Blinatumomab, a bispecific T-cell engager, requires the activity of CD3+ T-cells for tumor lysis. This pooled analysis aimed to re-examine baseline T regulatory cells (Tregs) as a biomarker of blinatumomab efficacy across multiple clinical trials and provide insights into peripheral T-cell dynamics during blinatumomab therapy in hematological malignancies.

Tregs and peripheral T-cells were enumerated by fluorescence-activated cell sorting and were statistically evaluated using the Wilcoxon rank-sum test and linear mixed effect modeling, respectively.

Comparable baseline percentages of Tregs were observed in responders and non-responders of blinatumomab treatment (N = 325) in adults with leukemia or lymphoma. Peripheral T-cell count recovery occurred early during blinatumomab dosing, and before blinatumomab-free interval in patients (N = 233) from 4 clinical trials.

The pooled analysis revealed that baseline Treg levels do not serve as a predictive marker for blinatumomab response and that there is rapid peripheral T-cell recovery following blinatumomab dosing. These results suggest that patients with varying levels of Tregs can benefit from blinatumomab treatment and that blinatumomab-free intervals of 7 days may suffice in blinatumomab treatment regimens.

DNA damage is one of the primary mechanisms underlying cancer and other chronic degenerative diseases. Early evaluation of this damage in the affected cells and tissues is crucial for understanding pathogenesis and implementing effective prevention strategies. However, isolating target cells from affected organs, such as the lungs, can be challenging. Therefore, an alternative approach is to evaluate genotoxic damage in surrogate cells. Pulmonary alveolar macrophages are ideally suited for this purpose because they are in close contact with the target cells of the bronchial and alveolar epithelium, share the exact mechanisms and levels of exposure, and are easily recoverable in large numbers. This review comprehensively lists all studies using alveolar macrophages as surrogate cells to show genotoxic lung damage in humans or laboratory animals. These investigations provide fundamental information on the mechanisms of DNA damage in the lung and allow for better assessment and management of risk following exposure to inhalable genotoxic agents. Furthermore, they may be a valuable tool in cancer chemoprevention, helping the right choice of agents for clinical trials.

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

Approximately 15% of locally advanced colorectal cancers (CRC) have DNA mismatch repair deficiency (dMMR), resulting in high microsatellite instability and a high tumour mutational burden. These cancers are frequently sensitive to therapy with immune-checkpoint inhibitors (ICIs) in the metastatic setting. This sensitivity seems to be even more pronounced in locally advanced disease, and organ preservation has become a realistic aim in ongoing clinical trials involving patients with dMMR rectal cancer. By contrast, metastatic CRCs with proficient DNA mismatch repair (pMMR) are generally resistant to ICIs, although a proportion of locally advanced pMMR tumours seem to have a high degree of sensitivity to ICIs. In this Review, we describe the current and emerging clinical evidence supporting the use of neoadjuvant ICIs in patients with dMMR and pMMR CRC, and the potential advantages (based on a biological rationale) of such an approach. We discuss how neoadjuvant 'window-of-opportunity' trials are being leveraged to progress biomarker discovery and we provide an overview of potential predictive biomarkers of response to ICIs, exploring the challenges faced when evaluating such biomarkers in biopsy-derived samples. Lastly, we describe how these discoveries might be used to drive a rational approach to trialling novel immunotherapeutic strategies in patients with pMMR CRC, with the ultimate aim of disease eradication and the generation of long-term immunosurveillance.

Background A minority of patients receiving stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) are not good responders. Radiomic features can be used to generate predictive algorithms and biomarkers that can determine treatment outcomes and stratify patients to their therapeutic options. This study investigated and attempted to validate the radiomic and clinical features obtained from early-stage and oligometastatic NSCLC patients who underwent SBRT, to predict local response. Methodology A single-institution, Institutional Review Board (IRB)-approved retrospective review was conducted on adult patients with early-stage and oligometastatic SBRT-treated NSCLC at the Jewish General Hospital. The study included 98 patients (82 with early-stage NSCLC and 16 with oligometastatic disease), with a median age of 76 years and a male-to-female ratio of 46:52. A total of 116 lesions were treated with SBRT between 2009 and 2022. Radiomics features (n = 107) were extracted from CT planning scans using PyRadiomics, and clinical data were collected for all 98 patients. Local response was assessed according to Response Evaluation Criteria In Solid Tumors (RECIST 1.1) criteria. Classification models, including support vector machines, random forests, adaptive boosting, and multi-layer perceptrons (MLPs), were used. Models were trained using a fivefold cross-validation scheme. Their performances were measured with receiver operating characteristic plots on the validation folds. Using the importance of the permutation feature, predictive biomarkers were identified. Results The most predictive model, incorporating all patients and using an MLP classifier with Adaptive Synthetic (ADASYN) sampling, a combined-input approach, and a radiomic filter, achieved an area under the curve (AUC) of 0.94 ± 0.05. When oligometastatic patients were omitted, the best model (AUC 0.95 ± 0.06) was also predictive, using a support vector classification (SVC) radial basis function (RBF) classifier, ADASYN sampling, and a clinical-based input. Treatment site and performance status, along with radiomic features such as first-order root-mean-squared-intensity, first-order skewness, and gray-level nonuniformity, were found to be predictive biomarkers. Conclusions The predictive models generated and the biomarkers identified could be used in clinical decision support systems for SBRT-treated NSCLC patients. Additionally, treatment site, performance status, and radiomic features were the most predictive variables.

