[Interpretation on the update of 2022 WHO classification of neuroendocrine tumors].

The 5th edition of the WHO Endocrine and Neuroendocrine Oncology Blue Book, released in 2022, contained some changes in the classification of neuroendocrine tumors. A brief summary of the main changes has been provided in this section. Mainly summarized as changes in naming, differentiation and classification of neuroendocrine tumors, and tumor grading systems related to anatomical locations, morphological characteristics of neuroendocrine tumors in different locations, auxiliary diagnostic and prognostic/therapeutic markers, differential diagnosis and diagnostic difficulties of neuroendocrine tumors.

Exploring the role of cellular senescence in cancer prognosis across multiple tumor types.

Background: Cellular senescence is a common biological process with a well-established link to cancer. However, the impact of cellular senescence on tumor progression remains unclear. To investigate this relationship, we utilized transcriptomic data from a senescence gene set to explore the connection between senescence and cancer prognosis. Methods: We developed the senescence score by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. We obtained transcriptomic information of the senescence gene set from The Cancer Genome Atlas (TCGA) program. Additionally, we created a nomogram that integrates these senescence scores with clinical characteristics, providing a more comprehensive tool for prognosis evaluation. Results: We calculated the senescence score based on the expression level of 42 senescence-related genes. We established the nomogram based on the senescence score and clinical characteristics. The senescence score showed a positive correlation with epithelial-to-mesenchymal transition, cell cycle, and glycolysis, and a negative correlation with autophagy. Furthermore, we carried out Gene Ontology (GO) analysis to explore the signaling pathways and biological process in different senescence score groups. Conclusions: The senescence score, a novel tool constructed in this study, shows promise in predicting survival outcomes across various cancer types. These findings not only highlight the complex interplay between senescence and cancer but also indicate that cellular senescence might serve as a biomarker for tumor prognosis.

How receptor tyrosine kinase-like orphan receptor 1 meets its partners in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in western societies, recognized by clinical and molecular heterogeneity. Despite the success of targeted therapies, acquired resistance remains a challenge for relapsed and refractory CLL, as a consequence of mutations in the target or the upregulation of other survival pathways leading to the progression of the disease. Research on proteins that can trigger such pathways may define novel therapies for a successful outcome in CLL such as the receptor tyrosine kinase-like orphan receptor 1 (ROR1). ROR1 is a signaling receptor for Wnt5a, with an important role during embryogenesis. The aberrant expression on CLL cells and several types of tumors, is involved in cell proliferation, survival, migration as well as drug resistance. Antibody-based immunotherapies and small-molecule compounds emerged to target ROR1 in preclinical and clinical studies. Efforts have been made to identify new prognostic markers having predictive value to refine and increase the detection and management of CLL. ROR1 can be considered as an attractive target for CLL diagnosis, prognosis, and treatment. It can be clinically effective alone and/or in combination with current approved agents. In this review, we summarize the scientific achievements in targeting ROR1 for CLL diagnosis, prognosis, and treatment.

Labeled-free quantitative proteomic analysis of cervical squamous cell carcinoma identifies potential protein biomarkers.

Background: Cervical cancer remains a prevalent cancer among women, and reliance on surgical and radio-chemical therapies can irreversibly affect patients' life span and quality of life. Thus, early diagnosis and further exploration into the pathogenesis of cervical cancer are crucial. Mass spectrometry technology is widely applied in clinical practice and can be used to further investigate the protein alterations during the onset of cervical cancer. Methods: Employing labeled-free quantitative proteomics technology and bioinformatics tools, we analyzed and compared the differential protein expression profiles between normal cervical squamous cell tissues and cervical squamous cell cancer tissues. GEPIA is an online website for analyzing the RNA sequencing expression data of tumor and normal tissue data from the TCGA and the GTEx databases. This approach aided in identifying qualitative and quantitative changes in key proteins related to the progression of cervical cancer. Results: Compared to normal samples, a total of 562 differentially expressed proteins were identified in cervical cancer samples, including 340 up-regulated and 222 down-regulated proteins. Gene ontology functional annotation, and KEGG pathway, and enrichment analysis revealed that the differentially expressed proteins mainly participated in metabolic pathways, spliceosomes, regulation of the actin cytoskeleton, and focal adhesion signaling pathways. Specifically, desmoplakin (DSP), protein phosphatase 1, regulatory (inhibitor) subunit 13 like (PPP1R13L) and ANXA8 may be involved in cervical tumorigenesis by inhibiting apoptotic signal transmission. Moreover, we used GEPIA database to validate the expression of DSP, PPP1R13L and ANXA8 in human cancers and normal cervix. Conclusion: In this study, we identified 562 differentially expressed proteins, and there were three proteins expressed higher in the cervical cancer tissues. The functions and signaling pathways of these differentially expressed proteins lay a theoretical foundation for elucidating the molecular mechanisms of cervical cancer.

