Cancer biomarker discovery and validation

Biomarkers of genotoxic damage in pulmonary alveolar macrophages: a review

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.

Potential of plasma biomarkers for heart failure prediction, management, and prognosis: A multiomics perspective

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.

Neoadjuvant immunotherapy for dMMR and pMMR colorectal cancers: therapeutic strategies and putative biomarkers of response

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.

Models and Biomarkers for Local Response Prediction in Early-Stage and Oligometastatic Non-small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Using Machine Learning

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.

Molecular biomarkers for predicting complete response to preoperative chemoradiation in people with locally advanced rectal cancer

OBJECTIVES

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.

Radiomics As Biomarkers for the Treatment of Non-small Cell Lung Cancer With Stereotactic Body Radiation Therapy: A Review of Concepts

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.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells

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.

Identification of an autophagy- and macropinocytosis-related prognostic signature for the prediction of prognosis and therapeutic response in gastric cancer

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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: A Review of Genetics, Current Biomarkers and Personalised Treatments

BACKGROUND

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.

RECENT FINDINGS

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.

CONCLUSION

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.

Peripheral immune biomarkers for immune checkpoint inhibition of solid tumours

BACKGROUND

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.

CONCLUSION

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.

HIGHLIGHTS

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.

Gold nanomaterials: important vectors in biosensing of breast cancer biomarkers

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.

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives

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.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

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.

Cox proportional hazards regression in small studies of predictive biomarkers

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: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer

Background/aim

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.

Materials and methods

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.

Results

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.

Conclusion

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.

Biomarker in Hepatocellular Carcinoma

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.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

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.

MicroRNA Expression in Endometrial Cancer: Current Knowledge and Therapeutic Implications

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.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

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.

CONTENT

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.

SUMMARY AND OUTLOOK

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.

Advancements in biosensors for cancer detection: revolutionizing diagnostics

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.

Combining causal and correlative approaches to discover biomarkers of response to paclitaxel

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.

Droplet-Based Preparation of ZnO-nanostructure Array for Microfluidic Fluorescence Biodetection

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.

Current State of Knowledge on Blood and Tissue-Based Biomarkers for Opisthorchis viverrini-induced Cholangiocarcinoma: A Review of Prognostic, Predictive, and Diagnostic Markers

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.

Nanoprojectile Secondary Ion Mass Spectrometry Enables Multiplexed Analysis of Individual Hepatic Extracellular Vesicles

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.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

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.

Circulating exosomal miRNAs as a promising diagnostic biomarker in cancer

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.

Enhanced Electrochemical Biosensing of the Sp17 Cancer Biomarker in Serum Samples via Engineered Two-Dimensional MoS2 Nanosheets on the Reduced Graphene Oxide Interface

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 offers insights into disease aetiology

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.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review)

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.

ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery

BACKGROUND

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.

RESULTS

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

RESEARCH

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.

CONCLUSIONS

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.

Tumor-Specific Growth Factor (TSGF): A Futuristic Tumor Biomarker in Early Diagnosis of Cancer

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.

Evaluation of IL-6, IL-25 & IL-35 in the COVID 19 Patients and their Correlation to Demography Data in the Symptomatic Patients

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.

Combining the amplification refractory mutation system and high-resolution melting analysis for KRAS mutation detection in clinical samples

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.

Defining the nuanced nature of redox biology in post-traumatic stress disorder

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.

Biomarker Detection in Early Diagnosis of Cancer: Recent Achievements in Point-of-Care Devices Based on Paper Microfluidics

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.

A review of prognostic and predictive biomarkers in breast cancer

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.

Recent Trends in Metal Nanoparticles Decorated 2D Materials for Electrochemical Biomarker Detection

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.

The potential of three whole blood microRNAs to predict outcome and monitor treatment response in sarcoid-bearing equids

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.

Investigating the Function of Human Jumping Translocation Breakpoint Protein (hJTB) and Its Interacting Partners through In-Solution Proteomics of MCF7 Cells

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.

Development and verification of new monoclonal orthopedia homeobox (OTP) specific antibodies for pulmonary carcinoid diagnostics

Background

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.

