An easy-operation aptasensor for simultaneous detection of multiple tumor-associated exosomal proteins based on multicolor fluorescent DNA nanoassemblies.

As a promising liquid biopsy biomarker, exosomes have demonstrated great potential and advantages in the noninvasive tumor diagnosis. However, an accurate and sensitive method for tumors-associated exosomes detection is scarce. Herein, we presented an easy-operation aptasensor which simultaneously detect multiple exosomal proteins by using multicolor fluorescent DNA nanoassemblies (FDNs) and CD63 aptamer-modified magnetic beads (MNPs-AptCD63). In this system, the FDNs were firstly constructed by encapsulating different quantum dots (QDs) into rolling circle amplification (RCA) products that contained different aptamer sequences. Thus, the FDNs could selectively recognize the different exosomal proteins captured by the MNPs-AptCD63, and achieve the multiplex and sensitive detection according to the fluorescence of QDs. Benefiting from the signal amplification capacity and high selectivity of FDNs, this aptasensor not only could detect exosomes as low as 650 particles/µL, but also showed accurate analysis in clinical samples. In addition, we can also achieve point-of-care testing (POCT) due to the simple analysis steps and naked-eye observable fluorescence of QDs under the ultraviolet irradiation. We believe that our aptasensor could provide a promising platform for exosomes-based personalized diagnosis and precise monitoring of human health.

Efficiency of electrochemical immuno- vs. apta(geno)sensors for multiple cancer biomarkers detection.

The interest in biosensors technology has been constantly growing over the last few years. It is still the biggest challenge to design biosensors able to detect two or more analytes in a single measurement. Electrochemical methods are frequently used for this purpose, mainly due to the possibility of applying two or more different redox labels characterized by independent and distinguished electrochemical signals. In addition to antibodies, nucleic acids (aptamers) have been increasingly used as bioreceptors in the construction of such sensors. Within this review paper, we have collected the examples of electrochemical immuno- and geno(apta)sensors for simultaneous detection of multiple analytes. Based on many published literature examples, we have emphasized the recent application of multiplexed platforms for detection of cancer biomarkers. It has allowed us to compare the progress in design strategies, including novel nanomaterials and amplification of signals, to get as low as possible limits of detection. We have focused on multi-electrode and multi-label strategies based on redox-active labels, such as ferrocene, anthraquinone, methylene blue, thionine, hemin and quantum dots, or metal ions such as Ag+, Pb2+, Cd2+, Zn2+, Cu2+ and others. We have finally discussed the possible way of development, challenges and prospects in the area of multianalyte electrochemical immuno- and geno(apta)sensors.

Multiplexed Immunophenotyping of Lymphoma Tissue Samples.

High-plex imaging techniques enable the detection and quantification of a multitude of markers in tissue biopsies at single-cell or near-single-cell resolution. In lymphoma, this can facilitate the detection and characterization of cellular phenotypes and interactions, describing both tumor and microenvironmental cells. In combination with other techniques, high-plex imaging allows the investigation of biological mechanisms and clinically relevant biomarkers. CO-Detection by IndEXing (CODEX), one of such techniques, is based on antibodies labeled with unique DNA oligonucleotides that can be visualized by complementary reporter oligonucleotides coupled to a fluorophore. Here, we provide an overview of the key steps of a CODEX-based project, including (1) antibody panel design, (2) cohort selection, (3) staining and imaging, (4) data analysis. By sharing our CODEX protocol and our experience with FFPE tissue samples, we aim to encourage wider use of this powerful technique in lymphoma research and improve insight into cellular composition and spatial dynamics for improved diagnostics and therapy.

Detection of Circulating Tumor DNA in Lymphoma Patients.

Liquid biopsy has become an opportunity in lymphoma diagnostics, since plasma circulating tumor DNA (ctDNA) is an easily accessible source of tumor DNA, which can be complementary to tissue biopsy. Through ctDNA, lymphomas can be molecularly characterized, tumor clonal evolution can be tracked, while monitoring minimal residual diseases during and after therapy. Here, we describe the methodology of cancer personalized profiling by deep sequencing (CAPP-seq) that we use for ctDNA qualification and quantification.

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.

Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery.

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

Electrochemical biosensors for early detection of breast cancer.

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

Circular RNAs in laryngeal cancer.

Laryngeal cancer remains a significant global health concern, with poor prognosis for advanced-stage disease highlighting the need for novel diagnostic, prognostic, and therapeutic approaches. Circular RNAs (circRNAs), a class of covalently closed non-coding RNAs, have emerged as important regulators of gene expression and cellular processes in various cancers, including laryngeal cancer. This review summarizes the current understanding of circRNAs in laryngeal cancer, covering their biogenesis, regulatory mechanisms, and potential clinical applications. We explore the diverse functions of circRNAs, including their roles as miRNA sponges, protein interactors, and direct mRNA regulators, and their influence on key cellular processes such as proliferation, invasion, and metastasis. The review highlights promising circRNAs as diagnostic and prognostic biomarkers, as well as potential therapeutic targets. We also outline current strategies for circRNA modulation, including suppression techniques like RNA interference and CRISPR/Cas systems, and overexpression methods using vectors and synthetic circRNAs.

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review).

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer­specific biomarkers. The availability of high­throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation­based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease­free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo­ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease­free survival of patients with breast cancer through efficient medicine.

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor.

BACKGROUND: Lactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. METHODS: Gene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. RESULTS: The immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor's development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. CONCLUSION: This study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

MRPL13 is a metastatic and prognostic marker of breast cancer: A silico analysis accompanied with experimental validation.

BACKGROUND: Although progress has been made in accurate diagnosis and targeted treatments, breast cancer (BC) patients with metastasis still present a grim prognosis. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, there is an urgent need to find new metastatic and prognostic biomarkers for BC patients. METHODS: We were dedicated to identifying genes linked to metastasis and prognosis in breast cancer through a combination of in silico analysis and experimental validation. RESULTS: A total of 25 overlap differentially expressed genes were identified. Ten hub genes (namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA) were identified and confirmed. MRPL13, TCEB1 and GOLT1B were shown to be associated with the worse overall survival (OS) and were optionally chosen for further verification by western blot. Only MRPL13 was found associated with cell invasion, and the expression of MRPL13 in metastatic BC was significantly higher than in primary BC. CONCLUSION: We proposed MRPL13 could be a potential novel biomarker for the metastasis and prognosis of breast cancer.

Expression and Significance of the Circular RNA circ_0001438 in the Development of Gastric Cancer.

Gastric cancer has become a great challenge to human health in the world. We studied the expression and role of the circular RNA 0001438 (circ_0001438) with the aim of finding a biomarker to assess the prognosis of gastric cancer. Through a polymerase chain reaction, circ_0001438 expression in gastric cancer was detected. Chi-square test, multi-factor Cox regression, and Kaplan-Meier analyses were used to determine the association between circ_0001438 and the patients' clinical condition and prognosis. Using the luciferase reporter gene system, the interaction between circ_0001438 and miR-1290 was analyzed, and the regulatory impact of circ_0001438/miR-1290 on the activity of gastric cancer cells was examined flowing the Transwell assay and CCK8 assay. In gastric cancer tissues and cells, circ_0001438 expression was downregulated, and miR-1290 expression was upregulated and the two were negatively correlated. miR-1290 inhibitors were transfected and significantly increased the activity of circ_0001438 luciferase, while miR-1290 mimics decreased the activity. Overexpression of circ_0001438 decreased miR-1290 expression and inhibited the proliferation and metastasis of gastric cancer cells, which was reversed when miR-1290 mimics were transfected. Additionally, there was a correlation between circ_0001438 expression and lymph node metastases, tumor size, and TNM stage of gastric cancer. Low circ_0001438 expression predicts poor prognosis of gastric cancer patients. circ_0001438 is a biomarker for tumor development and clinical prognosis in gastric cancer. It works by downregulating miR-1290 to control the activity of gastric cancer cells.

Analysis of the function, mechanism and clinical application prospect of TRPS1, a new marker for breast cancer.

