Early detection of cancer

The impacts of dipeptidyl- peptidase 4 (DPP-4) inhibitors on common female malignancies: A systematic review

The inhibition of dipeptidyl- peptidase 4 (DPP-4) is an essential therapy for controlling hyperglycemia in patients with type 2 diabetes (T2DM). However, the role of DPP-4 in cancer is not yet clear, with some studies suggesting that it may either promote or suppress tumors. This makes it crucial to have personalized treatment for diabetic women with cancer to effectively manage their diabetes whilst and preventing cancer mortality. To address this issue, we conducted an integrative in-silico analysis and systematic review of the literature to comprehensively examine the relationship between DPP-4 expression and the effects of its inhibitors on prevalent female malignancies. We specifically chose studies that examined the effects of DPP-4 expression and DPP-4 inhibition (DPP-4i) on prevalent cancers in women, such as breast cancer (BC), ovarian cancer (OV), cervical cancer (CC), and endometrial cancer (EC). These studies comprised those conducted both in vivo and in vitro. The review of the literature indicated that DPP-4i may worsen aggressive traits such as metastasis, Epithelial-to-mesenchymal transition (EMT), and chemotherapy resistance in BC cells. However, cohort studies on diabetic and BC patients did not confirm these findings. In vitro studies indicate that on OV, DPP-4 upregulation has been shown to prevent metastasis, while CCappears to be influenced by DPP-4 expression in terms of cell migration. sitagliptin, a pharmaceutical inhibitor of DPP-4, had a significant impact on reducing adhesion in CC cells in vitro. Overexpression of DPP-4 increased cell migration and proliferation in CC and EC cells, and hence the application of sitagliptin is expected to prevent this effect. On the other hand, the result of in-silico data confirmed that a significant correlation exists between DPP-4 expression and immune cell infiltration in breast, ovarian, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) as well as downregulated in these cancers compared to their normal tissue samples. Furthermore, a significant (p < 0.05) effect on OS of BC and CESC patients has been reported due to the elevation of DPP-4 methylation on a specific CPG Island. These findings could aid in creating specialized treatments for diabetic women with specific malignancies, but caution should be exercised when considering the patient's medical history and cancer type.

Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania

OBJECTIVE

To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania.

DATA SOURCES AND STUDY SETTING

We analyzed data from the Pennsylvania cancer registry (2010-2018) for adult Medicaid beneficiaries aged 21-64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council).

STUDY DESIGN

We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs.

DATA COLLECTION/EXTRACTION METHODS

N/A.

PRINCIPAL FINDINGS

Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (-3.9 percentage points; 95% CI: -7.2, -0.5; p = 0.02), particularly for screening-amenable cancers (-5.5 percentage points; 95% CI: -10.4, -0.6; p = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers.

CONCLUSIONS

In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.

Synergistic Assembly of 1DZnO and Anti-CYFRA 21-1: A Physicochemical Approach to Optical Biosensing

Objective: We conducted a comprehensive physicochemical analysis of one-dimensional ZnO nanowires (1DZnO), incorporating anti-CYFRA 21-1 immobilization to promote fast optical biomarker detection up to 10 ng ml-1. Impact Statement: This study highlights the effectiveness of proof-of-concept 1DZnO nanoplatforms for rapid cancer biomarker detection by examining the nanoscale integration of 1DZnO with these bioreceptors to deliver reliable photoluminescent output signals. Introduction: The urgent need for swift and accurate prognoses in healthcare settings drives the rise of sensitive biosensing nanoplatforms for cancer detection, which has benefited from biomarker identification. CYFRA 21-1 is a reliable target for the early prediction of cancer formation that can be perceptible in blood, saliva, and serum. However, 1DZnO nanostructures have been barely applied for CYFRA 21-1 detection. Methods: We assessed the nanoscale interaction between 1DZnO and anti-CYFRA 21-1 antibodies to develop rapid CYFRA 21-1 detection in two distinct matrices: PhosphateBuffered Saline (PBS) buffer and artificial saliva. The chemical modifications were tracked utilizing Fourier transform infrared spectroscopy, while transmission electron microscopy and energy dispersive spectroscopy confirmed antigen-antibody interplay over nanostructures. Results: Our results show high antibody immobilization efficiencies, affirming the effectiveness of 1DZnO nanoplatforms for rapid CYFRA 21-1 testing within a 5-min detection window in both PBS and artificial saliva. Photoluminescence measurements also revealed distinct optical responses across biomarker concentrations ranging from 10 to 1,000 ng ml-1. Conclusion: Discernible PL signal responses obtained after 5 min affirm the potential of 1DZnO nanoplatforms for further advancement in optical biomarker detection for application in early cancer prognosis.

A stable thymidine kinase 1 tetramer for improved quality control of serum level quantification

DNA synthesis is a critical process for cell growth and division. In cancer patients, an enzyme called thymidine kinase 1 (TK1) is often elevated in the blood, making it a valuable biomarker for cancer diagnosis and treatment. However, previous studies have shown that recombinant TK1 can exist in unstable mixtures of tetramers and dimers, leading to inconsistent results and potentially affecting accuracy. To address this issue, we hypothesized that incorporating tetrameric coiled-coil peptides could enhance TK1 self-assembly into stable tetramers without requiring additional adenosine triphosphate. In this study, we successfully expressed a recombinant TK1 tetramer protein in the Escherichia coli system. We optimized the induction conditions, significantly increasing protein expression levels, functionality, and solubility. Size exclusion chromatography confirmed the formation of a tetrameric structure in the expressed TK1 protein, with a molecular weight of 127.2 KDa, consistent with our expectations. We also found that the TK1 tetramer exhibited higher affinity with anti-TK1 IgY than wild-type TK1, as shown by enzyme-linked immunosorbent assay experiments. Moreover, the TK1 tetramer demonstrated good stability against heating, freeze-thawing and lyophilization with almost no immunoactivity lost. These findings suggest that recombinant TK1 tetramers have the potential to serve as calibrators in diagnostic assay kits, becoming promising candidates for quality control of clinical laboratory and in vitro diagnostic reagents.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

CRISPR for companion diagnostics in low-resource settings

New point-of-care tests (POCTs), which are especially useful in low-resource settings, are needed to expand screening capacity for diseases that cause significant mortality: tuberculosis, multiple cancers, and emerging infectious diseases. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostic (CRISPR-Dx) assays have emerged as powerful and versatile alternatives to traditional nucleic acid tests, revealing a strong potential to meet this need for new POCTs. In this review, we discuss CRISPR-Dx assay techniques that have been or could be applied to develop POCTs, including techniques for sample processing, target amplification, multiplex assay design, and signal readout. This review also describes current and potential applications for POCTs in disease diagnosis and includes future opportunities and challenges for such tests. These tests need to advance beyond initial assay development efforts to broadly meet criteria for use in low-resource settings.

