Biomarker Discovery via N-Glycoproteomics.

Posttranslational modifications (PTMs) of proteins regulate several biological processes, and investigating their diversity is crucial for understanding the mechanisms of cell regulation. Glycosylation is one of the most complex posttranslational modifications that control fundamental cellular processes such as protein folding, protein trafficking, host-pathogen interactions, cell adhesion, and cytokine receptor signaling networks. N-linked glycosylation denotes the attachment of glycans (oligosaccharides) to a nitrogen atom of asparagine (N) residues in the consensus motif Asn-X-Ser/Thr (NXS/T), where X is any amino acid except proline. Therefore, mutations in this posttranslational modification (i.e., N-glycosylation) site cause many human genetic diseases, including cancer. In the past decade, high-throughput quantitative proteome profiling tools have significantly renewed our interest in discovering novel cancer diagnostic or prognostic biomarkers through the simultaneous examination of the enormous amount of high-quality data of thousands of proteins and genes in complex biological systems. In this chapter, we describe how aberrant N-linked glycopeptides could be selectively identified as novel single tumor markers through the use of mass spectrometry (MS)-based proteomics, also known as Solid-phase extraction of N-glycopeptides (SPEG), and reasonable hypotheses that have the potential capacity to revolutionize biomarker discovery and bring those markers to the clinic as early as possible.

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Implications of RNA pseudouridylation for cancer biology and therapeutics: a narrative review.

BACKGROUND: Pseudouridine (Ψ), a C5-glycoside isomer of uridine, stands as one of the most prevalent RNA modifications in all RNA types. Distinguishing from the C-N bond linking uridine to ribose, the link between Ψ and ribose is a C-C bond, endowing Ψ modified RNA distinct properties and functions in various biological processes. The conversion of uridine to Ψ is governed by pseudouridine synthases (PUSs). RNA pseudouridylation is implicated in cancer biology and therapeutics. OBJECTIVES: In this review, we will summarize the methods for detecting Ψ, the process of Ψ generation, the impact of Ψ modification on RNA metabolism and gene expression, the roles of dysregulated Ψ and pseudouridine synthases in cancers, and the underlying mechanism. METHODS: We conducted a comprehensive search of PubMed from its inception through February 2024. The search terms included "pseudouridine"; "pseudouridine synthase"; "PUS"; "dyskerin"; "cancer"; "tumor"; "carcinoma"; "malignancy"; "tumorigenesis"; "biomarker"; "prognosis" and "therapy". We included studies published in peer-reviewed journals that focused on Ψ detection, specific mechanisms involving Ψ and PUSs, and prognosis in cancer patients with high Ψ expression. We excluded studies lacking sufficient methodological details or appropriate controls. RESULTS: Ψ has been recognized as a significant biomarker in cancer diagnosis and prognosis. Abnormal Ψ modifications mediated by various PUSs result in dysregulated RNA metabolism and impaired RNA function, promoting the development of various cancers. Overexpression of PUSs is common in cancer cells and predicts poor prognosis. PUSs inhibition arrests cell proliferation and enhances apoptosis in cancer cells, suggesting PUS-targeting cancer therapy may be a potential strategy in cancer treatment. DISCUSSION: High Ψ levels in serum, urine, and saliva may suggest cancer, but do not specify the type, requiring additional lab markers and imaging for accurate diagnosis. Standardized detection methods are also crucial for reliable results. PUSs are linked to cancer, but more researches are needed to understand their mechanisms in different cancers. Anticancer treatments targeting PUSs are still under developed.

Clinical Utility of Circulating Cell-Free DNA as a Liquid Biopsy in Cats With Various Tumours.

Only a limited number of tumour biomarkers are currently available in veterinary medicine, particularly in cats. Cell-free DNA (cfDNA) is an extracellular DNA fragment released upon cell death and is considered a minimally invasive biomarker for the diagnosis and monitoring of various human malignancies. This study aimed to clarify the utility of circulating cfDNA as a liquid biopsy for various feline tumours. Plasma samples were collected from 44 cats with various tumours, 24 cats with other diseases and 10 healthy controls. A follow-up study was conducted in three tumour-bearing patients. All cfDNA concentrations were quantified via real-time polymerase chain reaction (PCR), which provided short and long fragments of a newly identified feline LINE-1 gene. We found that cfDNA levels were significantly higher in cats with various tumours than in those with other diseases or healthy controls. The cfDNA concentration was not correlated with serum amyloid A (SAA) levels. Cats with tumours exhibited elevated cfDNA levels that predicted tumour-bearing with a sensitivity and specificity of 50.5% and 91.2%, respectively (AUC 0.736; p < 0.001). In lymphoma cases, cats with high cfDNA levels had significantly shorter survival times than those with low cfDNA levels (median: 33 days vs. 178 days; p = 0.003). In addition, the cfDNA levels of the three patients correlated with clinical status during follow-up. Collectively, these findings indicate the potential of cfDNA as a useful biomarker for the diagnosis, therapeutic monitoring and prognostic assessment of tumours in cats.