It has been discovered that Trichorhinophalangeal Syndrome-1 (TRPS1), a novel member of the GATA transcription factor family, participates in both normal physiological processes and the development of numerous diseases. Recently, TRPS1 has been identified as a new biomarker to aid in cancer diagnosis and is very common in breast cancer (BC), especially in triple-negative breast cancer (TNBC). In this review, we discussed the structure and function of TRPS1 in various normal cells, focused on its role in tumorigenesis and tumor development, and summarize the research status of TRPS1 in the occurrence and development of BC. We also analyzed the potential use of TRPS1 in guiding clinically personalized precision treatment and the development of targeted drugs.

Biological functions and molecular mechanisms of exosome-derived circular RNAs and their clinical implications in digestive malignancies: the vintage in the bottle.

BACKGROUND: Circular RNAs (circRNAs) are identified as a novel family of endogenous RNA molecules through 'back-splicing' and covalently linked at the 5' and 3' ends. Emerging researches have demonstrated circRNAs are stable and abundant in exosomes called exosomal circRNAs (exo-circRNA). MATERIALS AND METHODS: We searched recent studies and references to summary the research progress of exosomal circRNA. RESULTS: Recent studies have revealed that exosome-derived circRNAs including exo-CDR1as, exo-circRanGAP1, exo-circIAR play vital roles in cell proliferation and apoptosis, epithelial mesenchymal transition, invasion and metastasis, angiogenesis, immune evasion, cellular crosstalk, cancer cachexia through a variety of biological mechanisms, such as serving as microRNA sponges, interacting with RNA binding proteins, regulating gene transcription, N6-Methyladenosine modification and so on. Due to their characteristics of origin, structure, properties and biological functions, exo-circRNAs are expected to apply in precious diagnosis and prognostic indicators, improving drug and radiation resistance and sensitivity, becoming biological therapeutic targets. CONCLUSION: We summarize the update of digestive malignancies associated exo-circRNAs in biogenesis, biological functions, molecular mechanisms, clinical implications, potential applications and experimental technique in order to effectively promote transformation and application in the future.

Reflection mode polarimetry guides laser mass spectrometry to diagnostically important regions of human breast cancer tissue.

To enhance the clinical utility of mass spectrometry (MS), lengthy dwell times on less informative regions of patient specimens (e.g., adipose tissue in breast) must be minimized. Additionally, a promising variant of MS known as picosecond infrared laser MS (PIRL-MS) faces further challenges, namely, lipid contamination when probing adipose tissue. Here we demonstrate on several thick non-sectioned resected human breast specimens (healthy and malignant) that reflection-mode polarimetric imaging can robustly guide PIRL-MS toward regions devoid of significant fat content to (1) avoid signal contamination and (2) shorten overall MS analysis times. Through polarimetric targeting of non-fat regions, PIRL-MS sampling revealed feature-rich spectral signatures including several known breast cancer markers. Polarimetric guidance mapping was enabled by circular degree-of-polarization (DOP) imaging via both Stokes and Mueller matrix polarimetry. These results suggest a potential synergistic hybrid approach employing polarimetry as a wide-field-imaging guidance tool to optimize efficient probing of tissue molecular content using MS.

The signature of SARS-CoV-2-related genes predicts the immune therapeutic response and prognosis in breast cancer.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an exceptionally contagious single-stranded RNA virus with a strong positive contagion. The COVID-19 pandemic refers to the swift worldwide dissemination of SARS-CoV-2 infection, which began in late 2019. The COVID-19 epidemic has disrupted many cancer treatments. A few reports indicate that the prevalence of SARS-CoV-2 has disrupted the treatment of breast cancer patients (BCs). However, the role of SARS-CoV-2 in the occurrence and prognosis of BC has not been elucidated. Here, we applied bioinformatics to construct a prognostic signature of SARS-CoV-2-related genes (SCRGs). Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to extract co-expressed genes of differentially expressed genes (DEGs) in breast cancer and SCRGs. Then, we utilized the least absolute shrinkage and selection operator (LASSO) algorithm and univariate regression analysis to screen out three hub genes (DCTPP1, CLIP4 and ANO6) and constructed a risk score model. We further analyzed tumor immune invasion, HLA-related genes, immune checkpoint inhibitors (ICIs), and sensitivity to anticancer drugs in different SARS-CoV-2 related risk subgroups. In addition, we have developed a nomination map to expand clinical applicability. The results of our study indicate that BCs with a high-risk score are linked to negative outcomes, lower immune scores, and reduced responsiveness to anticancer medications. This suggests that the SARS-CoV-2 related signature could be used to guide prognosis assessment and treatment decisions for BCs.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.

