Cancer Diagnostic Biomarkers Research Articles

Extracellular Vesicle lncRNAs as Key Biomolecules for Cell-to-Cell Communication and Circulating Cancer Biomarkers

Extracellular vesicles (EVs) are secreted by almost every cell type and are considered carriers of active biomolecules, such as nucleic acids, proteins, and lipids. Their content can be uptaken and released into the cytoplasm of recipient cells, thereby inducing gene reprogramming and phenotypic changes in the acceptor cells. Whether the effects of EVs on the physiology of recipient cells are mediated by individual biomolecules or the collective outcome of the total transferred EV content is still under debate. The EV RNA content consists of several types of RNA, such as messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA), the latter defined as transcripts longer than 200 nucleotides that do not code for proteins but have important established biological functions. This review aims to update our insights on the functional roles of EV and their cargo non-coding RNA during cancer progression, to highlight the utility of EV RNA as novel diagnostic or prognostic biomarkers in cancer, and to tackle the technological advances and limitations for EV RNA identification, integrity assessment, and preservation of its functionality.

Immune Cell-Derived Exosomes: A Cell-Free Cutting-Edge Tumor Immunotherapy.

Extracellular vesicles (EVs) are cellular communication molecules and are classified into three major subpopulations, such as microvesicles, apoptotic bodies, and exosomes. Among these, exosomes-based cancer research is a cutting-edge investigation approach to cancer understanding. During cancer progression , tumor-derived exosomes can reprogram the cellular system and promote cancer. Circulating exosomes in the body fluids such as blood, plasma, serum, saliva, CSF, sweat, and tears play a key role in identifying diagnostic and prognostic cancer biomarkers. Diverse therapeutic sources of exosomes including stem cells, plants, and immune cells, etc. exhibit significant cancer-healing properties. Although cancer-targeting immunotherapy is an effective strategy, it has limitations such as toxicity, and high costs. In comparison, immune cell-derived exosomes-based immunotherapy is a cell-free approach for cancer treatment and has advantages like less toxicity, biocompatibility, reduced immunogenicity, and efficient, target-specific cancer therapeutic development. This review highlights the therapeutic signature of immune cell-derived exosomes for cancer treatment.

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein–Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

Identification of Plasma Protein Biomarkers for Predicting Lung Cancer.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating the development of effective early diagnostic strategies. Despite advancements in imaging and screening technologies, late-stage diagnoses remain common, limiting treatment options and reducing survival rates. Thus, there is a critical need for reliable, minimally invasive biomarkers to improve early detection and patient outcomes. Plasma protein biomarkers offer promising potential for early lung cancer detection and continuous disease monitoring. This study explored the potential of specific plasma protein markers as early indicators of lung cancer. Plasma samples were collected from normal healthy individuals and lung cancer patients, and protein purification and analysis were conducted using LC-MS/MS. A mixed-effect model was applied to select lung cancer-related protein markers based on label-free relative quantification values. We identified 29 proteins with potential for early lung cancer diagnosis, including complement proteins (CFB, C3, C8G, C1QA, C1R, C6), orosomucoid proteins (ORM1, ORM2), ceruloplasmin (CP), alpha-1-B glycoprotein (A1BG), and others. These proteins play diverse roles in immune response, inflammation, and cell signaling, suggesting their relevance in lung cancer pathophysiology. Our findings suggest the potential of plasma proteins as early diagnostic biomarkers for lung cancer. Further validation in larger cohorts is needed to confirm their clinical utility. Integrating these biomarkers into existing diagnostic modalities could enhance early detection accuracy, leading to improved patient outcomes.

Identification of Volatile Organic Compounds (Distinct Biomarkers) Emitted by Cancer Cells Using Gas Chromatography-mass Spectrometry – A Review

The utilization of Gas Chromatography-Mass Spectrometry (GC-MS) for the detection of Volatile Organic Compounds (VOCs) as potential biomarkers in cancer diagnostics is an evolving and promising field. This review outlines current exploratory and pilot studies that lay a fundamental groundwork for future research. Due to the complexity of the fecal and respiratory volatilome, influenced by a myriad of biological and environmental factors, it is crucial to expand research demographics to enhance data robustness and ensure the applicability of findings across diverse populations. We have also, herein, highlighted the dual capability of VOC analysis to detect cancer and differentiate between its stages, which is vital for customizing patient treatment plans and monitoring therapeutic efficacy. Furthermore, establishing quantifiable thresholds for VOC concentrations is essential for their clinical adoption as reliable indicators of cancer. The integration of GC-MS with machine learning and Artificial Intelligence (AI) could be useful for comprehensive metabolic profiles and facilitating the development of non-invasive and real-time diagnostic tools. The adoption of multi-omics approach promises a deeper understanding of cancer biology, potentially leading to personalized medicine and strategies finely tuned to individual molecular profiles. Hence, the research on VOCs as cancer biomarkers is set to offer transformative advancements in diagnostic technologies, ultimately improving patient outcomes through tailored therapeutic interventions.

