Cancer Diagnostic Biomarkers Research Articles

Exploration of PVT1 as a biomarker in prostate cancer.

Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.

Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125

Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments. However, they require more sophisticated methods of analysis due to the potential influence of pathological patients in raw clinical datasets. In this paper we develop a novel convolutional neural network (CNN) model trained on synthetic data to identify underlying healthy distributions within pathological admixtures. We present both the methodology to generate synthetic data and the CNN model. We evaluate the CNN using two synthetic test datasets, including samples from a proposed benchmark for indirect methods (RIBench) and show significant improvements compared to the reported state-of-the-art method based on the benchmark (refineR). We also demonstrate a real-world application of the model, estimating age-specific RIs for cancer antigen 125 (CA-125), a crucial biomarker for ovarian cancer diagnostics. Our results show that CA-125 RIs are strongly age-dependent, which could have important diagnostic consequences.

Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer

BackgroundProstate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear.MethodsSomatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored.ResultsTEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients’ sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT’s potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated.ConclusionsTEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Circulating Exosomal Transfer RNA-Related Fragments as Diagnostic Biomarkers for Non-small Cell Lung Cancer.

Exosomal transfer RNA-derived fragments [exo-tRF] possess the capacity to be employed as biomarkers for several types of cancer. We aim to ascertain the diagnostic significance of exosomal 5'tRF-TyrGTA and 5'tRF-ValTAC in non-small cell lung cancer [NSCLC]. Ultracentrifugation was deployed to obtain serum exosomes from NSCLC patients and healthy donors. The acquired exosomes were then confirmed by transmission electron microscopy [TEM], qNano, and western blot [WB] techniques. The level of exo- tRF expression was validated by the use of microarrays and RT-qPCR. The diagnostic performance of exo-tRFs for NSCLC was determined through the receiver operating characteristic curve [ROC]. Exosomal 5'tRF-TyrGTA and 5'tRF-ValTAC were significantly downregulated in both early- and late-stage NSCLC patients compared to healthy donors, representing favorable diagnostic efficiency for NSCLC. In addition, the exosomal 5'tRF-TyrGTA level was correlated with tumor stage and lymph node metastasis. Exosomal 5'tRF-TyrGTA and 5'tRF-ValTAC can serve as potential biomarkers for NSCLC.

Expression profile and functional analysis of miR-301b in patients with breast cancer: A bioinformatics, biochemical, and histopathological study

BackgroundmicroRNAs (miRNAs) are crucial regulators of various biological processes and molecular functions. Aberrant miRNA expression has been linked in many studies to neoplastic transformation. Among these miRNAs, dysregulation of miR-301b-5p was associated with different types of cancer including breast cancer. Although many research works have investigated the function of miR-301b in carcinogenesis, few have examined its expression, biological, and clinical implications in breast cancer. Methodswe examined the expression levels of miR-301b-5p in human cancerous breast tissue compared to normal breast controls using different bioinformatic tools and RT-qPCR analyses. Resultswe detected that miR-301b-5p was differentially expressed in cancerous breast tissue when compared to normal controls. MiR-301b-5p was detected to be upregulated in high-grade (Grade 3) and triple-negative breast cancers. A significant strong positive correlation was detected between miR-301b and Ki-67, the commonly used proliferative marker in breast cancer. Bioinformatics analyses using the KM plotter revealed that miR-301b has significant prognostic power in assessing the OS of patients with breast cancer. The study also identified many fundamental biological processes and regulatory pathways associated with the investigated miR-301b-related hub genes. Interestingly, the expression pattern and prognostic significance of PTEN, the top hub gene regulated by miR-301b, highlighted the prognostic significance of PTEN in breast cancer. ConclusionThe current study findings suggest the potential use of miR-301b-5p as a possible diagnostic and prognostic biomarker in breast cancer. Moreover, this study emphasized the clinical and biological relevance of miR-301b-5p in breast cancer.