BACKGROUND: Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. METHODS: Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. RESULTS: Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones. CONCLUSIONS: Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic. CLINICALTRIALS: gov/ct2/show/NCT05432128 .

Circular RNA landscape in extracellular vesicles from human biofluids.

BACKGROUND: Circular RNAs (circRNAs) have emerged as a prominent class of covalently closed single-stranded RNA molecules that exhibit tissue-specific expression and potential as biomarkers in extracellular vesicles (EVs) derived from liquid biopsies. Still, their characteristics and applications in EVs remain to be unveiled. METHODS: We performed a comprehensive analysis of EV-derived circRNAs (EV-circRNAs) using transcriptomics data obtained from 1082 human body fluids, including plasma, urine, cerebrospinal fluid (CSF), and bile. Our validation strategy utilized RT-qPCR and RNA immunoprecipitation assays, complemented by computational techniques for analyzing EV-circRNA features and RNA-binding protein interactions. RESULTS: We identified 136,327 EV-circRNAs from various human body fluids. Significantly, a considerable amount of circRNAs with a high back-splicing ratio are highly enriched in EVs compared to linear RNAs. Additionally, we discovered brain-specific circRNAs enriched in plasma EVs and cancer-associated EV-circRNAs linked to clinical outcomes. Moreover, we demonstrated that EV-circRNAs have the potential to serve as biomarkers for evaluating immunotherapy efficacy in non-small cell lung cancer (NSCLC). Importantly, we identified the involvement of RBPs, particularly YBX1, in the sorting mechanism of circRNAs into EVs. CONCLUSIONS: This study unveils the extensive repertoire of EV-circRNAs across human biofluids, offering insights into their potential as disease biomarkers and their mechanistic roles within EVs. The identification of specific circRNAs and the elucidation of RBP-mediated sorting mechanisms open new avenues for the clinical application of EV-circRNAs in disease diagnostics and therapeutics.

Exploring the gut microbiome's role in colorectal cancer: diagnostic and prognostic implications.

The intricate interplay between the gut microbiome and colorectal cancer (CRC) presents novel avenues for early diagnosis and prognosis, crucial for improving patient outcomes. This comprehensive review synthesizes current findings on the gut microbiome's contribution to CRC pathogenesis, highlighting its potential as a biomarker for non-invasive CRC screening strategies. We explore the mechanisms through which the microbiome influences CRC, including its roles in inflammation, metabolism, and immune response modulation. Furthermore, we assess the viability of microbial signatures as predictive tools for CRC prognosis, offering insights into personalized treatment approaches. Our analysis underscores the necessity for advanced metagenomic studies to elucidate the complex microbiome-CRC nexus, aiming to refine diagnostic accuracy and prognostic assessment in clinical settings. This review propels forward the understanding of the microbiome's diagnostic and prognostic capabilities, paving the way for microbiome-based interventions in CRC management.

Nasopharyngeal carcinoma-associated inflammatory cytokines: ongoing biomarkers.

Nasopharyngeal carcinoma (NPC) is a neoplasm related to inflammation; the expression of cytokines, such as CCL3, CCL4, CCL20, IL-1α, IL-1ß, IL-6, IL-8, and IL-10, among others, is presumed to be associated with NPC occurrence and development. Therefore, the circulating levels of these cytokines may be potential biomarkers for assessing tumor aggressiveness, exploring cellular interactions, and monitoring tumor therapeutic responses. Numerous scholars have comprehensively explored the putative mechanisms through which these inflammatory factors affect NPC progression and therapeutic responses. Moreover, investigations have focused on elucidating the correlation between the systemic levels of these cytokines and the incidence and prognosis of NPC. This comprehensive review aims to delineate the advancements in research concerning the relationship between inflammatory factors and NPC while considering their prospective roles as novel prognostic and predictive biomarkers in the context of NPC.