Harnessing the potential of genomic characterization of mutational profiles to improve early diagnosis of lung cancer

INTRODUCTION

Lung Cancer (LC) continues to be a leading cause of cancer-related mortality globally, largely due to the asymptomatic nature of its early stages and the limitations of current diagnostic methods such as Low-Dose Computed Tomography (LDCT), whose often result in late diagnosis, highlighting an urgent need for innovative, minimally invasive diagnostic techniques that can improve early detection rates.

AREAS COVERED

This review delves into the potential of genomic characterization and mutational profiling to enhance early LC diagnosis, exploring the current state and limitations of traditional diagnostic approaches and the revolutionary role of Liquid Biopsies (LB), including cell-free DNA (cfDNA) analysis through fragmentomics and methylomics. New genomic technologies that allow for earlier detection of LC are scrutinized, alongside a detailed discussion on the literature that shaped our understanding in this field.

EXPERT OPINION

Despite the promising advancements in genomic characterization techniques, several challenges remain, such as the heterogeneity of LC mutations, the high cost, and limited accessibility of Next-Generation Sequencing (NGS) technologies. Additionally, there is a critical need of standardized protocols for interpreting mutational data. Future research should focus on overcoming these barriers to integrate these novel diagnostic methods into standard clinical practice, potentially revolutionizing the management of LC patients.

Recent Advances in Hydrogel-Based Biosensors for Cancer Detection

Early cancer detection is crucial for effective treatment, but current methods have limitations. Novel biomaterials, such as hydrogels, offer promising alternatives for developing biosensors for cancer detection. Hydrogels are three-dimensional and cross-linked networks of hydrophilic polymers that have properties similar to biological tissues. They can be combined with various biosensors to achieve high sensitivity, specificity, and stability. This review summarizes the recent advances in hydrogel-based biosensors for cancer detection, their synthesis, their applications, and their challenges. It also discusses the implications and future directions of this emerging field.

Roles of LncRNA ARSR in tumor proliferation, drug resistance, and lipid and cholesterol metabolism

Cancer is one of the most serious diseases that threaten human life and health. Among all kinds of diseases, the mortality rate of malignant tumors is the second highest, second only to cardio-cerebrovascular diseases. Cancer treatment typically involves imaging, surgery, and pathological analysis. When patients are identified as carcinoma by the above means, there are often problems of distant metastasis, delayed treatment, and drug tolerance, indicating that patients have some poor prognosis and overall survival. Hence, the development of novel molecular biomarkers is of great clinical importance. In recent years, as an important mediator of material and information exchange between cells in the tumor microenvironment, lncRNA have attracted widespread attention for their roles in tumor development. In this review, we comprehensively summarize the up-to-date knowledge of lncARSR on diverse cancer types which mainly focuses on tumor proliferation, drug tolerance, and lipid and cholesterol metabolism, highlighting the potential of lncARSR as a diagnostic and prognostic biomarker and even a therapeutic target. In our final analysis, we provide a synthesized overview of the directions for future inquiry into lncARSR, and we are eager to witness the advancement of research that will elucidate the multifaceted nature of this lncRNA.

Smartphone-based point-of-care photoelectrochemical immunoassay coupling with ascorbic acid-triggered photocurrent-polarity conversion switching

Photocurrent-polarity conversion strategies are typically realized by constructing complex photovoltaic electrodes or changing the relevant conditions, but most involve poor photogenerated carrier transfer efficiency and cumbersome experimental steps. To this end, a photoelectrochemical (PEC) biosensor by utilizing ascorbic acid (AA)-induced photocurrent-polarity-switching was proposed for the detection of carcinoembryonic antigen (CEA). Under light excitation, the electron donor AA was oxidized by the photogenerated holes of photoactive material Co-NC/CdS, resulting in the conversion of cathodic photocurrent to the anodic direction. In the presence of the target CEA, alkaline phosphatase (ALP) was introduced into the microplates by the sandwiched immunoreaction, which then catalyzed the production of AA from ascorbic acid-2-phosphate (AAP). Finally, the catalytic product AA was transferred onto Co-NC/CdS-modified screen-printed carbon electrode, thus activating photocurrent-polarity-switching platform. The anodic photocurrent values gradually increased with increasing CEA concentration in the range of 0.02-80 ng mL-1 and reached a limit of detection (LOD) of 8.47 pg mL-1 (S/N = 3). In addition, the results of actual sample detection prove the reliability of the constructed PEC biosensor. Importantly, this work relies on a mobile smartphone wireless Bluetooth device coupled with the PEC biosensor for immediate detection, providing another idea for detecting CEA in clinical diagnosis.

Simultaneous Detection of Collagen I Alpha II and Cytokeratin 19 mRNA by Multiplex qPCR in Liquid Biopsy in Diagnosis of Patients with Resectable Solid Tumors

The early detection of tumors is one of the key factors in increasing overall survival in cancer patients. A wide range of cancers still do not have a system of early diagnosis; therefore, the development of new non-invasive tools in this line is essential. Accordingly, the objective of our work was to develop a non-invasive screening method for the early detection of various carcinomas in plasma using a panel that combines two markers using RT-qPCR. A retrospective case-control study was conducted to develop a cancer screening test based on the detection of stromal and epithelial biomarkers (COL1A2 and KRT19) in plasma. The expression of biomarkers was evaluated using multiplex quantitative PCR applied to 47 cases with non-metastatic tumors and 13 control participants. For both biomarkers, a cut-off value was stablished using Youden's J index through ROC curve analysis and areas under the curve (AUC) were calculated. The plasma mRNA expression level of both biomarkers was significantly higher in diseased versus healthy patients. Moreover, ROC curve analysis showed an AUC value of 0.897 for the combined model. This model also resulted in a cutoff value of 0.664, as well as a sensitivity of 83% and a specificity of 84.6%. These results suggest that the plasma expression levels of COL1A2 and KRT19 could a have potential role in detecting various types of cancer at the early stages. The combined analysis of both stromal and epithelial biomarkers would provide a non-invasive screening method that would allow us to differentiate patients with an active neoplastic process.