Tumor-exosomal miR-205-5p as a diagnostic biomarker for colorectal cancer.

Tumor-derived exosomal miRNAs play crucial roles in cancer diagnosis. Current studies aim to identify exosomal miRNAs associated with colorectal cancer (CRC) that are noninvasive, sensitive, and specific. Exosomes were extracted from CRC patients and healthy donors via ultracentrifugation, followed by verification via transmission electron microscopy (TEM), qNano, and Western blot analysis. The differential expression levels and clinical characteristics of miR-205-5p were analyzed in CRC via data from The Cancer Genome Atlas (TCGA). Real-time quantitative PCR was used to assess the expression levels of exosomal miRNAs in 157 primary CRC patients, 20 patients with benign diseases, and 135 healthy donors. Predictions regarding target genes were made to guide further exploration of the disease's etiopathogenesis through bioinformatics. Compared with that in healthy donors, the expression of miR-205-5p in colorectal cancer (CRC) patients was significantly lower, as determined through analysis of the TCGA database. We conducted a prediction and analysis of the functional enrichment of downstream target genes regulated by miR-205-5p. A lower level of exosomal miR-205-5p in the serum of CRC patients than in that of healthy controls (p < 0.0001) and patients with benign disease (p < 0.0001) was observed. Furthermore, the expression levels of exosomal miR-205-5p were significantly lower in early-stage CRC patients than in the comparison groups (p<0.001 and p < 0.0001). Notably, the expression levels of exosomal miR-205-5p significantly increased postoperatively (p = 0.0053). The present study demonstrated that serum exosomal miR-205-5p may be a diagnostic biomarker for CRC.

Biological functions and therapeutic potential of CKS2 in human cancer.

The incidence of cancer is increasing worldwide and is the most common cause of death. Identification of novel cancer diagnostic and prognostic biomarkers is important for developing cancer treatment strategies and reducing mortality. Cyclin-dependent kinase subunit 2 (CKS2) is involved in cell cycle and proliferation processes, and based on these processes, CKS2 was identified as a cancer gene. CKS2 is expressed in a variety of tissues in the human body, but its abnormal expression is associated with cancer in a variety of systems. CKS2 is generally elevated in cancer, plays a role in almost all aspects of cancer biology (such as cell proliferation, invasion, metastasis, and drug resistance) through multiple mechanisms regulating certain important genes, and is associated with clinicopathological features of patients. In addition, CKS2 expression patterns are closely related to cancer type, stage and other clinical variables. Therefore, CKS2 is considered as a tool for cancer diagnosis and prognosis and may be a promising tumor biomarker and therapeutic target. This article reviews the biological function, mechanism of action and potential clinical significance of CKS2 in cancer, in order to provide a new theoretical basis for clinical molecular diagnosis, molecular targeted therapy and scientific research of cancer.

Diagnostic and prognostic value of CD44v9 and TIM3 expression in CK‑ and CK+ regions in gastric cancer tissues.

The specificity and sensitivity of the current diagnostic and prognostic biomarkers for gastric cancer (GC) are limited. The present study aimed to evaluate the diagnostic and prognostic significance of cluster-of-differentiation gene 44 variant isoform 9 (CD44v9) and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) expression levels alone or combined in the tumor tissues of patients with GC and reveal the roles of CD44v9 and TIM3 in the cytokeratin (CK)+ and CK- regions. Multiplex immunofluorescence staining was performed for CD44v9, TIM3 and CK using a tissue microarray. The tissues were divided into three regions based on CK expression: Total, CK+, and CK- regions. The diagnostic and prognostic value was evaluated using receiver operating characteristic curves, Kaplan-Meier and Cox regression analyses. The results demonstrated that the density of cells expressing CD44v9, TIM3 and co-expressing CD44v9 and TIM3 (CD44v9/TIM3) in both the CK+ and CK- regions of tumor tissues was significantly higher than those in normal tissues (P<0.001). Moreover, the expression of CD44v9 in the CK- region was significantly positively correlated with age and tumor grade (P<0.05), and the expression of CD44v9/TIM3 in the CK- region of tumor tissues was significantly positively correlated with age, tumor grade and metastasis (P<0.05). Furthermore, the area under the curve for TIM3 expression in the CK+ region was 0.709, with a sensitivity of 45.83% and a specificity of 85.54% (P<0.001). High expression of CD44v9 in the CK- region was also significantly associated with poor survival and independently predicted a poor prognosis in patients with GC (hazard ratio, 2.387; 95% confidence interval, 1.384-4.118; P<0.01). In conclusion, dividing tissue regions based on CK expression is important for the diagnosis of GC. The expression of TIM3 in the CK+ region demonstrated diagnostic potential for GC, and high expression of CD44v9 in the CK- region was an independent prognostic risk factor for patients with GC.