Many cancer biomarkers such as the circulating tumor cells/microemboli (CTCs/CTM) have been reported significant associations with clinical outcomes. However, different biomarkers have different sensitivities and specificities for cancer types and cohort patients, and synergistic effects between certain biomarkers have also been observed, leading to the inaccurate, fluctuating, and even controversial results when multiple biomarkers are analyzed together. In this paper, a novel combinational index, P-score, was developed for monitoring and predicting the disease condition of lung cancer patients during follow-up visits.

There were totally 13 return patients with 54 blood samples involved in this study to examine the number of CTC and CTM. Information from one group of 7 patients including 27 blood samples with published clinical data was employed to develop while those from another group of 4 patients containing 14 blood samples with unpublished clinical data were used to validate the P score in prediction. Enumerations were based on immunofluorescent staining images. Distributions of CTC/CTM and their frequencies in stratified patients were carefully examined and analyzed the ROC curve and AUC value to develop the P score and P score-based prediction model.

We found that the predictive power of P-score was not only comparable to the traditional cancer marker, in comparison with individual CTC/CTM, more false positives could be corrected by using P-score, thereby to improve the accuracy of analysis. From our preliminary validation tests, the prognosis and disease progression monitored longitudinally by P-score were further confirmed by clinical outcome data from physicians and its sensitivity was even better than those from individual biomarkers. We believe that this novel combinational indicator could be a promising tool to interpret clinical outcomes more accurately from multiple factors, particularly useful for the early prognosis and longitudinal monitoring in cancer patient management.

This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: Primary objectives To identify and estimate the prognostic value of molecular biomarkers as predictors of pathological complete response to neoadjuvant chemoradiotherapy in people with locally advanced rectal cancer. summarises the review question in population, index prognostic factor, comparator prognostic factor(s), outcome, timing, and setting (PICOTS) format. [Table: see text] [Figure: see text] Secondary objectives To explore the following biomarker measurement, treatment, and study design factors as possible sources of heterogeneity in the association between the prognostic factor and pathological response: type of assay/measurement method, biomarker positivity criteria or cut-off point, chemotherapy regimen, and radiotherapy regimen.

Stereotactic body radiation therapy (SBRT) is currently the alternative for inoperable early-stage and oligometastatic non-small cell lung cancer (NSCLC) patients. While most patients are good responders among this specific group, some patients do not experience the benefits of this treatment. Even though physicians use clinical variables and semantic radiological features to make treatment decisions, medical images contain a wealth of personalized pathophysiological information that can be extracted and used for clinical decision support systems. In the form of radiomics features, details unique to each patient's medical scans can be utilized to create predictive models and to identify biomarking signatures. Then, these tools and indices can predict treatment outcomes and categorize patients to the most optimal treatment regimen. A conceptual review of relevant topics centered around the identification and development of radiomic-based biomarkers for SBRT-treated NSCLC was conducted. To begin with, an overview of the nature and management of non-small cell lung cancer was provided. To continue, biomarkers were defined in the context of cancer care. Then, the uses of stereotactic body radiation therapy in the treatment of NSCLC were further explained. Finally, the study of radiomics was discussed, and the uses and limitations of radiomic features and ML for SBRT-treated NSCLC were expanded upon. Radiomics-based biomarkers and predictive algorithmic models can potentially improve the SBRT treatment of early-stage and oligometastatic NSCLC by providing personalized support systems to healthcare professionals. While many institutions are attempting to optimize their biomarkers and AI-based tools for clinical use, additional prospective studies are needed to properly ensure their efficacy. As such, the improvements made in the field of personalized medicine are promising.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Traditional liquid biopsy markers show a low rate of positivity and accurate in gastric cancer. With the rapid advancement of sequencing technology, scientists have identified promising research avenues in this field. Autophagy and macropinocytosis utilize diverse pathways and mechanisms to supply resources and fuel for tumor growth. Nonetheless, their potential interplay introduces an untapped avenue for the discovery of novel tumor biomarkers.

To develop an innovative prognostic signature based on autophagy- and micropinocytosis-related genes, with the aim to predict the outcome and therapeutic response of gastric cancer patients. Additionally, to validate the prognostic impact of this signature, and elucidate the role of representative molecules in gastric cancer.

To construct and validate a prognostic signature for gastric cancer, bioinformatics methods such as COX regression, LASSO regression, survival analysis, ROC curve, and nomogram were utilized based on the sequencing and clinical data of gastric cancer patients retrieved from the TCGA and GEO databases. GSEA functional enrichment analyses were employed to predict the biological functions. Meanwhile, qRT-PCR and Western blot experiments were utilized to quantify the mRNA and protein expression levels. Furthermore, the EdU assay and colony formation assay were utilized to examine the cell proliferation ability while the Transwell assays were conducted to assess the migration and invasion abilities of gastric cancer cells.

Through consistency clustering and univariate COX analyses, potential prognostic genes involved in both autophagy and macropinocytosis were identified. Based on these genes, a 9-gene signature was constructed, which demonstrated high accuracy in predicting gastric cancer patients' survival period, immunotherapeutic response, and chemotherapy drug tolerance. Furthermore, qRT-PCR analyses of gastric cancer tissue samples showed that the representative genes of this signature were aberrantly overexpressed in gastric cancer, with MATN3, as the most notable molecule, exhibiting significant carcinogenic effects on cancer cells by actively regulating their proliferation, migration, and invasion abilities.

Our newly created prognostic signature possesses significant potential as a biomarker for gastric cancer, while MATN3 is identified as an oncogenic factor in gastric cancer. This brings to light new perspectives, which can contribute to enhancing the diagnosis and treatment of gastric cancer.