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

Realization of high-performance biosensor through sandwich analysis utilizing weak value amplification

A label-free optical sandwich immunoassay sensor, utilizing weak value amplification and total internal reflection, was devised for real-time, high-sensitivity analysis and detection of low-concentration targets. 3D printed channels and sodium chloride solution were employed to ensure reproducibility, reliability, and stability of the measurements for calibration. The sandwich structure demonstrated enhanced responsiveness in the proposed optical biosensor through a comparative analysis of the direct assay and sandwich assay for detecting alpha-fetoprotein (AFP) at the same concentration. By optimizing the binding sequences of the coating antibody, target, and detection antibody in the sandwich method, a more suitable sandwich sensing approach based on weak value amplification was achieved. With this approach, the limit of detection (LOD) of 6.29 ng/mL (pM level) for AFP in PBS solution was achieved. AFP testing and regeneration experiments in human serum have proved the feasibility of our methods in detecting complex samples and the reusability of sensing chips. Additionally, the method demonstrated excellent selectivity for unpaired antigens. The efficacy of this methodology was evaluated by simultaneously detecting AFP, carcinoembryonic antigen (CEA), and CA15-3 on a singular sensor chip. In conclusion, the label-free sandwich immunoassay sensing scheme holds promise for advancing the proposed optical sensors based on weak value amplification in early diagnosis and prevention applications. Compared to other biomarker detection methods, it will be easier to promote in practical applications.

Atomic force microscopy as a nanomechanical tool for cancer liquid biopsy

Liquid biopsies have been receiving tremendous attention for their potential to reshape cancer management. Though current studies of cancer liquid biopsy primarily fucus on applying biochemical assays to characterize the genetic/molecular profiles of circulating tumor cells (CTCs) and their secondary products shed from tumor sites in bodily fluids, delineating the nanomechanical properties of tumor-associated materials in liquid biopsy specimens yields complementary insights into the biology of tumor dissemination and evolution. Particularly, atomic force microscopy (AFM) has become a standard and versatile toolbox for characterizing the mechanical properties of living biological systems at the micro/nanoscale, and AFM has been increasingly utilized to probe the nanomechanical properties of various tumor-derived analytes in liquid biopsies, including CTCs, tumor-associated cells, circulating tumor DNA (ctDNA) molecules, and extracellular vesicles (EVs), offering additional possibilities for understanding cancer pathogenesis from the perspective of mechanobiology. Herein, the applications of AFM in cancer liquid biopsy are summarized, and the challenges and future directions of AFM as a nanomechanical analysis tool in cancer liquid biopsy towards clinical utility are discussed and envisioned.

It is feasible and safe to stop specialized follow-up of asymptomatic lower-risk chronic lymphocytic leukemia

ABSTRACT

Approximately half of patients with chronic lymphocytic leukemia (CLL) will never require treatment; nonetheless, they are recommended life-long specialized follow-up (sFU). To prioritize health care resources, local hospital management implemented ending sFU in asymptomatic patients with CLL International Prognostic Index (CLL-IPI) and CLL without need of treatment (CLL-WONT) low-to-intermediate risk, who were covered by universal health care. To evaluate the feasibility and safety of ending sFU, we investigated 3-year clinical outcomes among 112 patients selected by clinical assessment to end sFU as compared with 88 patients selected to continue sFU. Patients who ended sFU were older, but otherwise lower risk compared with patients continuing sFU. Overall survival (OS) was similar in patients ending and continuing sFU (3-year OS, 87% and 80%, respectively; P = .16). Hospital visits per patient-year were lower (median 0.7 vs 4.3, P < .0001) and time to first infection was longer (P = .035) in patients ending sFU compared with those who continued sFU, including shorter in-hospital antimicrobial treatment (median 4 vs 12 days, respectively; P = .026). Finally, 1 in 6 patients were rereferred, including 4 patients meeting international workshop on CLL criteria for need of treatment. This also resulted in a lower 3-year first treatment rate for patients ending sFU compared with patients continuing sFU (4% vs 23%, respectively; P < .0001). In conclusion, it is feasible and safe to end sFU for patients with CLL who have low-to-intermediate risk CLL-IPI and CLL-WONT scores upon thorough clinical evaluation before ending sFU.

Reprogramming hematopoietic stem cell metabolism in lung cancer: glycolysis, oxidative phosphorylation, and the role of 2-DG

Hematopoietic stem cells (HSCs) exhibit significant functional and metabolic alterations within the lung cancer microenvironment, contributing to tumor progression and immune evasion by increasing differentiation into myeloid-derived suppressor cells (MDSCs). Our aim is to analyze the metabolic transition of HSCs from glycolysis to oxidative phosphorylation (OXPHOS) in lung cancer and determine its effects on HSC functionality. Using a murine Lewis Lung Carcinoma lung cancer model, we conducted metabolic profiling of long-term and short-term HSCs, as well as multipotent progenitors, comparing their metabolic states in normal and cancer conditions. We measured glucose uptake using 2-[N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)Amino]-2-Deoxyglucose (2-NBDG) and assessed levels of lactate, acetyl-coenzyme A, and ATP. Mitochondrial functionality was evaluated through flow cytometry, alongside the impact of the glucose metabolism inhibitor 2-DG on HSC differentiation and mitochondrial activity. HSCs under lung cancer conditions showed increased glucose uptake and lactate production, with an associated rise in OXPHOS activity, marking a metabolic shift. Treatment with 2-DG led to decreased T-HSCs and MDSCs and an increased red blood cell count, highlighting its potential to influence metabolic and differentiation pathways in HSCs. This study provides novel insights into the metabolic reprogramming of HSCs in lung cancer, emphasizing the critical shift from glycolysis to OXPHOS and its implications for the therapeutic targeting of cancer-related metabolic pathways.