Preparation and identification of a fluorescent probe with CsPbBr3perovskite quantum dots and CD44v6 specific peptide for gastric cancer imaging.

Since the sensitivity and accuracy of traditional detection for early gastric cancer diagnosis are still insufficient, it is significant to continuously optimize the optical molecular imaging detection technology based on an endoscopic platform. The signal intensity and stability of traditional chemical fluorescent dyes are low, which hinders the clinical application of molecular imaging detection technology. This work developed a probe based on perovskite quantum dots (PQDs) and peptide ligands. By utilizing CsPbBr3perovskite PQDs modified by azithromycin (AZI), combined with the specific polypeptide ligand of CD44v6, a gastric cancer biomarker, the perovskite-based probe (AZI-PQDs probe) which can specifically identify gastric cancer tumor was prepared. Owing to the high photoluminescence quantum yield of CsPbBr3PQDs, the naked eye can observe the imaging under the excitation of the hand-held ultraviolet light source. AZI-PQDs probe can accurately identify gastric cancer cells, tissues, and xenograft models with experiments ofex vivoandin vivofluorescence imaging detection. It also exhibited low toxicity and immunogenicity, indicating the safety of the probe. This work provides a probe combined with cancer specificity and a reliable fluorescent signal that has the potential for application in gastric cancer optical molecular imaging.

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.

Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

Influence of Genetic Polymorphisms on the Age at Cancer Diagnosis in a Homogenous Lynch Syndrome Cohort of Individuals Carrying the MLH1:c.1528C>T South African Founder Variant.

Background: High variability in the age at cancer diagnosis in Lynch syndrome (LS) patients is widely observed, even among relatives with the same germline pathogenic variant (PV) in the mismatch repair (MMR) genes. Genetic polymorphisms and lifestyle factors are thought to contribute to this variability. We investigated the influence of previously reported genetic polymorphisms on the age at cancer diagnosis in a homogenous LS cohort with a South African founder germline PV c.1528C>T in the MLH1 gene. Methods: A total of 359 LS variant heterozygotes (LSVH) from 60 different families were genotyped for specific genetic polymorphisms in GSTM1, GSTT1, CYP1A1, CYP17, PPP2R2B, KIF20A, TGFB1, XRCC5, TNF, BCL2, CHFR, CDC25C, ATM, TTC28, CDC25C, HFE, and hTERT genes using Multiplex Polymerase Chain Reaction and MassArray methods. Kaplan-Meier survival analysis, univariate and multivariate Cox proportional hazards gamma shared frailty models adjusted for sex were used to estimate the association between age at cancer diagnosis and polymorphism genotypes. A p-value < 0.05 after correcting for multiple testing using the Benjamini-Hochberg method was considered significant at a 95% confidence interval. Results: We identified three genotypes in the cell-cycle regulation, DNA repair, and xenobiotic-metabolism genes significantly associated with age at cancer diagnosis in this cohort. The CYP1A1 rs4646903 risk (GG) and CDC25C rs3734166 polymorphic (GA+AA) genotypes were significantly associated with an increased risk of a younger age at cancer diagnosis (Adj HR: 2.03 [1.01-4.08], p = 0.034 and Adj HR: 1.53 [1.09-2.14], p = 0.015, respectively). LSVH who were heterozygous for the XRCC5 rs1051685 SNP showed significant protection against younger age at cancer diagnosis (Adj HR: 0.69 [CI, 0.48-0.99], p = 0.043). The risk of a younger age at any cancer diagnosis was significantly high in LS carriers of one to two risk genotypes (Adj HR: 1.49 [CI: 1.06-2.09], corrected p = 0.030), while having one to two protective genotypes significantly reduced the risk of developing any cancer and CRC at a younger age (Adj HR: 0.52 [CI: 0.37-0.73], and Adj HR: 0.51 [CI: 0.36-0.74], both corrected p < 0.001). Conclusions: Polymorphism genotypes in the cell-cycle regulation, DNA repair, and xenobiotic metabolizing genes may influence the age at cancer diagnosis in a homogenous LS cohort with a South African founder germline PV c.1528C>T in the MLH1 gene.

