, Miroslav Samaržija and Lara Batičić
Biomedicines 2023, 11(7), 1982; https://doi.org/10.3390/biomedicines11071982 - 13 Jul 2023
Cited by 4 | Viewed by 3202
Abstract
Small cell lung cancer (SCLC) is an aggressive malignancy characterized by rapid proliferation, early dissemination, acquired therapy resistance, and poor prognosis. Early diagnosis of SCLC is crucial since most patients present with advanced/metastatic disease, limiting the potential for curative treatment. While SCLC exhibits [...] Read more.
Small cell lung cancer (SCLC) is an aggressive malignancy characterized by rapid proliferation, early dissemination, acquired therapy resistance, and poor prognosis. Early diagnosis of SCLC is crucial since most patients present with advanced/metastatic disease, limiting the potential for curative treatment. While SCLC exhibits initial responsiveness to chemotherapy and radiotherapy, treatment resistance commonly emerges, leading to a five-year overall survival rate of up to 10%. New effective biomarkers, early detection, and advancements in therapeutic strategies are crucial for improving survival rates and reducing the impact of this devastating disease. This review aims to comprehensively summarize current knowledge on diagnostic options, well-known and emerging biomarkers, and SCLC treatment strategies and discuss future perspectives on this aggressive malignancy. Full article
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12 pages, 1333 KiB  
Review
Optimal Selection of IFN-α-Inducible Genes to Determine Type I Interferon Signature Improves the Diagnosis of Systemic Lupus Erythematosus
by Veronique Demers-Mathieu
Biomedicines 2023, 11(3), 864; https://doi.org/10.3390/biomedicines11030864 - 12 Mar 2023
Cited by 6 | Viewed by 2094
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies specific to self-molecules in the nucleus, cytoplasm, and cell surface. The diversity of serologic and clinical manifestations observed in SLE patients challenges the development of diagnostics and tools [...] Read more.
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies specific to self-molecules in the nucleus, cytoplasm, and cell surface. The diversity of serologic and clinical manifestations observed in SLE patients challenges the development of diagnostics and tools for monitoring disease activity. Elevated type I interferon signature (IFN- I) in SLE leads to dysregulation of innate and adaptive immune function, resulting in autoantibodies production. The most common method to determine IFN-I signature is measuring the gene expression of several IFN-α-inducible genes (IFIGs) in blood samples and calculating a score. Optimal selection of IFIGs improves the sensitivity, specificity, and accuracy of the diagnosis of SLE. We describe the mechanisms of the immunopathogenesis of IFN-I signature (IFNα production) and its clinical consequences in SLE. In addition, we explore the association between IFN-I signature, the presence of autoantibodies, disease activity, medical therapy, and ethnicity. We discuss the presence of IFN-I signature in some patients with other autoimmune diseases, including rheumatoid arthritis, systemic and multiple sclerosis, Sjogren’s syndrome, and dermatomyositis. Prospective studies are required to assess the role of IFIG and the best combination of IFIGs to monitor SLE disease activity and drug treatments. Full article
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