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May 2026 DOI 10.14302/issn.2572-3030.jcgb-26-6307
Faisst Arne-C.Corresponding author
The development of tumor biomarkers derived from blood, or its components, has become pivotal in advancing early cancer diagnosis. Malignant transformations induce cancer-specific alterations in the transcriptome, proteome, and secretome of tumor cells. Recent studies highlighted similar alterations in peripheral blood mononuclear cells (PBMCs) in cancer patients, which appear to mirror the state of transformation in tumor cells. These findings suggest an intercellular communication–driven mechanism rather than a systemic inflammatory response and, in addition to current ctDNA-based liquid biopsy biomarkers, point to a novel, simple, and highly robust approach for the early detection of cancer. Using this phenomenon to advance PBMC-based biomarker development, it will be essential to achieve 3D in vitro tumor models that reproduce a highly physiological tumor microenvironment (TME). Likewise, more enhanced 3D ex vivo models are required to enable the replication of cell-to-cell and organ-to-organ communication. These systems will guide the self-organization of mixed microenvironments derived from different tissues and enable them to accurately reproduce the molecular connections underlying these alterations. In this study, an innovative new modular 3D co-culturing approach was used to expose PBMCs to lung tumoroids, under physiologically relevant conditions. Changes in DNA fragmentation of PBMCs in the presence of lung cancer were quantified and used as a biomarker. To validate the predictiveness of this biomarker, our results were compared with clinical data from a clinical evaluation study. Similar to the clinical trial observations, PBMCs, when exposed to lung tumoroids, showed a significantly lower level of DNA fragmentation (37%). This modular 3D co-culturing model showed a predictiveness of the clinical data of > 90%, demonstrating its power to monitoring cell-to-cell communication effects and support the development of blood-based biomarkers.
Mar 2018
Liu XiaopingCorresponding author
School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China
An important use of proteomics data from Mass Spectrometry (MS) is the classification of tumor types with respect to peptides in specific cancer types. It is highly critical to find an optimal set of markers among specific cancer peptides whose expression can be clinically utilized to build assays for the diagnosis or to track the progression of specific cancer types. A number of feature selection algorithms have been proposed to obtain the classification of MS data. In this article, we proposed an improved feature selection algorithm based on feature weighting. Relief algorithm can calculate the weight of different features according to the correlation between their characteristics and categories. F-score is a simple filter-based feature selection method by evaluating how two sets of real numbers discriminate from each other. The main goal of this paper is to introduce a new feature weighting selection algorithm combining score from f-value and weight from relief, which is more accurate when classifying high-resolution MALDI-TOF (matrix-assisted laser desorption and ionization time-of-flight) MS data. We have developed a four-step strategy for data processing based on: (1) Align the study sets by binning of raw MS data, (2) local maximum search(LMS) peak detection, (3) a new combination feature weighting selection algorithm and (4) support vector machines achieve a satisfactory performance of identifying cancer and the healthy. The best parameter set for LMS were achieved with control variable method, which achieve an average accuracy of 97.4167% (sd = 0.0146) and the best accuracy of 98.6111% in 1000 independent 10 -fold cross validations.
Nov 2025 DOI 10.14302/issn.2326-0793.jpgr-25-5573
E. Imiruaye OghenetegaCorresponding author
Advancements in proteomic and genomic technologies have transformed molecular biology by enabling comprehensive analysis of biological systems at the molecular level. This literature review explores the evolution, methodologies, and practical applications of key proteomic and genomic techniques. In proteomics, tools such as two-dimensional electrophoresis, mass spectrometry, Western blotting, Edman degradation, and functional protein microarrays have facilitated high-throughput protein identification, post-translational modification analysis, and biomarker discovery. Similarly, genomic methodologies like PCR, recombinant DNA technology, gel electrophoresis, and Southern blotting have revolutionized gene detection, manipulation, and expression profiling. The review also highlights the interdisciplinary impact of these technologies across clinical diagnostics, oncology, autoimmune disorders, infectious disease surveillance, cardiovascular research, and personalized nutrition. Integrative approaches combining proteomics and genomics are enabling the discovery of novel therapeutic targets, improving disease classification, and advancing precision medicine. Despite current limitations, such as the absence of amplification techniques for proteins and challenges in data interpretation, ongoing innovations promise to bridge these gaps. This synthesis underscores the pivotal role of molecular techniques in deepening our understanding of human biology and accelerating biomedical advancements for improved healthcare outcomes.