Cancer and the Microbiome of the Human Body

Cancer remains a public health concern worldwide, with its incidence increasing worldwide and expected to continue growing during the next decades. The microbiome has emerged as a central factor in human health and disease, demonstrating an intricate relationship between the microbiome and cancer. Although some microbiomes present within local tissues have been shown to restrict cancer development, mainly by interacting with cancer cells or the host immune system, some microorganisms are harmful to human health and risk factors for cancer development. This review summarizes the recent evidence concerning the microbiome and some of the most common cancer types (i.e., lung, head and neck, breast, gastric, colorectal, prostate, and cervix cancers), providing a general overview of future clinical approaches and perspectives.

Using a community-engaged research process to plan, implement, and evaluate a cancer education program to improve knowledge and screening intentions among African American men

OBJECTIVE

We assessed acceptability, feasibility, and preliminary efficacy of a culturally appropriate, cancer education program to improve cancer knowledge, attitudes, subjective norms, and screening intentions for oropharynx, colon, and prostate cancers among African American men. We detailed the community-engaged research process used for African American men to design, implement, and evaluate the program.

MATERIALS AND METHODS

We recruited 84 (61 in-person, 23 online) African American men over 2-months across 4 churches in Middle Tennessee in 2021. A single group, pre-post-test design was used to evaluate the 2.5-h hybrid program. Scales used were: General self-efficacy for cancer screening; attitudes toward cancer; general cancer knowledge; and subjective norms related to cancer. One-item measured cancer screening intention. Taba robust partial correlation measured the degree of association between changes in means of each explanatory variable with changes in means of each outcome variable. IBM SPSS version 28 and R/RStudio version 3.6.0 was used for data analysis. We conducted three focus groups (n = 17) to assess program acceptability. Microsoft Excel version 26 was used to conduct thematic analysis for this data.

FINDINGS

Quantitative Significant differences were found in the pre/post comparisons of knowledge (mean difference: 0.22; p-value = 0.015), self-efficacy (mean difference: 0.23; p-value < 0.001), and prostate cancer screening intention (mean difference: 0.19; p-value = 0.049) scores. This indicates the mean score for knowledge, self-efficacy, and prostate cancer screening intention was significantly higher post-intervention. Qualitative Focus group themes were: (1) Impact of Program on Participants Psychosocial Health (2) Perspectives on Life after the program. (3) Views on Programmatic Components; (4) Recommendations for Program Improvement.

CONCLUSIONS

Results demonstrate our program is feasible, acceptable, and could increase cancer screening intentions and behavior. Psychosocial providers should demonstrate cultural awareness and humility when providing services to address the psychological and social needs for cancer screening among African American men.

The CD47/TSP-1 axis: a promising avenue for ovarian cancer treatment and biomarker research

BACKGROUND

Ovarian cancer (OC) remains one of the most challenging and deadly malignancies facing women today. While PARP inhibitors (PARPis) have transformed the treatment landscape for women with advanced OC, many patients will relapse and the PARPi-resistant setting is an area of unmet medical need. Traditional immunotherapies targeting PD-1/PD-L1 have failed to show any benefit in OC. The CD47/TSP-1 axis may be relevant in OC. We aimed to describe changes in CD47 expression with platinum therapy and their relationship with immune features and prognosis.

METHODS

Tumor and blood samples collected from OC patients in the CHIVA trial were assessed for CD47 and TSP-1 before and after neoadjuvant chemotherapy (NACT) and multiplex analysis was used to investigate immune markers. Considering the therapeutic relevance of targeting the CD47/TSP-1 axis, we used the CD47-derived TAX2 peptide to selectively antagonize it in a preclinical model of aggressive ovarian carcinoma.

RESULTS

Significant reductions in CD47 expression were observed post NACT. Tumor patients having the highest CD47 expression profile at baseline showed the greatest CD4+ and CD8+ T-cell influx post NACT and displayed a better prognosis. In addition, TSP-1 plasma levels decreased significantly under NACT, and high TSP-1 was associated with a worse prognosis. We demonstrated that TAX2 exhibited a selective and favorable biodistribution profile in mice, localizing at the tumor sites. Using a relevant peritoneal carcinomatosis model displaying PARPi resistance, we demonstrated that post-olaparib (post-PARPi) administration of TAX2 significantly reduced tumor burden and prolonged survival. Remarkably, TAX2 used sequentially was also able to increase animal survival even under treatment conditions allowing olaparib efficacy.

CONCLUSIONS

Our study thus (1) proposes a CD47-based stratification of patients who may be most likely to benefit from postoperative immunotherapy, and (2) suggests that TAX2 is a potential alternative therapy for patients relapsing on PARP inhibitors.

Recent Advancements in the Application of Circulating Tumor DNA as Biomarkers for Early Detection of Cancers

Early detection of cancer is vital for increasing patient survivability chances. The three major techniques used to diagnose cancers are instrumental examination, tissue biopsy, and tumor biomarker detection. Circulating tumor DNA (ctDNA) has gained much attention in recent years due to advantages over traditional technology, such as high sensitivity, high specificity, and noninvasive nature. Through the mechanism of apoptosis, necrosis, and circulating exosome release in tumor cells, ctDNA can spread throughout the circulatory system and carry modifications such as methylations, mutations, gene rearrangements, and microsatellite instability. Traditional gene-detection technology struggles to achieve real-time, low-cost, and portable ctDNA measurement, whereas electrochemical biosensors offer low cost, high specificity alongside sensitivity, and portability for the detection of ctDNA. Therefore, this review focuses on describing the recent advancements in ctDNA biomarkers for various cancer types and biosensor developments for real-time, noninvasive, and rapid ctDNA detection. Further in the review, ctDNA sensors are also discussed in regards to their selections of probes for receptors based on the electrode surface recognition elements.