TopMarker: Computational screening biomarkers of hepatocellular carcinoma from transcriptome and interactome based on differential network topological parameters

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

PUM1-TRAF3 fusion protein activates non-canonical NF-κB signaling via rescued NIK in biliary tract cancer

Discovery and verification of diagnostic or therapeutic biomarkers for biliary tract cancer (BTC) is challenging owing to the low prevalence of the disease. Here, we identified and investigated the clinical impact of a fusion gene, Pumilio1-tumor necrosis factor receptor-associated factor 3 (PUM1-TRAF3), caused by 1;14 chromosomal translocation in BTC. PUM1-TRAF3 was initially identified in the RNA-sequencing of five BTC surgical tissues and confirmed by fluorescence in situ hybridization. Expression of the fusion gene was validated in an expanded cohort (5/55, 9.1%). Establishment and molecular assessment of PUM1-TRAF3 expressing BTC cells revealed that PUM1-TRAF3 activates non-canonical NF-κB signaling via NF-κB-inducing kinase (NIK). Abnormal TRAF3 activity, driven by competitive binding of PUM1-TRAF3 and TRAF3 to NIK, led to NIK rescue followed by P52/RelB nuclear translocation, all of which were reverted by an NIK inhibitor. The elevated expression of NIK and activated NF-κB signaling was observed in the PUM1-TRAF3-expressing regions of patient tissues. Expression of the PUM1-TRAF3 fusion was significantly correlated with strong NIK expression, which is associated with a poorer prognosis for patients with BTC. Overall, our study identifies a new fusion gene, PUM1-TRAF3, that activates NIK and non-canonical NF-κB signaling, which may be beneficial for developing precise treatment strategies for BTC.

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

CTHRC1 is associated with BRAF(V600E) mutation and correlates with prognosis, immune cell infiltration, and drug resistance in colon cancer, thyroid cancer, and melanoma.

Colon cancer, thyroid cancer, and melanoma are common malignant tumors that seriously threaten human health globally. The B-Raf proto-oncogene, serine/threonine kinase (BRAF)(V600E) mutation is an important driver gene mutation in these cancer types. In this study, we identified that collagen triple helix repeat containing 1 (CTHRC1) expression was associated with the BRAF(V600E) mutation in colon cancer, thyroid cancer, and melanoma. Based on database analysis and clinical tissue studies, CTHRC1 was verified to correlate with poor prognosis and worse clinicopathological features in colon cancer and thyroid cancer patients, but not in patients with melanoma. Several signaling pathways, immune cell infiltration, and immunotherapy markers were associated with CTHRC1 expression. Additionally, a high level of CTHRC1 was correlated with decreased sensitivity to antitumor drugs (vemurafenib, PLX-4720, dabrafenib, and SB-590885) targeting the BRAF(V600E) mutation. This study provides evidence of a significant correlation between CTHRC1 and the BRAF(V600E) mutation, suggesting its potential utility as a diagnostic and prognostic biomarker in human colon cancer, thyroid cancer, and melanoma.