Prostate cancer is the second leading cause of cancer deaths in men, second only to lung cancer. Despite this, diagnosis and prognosis methods remain limited, with effective treatments being few and far between. Traditionally, prostate cancer is initially tested for through a prostate serum antigen (PSA) test and a digital rectum examination (DRE), followed by confirmation through an invasive prostate biopsy. The DRE and biopsy are uncomfortable for the patient, so less invasive, accurate diagnostic tools are needed. Current diagnostic tools, along with genes that hold possible biomarker uses in diagnosis, prognosis and indications for personalised treatment plans, were reviewed in this article.

Several genes from multiple families have been identified as possible biomarkers for disease, including those from the MYC and ETS families, as well as several tumour suppressor genes, Androgen Receptor signalling genes and DNA repair genes. There have also been advances in diagnostic tools, including MRI-targeted and liquid biopsies. Several personalised treatments have been developed over the years, including those that target metabolism-driven prostate cancer or those that target inflammation-driven cancer.

Several advances have been made in prostate cancer diagnosis and treatment, but the disease still grows year by year, leading to more and more deaths annually. This calls for even more research into this disease, allowing for better diagnosis and treatment methods and a better chance of patient survival.

With the rapid adoption of immunotherapy for the treatment of cancer comes the pressing need for readily accessible biomarkers to guide immunotherapeutic strategies and offer insights into outcomes with specific treatments. Regular sampling of solid tumour tissues outside of melanoma for immune monitoring is not often feasible; conversely, routine, frequent interrogation of circulating immune biomarkers is entirely possible. As immunotherapies and immune checkpoint inhibitors, in particular, are more widely used in first-line, neoadjuvant, and metastatic settings, the discovery and validation of peripheral immune biomarkers are urgently needed across solid tumour types for improved prediction and prognostication of clinical outcomes in response to immunotherapy, as well as elucidation of mechanistic underpinnings of the intervention. Careful experimental design, encompassing both retrospective and prospective studies, is required in such biomarker identification studies, and concerted efforts are essential for their advancement into clinical settings.

In this review, we summarize shared immune features between the tumour microenvironment and systemic circulation, evaluate exploratory peripheral immune biomarker studies, and discuss associations between candidate biomarkers with clinical outcomes. We also consider integration of multiple peripheral immune parameters for better prediction and prognostication and discuss considerations in study design to further evaluate the clinical utility of candidate peripheral immune biomarkers for immunotherapy of solid tumours.

Peripheral immune biomarkers are critical for improved prediction and prognostication of clinical outcomes for patients with solid tumours treated with immune checkpoint inhibition. Candidate peripheral biomarkers, such as cytokines, soluble factors, and immune cells, have potential as biomarkers to guide immunotherapy of solid tumours. Multiple peripheral immune parameters may be integrated to improve prediction and prognostication. The potential of peripheral immune biomarkers to guide immunotherapy of solid tumours requires critical work in biomarker discovery, validation, and standardization.

Breast cancer (BC) is one of the most common malignant tumors in women worldwide, and its incidence is increasing every year. Early diagnosis and treatment are critical to improve the curability and prognosis of patients. However, existing detection methods often suffer from insufficient sensitivity and specificity, which limits their clinical application. Fortunately, the rapid development of nanotechnology offers new possibilities for diagnosing BC. For example, the unique physicochemical properties of gold nanomaterials (Au NMs), such as fascinating optical properties and quantum size effect, along with excellent biocompatibility and modifiability, enable them to manifest great potential in the field of biosensing, especially in the detection of BC biomarkers. Through fine surface modification and functionalization, Au NMs can accurately bind to specific antibodies, nucleic acids, and other biomolecules, thus achieving sensitive and precise detection of specific biomarkers. Here, we focus on the research progress of Au NMs as a key biosensing vector in BC biomarker detection. From four major perspectives of early diagnosis, prognostic evaluation, risk prediction, and bioimaging applications, we have thoroughly analyzed the broad application of Au NMs in BC biomarker detection and prospectively addressed its possible future trends. We hope this review will provide more comprehensive ideas for future researchers and promote the further development of this field.

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Predictive biomarkers are essential for personalized medicine since they select the best treatment for a specific patient. However, of all biomarkers that are evaluated, only few are eventually used in clinical practice. Many promising biomarkers may be erroneously abandoned because they are investigated in small studies using standard statistical techniques which can cause small sample bias or lack of power. The standard technique for failure time endpoints is Cox proportional hazards regression with a multiplicative interaction term between binary variables of biomarker and treatment. Properties of this model in small studies have not been evaluated so far, therefore we performed a simulation study to understand its small sample behavior. As a remedy, we applied a Firth correction to the score function of the Cox model and obtained confidence intervals (CI) using a profile likelihood (PL) approach. These methods are generally recommended for small studies of different design. Our results show that a Cox model estimates the biomarker-treatment interaction term and the treatment effect in one of the biomarker subgroups with bias, and overestimates their standard errors. Bias is however reduced and power is increased with Firth correction and PL CIs. Hence, the modified Cox model and PL CI should be used instead of a standard Cox model with Wald based CI in small studies of predictive biomarkers.

LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets. LUNGBANK functions not merely as a repository but as a sophisticated platform crucial for advancing lung cancer research, poised to facilitate significant discoveries.

LUNGBANK was meticulously designed to optimize every stage of biospecimen handling, from collection and storage to processing. Rigorous standard operating procedures and stringent quality control measures guarantee the integrity of collected biospecimens. Advanced data management protocols facilitate the efficient integration and analysis of various datasets, enhancing the depth and breadth of research possibilities in lung cancer.