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-throughput analysis of specific changes in HPs associated with HCC remains unrealized, due to the complex nature of biological fluids and the challenges of mining complex patterns in large data sets. The clinical diagnosis of HCC still lacks a non-destructive and accurate classification method, given the limited specificity of widely used biomarkers. To address these challenges, we have established a multifunctional platform that integrates artificial intelligence computation, hydrophilic interaction extraction of HPs, and MALDI-MS testing. This platform aims to achieve highly sensitive HP fingerprinting for accurate diagnosis of HCC. The method not only facilitates efficient detection of HPs, but also achieves a remarkable 100.00% diagnostic accuracy for HCC in a test cohort, supported by machine learning algorithms. By constructing a panel of HPs with 10 characteristic features, we achieved 98% accuracy in the test cohort for rapid diagnosis and identified 62 HPs deeply involved in pathways related to liver diseases. This integrated strategy provides new research directions for future biomarker studies as well as early diagnosis and individualized treatment of HCC.

Benefits and Harms of Interception and Early Detection of Cancer

Strategies for early detection and interception of cancer are based on 2 synergistic elements: proactive search for asymptomatic cancer, precancer, or cancer predisposition and proactive disruption of cancer evolution. Benefits and harms of both these elements will vary widely depending on the screened populations, the types of cancers targeted, the detection modalities, and the health care delivery approaches following diagnosis. This article attempts to identify common elements that can inform the evaluation of alternative strategies across many of these scenarios.

New paradigms to break barriers in early cancer detection for improved prognosis and treatment outcomes

The uncontrolled growth and spread of cancerous cells beyond their usual boundaries into surrounding tissues characterizes cancer. In developed countries, cancer is the leading cause of death, while in underdeveloped nations, it ranks second. Using existing cancer diagnostic tools has increased early detection rates, which is crucial for effective cancer treatment. In recent decades, there has been significant progress in cancer-specific survival rates owing to advances in cancer detection and treatment. The ability to accurately identify precursor lesions is a crucial aspect of effective cancer screening programs, as it enables early treatment initiation, leading to lower long-term incidence of invasive cancer and improved overall prognosis. However, these diagnostic methods have limitations, such as high costs and technical challenges, which can make accurate diagnosis of certain deep-seated tumors difficult. To achieve accurate cancer diagnosis and prognosis, it is essential to continue developing cutting-edge technologies in molecular biology and cancer imaging.

Cancer treatments: Past, present, and future

There is a rich history of cancer treatments which provides a number of important lessons for present and future cancer therapies. We outline this history by looking in the past, reviewing the current landscape of cancer treatments, and by glancing at the potential future cancer therapies.

Vanadium-doped metal-organic framework@Znln2S4 core-shell heterojunction-attenuated photoelectrochemical immunoassay

Considering that cancer has become the second leading cause of death in humans, it is essential to develop an analytical approach that can sensitively detect tumor markers for early detection. We report an attenuated photoelectrochemical (PEC) immunoassay based on the organic-inorganic heterojunction 10MIL-88B(FeV)/ZnIn2S4 (10M88B(FeV)/ZIS) as a photoactive material for monitoring carcinoembryonic antigen (CEA). The 10M88B(FeV)/ZIS heterojunctions have excellent light-harvesting properties and high electrical conductivity, which are attributed to the advantages of both organic and inorganic semiconductors, namely, remarkable photogenerated carrier separation efficiency and long photogenerated carrier lifetime. Horseradish peroxidase (HRP) in the presence of H2O2 can catalyze 3,3'-diaminofenamide (DAB) producing brown precipitates (oxDAB), which is then loaded onto the 10M88B(FeV)/ZIS heterojunction to reduce the photocurrent and enable the quantitative detection of CEA. Under optimal conditions, the photocurrent values of the PEC biosensor are linearly related to the logarithm of the CEA concentrations, ranging from 0.01 ng mL-1 to 100 ng mL-1 with a detection limit (LOD) of 4.0 pg mL-1. Notably, the accuracy of the PEC biosensor is in agreement with that of the human CEA enzyme-linked immunosorbent assay (ELISA) kit.

Recent Advances on the Metal-Organic Frameworks-Based Biosensing Methods for Cancer Biomarkers Detection

Sensitive and selective detection of cancer biomarkers is crucial for early diagnosis and treatment of cancer, one of the most dangerous diseases in the world. Metal-organic frameworks (MOFs), a class of hybrid porous materials fabricated through the assembly of metal ions/clusters and organic ligands, have attracted increasing attention in the sensing of cancer biomarkers, due to the advantages of adjustable size, high porosity, large surface area and ease of modification. MOFs have been utilized to not only fabricate active sensing interfaces but also arouse a variety of measurable signals. Several representative analytical technologies have been applied in MOF-based biosensing strategies to ensure high detection sensitivity toward cancer biomarkers, such as fluorescence, electrochemistry, electrochemiluminescence, photochemistry and colorimetric methods. In this review, we summarized recent advances on MOFs-based biosensing strategies for the detection of cancer biomarkers in recent three years based on the categories of metal nodes, and aimed to provide valuable references for the development of innovative biosensing platform for the purpose of clinical diagnosis.

Systematically Evaluating Cell-Free DNA Fragmentation Patterns for Cancer Diagnosis and Enhanced Cancer Detection via Integrating Multiple Fragmentation Patterns

Cell-free DNA (cfDNA) fragmentation patterns have immense potential for early cancer detection. However, the definition of fragmentation varies, ranging from the entire genome to specific genomic regions. These patterns have not been systematically compared, impeding broader research and practical implementation. Here, 1382 plasma cfDNA sequencing samples from 8 cancer types are collected. Considering that cfDNA within open chromatin regions is more susceptible to fragmentation, 10 fragmentation patterns within open chromatin regions as features and employed machine learning techniques to evaluate their performance are examined. All fragmentation patterns demonstrated discernible classification capabilities, with the end motif showing the highest diagnostic value for cross-validation. Combining cross and independent validation results revealed that fragmentation patterns that incorporated both fragment length and coverage information exhibited robust predictive capacities. Despite their diagnostic potential, the predictive power of these fragmentation patterns is unstable. To address this limitation, an ensemble classifier via integrating all fragmentation patterns is developed, which demonstrated notable improvements in cancer detection and tissue-of-origin determination. Further functional bioinformatics investigations on significant feature intervals in the model revealed its impressive ability to identify critical regulatory regions involved in cancer pathogenesis.