Jan 2024 DOI 10.14302/issn.2574-4496.jtc-23-4835
Hussein Saleh Hussein AbbasCorresponding author
The prevalence of thyroid cancer is rapidly increasing worldwide, majorly due to overdiagnosis and overtreatment methods of differentiated thyroid cancer. The emergent and potent preclinical models, high-throughput molecular techniques, and genetic expression microarrays have delivered deeper insights into understanding the molecular features in oncogenesis. Thus, molecular markers have become a promising tool in managing thyroid cancer for differentiating benign and malignant tumors, prognosis, recurrence, and determination of novel therapeutic targets. In differentiated thyroid cancer, molecular markers are majorly utilized for guiding the development of indeterminate thyroid nodules on fine needle aspiration (FNA) histologies. Dissimilar to this, in advanced thyroid cancer, molecular markers permit targeted treatment of a modified signaling cascade. Determining causal mutation of targeted kinase receptors in advanced thyroid cancer can depict a promising treatment strategy with mutation-targeted tyrosine kinase inhibitors to reduce progression and eradicate mutation effects when conventional methods fail to manage. This review will focus on the molecular landscape and discuss the impact of molecular markers on the prognosis, treatment, and surveillance of differentiated and anaplastic thyroid cancer.
Jan 2019 DOI 10.14302/issn.2572-3030.jcgb-18-2527
Zhang XiCorresponding author
Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, United States
As remarkable advances have been made in immunotherapies, the overall goal of immunotherapy has become the selection of patients and evaluating the benefits of treatment. One of the major obstacles to develop immunotherapies is the lack of effective immune monitoring. Monitoring of key changes in the immune system during immunotherapy (immunomonitoring) provides important insights into efficacy as well as the immune mechanisms of response at the molecular and cellular levels. Immunomonitoring techniques include traditional immunoassays that use specific antibodies to recognize the analytes of interest, new high-throughput immunoassays that target immune cells and nucleic acids, and less classical immunogenomic approaches that rely on genome-wide profiling and computational analysis on various types of clinical samples. Substantial progress has been made in the application of immunomonitoring strategies to pre-clinical and clinical studies, especially for patients with cancer and infectious diseases. Current and emerging immunoassays performed in clinical practice will be examined herein, and immunogenomic approaches that complement these techniques will be highlighted and compared with traditional methods. Finally, we will discuss several new computational methods for analyzing gene signatures for immunomonitoring, including gene expression data profiling by microarray, the nCounter technique, regular RNA-seq, and single-cell RNA-seq. Novel immunomonitoring techniques, especially immunogenomic approaches, will continue to be developed to facilitate assessment of immunotherapeutic response and predict patient outcomes in cancer and infectious disease.
Dec 2018 DOI 10.14302/issn.2377-2549.jndc-18-2430
P. Richie Jr JohnCorresponding author
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033
Introduction: Analysis of 8-oxodG is usually conducted by either chromatography-based methods or by immunochemical methods commonly used based upon their low cost and high-throughput. However, concern regarding the accuracy of ELISA methods has complicated their use. We directly compare the levels of urinary 8-oxodG obtained by HPLC-MS/MS with three commercially available ELISA kits in this report. Methods: In the current study, a total of 9 human urine samples were analyzed by LC-MS/MS and three commonly used commercial available ELISA kits. Results: We found that urinary 8-oxodG levels analyzed by HPLC-MS/MS [1.4 ± 0.3 nmol/mmol creatinine) were 7.6- to 23.5-fold lower than those detected by ELISA. Overall, the correlations between ELISA and HPLC-MS/MS were poor but were improved after SPE purification for kits from ENZO (P = 0.2817 without SPE; P = 0.0086 with SPE) and Abcam (P = 0.0596 without SPE; P = 0.0473 with SPE). Discussion and Conclusion: While we confirmed that SPE purification can improve the correlation between the selected ELISA kits and HPLC-MS/MS, HPLC-MS/MS is still the method of choice to accurately assess the levels of 8-oxodG in human urine.
Aug 2018 DOI 10.14302/issn.2690-4721.ijcm-18-2217
Samaras ShivanthiCorresponding author
Molecular Microbiology, School of Allied Health Sciences, Faculty of Health & Life Sciences, Hawthorn Building, The Gateway, De Montfort University, Leicester, LE1 9BH United Kingdom
Background Overuse of beta-lactam antibiotics has lead to selection for extended-spectrum β-lactamase (ESBL) producing Enterobacteriaceae, a major cause of antibiotic resistant urinary tract infections (UTIs). Standard detection methods are time-consuming, with disputed accuracy. This study describes a novel real-time PCR method to detect CTX-M, SHV, OXA and TEM. Methods 179 Enterobacteriaceae isolates from UTIs were collected from the Leicester Royal Infirmary, UK. A multiplex Plexor®-based real-time PCR assay detected ESBLs using their specific amplicon melting temperature, during each cycle, removing the need for a melt-curve analysis. Validation was achieved by end-point PCR and disk diffusion. Results The method was able to produce rapid and accurate results, achieving a sensitivity and specificity of 94.9% and 72% respectively, and the assay can differentiate between the different ESBL genes, with ease. Conclusions With further investigation, a Plexor®-based assay could form the basis of a high-throughput kit that health services could use to detect ESBLs or other antibiotic resistance genes.