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Clinical Prognostic and Predictive Biomarkers—2nd Edition

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2951

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Special Issue Editors

Prof. Dr. Yuli Huang

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Guest Editor

Department of Cardiology, Shunde Hospital, Southern Medical University, Foshan 528300, China
Interests: cardiovascular disease; heart failure; diabetes; biomarkers; public health; prevention
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yong Yuan

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Guest Editor

Department of Cardiology, Zhongshan City People's Hospital, Sun Yat-sen University, Zhongshan, China
Interests: cardiovascular disease; heart failure; biomarkers; coronary artery disease
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peisong Chen

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Guest Editor

Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
Interests: laboratory medicine; precision medicine; risk prediction; clinical biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomarkers are measurement biological variables, which can be detected in organ tissues, blood, or other body fluids. They can be mainly divided into two main types: prognostic and predictive biomarkers. Prognostic biomarkers are associated with the clinical outcomes (e.g., disease progression and recurrence, death) of the interested diseases, and are used to identify those with more aggressive disease status. Predictive biomarkers are used to identify individuals with a higher likelihood of response to a particular treatment, which allows better identification of those who are more likely to benefit from a given treatment. Generally, biomarkers can be either prognostic or predictive, while in some cases they could be used as both prognostic and predictive.

With the great advances in proteomics, metabolomics, functional genomics, and bioinformatics, more and more novel biomarkers are discovering. They play an important role in identifying high-risk individuals, diagnosing disease conditions, and predicting response to therapy and prognosis in multiple fields of clinical medicine, including cardiovascular disease, diabetes, and cancer. Furthermore, they allow us to better understand the mechanisms and molecular pathways of disease development and progression. This deeper knowledge of biomarkers offers the opportunity to develop novel precision and personalized therapies.

In this Special Issue, we aim to provide a platform for communication on the progress of biomarkers identification and utilization in healthcare. The welcomed topics include but are not limited to biomarkers in cardiovascular disease, diabetes, acute and chronic venous disease, and cancer.

Prof. Dr. Yuli Huang
Prof. Dr. Yong Yuan
Prof. Dr. Peisong Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

biomarkers
prognostic
predictive
risk stratification
cardiovascular disease
diabetes
cancer
hypertension
heart failure

Related Special Issue

Clinical Prognostic and Predictive Biomarkers in Diagnostics (20 articles)

Published Papers (3 papers)

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Research

12 pages, 1025 KiB

Open AccessArticle

Exploring PGE2 and LXA4 Levels in Migraine Patients: The Potential of LXA4-Based Therapies

by Idris Kocaturk, Sedat Gulten, Bunyamin Ece and Fatma Mutlu Kukul Guven

Diagnostics 2024, 14(6), 635; https://doi.org/10.3390/diagnostics14060635 - 17 Mar 2024

Viewed by 688

Abstract

Neurogenic inflammation plays a significant role in the pathogenesis of migraines. This study aimed to investigate the serum levels of prostaglandin E2 (PGE2), lipoxin A4 (LXA4), and other inflammatory biomarkers (C-reactive protein, fibrinogen) in migraine patients. In total, 53 migraine patients and 53 healthy controls were evaluated. Blood serum samples were collected during both attack and interictal periods and compared with the control group. In both the attack and interictal periods, PGE2 and LXA4 values were significantly lower in migraine patients compared to the control group (p < 0.001). Additionally, PGE2 values during the attack period were significantly higher than those during the interictal period (p = 0.016). Patients experiencing migraine attacks lasting ≥ 12 h had significantly lower serum PGE2 and LXA4 levels compared to those with attacks lasting < 12 h (p = 0.028 and p = 0.009, respectively). In ROC analysis, cut-off values of 332.7 pg/mL for PGE2 and 27.2 ng/mL for LXA4 were determined with 70–80% sensitivity and specificity. In conclusion, PGE2 and LXA4 levels are significantly lower in migraine patients during both interictal and attack periods. Additionally, the levels of LXA4 and PGE2 decrease more with the prolongation of migraine attack duration. Our findings provide a basis for future treatment planning. Full article

(This article belongs to the Special Issue Clinical Prognostic and Predictive Biomarkers—2nd Edition)

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22 pages, 2199 KiB

Open AccessArticle

Role of Clinical Risk Factors and B-Type Natriuretic Peptide in Assessing the Risk of Asymptomatic Cardiotoxicity in Breast Cancer Patients in Kazakhstan

by Zhenisgul Tlegenova, Saule Balmagambetova, Bekbolat Zholdin, Gulnara Kurmanalina, Iliada Talipova, Arip Koyshybaev, Gulmira Sultanbekova, Mira Baspayeva, Saule Madinova, Kulparshan Kubenova, Aiganym Amanova and Amin Tamadon