LUNGBANK has amassed a comprehensive collection of biospecimens essential for unraveling the intricate molecular mechanisms of lung cancer. The integration of state-of-the-art technologies ensures the acquisition of top-tier data, fostering breakthroughs in translational and histological research. Moreover, the establishment of patient-derived systems by LUNGBANK underscores its pivotal role in personalized medicine approaches.

The establishment of LUNGBANK marks a significant milestone in addressing the critical challenges of lung cancer research. By providing researchers with high-quality biospecimens and advanced research tools, LUNGBANK not only supports Project LUNGMARK's objectives but also contributes extensively to the broader landscape of personalized medicine. It promises to enhance our understanding of lung cancer initiation, progression, and therapeutic interventions tailored to individual patient needs, thereby advancing the field towards more effective diagnostic and therapeutic strategies.

Liver cancer is one of the most prevalent types of cancer and a major contributor to the socioeconomic burden worldwide. The pathogenesis of hepatocellular carcinoma (HCC) is contributed by various etiological factors like virus infection, excessive alcohol consumption, exposure to toxins, or metabolic disorders. Majority of patients are diagnosed with late-stage HCC, which restricts its management to only palliative care. HCC, if diagnosed early, increases the survival and quality of life. Currently available biomarker (alpha-fetoproteins) have several limitations, that impede the early diagnosis and staging of cancer. This warrants the continous search in pursuit of a novel biomarker. Several research works in diverse areas have contributed to the identification of various novel biomarkers that have shown multifaceted application in early disease diagnosis, which further aid in targeted and effective therapy that can prevent cancer progression. This improves the overall health status of the patient along with significant reduction in caretaker's burden. With the aid of novel technologies, several biomarkers have been investigated and validated in mutliple preliminary research works. Therefore in this review, we have outlined various novel biomarkers that showed promising outcomes in their trials and we have highlighted the developing areas that act as game changers in cancer diagnosis and management.

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Background and Objectives: An extracellular vesicle is part of a class of submicron particles derived from cells, mediating cellular crosstalk through microRNA (miRNA). MiRNA is a group of RNA molecules, each of which consists of 15-22 nucleotides and post-transcriptionally modulates gene expression. The complementary mRNAs-onto which the miRNAs hybridize-are involved in processes such as implantation, tumor suppression, proliferation, angiogenesis, and metastasis that define the entire tumor microenvironment. The endometrial biopsy is a standard technique used to recognize cellular atypia, but other non-invasive markers may reduce patient discomfort during the use of invasive methods. The present study aims to examine the distribution and the regulation of the differentially expressed miRNAs (DEMs) and EV-derived substances in women with endometrial cancer. Materials and Methods: We systematically searched the PubMed, EMBASE, Scopus, Cochrane Library, and ScienceDirect databases in April 2023, adopted the string "Endometrial Neoplasms AND Exosomes", and followed the recommendations in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We selected all the studies that included patients with endometrial cancer and that described the regulation of miRNA molecules in that context. The differences in molecule expression between patients and controls were evaluated as significant when the proteins had a fold change of ±1.5. Results: Seventeen records fulfilled the inclusion criteria: a total of 371 patients and 273 controls were analyzed. The upregulated molecules that had the widest delta between endometrial cancer patients and controls-relative expression ≥ 1 > 3 log2(ratio)-were miR-20b-5p, miR-204-5p, miR-15a-5p, and miR-320a. In particular, miR-20b-5p and miR-204-5p were extracted from both serum and endometrial specimens, whereas miR-15a-5p was only isolated from plasma, and miR-320a was only extracted from the endometrial specimens. In parallel, the most downregulated miRNA in the endometrial cancer patients compared to the healthy subjects was miR-320a, which was found in the endometrial specimens. Conclusions: Although their epigenetic regulation remains unknown, these upregulated molecules derived from EVs are feasible markers for the early detection of endometrial cancer. The modulation of these miRNA molecules should be assessed during different treatments or if recurrence develops in response to a targeted treatment modality.

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Cancer stands as the reigning champion of life-threatening diseases, casting a shadow with the highest global mortality rate. Unleashing the power of early cancer treatment is a vital weapon in the battle for efficient and positive outcomes. Yet, conventional screening procedures wield limitations of exorbitant costs, time-consuming endeavors, and impracticality for repeated testing. Enter bio-marker-based cancer diagnostics, which emerge as a formidable force in the realm of early detection, disease progression assessment, and ultimate cancer therapy. These remarkable devices boast a reputation for their exceptional sensitivity, streamlined setup requirements, and lightning fast response times. In this study, we embark on a captivating exploration of the most recent advancements and enhancements in the field of electrochemical marvels, targeting the detection of numerous cancer biomarkers. With each breakthrough, we inch closer to a future where cancer's grip on humanity weakens, guided by the promise of personalized treatment and improved patient outcomes. Together, we unravel the mysteries that cancer conceals and illuminate a path toward triumph against this daunting adversary. This study celebrates the relentless pursuit of progress, where electrochemical innovations take center stage in the quest for a world free from the clutches of carcinoma.

We recently discovered a putative paclitaxel response predictive biomarker for glioblastoma and breast cancer using the whole genome CRISPR knockout screen. The biomarker candidate was validated in two independent breast cancer patient cohorts that received taxane treatment. To further evaluate the potential application of this biomarker in the clinic for patients with glioblastoma, a prospective validation in cohorts of patients with glioblastoma is essential and will be performed as part of our ongoing phase II clinical trial (NCT04528680). The validation of novel biomarkers of susceptibility to therapy is critical to elucidate the efficacy signal of therapeutic agents. This is especially important in the context of glioblastoma, where therapeutic benefit is variable and unpredictable, leading to negative trials, yet the outcome of subset of patients has outperformed expectations.