The rapidly changing field of predictive biomarkers of non-small cell lung cancer.

Lung cancer is a leading cause of cancer-related death worldwide in both men and women, however mortality in the US and EU are recently declining in parallel with the gradual cut of smoking prevalence. Consequently, the relative frequency of adenocarcinoma increased while that of squamous and small cell carcinomas declined. During the last two decades a plethora of targeted drug therapies have appeared for the treatment of metastasizing non-small cell lung carcinomas (NSCLC). Personalized oncology aims to precisely match patients to treatments with the highest potential of success. Extensive research is done to introduce biomarkers which can predict the effectiveness of a specific targeted therapeutic approach. The EGFR signaling pathway includes several sufficient targets for the treatment of human cancers including NSCLC. Lung adenocarcinoma may harbor both activating and resistance mutations of the EGFR gene, and further, mutations of KRAS and BRAF oncogenes. Less frequent but targetable genetic alterations include ALK, ROS1, RET gene rearrangements, and various alterations of MET proto-oncogene. In addition, the importance of anti-tumor immunity and of tumor microenvironment has become evident recently. Accumulation of mutations generally trigger tumor specific immune defense, but immune protection may be upregulated as an aggressive feature. The blockade of immune checkpoints results in potential reactivation of tumor cell killing and induces significant tumor regression in various tumor types, such as lung carcinoma. Therapeutic responses to anti PD1-PD-L1 treatment may correlate with the expression of PD-L1 by tumor cells. Due to the wide range of diagnostic and predictive features in lung cancer a plenty of tests are required from a single small biopsy or cytology specimen, which is challenged by major issues of sample quantity and quality. Thus, the efficacy of biomarker testing should be warranted by standardized policy and optimal material usage. In this review we aim to discuss major targeted therapy-related biomarkers in NSCLC and testing possibilities comprehensively.

New challenges in hepatocellular carcinoma: A role for PIWI-interacting RNAs?

Hepatocellular carcinoma (HCC) is the most common and deadliest subtype of liver cancer worldwide and, therefore, poses an enormous threat to global health. Understanding the molecular mechanisms underlying the development and progression of HCC is central to improving our clinical approaches. PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that bind to PIWI family proteins to regulate gene expression at transcriptional and post-transcriptional levels. A growing body of work shows that the dysregulation of piRNAs plays a crucial role in the progression of various human cancers. In this editorial, we report on the current knowledge of HCC-associated piRNAs and their potential clinical utility. Based on the editorial by Papadopoulos and Trifylli, on the role and clinical evaluation of exosomal circular RNAs in HCC, we highlight this other emerging class of non-coding RNAs.

Prognostic relevance of high expression of kynurenine pathway markers in glioblastoma.

Glioblastoma (GBM) continues to exhibit a discouraging survival rate despite extensive research into new treatments. One factor contributing to its poor prognosis is the tumor's immunosuppressive microenvironment, in which the kynurenine pathway (KP) plays a significant role. This study aimed to explore how KP impacts the survival of newly diagnosed GBM patients. We examined tissue samples from 108 GBM patients to assess the expression levels of key KP markers-tryptophan 2,3-dioxygenase (TDO2), indoleamine 2,3-dioxygenase (IDO1/2), and the aryl hydrocarbon receptor (AhR). Using immunohistochemistry and QuPath software, three tumor cores were analyzed per patient to evaluate KP marker expression. Kaplan-Meier survival analysis and stepwise multivariate Cox regression were used to determine the effect of these markers on patient survival. Results showed that patients with high expression of TDO2, IDO1/2, and AhR had significantly shorter survival times. This finding held true even when controlling for other known prognostic variables, with a hazard ratio of 3.393 for IDO1, 2.775 for IDO2, 1.891 for TDO2, and 1.902 for AhR. We suggest that KP markers could serve as useful tools for patient stratification, potentially guiding future immunomodulating trials and personalized treatment approaches for GBM patients.

Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis.

OBJECTIVE: To conduct a systematic review and meta-analysis of case-control and cohort human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on esophageal cancer (EC), cancer of the gastroesophageal junction (GEJ), and gastric cancer (GC) in blood and tissue. BACKGROUND: Upper gastrointestinal cancers (UGC), predominantly EC, GEJ, and GC, are malignant tumour types with high morbidity and mortality rates. Numerous studies have focused on metabolomic profiling of UGC in recent years. In this systematic review and meta-analysis, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with EC, GEJ and GC. METHODS: Following the PRISMA procedure, a systematic search of four databases (Embase, PubMed, MEDLINE, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of EC, GEJ and GC was conducted and registered at PROSPERO (CRD42023486631). The Newcastle-Ottawa Scale (NOS) was used to benchmark the risk of bias for case-controlled and cohort studies. QUADOMICS, an adaptation of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy) tool, was used to rate diagnostic accuracy studies. Original articles comparing metabolite patterns between patients with and without UGC were included. Two investigators independently completed title and abstract screening, data extraction, and quality evaluation. Meta-analysis was conducted whenever possible. We used a random effects model to investigate the association between metabolite levels and UGC. RESULTS: A total of 66 original studies involving 7267 patients that met the required criteria were included for review. 169 metabolites were differentially distributed in patients with UGC compared to healthy patients among 44 GC, 9 GEJ, and 25 EC studies including metabolites involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and lipid metabolism. Phosphatidylcholines, eicosanoids, and adenosine triphosphate were among the most frequently reported lipids and metabolites of cellular respiration, while BCAA, lysine, and asparagine were among the most commonly reported amino acids. Previously identified lipid metabolites included saturated and unsaturated free fatty acids and ketones. However, the key findings across studies have been inconsistent, possibly due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group. CONCLUSION: Thus far, metabolomic studies have provided new opportunities for screening, etiological factors, and biomarkers for UGC, supporting the potential of applying metabolomic profiling in early cancer diagnosis. According to the results of our meta-analysis especially BCAA and TMAO as well as certain phosphatidylcholines should be implicated into the diagnostic procedure of patients with UGC. We envision that metabolomics will significantly enhance our understanding of the carcinogenesis and progression process of UGC and may eventually facilitate precise oncological and patient-tailored management of UGC.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Advancing neoadjuvant therapies in resectable non-small cell lung cancer: implications for novel treatment strategies and biomarker discovery.

The delivery of neoadjuvant and perioperative therapies for non-small cell lung cancer has been radically altered by significant advances and by the incorporation of targeted therapies as well as immune checkpoint inhibitors alone or alongside conventional chemotherapy. This evolution has been particularly notable in the incorporation of immunotherapy and targeted therapy into the treatment of resectable NSCLC, where recent FDA approvals of drugs such as nivolumab and pembrolizumab, in combination with platinum doublet chemotherapy, have led to considerable improvements in pathological complete response rates and the potential for enhanced long-term survival outcomes. This review emphasizes the growing importance of biomarkers in optimizing treatment selection and explores the impact of emerging studies that challenge existing treatment paradigms and investigate novel therapeutic combinations poised to redefine standard of care practices. Furthermore, the discussion extends to the unmet needs within perioperative treatment assessment and prognostication, highlighting the prospective value of biomarkers in evaluating treatment responses and prognosis.

Cystatin SN (CST1) Is a Poor Independent Prognostic Biomarker for Gastrointestinal Diffuse Large B-Cell Lymphoma.