Diagnostics 2023, 13(23), 3557; https://doi.org/10.3390/diagnostics13233557 - 28 Nov 2023

Viewed by 970

Abstract

The asymptomatic progression of chemotherapy-induced cardiotoxicity poses a significant risk to breast cancer patients. In the present single-center cohort study, a predictive model for evaluating the risk of cardiotoxicity during or by the end of chemotherapy was designed. The risk-prediction nomogram was delineated and assessed. In total, 34 patients out of 120 developed asymptomatic cardiotoxicity (28.3%). Of six explored biomarkers, only B-type natriuretic peptide showed a reliable pattern of incremental increase, revealing statistical significance between cardiotoxicity “+” and “−” groups by visit 4 or by the 9th month of monitoring (p 0.006). The following predictors were included in the model: age, hypertension, diabetes mellitus, baseline glomerular filtration rate, 6 min walk test measured at visit 4, BNP values at visit 4, left ventricular ejection fraction levels at visit 4, a total dose of radiotherapy received, and anthracycline cumulative doses. The model’s AUC was 0.72 (95% CI 0.59; 0.86), evidencing the satisfactory predictive ability of the model; sensitivity 100% (95% CI 90.36; 100.0) at a specificity of 66.67% (95% CI 50.33; 79.79); PPV 54.1% [95% CI 47.13; 60.91]; PVN 100% [95% CI 94.64; 100.00]. The calibration plot showed satisfactory agreement between predicted and actual chances (p = 0.98). The designed model can be applied in settings lacking speckle tracking echocardiography. Full article

(This article belongs to the Special Issue Clinical Prognostic and Predictive Biomarkers—2nd Edition)

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11 pages, 1429 KiB

Open AccessArticle

Predicting Peri-Operative Outcomes in Patients Treated with Percutaneous Thermal Ablation for Small Renal Masses: The SuNS Nephrometry Score

by Gennaro Musi, Stefano Luzzago, Giovanni Mauri, Francesco Alessandro Mistretta, Gianluca Maria Varano, Chiara Vaccaro, Sonia Guzzo, Daniele Maiettini, Ettore Di Trapani, Paolo Della Vigna, Roberto Bianchi, Guido Bonomo, Matteo Ferro, Zhe Tian, Pierre I. Karakiewicz, Ottavio de Cobelli, Franco Orsi and Mattia Luca Piccinelli

Diagnostics 2023, 13(18), 2955; https://doi.org/10.3390/diagnostics13182955 - 15 Sep 2023

Cited by 1 | Viewed by 969

Abstract

Our objective was to develop a new, simple, and ablation-specific nephrometry score to predict peri-operative outcomes and to compare its predictive accuracy to PADUA and RENAL scores. Overall, 418 patients were treated with percutaneous thermal ablation (microwave and radiofrequency) between 2008 and 2021. The outcome of interest was trifecta status (achieved vs. not achieved): incomplete ablation or Clavien–Dindo ≥ 3 complications or postoperative estimated glomerular filtration rate decrease ≥ 30%. First, we validated the discrimination ability of the PADUA and RENAL scoring systems. Second, we created and internally validated a novel scoring (SuNS) system, according to multivariable logistic regression models. The predictive accuracy of the model was tested in terms of discrimination and calibration. Overall, 89 (21%) patients did not achieve trifecta. PADUA and RENAL scores showed poor ability to predict trifecta status (c-indexes 0.60 [0.53–0.67] and 0.62 [0.55–0.69], respectively). We, therefore, developed the SuNS model (c-index: 0.74 [0.67–0.79]) based on: (1) contact surface area; (2) nearness to renal sinus or urinary collecting system; (3) tumour diameter. Three complexity classes were created: low (3–4 points; 11% of no trifecta) vs. moderate (5–6 points; 30% of no trifecta) vs. high (7–8 points; 65% of no trifecta) complexity. Limitations include the retrospective and single-institution nature of the study. In conclusion, we developed an immediate, simple, and reproducible ablation-specific nephrometry score (SuNS) that outperformed PADUA and RENAL nephrometry scores in predicting peri-operative outcomes. External validation is required before daily practice implementation. Full article

(This article belongs to the Special Issue Clinical Prognostic and Predictive Biomarkers—2nd Edition)

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