Dynamic Addressing Molecular Robot (DAMR): An Effective and Efficient Trial-and-Error Approach for the Analysis of Single Nucleotide Polymorphisms

Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial-and-error-based molecular recognition approaches have always been challenged in distinguishing minimal differences between targets and non-targets, such as single nucleotide polymorphisms (SNPs) of oligonucleotides. To address these challenges, here, a novel concept of dynamic addressing analysis is proposed. In this concept, by dissecting the regions of the target and creating a corresponding recognizer, it is possible to eliminate the inaccuracy and inefficiency of recognition. To achieve this concept, a Dynamic Addressing Molecular Robot (DAMR), a DNA-based dynamic addressing device is developed which is capable of dynamically locating targets. DAMR is designed to first bind to the conserved region of the target while addressing the specific region dynamically until accurate recognition is achieved. DAMR has provided an approach for analyzing low-resolution targets and has been used for analyzing SNP of miR-196a2 in both cell and serum samples, which has opened new avenues for effective and efficient molecular recognition.

Discovery and validation of combined biomarkers for the diagnosis of esophageal intraepithelial neoplasia and esophageal squamous cell carcinoma

Early diagnosis and intervention of esophageal squamous cell carcinoma (ESCC) can improve the prognosis. The purpose of this study was to identify biomarkers for ESCC and esophageal precancerous lesions (intraepithelial neoplasia, IEN). Based on the proteomic and genomic data of esophageal tissue including previously reported data, up-regulated proteins with copy number amplification in esophageal cancer were screened as candidate biomarkers. Five proteins, including KDM2A, RAD9A, ECT2, CYHR1 and TONSL, were confirmed by immunohistochemistry on ESCC and normal esophagus (NE). Then, we investigated the expression of 5 proteins in 236 participants (60 NEs, 93 IENs and 83 ESCCs) which were randomly divided into training set and test set. When distinguishing ESCC from NE, the area under curve (AUC) of the multiprotein model was 0.940 in the training set, while the lowest AUC of a protein was 0.735. In the test set, the results were similar. When distinguishing ESCC from IEN or distinguishing IEN from NE, the diagnostic efficiency of the multi-protein models were also improved compared with that of single protein. Our findings suggest that combined detection of KDM2A, RAD9A, ECT2, CYHR1 and TONSL can be used as potential biomarkers for the early diagnosis of ESCC and precancerous lesion development prediction. SIGNIFICANCE: Candidate biomarkers including KDM2A, RAD9A, ECT2, CYHR1 and TONSL screened by integrating genomic and proteomic data from the esophagus can be used as potential biomarkers for the early diagnosis of esophageal squamous cell carcinoma and precancerous lesion development prediction.

Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs

Accurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs. We include 4606 samples subjected to different types of cfDNA sequencing to develop EIMIT, and subsequently evaluate classification performance of linear projections of EMIT on six datasets and an additional inhouse testing set encopassing whole-genome, whole-genome bisulfite and 5-hydroxymethylcytosine sequencing. The linear projection of representations from EMIT achieved area under the receiver operating curve (AUROC) values ranged from 0.895 (0.835-0.955) to 0.996 (0.994-0.997) across these six datasets, outperforming its baseline by significant margins. Additionally, we showed that linear projection of EMIT representations can achieve an AUROC of 0.962 (0.914-1.0) in identification of lung cancer on an independent testing set subjected to whole-exome sequencing. The findings of this study indicate that a transformer-based deep learning model can learn cancer-discrimative representations from cfDNA end-motifs. The representations of this deep learning model can be exploited for discriminating patients with and without cancer.

Trends and frontiers in signal amplification for aptamer-based tumor detection: A bibliometric analysis

BACKGROUND

Malignant tumors are one of the leading causes of death worldwide, imposing a substantial economic and social burden. Early detection is the key to improving cure rates and reducing mortality rates, which requires the development of sensitive early detection technologies. Signal amplification techniques play a crucial role in aptamer-based early detection of tumors and are increasingly garnering attention from researchers.

AIM

To investigate the current research status, developmental trajectories, and hotspots in signal amplification for aptamer-based tumor detection through bibliometric analysis.

METHODS

English publications pertaining to signal amplification in aptamer-based tumor detection were retrieved from the Web of Science Core Collection database. VOSviewer and CiteSpace software were employed to analyze various information within this field, including countries, institutions, authors, co-cited authors, journals, co-cited journals, cited references, and keywords.

RESULTS

A total of 757 publications were included in this study. China accounted for 85.47% of all publications, with Nanjing University (China) emerging as the institution with the highest publication output. The most influential authors and journals were Hasanzadeh M. from Iran and "Biosensors and Bioelectronics", respectively. Exosomes and carcinoembryonic antigen (CEA) stood out as the most researched tumor-related molecules. Currently, the predominant signal amplification technique, nanomaterial, and signal transduction method were identified as hybridization chain reactions, gold nanoparticles, and electrochemical methods, respectively. Over the past 3 years, exosomes, CEA, electrochemical biosensors, and nanosheets have emerged as research hotspots, exhibiting a robust burst of intensity.

CONCLUSION

This study is the first bibliometric analysis of literature on signal amplification in aptamer-based tumor detection and elucidates the current status, hotspots, and prospective research directions within this realm. Additionally, it provides an important reference for researchers.

Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

Site-Specific Cascade-Activatable Fluorogenic Nanomicelles Enable Precision and Accuracy Imaging of Pulmonary Metastatic Tumor

The precise localization of metastatic tumors with subtle growth is crucial for timely intervention and improvement of tumor prognosis but remains a paramount challenging. To date, site-specific activation of fluorogenic probes for single-stimulus-based diagnosis typically targets an occult molecular event in a complex biosystem with limited specificity. Herein, we propose a highly specific site-specific cascade-activated strategy to enhance detection accuracy, aiming to achieve the accurate detection of breast cancer (BC) lung metastasis in a cascade manner. Specifically, cascade-activatable NIR fluorogenic nanomicelles HPNs were constructed using ultra-pH-sensitive (UPS) block copolymers as carriers and nitroreductase (NTR)-activated fluorogenic reporters. HPNs exhibit programmable cascade response characteristics by first instantaneous dissociating under in situ tumor acidity, facilitating deep tumor penetration followed by selective fluorescence activation through NTR-mediated enzymatic reaction resulting in high fluorescence ON/OFF contrast. Notably, this unique feature of HPNs enables high-precision diagnosis of orthotopic BC as well as its lung metastases with a remarkable signal-to-background ratio (SBR). This proposed site-specific cascade activation strategy will offer opportunities for a specific diagnosis with high signal fidelity of various insidious metastatic lesions in situ across different diseases.