BACKGROUND Gastrointestinal diffuse large B-cell lymphoma (GI-DLBCL) is the most common histological subtype of extra-nodal DLBCL, but the risk factors, prognostic biomarkers, histopathological classifications, and treatment strategies have not had significant progress. Emerging evidence shows that cystatin SN (CST1) is involved in tumor progression in several cancer types, but its role in GI-DLBCL has not been revealed. MATERIAL AND METHODS We established a cohort consisting of 84 patients with GI-DLBCL who underwent surgical resection. The expression of CST1 in the cohort was investigated by immunohistochemistry, which divided the patients into subgroups with low or high expression of CST1. Moreover, the CST1 expression in GI-DLBCL tissues or adjacent GI tissues were compared with RT-qPCR. The correlation between CST1 expression and clinicopathological factors was analyzed with the chi-square test. The prognostic significance of CST1 was estimated by univariate and multivariate analysis, and statistical significance was analyzed with the log-rank test. RESULTS CST1 was aberrantly upregulated in GI-DLBCL tissues compared with in non-tumor GI tissues. High expression of CST1 indicated poor prognosis of GI-DLBCL (P=0.012), and CST1 can be regarded as an independent prognostic biomarker of GI-DLBCL (hazard ratio=3.07). In our study, serum lactate dehydrogenase (P=0.002), performance status (P=0.003), Lugano stage (P=0.002), and International Prognostic Index (P=0.001) were also prognostic factors of GI-DLBCL. CONCLUSIONS CST1 is an independent prognostic biomarker of GI-DLBCL, indicating unfavorable prognosis. Our results suggested that CST1 detection can be a promising method to stratify high-risk patients and guide individual treatment.

Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue.

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope's detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

High Caveolin-1 mRNA expression in triple-negative breast cancer is associated with an aggressive tumor microenvironment, chemoresistance, and poor clinical outcome.

BACKGROUND: Currently, there are few treatment-predictive and prognostic biomarkers in triple-negative breast cancer (TNBC). Caveolin-1 (CAV1) is linked to chemoresistance and several important processes involved in tumor progression and metastasis, such as epithelial-mesenchymal transition (EMT). Herein, we report that high CAV1 gene expression is an independent factor of poor prognosis in TNBC. METHODS: CAV1 gene expression was compared across different molecular features (e.g., PAM50 subtypes). CAV1 expression was assessed in relation to clinical outcomes using Cox regression adjusted for clinicopathological predictors. Differential gene expression and gene set enrichment analyses were applied to compare high- and low-expressing CAV1 tumors. Tumor microenvironment composition of high- and low-expressing CAV1 tumors was estimated using ECOTYPER. Tumor tissue microarrays were used to evaluate CAV1 protein levels in stromal and malignant cells. RESULTS: In the SCAN-B (n = 525) and GSE31519 (n = 327) cohorts, patients with CAV1-high tumors had an increased incidence of early recurrence adjusted HR 1.78 (95% CI 1.12-2.81) and 2.20 (95% CI 1.39-3.47), respectively. In further analysis, high CAV1 gene expression was associated with a molecular profile indicating altered metabolism, neovascularization, chemoresistance, EMT, suppressed immune response, and active tumor microenvironment. Protein levels of CAV1 in malignant and stromal cells were not correlated with CAV1 gene expression. CONCLUSION: CAV1 gene expression in TNBC is a biomarker that merits further investigation in clinical trials and as a therapeutic target.

KLK7 expression in human tumors: a tissue microarray study on 13,447 tumors.

BACKGROUND: Kallikrein-related peptidase 7 (KLK7) is a chymotrypsin-like serine protease which is essential for the desquamation of corneocytes and thus plays a pivotal role in maintaining skin homeostasis. In cancer, KLK7 overexpression was suggested to represent a route for metastasis through cleavage of cell junction and extracellular matrix proteins of cancer cells. METHODS: To comprehensively determine KLK7 protein expression in normal and neoplastic tissues, a tissue microarray containing 13,447 samples from 147 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. RESULTS: KLK7 positivity was found in 64 of 147 tumor categories, including 17 tumor categories with at least one strongly positive case. The highest rate of KLK7 positivity was found in squamous cell carcinomas from various sites of origin (positive in 18.1%-63.8%), ovarian and endometrium cancers (4.8%-56.2%), salivary gland tumors (4.8%-13.7%), bilio-pancreatic adenocarcinomas (20.0%-40.4%), and adenocarcinomas of the upper gastrointestinal tract (3.3%-12.5%). KLK7 positivity was linked to nodal metastasis (p = 0.0005), blood vessel infiltration (p = 0.0037), and lymph vessel infiltration (p < 0.0001) in colorectal adenocarcinoma, nodal metastasis in hepatocellular carcinoma (p = 0.0382), advanced pathological tumor stage in papillary thyroid cancer (p = 0.0132), and low grade of malignancy in a cohort of 719 squamous cell carcinomas from 11 different sites of origin (p < 0.0001). CONCLUSIONS: These data provide a comprehensive overview on KLK7 expression in normal and neoplastic human tissues. The prognostic relevance of KLK7 expression and the possible role of KLK7 as a drug target need to be further investigated.