The Diagnostic Value of bpMRI in Prostate Cancer: Benefits and Limitations Compared to mpMRI.

Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and mpMRI are essential for prostate cancer management. The PI-RADS system helps assess malignancy risk based on imaging. While mpMRI, which includes T1, T2, DWI, and dynamic contrast-enhanced imaging (DCE), is the standard, bpMRI offers a contrast-free alternative using only T2 and DWI. This reduces costs, acquisition time, and the risk of contrast-related side effects but has limitations in detecting higher-risk PI-RADS 3 and 4 lesions. This study compared bpMRI's diagnostic accuracy to mpMRI, focusing on prostate volume and PI-RADS scoring. Both methods showed strong inter-rater agreement for prostate volume (ICC 0.9963), confirming bpMRI's reliability in this aspect. However, mpMRI detected more complex conditions, such as periprostatic fat infiltration and iliac lymphadenopathy, which bpMRI missed. While bpMRI offers advantages like reduced cost and no contrast use, it is less effective for higher-risk lesions, making mpMRI more comprehensive.

Construction of a chemiluminescent biosensor based on enzymatic extension and click chemistry for sensitive measurement of MGMT activity in human breast tissues.

O6-methylguanine methyltransferase (MGMT) is responsible for dealkylation of naturally occurring O6-methylguanines, and it is closely related with DNA replication, transcription, and cancers. Herein, we develop a chemiluminescent biosensor based on enzymatic extension and click chemistry for sensitive measurement of MGMT activity. When MGMT is present, the MGMT-catalyzed demethylation reaction initiates the cleavage of biotinylated dumbbell probes by PvuII restrictive enzyme, releasing two DNA fragments with 3'-OH end. The resultant DNA fragments can trigger terminal transferase (TdT)- and click chemistry-assisted isothermal amplification to obtain abundant G-rich sequences. The G-rich sequences can be captured by magnetic beads to produce a high chemiluminescence signal. This biosensor can greatly amplify the chemiluminescence signal, facilitating label-free and template-free measurement of MGMT. Especially, the introduction of dumbbell probe and PvuII enzyme can efficiently eliminate the false positive and improve the assay specificity. This biosensor possesses high sensitivity with a detection limit of 1.4 × 10-9 ng/µL, and it may accurately quantify the intracellular MGMT. Importantly, this biosensor can be used to screen the MGMT inhibitors and distinguish the MGMT level in breast tumor tissues and normal tissues, with great potential in drug discovery and cancer diagnosis.

Is prostatic adenocarcinoma detectable by urine cytology-A multicenter retrospective review.

INTRODUCTION: Urine cytology is robust for the diagnosis of urothelial lesions, but data on the detection rates of prostatic adenocarcinoma in urine cytology is limited. In this study, a multicenter review was performed to define the clinical role of urine cytology in diagnosis of prostatic adenocarcinoma. METHODS: Cytologic diagnoses of lower tract urine cytology specimens with histology-proven prostatic adenocarcinoma from three institutions, from a period of over two decades, were reviewed. Clinicopathological parameters-tumor grade, stage, histologic features, and preanalytical factors-prostate-specific antigen (PSA) level and lesion size, were retrieved and compared with cytologic diagnoses. RESULTS: In total, 2115 urine cytology specimens from 1119 patients were retrieved. The atypia (or above/C3+) and suspicious (or above/C4+) rates were 19.48% and 3.36%. Bilobar and extracapsular involvement, lymphovascular invasion, Gleason score, and International Society of Urological Pathology grade were associated with a positive urine diagnosis (p < 0.05). The atypia (C3+) and suspicious (C4+) rates of urine cytology in patients with a PSA level of ≤4.0 ng/mL was paradoxically higher (p < 0.01), but PSA levels correlated positively with urine diagnosis at higher cutoffs (>10, >20, >50, >100 ng/mL). All these factors remained significant on multivariate analysis (p < 0.05), including a negative correlation with low-PSA (≤4.0 ng/mL, p = 0.001) and positive correlation with high-PSA (>20 ng/mL, p = 0.020). Lesion size and multifocality were not associated with urine cytology diagnosis (p > 0.05). CONCLUSION: Urine cytology showed low sensitivity in detection of prostatic adenocarcinoma. Detection rates were largely positively correlated with PSA levels but not for lesion size nor multifocality, limiting its clinical utility.

Interventions to improve timely cancer diagnosis: an integrative review.