Nanostructure-enhanced biodetection is widely used for early diagnosis and treatment, which plays an essential role in improving the cure rates of cancer patients. ZnO nanostructure-based fluorescence immunoassay has been demonstrated to enable effective and sensitive detection of cancer biomarkers for their excellent biocompatibility, high electrical point, and unique fluorescence enhancement properties. Further optimization of such fluorescence detection technology is still in demand to meet the requirements of highly sensitive, multiplex detection, and user-friendly devices. Droplet microfluidics is a promising platform for high-throughput analysis of biological assays, and they have been intensively used in analytical chemistry and synthesis of nanoparticles. Here, we propose a simple droplet chip, where a static droplet array was successfully obtained for in situ growth of ZnO nanostructures with varied diameters by changing the entire growth time and replenishment interval. This device provides a novel and alternative approach for patterned growth of ZnO nanostructures and understanding the growth condition of ZnO nanostructures in static droplet, which offers some guidance toward the design of multiple fluorescence amplification platforms potentially for biosensing. As a demonstration, we used the patterned grown ZnO nanostructures for multiple detection of cancer biomarkers, achieving a low limit of detection as low as 138 fg/mL in the human α-fetoprotein assay and 218 fg/mL in the carcinoembryonic antigen assay with a large dynamic range of 8 orders. These results suggest that such multifunctional microfluidic devices may be useful tools for efficient fluorescence diagnostic assays.

Cholangiocarcinoma (CCA) is a prevalent cancer in Southeast Asia, with Opisthorchis viverrini (O.viverrini) infection being the primary risk factor. Most CCA cases in this region are diagnosed at advanced stages, leading to unfavorable prognoses. The development of stage-specific biomarkers for Opisthorchis viverrini-induced cholangiocarcinoma (Ov-CCA) holds crucial significance, as it facilitates early detection and timely administration of curative interventions, effectively mitigating the high morbidity and mortality rates associated with this disease in the Great Mekong region. Biomarkers are a promising approach for early detection, prognosis, and targeted treatment of CCA. Disease-specific biomarkers facilitate early detection and enable monitoring of therapy effectiveness, allowing for any necessary corrections. This review provides an overview of the potential O. viverrini-specific molecular biomarkers and important markers for diagnosing and monitoring Ov-CCA, discussing their prognostic, predictive, and diagnostic value. Despite the limited research in this domain, several potential biomarkers have been identified, encompassing both worm-induced and host-induced factors. This review offers a thorough examination of historical and contemporary progress in identifying biomarkers through multiomics techniques, along with their potential implications for early detection and treatment.

Extracellular vesicles (EVs) are nanoscale lipid bilayer particles secreted by cells. EVs may carry markers of the tissue of origin and its disease state, which makes them incredibly promising for disease diagnosis and surveillance. While the armamentarium of EV analysis technologies is rapidly expanding, there remains a strong need for multiparametric analysis with single EV resolution. Nanoprojectile (NP) secondary ion mass spectrometry (NP-SIMS) relies on bombarding a substrate of interest with individual gold NPs resolved in time and space. Each projectile creates an impact crater of 10-20 nm in diameter while molecules emitted from each impact are mass analyzed and recorded as individual mass spectra. We demonstrate the utility of NP-SIMS for statistical analysis of single EVs derived from normal liver cells (hepatocytes) and liver cancer cells. EVs were captured on antibody (Ab)-functionalized gold substrate and then labeled with Abs carrying lanthanide (Ln) MS tags (Ab@Ln). These tags targeted four markers selected for identifying all EVs, and specific to hepatocytes or liver cancer. NP-SIMS was used to detect Ab@Ln-tags colocalized on the same EV and to construct scatter plots of surface marker expression for thousands of EVs with the capability of categorizing individual EVs. Additionally, NP-SIMS revealed information about the chemical nanoenvironment where targeted moieties colocalized. Our approach allowed analysis of population heterogeneity with single EV resolution and distinguishing between hepatocyte and liver cancer EVs based on surface marker expression. NP-SIMS holds considerable promise for multiplexed analysis of single EVs and may become a valuable tool for identifying and validating EV biomarkers of cancer and other diseases.

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Cancer belongs to multifactorial diseases characterized by uncontrolled growth and proliferation of abnormal cells. Breast cancer, non-small cell lung cancer, and colorectal cancer are the most frequently diagnosed malignancies with a high mortality rate. These carcinomas typically contain multiple genetically distinct subpopulations of tumor cells leading to tumor heterogeneity, which promotes the aggressiveness of the disease. Early diagnosis is necessary to increase patient progression-free survival. Particularly, miRNAs present in exosomes derived from tumors represent potential biomarkers suitable for early cancer diagnosis. Identification of miRNAs by liquid biopsy enables a personalized approach with the subsequent better clinical management of patients. This review article highlights the potential of circulating exosomal miRNAs in early breast, non-small cell lung, and colorectal cancer diagnosis.