Gut Microbiota Signatures in Colorectal Cancer as a Potential Diagnostic Biomarker in the Future: A Systematic Review.

The gut microbiota has acquired significant attention in recent years for its potential as a diagnostic biomarker for colorectal cancer (CRC). In this literature review, we looked at the studies exploring alterations in gut microbiota composition associated with CRC, the potential mechanisms linking gut dysbiosis to CRC development, and the diagnostic approaches utilizing gut microbiota analysis. Our research has led to the conclusion that individuals with CRC often display alterations in their gut microbiota composition compared to healthy individuals. These alterations can include changes in the diversity, abundance, and type of bacteria present in the gut. While the use of gut microbiota as a diagnostic biomarker for CRC holds promise, further research is needed to validate its effectiveness and standardize testing protocols. Additionally, considerations such as variability in the microbiota composition among individuals and potential factors must be addressed before microbiota-based tests can be widely implemented in clinical practice.

Differential expression of long non-coding RNAs in colon cancer: Insights from transcriptomic analysis

BackgroundColon Cancer (CC) incidence has sharply grown in recent years. Long non-coding RNAs (lncRNA) are produced by a group of non-protein-coding genes, and have important functions in controlling gene expression and impacting the biological features of various malignancies including CC. MethodsOur research focused on examining the function of lncRNAs in the development of colon cancer. To this end, we selected and analyzed a dataset (GSE104836) from the GEO database, which contained information about the expression of mRNAs and lncRNAs in both colon cancer tissues and normal adjacent paired tumor tissues. The DESeq2 R package in Bioconductor was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) that showed differences in expression levels. Next, by literature review of previous studies, we chose two lncRNAs (FENDRR and LINC00092) for additional studies. To validate our findings, a series of tests were performed on a total of 31 tumor tissues and normal paired adjacent tumor tissues. The lncRNA expression levels were assessed in tumor tissues as well as in surrounding normal tumor tissues. ResultsThe data confirmed that just two particular lncRNAs, FENDRR and LINC00092, had considerably decreased expression levels throughout all stages of cancer. In addition, the survival assay was conducted using the GEPIA2 software, revealing that a reduced expression of FENDRR is correlated with a reduced overall survival. Furthermore, our investigation using receiver operating characteristic (ROC) methodology revealed that these two lncRNAs had significant discriminatory ability between colon cancer and normal tissues. To determine the cause of the decrease in the activity of these two long non-coding RNAs (lncRNAs), we used methylation-specific PCR (MSP) to examine the methylation pattern of their promoter regions. Our investigation revealed hypermethylation in the promoter regions of FENDRR and LINC00092 within tumor tissues compared to normal adjacent tumor tissues. ConclusionTaken together, our findings revealed the lncRNAs signatures as potential therapeutic targets and molecular diagnostic biomarkers in colon cancer. Furthermore, the evidence provided substantiates the important role of promoter methylation in regulating the expression levels for both of these lncRNAs.

Urinary Metabolite Diagnostic and Prognostic Liquid Biopsy Biomarkers of Lung Cancer in Nonsmokers and Tobacco Smokers.

Nonsmokers account for 10% to 13% of all lung cancer cases in the United States. Etiology is attributed to multiple risk factors including exposure to secondhand smoking, asbestos, environmental pollution, and radon, but these exposures are not within the current eligibility criteria for early lung cancer screening by low-dose CT (LDCT). Urine samples were collected from two independent cohorts comprising 846 participants (exploratory cohort) and 505 participants (validation cohort). The cancer urinary biomarkers, creatine riboside (CR) and N-acetylneuraminic acid (NANA), were analyzed and quantified using liquid chromatography-mass spectrometry to determine if nonsmoker cases can be distinguished from sex and age-matched controls in comparison with tobacco smoker cases and controls, potentially leading to more precise eligibility criteria for LDCT screening. Urinary levels of CR and NANA were significantly higher and comparable in nonsmokers and tobacco smoker cases than population controls in both cohorts. Receiver operating characteristic analysis for combined CR and NANA levels in nonsmokers of the exploratory cohort resulted in better predictive performance with the AUC of 0.94, whereas the validation cohort nonsmokers had an AUC of 0.80. Kaplan-Meier survival curves showed that high levels of CR and NANA were associated with increased cancer-specific death in nonsmokers as well as tobacco smoker cases in both cohorts. Measuring CR and NANA in urine liquid biopsies could identify nonsmokers at high risk for lung cancer as candidates for LDCT screening and warrant prospective studies of these biomarkers.