Understanding the Landscape of Multi-Cancer Detection Tests: The Current Data and Clinical Considerations

Multi-cancer detection (MCD) tests are blood-based assays that screen for multiple cancers concurrently and offer a promising approach to improve early cancer detection and screening uptake. To date, there have been two prospective interventional studies evaluating MCD tests as a screening tool in human subjects. No MCD tests are currently approved by the FDA, but there is one commercially available MCD test. Ongoing trials continue to assess the efficacy, safety, and cost implications of MCD tests. In this review, we discuss the performance of CancerSEEK and Galleri, two leading MCD platforms, and discuss the clinical consideration for the broader application of this new technology.

Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains

Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of multi-material heterochains, which show improved sensitivity, throughput, and accuracy compared to standard ELISA kits. By controlling the material combination and the number of ligand nanoparticles (NPs), we observe robust near-field enhancement as well as both strong electromagnetic resonance in polymer-semiconductor heterochains. In particular, their optical signals show a linear response to the coordination number of the semiconductor NPs in a wide range. Accordingly, a visible nanophotonic biosensor is developed by functionalizing antibodies on central polymer chains that can identify target proteins attached to semiconductor NPs. This allows for the specific detection of multiple protein biomarkers from healthy people and pancreatic cancer patients in one step with an ultralow detection limit (1 pg/mL). Furthermore, rapid and high-throughput quantification of protein expression levels in diverse clinical samples such as buffer, urine, and serum is achieved by combining a neural network algorithm, with an average accuracy of 97.3%. This work demonstrates that the heterochain-based biosensor is an exemplary candidate for constructing next-generation diagnostic tools and suitable for many clinical settings.

Development and validation of a TAAbs and TAAs based non-invasive model for diagnosing lung cancer

Background

Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection.

Methods

1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis.

Results

The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis.

Conclusions

This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.

Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD)

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

Multi-MXene assisted large-scale manufacturing of electrochemical biosensors based on enzyme-nanoflower enhanced electrodes for the detection of H2O2 secreted from live cancer cells

In situ monitoring of H2O2 in cellular microenvironments plays a critical role in the early diagnosis and pretreatment of cancer, but is limited by the lack of efficient and low-cost strategies for the large-scale preparation of real-time biosensors. Herein, a universal strategy for MXene-based composite inks combined with a scalable screen-printing process is validated in large-scale manufacturing of electrochemical biosensors for in situ detection of H2O2 secreted from live cells. Compositing biocompatible carboxymethyl cellulose (CMCS) with excellent conductive MXene, a water-based ink electrode (MXene/CMCS) with tunable viscosity is efficiently printed with desirable printing accuracy. Subsequently, the MXene/CMCS@HRP electrochemical biosensor exhibits stable electrochemical performance through HRP nanoflower modification, showing rapid electron transport and high electrocatalytic capacity, and demonstrating a low limit of detection (0.29 μM) with a wide linear detection range (0.5 μM-3 mM), superior sensitivity (56.45 μA mM-1 cm-2), long-term stability and high anti-interference ability. Moreover, this electrochemical biosensor is effectively employed for in situ detection of H2O2 secreted from HeLa cells, revealing good biocompatibility and outstanding biosensing capability. This proposed strategy not only extends the possibility of low-cost biomedical devices, but also provides a promising approach for early diagnosis and treatment of cancer.

Glycosylated nanoplatforms: From glycosylation strategies to implications and opportunities for cancer theranostics

Glycosylated nanoplatforms have emerged as promising tools in the field of cancer theranostics, integrating both therapeutic and diagnostic functionalities. These nanoscale platforms are composed of different materials such as lipids, polymers, carbons, and metals that can be modified with glycosyl moieties to enhance their targeting capabilities towards cancer cells. This review provides an overview of different modification strategies employed to introduce glycosylation onto nanoplatforms, including chemical conjugation, enzymatic methods, and bio-orthogonal reactions. Furthermore, the potential applications of glycosylated nanoplatforms in cancer theranostics are discussed, focusing on their roles in drug delivery, imaging, and combination therapy. The ability of these nanoplatforms to selectively target cancer cells through specific interactions with overexpressed glycan receptors is highlighted, emphasizing their potential for enhancing efficacy and reducing the side effects compared to conventional therapies. In addition, the incorporation of diagnostic components onto the glycosylated nanoplatforms provided the capability of simultaneous imaging and therapy and facilitated the real-time monitoring of treatment response. Finally, challenges and future perspectives in the development and translation of glycosylated nanoplatforms for clinical applications are addressed, including scalability, biocompatibility, and regulatory considerations. Overall, this review underscores the significant progress made in the field of glycosylated nanoplatforms and their potential to revolutionize cancer theranostics.

Architectural and cytological features of epithelial dysplasia associated with transformation risk

OBJECTIVES

This study explored associations between histological features of dysplasia and malignant transformation, as well as genomic copy number alterations.

MATERIALS AND METHODS

Overall, 201 samples were collected from patients of oral leukoplakia. The associations of dysplastic features with malignant transformation and copy number alterations were investigated by Cox proportional hazards regression analysis and the Mann-Whitney U-test.

RESULTS

Eight individual histological features, such as irregular epithelial stratification (p = 0.001), mitoses high in epithelium (p = 0.033), extension of changes along minor gland ducts (p < 0.001), etc., were associated with greater risk of malignant transformation. A model including histological features and age showed good performance for predicting malignant transformation (area under receiver operating characteristic curve: 0.806). Irregular epithelial stratification (p = 0.007), abnormal nuclear shape (p = 0.005), abnormal cell size (p = 0.004), etc. were associated with greater genomic instability.