In the present investigation, we reported a label-free and highly effective immunosensor for the first time employing a nanostructured molybdenum disulfide nanosheets@reduced graphene oxide (nMoS2 NS@rGO) nanohybrid interface for the determination of sperm protein 17 (Sp17), an emerging cancer biomarker. We synthesized the nMoS2 NS@rGO nanohybrid using a one-step hydrothermal technique and then functionalized it with 3-aminopropyltriethoxysilane (APTES). Furthermore, the anti-Sp17 monoclonal antibodies were covalently attached to the APTES/nMoS2 NS@rGO/indium tin oxide (ITO) electrode utilizing 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide-N-hydroxy succinimide (EDC-NHS) coupling chemistry. Bovine serum albumin (BSA) was then used to block nonspecific binding regions on the anti-Sp17/APTES/nMoS2 NS@rGO/ITO bioelectrode. The morphological and structural features of the synthesized nanohybrid and the modified electrodes were studied using transmission electron microscopy, scanning electron microscopy with energy dispersive X-ray (EDX) composition studies, atomic force microscopy, Fourier transform infrared spectroscopy, and Raman spectroscopy. The immunoreaction between the Sp17 antigen and anti-Sp17 antibodies on the surface of the BSA/anti-Sp17/APTES/nMoS2 NS@rGO/ITO sensing bioelectrode was applied as the basis for the detection technique, which measured the electrocatalytic current and impedimetric response change. The designed BSA/anti-Sp17/APTES/nMoS2 NS@rGO/ITO bioelectrode showed improved amperometric and impedimetric biosensing performance in the response studies, including remarkable sensitivity (23.2 μA ng-1mL cm-2 and 0.48 kΩ mL ng-1 cm-2), wider linearity (0.05-8 and 1-8 ng mL-1), an excellent lower detection limit (0.13 and 0.23 ng mL-1), and a rapid response time of 20 min. The biosensor exhibited impressive storage durability lasting 7 weeks and showed remarkable precision in identifying Sp17 in serum samples from cancer patients, as confirmed using the enzyme-linked immunosorbent assay method.

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets.

Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment, https://marisshiny.

chop.edu/ROGUE/ ), a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells.

User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.

Despite the significant improvement in the treatment modalities, cancer is one of the fastest-growing chronic disease conditions all over the world. Genetic and Epigenetic alterations in the normal physiology of the cell are the key factor for tumor development. These changes can trigger the production of abnormal protein expressions through stimulation of different signaling pathways and can deeply affect normal cell growth and proliferation. Any altered protein expression, genetic variation, micro-RNA or post-translational protein modifications that indicate tumorigenesis can act as an early signal termed as biomarker. Cancer, being a multistep process with accumulating genetic and epigenetic alterations, could be detected early with suitable biomarkers. There are several proteins such as AFP, CA-125, PSA, troponin, CEA, osteopontin, CA 19-9 that act as biomarkers which help in early detection, prognosis, and monitoring of disease progression, a hunt for newer biomarkers with higher specificity and sensitivity is still ongoing. Tumor-specific growth factor (TSGF) is one such budding and prevailing tumor biomarker used for the early-stage detection of several types of carcinomas. TSGF is a gene that helps in tumor angiogenesis and gets released during the preliminary stages from cancer cells that ensure the vascular proliferation of the same. In this review, the clinical investigations of TSGF in different kinds of malignancy is discussed in detail and suggests the possibility of using TSGF as a biomarker in early diagnosis of cancer.

The severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, was first discovered in Wuhan, Hubei province, China. Cytokines play a critical role in COVID-19 infections through their inflammatory or anti-inflammatory activities. This study aimed to detect the diagnostic value of and the relationship between the interleukins under study, in addition to their relationship with demographic data in COVID-19 patients. Patients with a confirmed diagnosis of COVID-19 based on laboratory (PCR) results and the healthy control group were given their section of this investigation. The patient group had 120 COVID-19 patients, including 62 males and 58 females, while the control group consisted of 32 individuals (22 males and 10 females). The subdivision was then performed according to their vaccination status, chronic diseases, gender, and residence. Cytokine levels were detected using the ELISA technique. The immunological status of COVID-19 patients was determined by measuring interleukin (IL)-6, IL-25, and IL-35. During the research, it was found that IL-6 was highly significant in COVID-19 patients (0.001). However, its level was not significantly different (0.376) in patients regarding the type of chronic diseases, residence (0.353), and gender (0.574), but it was significantly different in vaccinated patients (0.029). It was also found that IL-6 is significantly correlated with IL-25 and IL-35. IL-25 was highly significant in COVID-19 patients (0.007), and there was a significant difference in its level in patients regarding the type of chronic disease (0.049). While there was no difference in terms of residence (0.421) and gender (0.681), corona vaccination showed a significant difference (0.047). IL-25 also had a significant correlation with IL-6 and IL-35. As for IL-35, it was significant in patients with COVID-19 (0.013) but not significantly different regarding chronic diseases (0.344), residence (0.877), or gender (0.800). However, it was significantly different in vaccinated patients, compared to the non-vaccinated ones. IL-35 was found to be significantly correlated with IL-25 and IL-6 (0.000). The examined interleukins increased in COVID-19 individuals. IL-6 remains an excellent marker for determining the immune state of patients with COVID-19. There were also strong correlations between the interleukins under study in COVID-19 patients. However, there was no relationship between age, residence, gender, and the concentration of studied cytokines. IL-25 increases significantly in COVID-19 patients suffering from chronic diseases. Therefore, it is more efficient in the follow-up of patients.