The Long Noncoding RNA PANDAR as a Prognostic and Diagnostic Biomarker in Panceratic Cancer ‏‏.

Pancreatic cancer (PC) is a lethal complication in the world, affecting around half a million individuals each year. The treatment of PC is relatively difficult due to the difficulty in making an early diagnosis. Most PC patients are confronted with locally metastatic or advanced diseases in the asymptomatic phase, and about 80% have late diagnosis with metastasis. Recently, long noncoding RNAs (lncRNAs) have drawn attention as a novel biological regulation layer. They take part in the regulation of mRNA and can be used as a prognostic factor or drug target. Based on their functions as regulators of PC initiation and progress, the lncRNAs can be categorized as tumor suppressors or oncogenic. They can be considered as a target for finding new biomarkers for prognosis, diagnosis, monitoring, and treating drug response in PC. Therefore, the present study summarizes the lncRNAs role in PC and the probable strategies to deal with their expression and controlling tumorigenesis and detection of the prognosis of PC.

Synergistic powering of DNA walker movement by endogenous dual enzymes for constructing dual-mode biosensors

To achieve highly sensitive and reliable detection of apurinic/apyrimidinic endonuclease 1 (APE1), a critical cancer diagnostic biomarker, we designed a DNA walker-based dual-mode biosensor, utilizing cellular endogenous dual enzymes (APE 1 and Flap endonuclease 1 (FEN 1)) to collaborate in activating and propelling DNA walker motion on DNA-functionalized Au nanoparticles. Incorporating both fluorescence and electrochemical detection modes, this system leverages signal amplification from DNA walker movement and cascade amplification through tandem hybridization chain reactions (HCR), achieving highly sensitive detection of APE 1. In the fluorescence mode, continuous DNA walker movement, initiated by APE1 and driven by FEN1, generates a robust signal response within a concentration range of 0.01–500 U mL−1, presenting a good linearity in the concentration range of 0.01–10 U mL−1, with a detection limit of 0.01 U mL−1. In the electrochemical detection module, the cascade upstream DNA walker and downstream HCR dual signal amplification strategy further enhances the sensitivity of APE1 detection, extending the linear range to 0.01–50 U mL−1 and reducing the detection limit to 0.002 U mL−1. Rigorous validation demonstrates the biosensor's specificity and anti-interference capability against multiple enzymes. Moreover, it effectively distinguishes cancer cells from normal cell lysates, exhibiting excellent stability and consistency in the dual-modes. Overall, our findings underscore the efficacy of the developed dual-mode biosensor for detecting APE1 in serum and cell lysates samples, indicating its potential for clinical applications in disease diagnosis.

Extraction and characterization of exosomes from the exhaled breath condensate and sputum of lung cancer patients and vulnerable tobacco consumers—potential noninvasive diagnostic biomarker source