Sensing and Imaging Agents for Cyclooxygenase Enzyme.

In this concept, we present a comprehensive study on the development and application of COX-2-specific fluorescent probes for cancer imaging and diagnosis. Targetting cancer cells and measuring cancer-related activities in specific organelles quickly and accurately are crucial factors for early diagnosis and research on cancer pathology and treatment. This concept explores a variety of probes which include BDIMC1-2, TPI-IMC, YZP1, IQ-1, ANQ-IMC-6, Niblue-C6-IMC, AZB-IMC2, CMP, CF3-Fluorocoxib, IBPC1, CoxFluor, etc. and each one demonstrates unique mechanisms and high selectivity towards COX-2 enzymes. These probes were designed to enhance fluorescence upon binding to COX-2 which enable precise visualization of tumor and inflamed tissues. The research emphasizes the importance of COX-2 as a biomarker in cancer diagnostics, particularly in identifying cancer stem cells and inflamed tissues. This concept highlights the potentiality of these probes in non-invasive imaging techniques which offering significant advancements in cancer diagnosis and monitoring. The in vivo and in vitro experiments, including applications in mouse models and human tissue samples, confirm the efficacy of these probes in providing detailed imaging for clinical and research applications.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Prostate cancer (PCa) is a leading malignancy affecting men globally, contributing significantly to cancer-related morbidity and mortality. Our study aims to explore causal relationships between blood protein ratios (BPR) and PCa using a Mendelian randomization (MR) approach, potentially identifying new diagnostic and therapeutic targets. Methods: A two-sample MR method was employed, utilizing genetic variants as instrumental variables (IVs) to infer causality between circulating BPR and PCa. Data on BPR were obtained from a proteomics study of the UK Biobank, while PCa data were sourced from FinnGen, the PRACTICLE Consortium, and the GWAS Catalog. Stringent criteria were applied for IV selection, and statistical analyses included the inverse-variance weighted (IVW) method with sensitivity analyses to address pleiotropy and heterogeneity. Results: Significant causal associations were identified between several BPR and PCa. Notably, the ratios of CEBPB/PXN, APBB1IP/NCF2, APP/EGF, and CRKL/ EGF were found to be protective against PCa, while the ratios of ARHGAP1/RAD23B, EGF/ TNFSF14, and GOLM2/STC1 were identified as risk factors. Reverse MR analysis suggested that PCa might act as a protective factor for the GOLM2/STC1 ratio. Sensitivity analyses confirmed the robustness of these findings. Conclusions: This study elucidates significant causal relationships between 7 BPR and PCa, offering new insights for diagnosis, treatment evaluation, and personalized therapeutic strategies. Future research should focus on validating these findings and exploring the underlying biological mechanisms to improve PCa management.

Detection of Catalase and 4-Hydroxynonenal as Detective Biomarkers in Iraqi Breast Cancer

Breast cancer is the most common malignant tumor among women and a leading cause of cancer-related death. Early diagnosis is critical for improving treatment outcomes. The objective of this study is to determine and examine catalase (CAT) and 4-hydroxynonenal (4-HNE) markers as non-invasive biomarkers for early detection in Iraqi breast cancer. A case-control study of 150 participants was further divided into 75 women with a diagnosis of breast cancer and 75 healthy controls. In the present study, blood samples were collected for the determination of serum CAT and 4-HNE levels using an enzyme-linked immunosorbent assay technique. The biochemical markers urea and creatinine were determined by automated Cobas c311. Hemoglobin concentration (HB)-and total white blood cell (WBC) count was done by an automated hematology analyzer. Statistical analyses were performed using the SPSS version 24, where receiver operating characteristics (ROC) analysis was used to assess diagnostic performance. CAT activity was significantly reduced in the patient group (6.07±1.19 pg/ml) compared to controls (12.11±2.74 pg/ml), while 4-HNE was significantly elevated in the patient group (11.43±2.41 pg/ml) compared to control (3.83±1.02 pg/ml). Among hematology analyzer, there was a significant difference in HB and WBC between patients and controls. Roc analyses demonstrated a sensitivity of 100% for CAT and 98% for 4-HNE. Reduced serum levels of CAT and elevated 4-HNE levels in Iraqi breast cancer may serve as new diagnostic biomarkers for breast cancer, highlighting their role in disease progression.

SB7-H4 is a diagnostic biomarker in epithelial ovarian cancer and correlates to platinum resistance.

Ovarian cancer, with its high mortality rate among gynecological cancers, is often diagnosed late due to the lack of early diagnostic symptoms and biomarkers. The tumor immune microenvironment has become a focal point in cancer diagnostic and therapeutic research. Among these, B7-H4, a checkpoint protein, plays a crucial role in immune regulation and tumor suppression, contributing to immune evasion within the tumor microenvironment. This study aims to identify the concentration of soluble B7-H4(sB7-H4) in the plasma of patients with ovarian cancer and to evaluate its clinical significance. Through a comprehensive analysis involving enzyme-linked immunosorbent assay, immunohistochemistry, and multicolor immunofluorescence, we quantified sB7-H4 levels in patient plasma and ascites, correlating these findings with tissue expression and clinical outcomes. Results indicated a strong association between high sB7-H4 levels and advanced disease, surgical outcomes, lymphatic metastasis, and platinum resistance. When compared with traditional biomarkers CA125 and HE4, sB7-H4, especially in conjunction with these markers, enhances the diagnostic accuracy for epithelial ovarian cancer, offering insights into disease progression and therapeutic efficacy. This comprehensive analysis suggests that sB7-H4 is a promising biomarker for EOC, providing valuable insights into diagnosis, stage differentiation, treatment effectiveness, and prognosis.