Cancer will affect more than one in three U.S. residents in their lifetime, and although the diagnosis will be made efficiently in most of these cases, roughly one in five patients will experience a delayed or missed diagnosis. In this integrative review, we focus on missed opportunities in the diagnosis of breast, lung, and colorectal cancer in the ambulatory care environment. From a review of 493 publications, we summarize the current evidence regarding the contributing factors to missed or delayed cancer diagnosis in ambulatory care, as well as evidence to support possible strategies for intervention. Cancer diagnoses are made after follow-up of a positive screening test or an incidental finding, or most commonly, by following up and clarifying non-specific initial presentations to primary care. Breakdowns and delays are unacceptably common in each of these pathways, representing failures to follow-up on abnormal test results, incidental findings, non-specific symptoms, or consults. Interventions aimed at 'closing the loop' represent an opportunity to improve the timeliness of cancer diagnosis and reduce the harm from diagnostic errors. Improving patient engagement, using 'safety netting,' and taking advantage of the functionality offered through health information technology are all viable options to address these problems.

The Accuracy of Skin Cancer Detection Rates with the Implementation of Dermoscopy Among Dermatology Clinicians: A Scoping Review.

Objective: The goal is to determine if the implementation of dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians and identify the optimal training method for dermatology clinicians to become proficient in dermoscopy. Methods: A comprehensive search through the A.T. Still Memorial Library, including the electronic health databases PubMed, Scopus, UpToDate, and CINAHL, was performed. Google Scholar search results were sorted by relevance, and the first 30 pages were included within the search due to the large quantity of results. The search keywords included "skin cancer diagnosis," "accuracy," "detection," "dermoscopy," and "dermatologists." The search was performed in July 2023. The date limitations used within the search parameters ranged from 2017 to 2023 to review the past seven years of publications. The search evaluated reference lists and encompassed those that met the inclusion and exclusion criteria. Dermatologists, dermatology physician assistants, dermatology nurse practitioners, and primary care practitioners were eligible for inclusion. The search included literature from any country. The English language was the only language permitted within the search. Gray literature was included in the search using news, press release, and MedRxiv. Results: A total of 28 articles met the inclusion criteria. All of the articles included were from peer-reviewed sources and in the English language. The articles came from 10 different countries of origin and were published from 2017 to 2023. The main results of the scoping review discovered that the use of dermoscopy improves the accuracy of skin cancer diagnosis. The results also demonstrated that dermoscopy training is highly variable; multiple different types of diagnostic algorithms are used in the professional medical education systems of the 10 countries included within the scoping review. The dermoscopy training algorithms recommended include pattern analysis, 7-point checklist, Menzies method, Triage Amalgamated Dermoscopy Algorithm, Australasian College of Dermatology Dermoscopy Course, 3-point checklist, ABCD rule, Skin Imaging College of China, and no particular algorithm. Of these, the three most commonly recommended included the 7-point checklist, Menzies method, and pattern analysis. Conclusion: The results demonstrated that dermoscopy improves the accuracy of skin cancer diagnosis for dermatology clinicians and primary care providers. Key implications of these findings for practice include earlier skin cancer detection, which can lead to reduced rates of morbidity and mortality, reduced overall healthcare costs, reduced number of benign lesions biopsied, and improved patient outcomes.

Diagnostic process of cancer in a health system without gatekeeping: a single centre survey analysis.

INTRODUCTION: It is essential to increase the rates of early diagnosis in cancer control, and the diagnostic process needs to be improved to achieve this goal. Previous studies showed that in countries where there is a gatekeeping system, there might be a delay in cancer diagnosis. Our aim is to examine the process of cancer diagnosis in a healthcare system without gatekeeping. METHOD: A quantitative descriptive study has been conducted in various outpatient clinics of Pendik Training and Research Hospital, between 1 February and 31 May 2019, with individuals aged over 18 and diagnosed with cancer in the last six months. The data was collected through a questionnaire filled in by face-to-face interview method. Patient's socio-economic characteristics, their symptoms at the time of the diagnosis and the diagnosis process were questioned. RESULT: The median diagnostic interval was 30 days (min-max 1-365), and the median patient interval was 60 (1-600) days. Patients pointed out that the diagnostic tests, especially the pathology reporting process, caused the diagnostic interval to be prolonged. Of the patients, 84% (n 135) stated that they did not consider their symptoms as a sign of serious illness. The patient interval was shortest with symptoms of haematuria and haematochezia and longest with dysuria and change in bladder habit. DISCUSSION: The study examined the diagnosis process in our health system, where patients can apply for health services at any stage. The results showed that there were no superior outcomes to those observed in primary care-led health systems. Patients reported that waiting times for medical tests led to prolongation of the diagnosis time. Cancer awareness of patients should also be increased to shorten patient admission times.