Functions of methyltransferase-like 3 in breast cancer: pathogenesis, drug resistance, and therapeutic target.

Breast cancer (BC) is a highly prevalent malignancy worldwide, with complex pathogenesis and treatment challenges. Research reveals that methyltransferase-like 3 (METTL3) is widely involved in the pathogenesis of several tumors through methylation of its target RNAs, and its role and mechanisms in BC are also extensively studied. In this review, we aim to provide a comprehensive interpretation of available studies and elucidate the relationship between METTL3 and BC. This review suggests that high levels of METTL3 are associated with the pathogenesis, poor prognosis, and drug resistance of BC, suggesting METTL3 as a potential diagnostic or prognostic biomarker and therapeutic target. Collectively, this review provides a comprehensive understanding of how METTL3 functions through RNA methylation, which provides a valuable reference for future fundamental studies and clinical applications.

Lectin microarray based glycan profiling of exosomes for dynamic monitoring of colorectal cancer progression.

BACKGROUND: Exosomes, as emerging biomarkers in liquid biopsies in recent years, offer profound insights into cancer diagnostics due to their unique molecular signatures. The glycosylation profiles of exosomes have emerged as potential biomarkers, offering a novel and less invasive method for cancer diagnosis and monitoring. Colorectal cancer (CRC) represents a substantial global health challenge and burden. Thus there is a great need for the aberrant glycosylation patterns on the surface of CRC cell-derived exosomes, proposing them as potential biomarkers for tumor characterization. RESULTS: The interactions of 27 lectins with exosomes from three CRC cell lines (SW480, SW620, HCT116) and one normal colon epithelial cell line (NCM460) have been analyzed by the lectin microarray. The result indicates that Ulex Europaeus Agglutinin I (UEA-I) exhibits high affinity and specificity towards exosomes derived from SW480 cells. The expression of glycosylation related genes within cells has been analyzed by high-throughput quantitative polymerase chain reaction (HT-qPCR). The experimental result of HT-qPCR is consistent with that of lectin microarray. Moreover, the limit of detection (LOD) of UEA-I microarray is calculated to be as low as 2.7 × 105 extracellular vehicles (EVs) mL-1 (three times standard deviation (3σ) of blank sample). The UEA-I microarray has been successfully utilized to dynamically monitor the progression of tumors in mice-bearing SW480 CRC subtype, applicable in tumor sizes ranging from 2 mm to 20 mm in diameter. SIGNIFICANCE: The results reveal that glycan expression pattern of exosome is linked to specific CRC subtypes, and regulated by glycosyltransferase and glycosidase genes of mother cells. Our findings illuminate the potential of glycosylation molecules on the surface of exosomes as reliable biomarkers for diagnosis of tumor at early stage and monitoring of cancer progression.

Concomitant decrease of E- and A-FABP expression predicts worse survival in urothelial bladder cancer patients.

Non-muscle invasive bladder cancers (NMIBC) pTa-pT1 are depicted by a high risk of recurrence and/or progression with an unpredictable clinical evolution. Our aim was to identify, from the original resection specimen, tumors that will progress to better manage patients. We previously showed that A-FABP (Adipocyte- Fatty Acid Binding Protein) loss predicted NMIBC progression. Here we determined by immunohistochemistry the prognostic value of E-FABP (Epidermal-Fatty Acid Binding Protein) expression in 210 tumors (80 pTa, 75 pT1, 55 pT2-T4). Thus, E-FABP low expression was correlated with a high grade/stage, the presence of metastatic lymph nodes, and visceral metastases (p < 0.001). Unlike A-FABP in NMIBC, E-FABP low expression was not associated with RFS or PFS in Kaplan-Meier analysis. But patients of the overall cohort with a high E-FABP expression had a longer mOS (53.8 months vs. 29.3 months, p = 0.029). The immunohistochemical analysis on the same NMIBC tissue sections revealed that when A-FABP is absent, a high E-FABP expression is detected. E-FABP could compensate A-FABP loss. Interestingly, patients, whose original tumor presents both low E-FABP and negative A-FABP, had the worse survival, those maintaining the expression of both markers had better survival. To conclude, the combined evaluation of A- and E-FABP expression allowed to stratify patients with urothelial carcinoma for optimizing treatment and follow-up.