The success of personalized medicine depends on the discovery of biomarkers that allow oncologists to identify patients that will benefit from a particular targeted drug. Molecular tests are mostly performed using tumor samples, which may not be representative of the tumor's temporal and spatial heterogeneity. Liquid biopsies, and particularly the analysis of circulating tumor DNA, are emerging as an interesting means for diagnosis, prognosis, and predictive biomarker discovery. In this study, the amplification refractory mutation system (ARMS) coupled with high-resolution melting analysis (HRMA) was developed for detecting two of the most relevant KRAS mutations in codon 12. After optimization with commercial cancer cell lines, KRAS mutation screening was validated in tumor and plasma samples collected from patients with pancreatic ductal adenocarcinoma (PDAC), and the results were compared to those obtained by Sanger sequencing (SS) and droplet digital polymerase chain reaction (ddPCR). The developed ARMS-HRMA methodology stands out for its simplicity and reduced time to result when compared to both SS and ddPCR but showing high sensitivity and specificity for the detection of mutations in tumor and plasma samples. In fact, ARMS-HRMA scored 3 more mutations compared to SS (tumor samples T6, T7, and T12) and one more compared to ddPCR (tumor sample T7) in DNA extracted from tumors. For ctDNA from plasma samples, insufficient genetic material prevented the screening of all samples. Still, ARMS-HRMA allowed for scoring more mutations in comparison to SS and 1 more mutation in comparison to ddPCR (plasma sample P7). We propose that ARMS-HRMA might be used as a sensitive, specific, and simple method for the screening of low-level mutations in liquid biopsies, suitable for improving diagnosis and prognosis schemes.

Post-traumatic stress disorder (PTSD) is a mental health disorder that arises after experiencing or witnessing a traumatic event. Despite affecting around 7% of the population, there are currently no definitive biological signatures or biomarkers used in the diagnosis of PTSD. Thus, the search for clinically relevant and reproducible biomarkers has been a major focus of the field. With significant advances of large-scale multi-omic studies that include genomic, proteomic, and metabolomic data, promising findings have been made, but the field still has fallen short. Amongst the possible biomarkers examined, one area is often overlooked, understudied, or inappropriately investigated: the field of redox biology. Redox molecules are free radical and/or reactive species that are generated as a consequence of the necessity of electron movement for life. These reactive molecules, too, are essential for life, but in excess are denoted as "oxidative stress" and often associated with many diseases. The few studies that have examined redox biology parameters have often utilized outdated and nonspecific methods, as well as have reported confounding results, which has made it difficult to conclude the role for redox in PTSD. Herein, we provide a foundation of how redox biology may underlie diseases like PTSD, critically examine redox studies of PTSD, and provide future directions the field can implement to enhance standardization, reproducibility, and accuracy of redox assessments for the use of diagnosis, prognosis, and therapy of this debilitating mental health disorder.

Microfluidics is very crucial in lab-on-a-chip systems for carrying out operations in a large-scale laboratory environment on a single chip. Microfluidic systems are miniaturized devices in which the fluid behavior and control can be manipulated on a small platform, with surface forces on the platform being greater than volumetric forces depending on the test method used. In recent years, paper-based microfluidic analytical devices (μPADs) have been developed to be used in point-of-care (POC) technologies. μPADs have numerous advantages, including ease of use, low cost, capillary action liquid transfer without the need for power, the ability to store reagents in active form in the fiber network, and the capability to perform multiple tests using various measurement techniques. These benefits are critical in the advancement of paper-based microfluidics in the fields of disease diagnosis, drug application, and environment and food safety. Cancer is one of the most critical diseases for early detection all around the world. Detecting cancer-specific biomarkers provides significant data for both early diagnosis and controlling the disease progression. μPADs for cancer biomarker detection hold great promise for improving cure rates, quality of life, and minimizing treatment costs. Although various types of bioanalytical platforms are available for the detection of cancer biomarkers, there are limited studies and critical reviews on paper-based microfluidic platforms in the literature. Hence, this article aims to draw attention to these gaps in the literature as well as the features that future platforms should have.

Breast cancer (BC) is a common cancer all over the world that affects women. BC is one of the leading causes of cancer mortality in women, which today has decreased with the advancement of technology and new diagnostic and therapeutic methods. BCs are histologically divided into in situ and invasive carcinoma, and both of them can be divided into ductal and lobular. The main function after the diagnosis of invasive breast cancer is which patient should use chemotherapy, which patient should receive adjuvant therapy, and which should not. If the decision is for adjuvant therapy, the next challenge is to identify the most appropriate treatment or combination of treatments for a particular patient. Addressing the first challenge can be helped by prognostic biomarkers, while addressing the second challenge can be done by predictive biomarkers. Among the molecular markers related to BC, ER, PR, HER2, and the Mib1/Ki-67 proliferation index are the most significant ones and are tightly confirmed in the standard care of all primary, recurrent, and metastatic BC patients. CEA and CA-15-3 antigens are the most valuable markers of serum tumors in BC patients. Determining the series of these markers helps monitor response to the treatment and early detection of recurrence or metastasis. miRNAs have been demonstrated to be intricate in mammary gland growth, proliferation, and formation of BC known to be incriminated in BC biology. By combining established prognostic factors with valid prognostic/predicted biomarkers, we can start the journey to personalized treatment for every recently diagnosed BC patient.

Technological advancements in the healthcare sector have pushed for improved sensors and devices for disease diagnosis and treatment. Recently, with the discovery of numerous biomarkers for various specific physiological conditions, early disease screening has become a possibility. Biomarkers are the body's early warning systems, which are indicators of a biological state that provides a standardized and precise way of evaluating the progression of disease or infection. Owing to the extremely low concentrations of various biomarkers in bodily fluids, signal amplification strategies have become crucial for the detection of biomarkers. Metal nanoparticles are commonly applied on 2D platforms to anchor antibodies and enhance the signals for electrochemical biomarker detection. In this context, this review will discuss the recent trends and advances in metal nanoparticle decorated 2D materials for electrochemical biomarker detection. The prospects, advantages, and limitations of this strategy also will be discussed in the concluding section of this review.