Noninvasive sample sources of exosomes, such as exhaled breath and sputum, which are in close proximity to the tumor microenvironment and may contain biomarkers indicative of lung cancer, are far more permissive than invasive sample sources for biomarker screening. Standardized exosome extraction and characterization approaches for low-volume noninvasive samples are critically needed. We isolated and characterized exhaled breath condensate (EBC) and sputum exosomes from healthy nonsmokers (n = 30), tobacco smokers (n = 30), and lung cancer patients (n = 40) and correlated the findings with invasive sample sources. EBC samples were collected by using commercially available R-Tubes. To collect sputum samples the participants were directed to take deep breaths, hold their breath, and cough in a collection container. Dynamic light scattering, nanoparticle tracking analysis, and transmission electron microscopy were used to evaluate the exosome morphology. Protein isolation, western blotting, exosome quantification via EXOCET, and Fourier transform infrared spectroscopy were performed for molecular characterization. Exosomes were successfully isolated from EBC and sputum samples, and their yields were adequate and sufficiently pure for subsequent downstream processing and characterization. The exosomes were confirmed based on their size, shape, and surface marker expression. Remarkably, cancer exosomes were the largest in size not only in the plasma subgroups, but also in the EBC (p < 0.05) and sputum (p = 0.0036) subgroups, according to our findings. A significant difference in exosome concentrations were observed between the control sub-groups (p < 0.05). Our research confirmed that exosomes can be extracted from noninvasive sources, such as EBC and sputum, to investigate lung cancer diagnostic biomarkers for research, clinical, and early detection in smokers.

Combined detection of serum EFNA1 and MMP13 as diagnostic biomarker for gastric cancer

We previously identified that serum EFNA1 and MMP13 were potential biomarker for early detection of esophageal squamous cell carcinoma. In this study, our aim is to explore the diagnostic value of serum EFNA1 and MMP13 for gastric cancer. We used enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of serum EFNA1 and MMP13 in 210 GCs and 223 normal controls. The diagnostic value of EFNA1 and MMP13 was evaluated in an independent cohorts of GC patients and normal controls (n = 238 and 195, respectively). Receiver operating characteristics were used to calculate diagnostic accuracy. In training and validation cohorts, serum EFNA1 and MMP13 levels in the GC groups were significantly higher than those in the normal controls (P < 0.001). The area under the curve (AUC) of the combined detection of serum EFNA1 and MMP13 for GC was improved (0.794), compared with single biomarker used. Similar results were observed in the validation cohort. Importantly, the combined measurement of serum EFNA1 and MMP13 to detect early-stage GC also had acceptable diagnostic accuracy in training and validation cohort. Combined detection of serum EFNA1 and MMP13 could help identify early-stage GC, suggesting that it may be a promising tool for the early detection of GC.

ECM1 and ANXA1 in urinary extracellular vesicles serve as biomarkers for breast cancer.

Although urinary extracellular vesicles (uEVs) have been extensively studied in various cancers, their involvement in breast cancer (BC) remains largely unexplored. The non-invasive nature of urine as a biofluid and its abundant protein content offer considerable potential for the early detection of breast cancer. This study analyzed the proteomic profiles of uEVs from BC patients and healthy controls (HC). The dysregulation of ECM1 and ANXA1 in the uEVs was validated in a larger cohort of 128 BC patients, 25 HC and 25 benign breast nodules (BBN) by chemiluminescence assay (CLIA). The expression levels of ECM1 and ANXA1 were also confirmed in the uEVs of MMTV-PyMT transgenic breast cancer mouse models. LC-MS/MS analysis identified 571 dysregulated proteins in the uEVs of BC patients. ECM1 and ANXA1 were selected for validation in 128 BC patients, 25 HC and 25 BBN using CLIA, as their fold change showed a significant difference of more than 10 with p-value<0.05. Protein levels of ECM1 and ANXA1 in uEVs were significantly increased in BC patients. In addition, the protein levels of ECM1 and ANXA1 in the uEVs of MMTV-PyMT transgenic mice were observed to increase progressively with the progression of breast cancer. We developed a simple and purification-free assay platform to isolate uEVs and quantitatively detect ECM1 and ANXA1 in uEVs by WGA-coupled magnetic beads and CLIA. Our results suggest that ECM1 and ANXA1 in uEVs could potentially serve as diagnostic biomarkers for breast cancer.