Adoption, acceptance, and use of a decision support tool to promote timely investigations for cancer in primary care.

BACKGROUND: The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities. OBJECTIVES: Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer. METHODS: Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data. RESULTS: The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population. CONCLUSIONS: The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs.

Cancer diagnosis based on laser-induced breakdown spectroscopy with bagging-voting fusion model.

Advances in cancer diagnostics play a pivotal role in increasing early detection of cancer. Integrating laser-induced breakdown spectroscopy (LIBS) with machine learning algorithms has attracted wide interest in cancer diagnosis. However, using a single model`s efficacy is limited by algorithm principles, making it challenging to meet the comprehensive needs of cancer diagnosis. Here, we demonstrate a bagging-voting fusion (BVF) algorithm for the detection and identification of multiple types of cancer. In the BVF model of this paper, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), and random forest (RF) models, which have relatively small homogeneity to obtain more comprehensive decision boundaries, are fused at both the training and decision levels. LIBS spectral data was collected from four types of serum samples, including liver cancer, lung cancer, esophageal cancer, and healthy control. LIBS detection was conducted on the samples, which were directly dropped onto ordered microarray silicon substrates and dried. The results showed that the BVF model achieved an accuracy of 92.53 % and a recall of 92.92 % across the four types of serum, outperforming the best single machine-learning model (SVM: accuracy 75.86 %, recall 77.50 %). Moreover, the BVF model with manual line selection feature extraction required only 140 s for a single detection and identification. In conclusion, the aforementioned results demonstrated that LIBS with BVF has excellent performance in detecting a multitude of cancers, and is expected to provide a new method for efficient and accurate cancer diagnosis.

Transforming cancer detection and treatment with nanoflowers.

Nanoflowers, an innovative class of nanoparticles with a distinctive flower-like structure, have garnered significant interest for their straightforward synthesis, remarkable stability, and heightened efficiency. Nanoflowers demonstrate versatile applications, serving as highly sensitive biosensors for rapidly and accurately detecting conditions such as diabetes, Parkinson's, Alzheimer's, and foodborne infections. Nanoflowers, with their intricate structure, show significant potential for targeted drug delivery and site-specific action, while also exhibiting versatility in applications such as enzyme purification, water purification from dyes and heavy metals, and gas sensing through materials like nickel oxide. This review also addresses the structural characteristics, surface modification, and operational mechanisms of nanoflowers. The nanoflowers play a crucial role in preventing premature drug leakage from nanocarriers. Additionally, the nanoflowers contribute to averting systemic toxicity and suboptimal therapy efficiency caused by hypoxia in the tumor microenvironment during chemotherapy and photodynamic therapy. This review entails the role of nanoflowers in cancer diagnosis and treatment. In the imminent future, the nanoflowers system is poised to revolutionize as a smart material, leveraging its exceptional surface-to-volume ratio to significantly augment adsorption efficiency across its intricate petals. This review delves into the merits and drawbacks of nanoflowers, exploring synthesis techniques, types, and their evolving applications in cancer.

Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis.

BACKGROUND/OBJECTIVES: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. METHODS: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. RESULTS: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. CONCLUSIONS: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications.

Exploring the potential of myo-inositol in thyroid disease management: focus on thyroid cancer diagnosis and therapy.

Thyroid cancer (TC) is a malignancy that is increasing in prevalence on a global scale, necessitating the development of innovative approaches for both diagnosis and treatment. Myo-inositol (MI) plays a crucial role in a wide range of physiological and pathological functions within human cells. To date, studies have investigated the function of MI in thyroid physiology as well as its potential therapeutic benefits for hypothyroidism and autoimmune thyroiditis. However, research in the field of TC is very restricted. Metabolomics studies have highlighted the promising diagnostic capabilities of MI, recognizing it as a metabolic biomarker for identifying thyroid tumors. Furthermore, MI can influence therapeutic characteristics by modulating key cellular pathways involved in TC. This review evaluates the potential application of MI as a naturally occurring compound in the management of thyroid diseases, including hypothyroidism, autoimmune thyroiditis, and especially TC. The limited number of studies conducted in the field of TC emphasizes the critical need for future research to comprehend the multifaceted role of MI in TC. A significant amount of research and clinical trials is necessary to understand the role of MI in the pathology of TC, its diagnostic and therapeutic potential, and to pave the way for personalized medicine strategies in managing this intricate disease.

Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.