Methods

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).

Results

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.

Conclusions

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.

Prognostic value of dysadherin in cancer: A systematic review and meta-analysis

Cancer is a leading cause of death worldwide and novel prognostic factors are reported with increasing numbers. Systematic reviews and meta-analyses on cumulative research data are crucial in estimating the true prognostic value of proposed factors. Dysadherin (FXYD Domain Containing Ion Transport Regulator 5; FXYD5) is a cell membrane glycoprotein that modulates Na+, K+-ATPase activity and cell-cell adhesion. It is abundantly expressed in a variety of cancer cells, but only in a limited number of normal cells and its levels are increased in many different tumor types. The expression or level of dysadherin has been suggested as an independent predictor for metastasis and poor prognosis by number of studies, yet we lack a definitive answer. In this study, we systematically evaluated the prognostic value of dysadherin in cancer and summarized the current knowledge on the subject. PubMed, Scopus, Web of Science and relevant clinical trial and preprint databases were searched for relevant publications and PRISMA and REMARK guidelines were applied in the process. After a careful review, a total of 23 original research articles were included. In each study, dysadherin was pointed as a marker for poor prognosis. Meta-analyses revealed 3- and 1.5-fold increases in the risk of death (fixed effects HR 3.08, 95% CI 1.88-5.06, RR 1.47, 95% CI 1.06-2.05 on overall survival, respectively) for patients with high (>50%) tumoral FXYD5 level. In many studies, a connection between dysadherin expression or level and metastatic behavior of the cancer as well as inverse correlation with E-cadherin level were reported. Thus, we conclude that dysadherin might be a useful prognostic biomarker in the assessment of disease survival of patients with solid tumors.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: A Systematic Review and Meta-Analysis

Background: Prognostic scores derived from the blood count have garnered significant interest as an indirect measure of the inflammatory pressure in cancer. The recently developed pan-immune-inflammation value (PIV), an equation including the neutrophil, platelet, monocyte, and lymphocyte levels, has been evaluated in several cohorts, although with variations in the tumor types, disease stages, cut-offs, and treatments. Therefore, we evaluated the association between survival and PIV in cancer, performing a systematic review and meta-analysis. Methods: We conducted a systematic review from the Pubmed, Medline, and Embase databases to filter the published studies until 17 May 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model. Results: Fifteen studies encompassing 4942 patients were included. In the pooled analysis of fifteen studies, the patients with higher PIV levels had significantly increased risk of death than those with lower PIV levels (HR: 2.00, 95% CI: 1.51−2.64, p < 0.001) and increased risk of progression or death (HR: 1.80, 95% CI: 1.39−2.32, p < 0.001). Analyses were consistent across several clinical scenarios, including non-metastatic or metastatic disease, different cut-offs (500, 400, and 300), and treatment with targeted therapy or immunotherapy (p < 0.001 for each). Conclusion: The available evidence demonstrates that PIV could be a prognostic biomarker in cancer. However, further research is needed to explore the promise of PIV as a prognostic biomarker in patients with non-metastatic disease or patients treated without immunotherapy or targeted therapy.

Expression of CD274 mRNA Measured by qRT-PCR Correlates With PD-L1 Immunohistochemistry in Gastric and Urothelial Carcinoma

Programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) is widely used to predict the clinical responses to immune checkpoint inhibitors (ICIs). However, PD-L1 IHC suffers from the complexity of multiple testing platforms and different cutoff values caused by the current one drug-one diagnostic test co-development approach for ICIs. We aimed to test whether PD-L1 (CD274) mRNA expression levels measured using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) can represent PD-L1 IHC and predict responses to ICI. The FDA-approved PD-L1 IHC results with 22C3 pharmDx (gastric cancer) and SP142 (urothelial carcinoma) were compared with CD274 mRNA expression levels via qRT-PCR using the same formalin-fixed, paraffin-embedded tissue blocks from 59 gastric cancer and 41 urothelial carcinoma samples. CD274 mRNA expression was identified using three independent sets of primers and TaqMan® probes targeting exon 1-2, exon 3-4, and exon 5-6. CD274 mRNA levels in spanning exon 1-2, exon 3-4, and exon 5-6 junctions of CD274 correlated well with PD-L1 expression (r²=0.81, 0.65, and 0.59, respectively). The area under the curve of exon 1-2 was the highest (0.783), followed by exon 3-4 (0.701), and exon 5-6 (0.671) of the CD274 gene against the PD-L1 combined positive score cutoff of 10. When CD274 mRNA expression was matched for response to immunotherapy, the overall response rate was higher in patients with high CD274 mRNA levels with a cutoff of 0.0722 (gastric cancer) and 0.0480 (urothelial carcinoma) than in those with low CD274 mRNA expression (P < 0.001 and P = 0.018, respectively). These results show that CD274 mRNA levels predicted ICI responses in patients with gastric or urothelial carcinomas and could be used as alternatives for PD-L1 IHC.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

Background

Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.

Methods

Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.

Results

Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.

Conclusion

Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09349-x.

[Gene expression profiling in melanoma diagnostics: problems and future application in clinical practice]

Difficulties in the diagnosis and differential diagnosis of melanoma in the work of a pathologist include not only conflicting structural and morphological features, but also the insufficient effectiveness of biochemical and some molecular markers in immunohistochemical studies. The review presents modern alternative methods for diagnosing malignant tumors based on the assessment of gene expression, the performance, objectivity and reliability of the determination of which may in the future have clinical application as an addition to histopathological methods in the diagnosis and differential diagnosis of various malignant neoplasms, including melanocytic neoplasms, which is changing the paradigm of routine medical practice, introducing diagnostic tests that carry molecular information into it.

Next-Generation Intelligent MXene-Based Electrochemical Aptasensors for Point-of-Care Cancer Diagnostics

Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of cancer. Recently, 2D nanomaterial-enabled advanced biosensors have shown potential towards the early diagnosis of cancer. The high surface area, surface functional groups availability, and excellent electrical conductivity of MXene make it the 2D material of choice for the fabrication of advanced electrochemical biosensors for disease diagnostics. MXene-enabled electrochemical aptasensors have shown great promise for the detection of cancer biomarkers with a femtomolar limit of detection. Additionally, the stability, ease of synthesis, good reproducibility, and high specificity offered by MXene-enabled aptasensors hold promise to be the mainstream diagnostic approach. In this review, the design and fabrication of MXene-based electrochemical aptasensors for the detection of cancer biomarkers have been discussed. Besides, various synthetic processes and useful properties of MXenes which can be tuned and optimized easily and efficiently to fabricate sensitive biosensors have been elucidated. Further, futuristic sensing applications along with challenges will be deliberated herein.

Lectin-Based Protein Microarray for the Glycan Analysis of Colorectal Cancer Biomarkers: The Insulin-Like Growth Factor System

Lectin-based protein microarrays are used for glycoprofiling of various kinds of biological samples. Here we describe lectin-based microarray assay in the reverse-phase format where glycoprotein samples are spotted onto microarray slide and then are incubated with set of lectins. This configuration allows high-throughput screening of a large cohort of samples by a set of lectins without need of separation of glycans from glycoproteins. We applied the described method for glycan analysis of glycoprotein biomarkers of colorectal cancer associated with the insulin-like growth factor system.

1H NMR based urinary metabolites profiling dataset of canine mammary tumors

The identification of efficient and sensitive biomarkers for non-invasive tests is one of the major challenges in cancer diagnosis. To address this challenge, metabolomics is widely applied for identifying biomarkers that detect abnormal changes in cancer patients. Canine mammary tumors exhibit physiological characteristics identical to those in human breast cancer and serve as a useful animal model to conduct breast cancer research. Here, we aimed to provide a reliable large-scale metabolite dataset collected from dogs with mammary tumors, using proton nuclear magnetic resonance spectroscopy. We identified 55 metabolites in urine samples from 20 benign, 87 malignant, and 49 healthy control subjects. This dataset provides details of mammary tumor-specific metabolites in dogs and insights into cancer-specific metabolic alterations that share similar molecular characteristics.