CONCLUSIONS

A Cox proportional hazards model using eight histological features and patient age reliably predicted the malignant potential of oral epithelial dysplasia. Identification of these histological features closely related to malignant transformation may aid the management of oral potentially malignant disorders and early detection of oral squamous cell carcinoma.

The Potential MicroRNA Diagnostic Biomarkers in Oral Squamous Cell Carcinoma of the Tongue

Oral squamous cell carcinoma (OSCC) of the tongue is a common type of head and neck malignancy with a poor prognosis, underscoring the urgency for early detection. MicroRNAs (miRNAs) have remarkable stability and are easily measurable. Thus, miRNAs may be a promising biomarker candidate among biomarkers in cancer diagnosis. Biomarkers have the potential to facilitate personalized medicine approaches by guiding treatment decisions and optimizing therapy regimens for individual patients. Utilizing data from The Cancer Genome Atlas, we identified 13 differentially expressed upregulated miRNAs in OSCC of the tongue. Differentially expressed miRNAs were analyzed by enrichment analysis to reveal underlying biological processes, pathways, or functions. Furthermore, we identified miRNAs associated with the progression of OSCC of the tongue, utilizing receiver operating characteristic analysis to evaluate their potential as diagnostic biomarkers. A total of 13 upregulated miRNAs were identified as differentially expressed in OSCC of the tongue. Five of these miRNAs had high diagnostic power. In particular, miR-196b has the potential to serve as one of the most effective diagnostic biomarkers. Then, functional enrichment analysis for the target gene of miR-196b was performed, and a protein-protein interaction network was constructed. This study assessed an effective approach for identifying miRNAs as early diagnostic markers for OSCC of the tongue.

Structural Defect-Enabled Magnetic Neutrality Nanoprobes for Ultra-High-Field Magnetic Resonance Imaging of Isolated Tumor Cells in Vivo

The identification of metastasis "seeds," isolated tumor cells (ITCs), is of paramount importance for the prognosis and tailored treatment of metastatic diseases. The conventional approach to clinical ITCs diagnosis through invasive biopsies is encumbered by the inherent risks of overdiagnosis and overtreatment. This underscores the pressing need for noninvasive ITCs detection methods that provide histopathological-level insights. Recent advancements in ultra-high-field (UHF) magnetic resonance imaging (MRI) have ignited hope for the revelation of minute lesions, including the elusive ITCs. Nevertheless, currently available MRI contrast agents are susceptible to magnetization-induced strong T2-decaying effects under UHF conditions, which compromises T1 MRI capability and further impedes the precise imaging of small lesions. Herein, this study reports a structural defect-enabled magnetic neutrality nanoprobe (MNN) distinguished by its paramagnetic properties featuring an exceptionally low magnetic susceptibility through atomic modulation, rendering it almost nonmagnetic. This unique characteristic effectively mitigates T2-decaying effect while concurrently enhancing UHF T1 contrast. Under 9 T MRI, the MNN demonstrates an unprecedentedly low r2/r1 value (≈1.06), enabling noninvasive visualization of ITCs with an exceptional detection threshold of ≈0.16 mm. These high-performance MNNs unveil the domain of hitherto undetectable minute lesions, representing a significant advancement in UHF-MRI for diagnostic purposes and fostering comprehensive metastasis research.

Tumour-specific activation of a tumour-blood transport improves the diagnostic accuracy of blood tumour markers in mice

BACKGROUND

The accuracy of blood-based early tumour recognition is compromised by signal production at non-tumoral sites, low amount of signal produced by small tumours, and variable tumour production. Here we examined whether tumour-specific enhancement of vascular permeability by the particular tumour homing peptide, iRGD, which carries dual function of binding to integrin receptors overexpressed in the tumour vasculature and is known to promote extravasation via neuropilin-1 receptor upon site-specific cleavage, might be useful to improve blood-based tumour detection by inducing a yet unrecognised vice versa tumour-to-blood transport.

METHODS

To detect an iRGD-induced tumour-to-blood transport, we examined the effect of intravenously injected iRGD on blood levels of α-fetoprotein (AFP) and autotaxin in several mouse models of hepatocellular carcinoma (HCC) or in mice with chronic liver injury without HCC, and on prostate-specific antigen (PSA) levels in mice with prostate cancer.

FINDINGS

Intravenously injected iRGD rapidly and robustly elevated the blood levels of AFP in several mouse models of HCC, but not in mice with chronic liver injury. The effect was primarily seen in mice with small tumours and normal basal blood AFP levels, was attenuated by an anti-neuropilin-1 antibody, and depended on the concentration gradient between tumour and blood. iRGD treatment was also able to increase blood levels of autotaxin in HCC mice, and of PSA in mice with prostate cancer.

INTERPRETATION

We conclude that iRGD induces a tumour-to-blood transport in a tumour-specific fashion that has potential of improving diagnosis of early stage cancer.

FUNDING

Deutsche Krebshilfe, DKTK, LOEWE-Frankfurt Cancer Institute.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Medical Applications of Silver and Gold Nanoparticles and Core-Shell Nanostructures Based on Silver or Gold Core: Recent Progress and Innovations

Nanoparticles (NPs) of noble metals such as silver (Ag NPs) or gold (Au NPs) draw the attention of scientists looking for new compounds to use in medical applications. Scientists have used metal NPs because of their easy preparation, biocompatibility, ability to influence the shape and size or modification, and surface functionalization. However, to fully use their capabilities, both the benefits and their potential threats should be considered. One possibility to reduce the potential threat and thus prevent the extinction of their properties resulting from the agglomeration, they are covered with a neutral material, thus obtaining core-shell nanostructures that can be further modified and functionalized depending on the subsequent application. In this review, we focus on discussing the properties and applications of Ag NPs and Au NPs in the medical field such as the treatment of various diseases, drug carriers, diagnostics, and many others. In addition, the following review also discusses the use and potential applications of Ag@SiO2 and Au@SiO2 core-shell nanostructures, which can be used in cancer therapy and diagnosis, treatment of infections, or tissue engineering.

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRβ sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis.

SIGNIFICANCE

Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

Utilizing nullomers in cell-free RNA for early cancer detection

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.