MicroRNAs (miRNAs) have been proposed as biomarkers for equine sarcoid (ES) disease. In this study, the suitability of three whole blood miRNAs to diagnose ES and to predict and monitor the outcome of therapy was explored. Using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), expression levels of eca-miR-127, eca-miR-379, and eca-miR-432 in whole blood of ES-affected equids before and at least one year after therapy were compared to those of unaffected control equids. Associations of age, sex, species, diagnosis, and therapy outcome with miRNA expression levels were examined using general linear models. In total, 48 ES-affected equids and 47 control equids were recruited. From the affected animals, 31 responded favorably to treatment, and 17 demonstrated a failure of therapy. None of the tested miRNAs were influenced by age. Male equids showed increased expression of eca-miR-127 compared to females and horses showed higher expression levels of eca-miR-379 and eca-miR-432 than donkeys. Eca-miR-127 was confirmed as a diagnostic discriminator between ES-affected and control equids. No difference in miRNA profiles before therapy was found when comparing ES-affected equids with success vs. failure of therapy. Eca-miR-379 and eca-miR-432 decreased over time in horses where therapy was successful, but not in those cases where it failed. Biological variables influence equine whole blood miRNA expression, which may complicate biomarker validation. While none of the tested miRNAs could predict the response to therapy in ES-affected equids and eca-miR-127 showed poor diagnostic accuracy for ES, eca-miR-379 and eca-miR-432 miRNAs might allow refinement of monitoring of success of ES therapy.

Human jumping translocation breakpoint (hJTB) gene is located on chromosome 1q21 and is involved in unbalanced translocation in many types of cancer. JTB protein is ubiquitously present in normal cells but it is found to be overexpressed or downregulated in various types of cancer cells, where this protein and its isoforms promote mitochondrial dysfunction, resistance to apoptosis, genomic instability, proliferation, invasion and metastasis. Hence, JTB could be a tumor biomarker for different types of cancer, such as breast cancer (BC), and could be used as a drug target for therapy. However, the functions of the protein or the pathways through which it increases cell proliferation and invasiveness of cancer cells are not well-known. Therefore, we aim to investigate the functions of JTB by using in-solution digestion-based cellular proteomics of control and upregulated and downregulated JTB protein in MCF7 breast cancer cell line, taking account that in-solution digestion-based proteomics experiments are complementary to the initial in-gel based ones. Proteomics analysis allows investigation of protein dysregulation patterns that indicate the function of the protein and its interacting partners, as well as the pathways and biological processes through which it functions. We concluded that JTB dysregulation increases the epithelial-mesenchymal transition (EMT) potential and cell proliferation, harnessing cytoskeleton organization, apical junctional complex, metabolic reprogramming, and cellular proteostasis. Deregulated JTB expression was found to be associated with several proteins involved in mitochondrial organization and function, oxidative stress (OS), apoptosis, and interferon alpha and gamma signaling. Consistent and complementary to our previous results emerged by using in-gel based proteomics of transfected MCF7 cells, JTB-related proteins that are overexpressed in this experiment suggest the development of a more aggressive phenotype and behavior for this luminal type A non-invasive/poor-invasive human BC cell line that does not usually migrate or invade compared with the highly metastatic MDA-MB-231 cells. This more aggressive phenotype of MCF7 cells related to JTB dysregulation and detected by both in-gel and in-solution proteomics could be promoted by synergistic upregulation of EMT, Mitotic spindle and Fatty acid metabolism pathways. However, in both JTB dysregulated conditions, several downregulated JTB-interacting proteins predominantly sustain antitumor activities, attenuating some of the aggressive phenotypical and behavioral traits promoted by the overexpressed JTB-related partners.

Orthopedia homeobox (OTP) has shown to be a useful prognostic marker to predict outcome in pulmonary carcinoids, which is also supported by the World Health Organization. However, the discontinuation of the initially used polyclonal antibody and absence of a reliable routinely applicable monoclonal OTP antibody hampers implementation in routine diagnostics. Here, new monoclonal antibodies directed against OTP were developed and verified on formalin-fixed paraffin-embedded tissue of pulmonary neuroendocrine tumors (NETs) for clinical diagnostics.

OTP specific monoclonal antibodies were produced from mice immunised with a recombinant human OTP protein fragment. Enzyme-linked immunosorbent assay (ELISA) positive hybridomas were evaluated using immunohistochemistry (IHC). Following epitope-mapping and isotyping, purified monoclonal antibodies were validated for IHC in formalin-fixed paraffin-embedded tissues, the optimal dilution was determined, and results were cross validated with the OTP polyclonal antibody (HPA039365, Atlas Antibodies). Staining protocols were optimized on two automated staining platforms and performance was harmonized using a tissue microarray (TMA).

Two clones (CL11222 and CL11225) were selected for purified monoclonal antibody (mAb) production. Intratumor heterogeneity assessment revealed similar performance for both clones. While clone CL11225 displayed a unique epitope compared to those present in the polyclonal antibody, this clone performed most similar to the polyclonal antibody. Cross-platform assessment revealed an excellent agreement for clone CL11225 while clone CL11222 showed somewhat discordant results on Dako.

New monoclonal OTP specific antibodies have been developed and verified on different automated immunohistochemical staining platforms. The OTP specific monoclonal antibodies showed excellent agreement with the often-used polyclonal antibody allowing application in routine diagnostics.