UBE2T is a diagnostic and prognostic biomarker for endometrial cancer.

Endometrial cancer (UCEC) is one of the most common malignant tumors in gynecology, and early diagnosis is crucial for its treatment. Currently, there is a lack of early screening tests specific to UCEC, and treatment advances are limited. It is crucial to identify more sensitive biomarkers for screening, diagnosis, and predicting UCEC. Previous studies have shown that UBE2T is involved in the development of various tumors such as breast cancer and liver cancer, but research on the role of UBE2T in UCEC is limited. Using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UALCAN databases, we analyzed the differential expression of UBE2T mRNA and protein in endometrial cancer (UCEC), along with its clinical relevance. A total of 113 clinical samples were collected, and immunohistochemistry and Western blot analysis were employed to validate bioinformatics analysis results. Volcano plots were generated using UBE2T and its differentially expressed genes, and a protein-protein interaction (PPI) network was constructed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to predict the functional role of UBE2T in UCEC progression. Correlation between UBE2T expression and patient survival was analyzed using TCGA data, and Kaplan-Meier survival curves were plotted. UBE2T is significantly overexpressed in UCEC and correlates with poor prognosis. Its overexpression is closely associated with mitosis, cell cycle regulation, and histological grade in UCEC patients. UBE2T is highly expressed in UCEC and suppresses anti-tumor immune responses in UCEC patients. It serves as a key participant in UCEC progression, associated with a range of adverse outcomes, and holds potential as a clinical diagnostic and prognostic biomarker.

Comprehensive analysis reveals that P4HA3 is a prognostic and diagnostic gastric cancer biomarker that can predict immunotherapy efficacy

Gastric cancer (GC) is one of the most challenging malignant tumors worldwide, primarily because of its high incidence and mortality rates. Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) has been established as a pivotal factor for facilitating cell proliferation, invasion, and metastasis across multiple human tumors. Nevertheless, the precise role of P4HA3in GC has not been fully elucidated. In this study, we used data from The Cancer Genome Atlas (TCGA) to examine the role of P4HA3 as a potential biomarker for predicting immunotherapy response in patients with GC. Our comprehensive analysis of data from the TCGA, TIMER, and other databases revealed a significant association between elevated P4HA3 expression in GC and adverse prognostic outcomes. Furthermore, we confirmed that P4HA3 expression was strongly correlated with immune infiltrating cells, immune infiltration markers, the tumor mutational burden (TMB), microsatellite instability (MSI), the immune score, the stromal score, and immune checkpoints, thus highlighting P4HA3 as a crucial and dependable therapeutic target within the context of immune-based antitumor strategies. Our findings suggest that P4HA3 may function as an immune-related biomarker in the pathogenesis and treatment of GC, indicating that P4HA3 is a promising prognostic and therapeutic target for this malignancy.

Value of miR-31 and miR-150-3p as diagnostic and prognostic biomarkers for breast cancer.

The most prevalent malignancy among women is breast cancer (BC). MicroRNAs (miRNAs) play a role in the initiation and progression of BC by influencing breast cancer stem cells (BCSCs) but the diagnostic and prognostic roles of those miRNAs on BC patients are still unknown. It was aimed to investigate expression profiles, diagnostic and prognostic potentials of BCSC-related miRNAs in different subtypes (Luminal A and B, HER2 + and TNBC) of BC patients. Expression analysis of 15 BCSC-related miRNAs was performed in 50 breast tumor tissues and 20 adjacent non-tumor tissues obtained from BC patients using the qRT-PCR method. The expression levels of miR-31 and miR-150-3p were significantly upregulated in the tumor tissues compared to the adjacent non-tumor tissues (p < 0.05). miR-31 expression upregulated in the Luminal A and Luminal B group compared to non-tumor tissue (p < 0.05). miR-31 expression was determined to be significantly higher in the Luminal group (Luminal A and B) compared to the aggressive group (HER2 + and TNBC) (p < 0.05). According to the ROC analysis, the area under the curve (AUC) of miR-31 and miR-150-3p were 0.66 with a sensitivity of 68% and a specificity of 70%. A significant inverse correlation was observed between miR-31 expression with metastatic carcinoma status, in situ component, and Ki67 value in tumors, and high miR-150-3p expression was correlated with p63 expression (p < 0.05). miR-31 and miR-150-3p have the potential to serve as biomarkers for guiding diagnosis, evaluating prognosis, and metastatic process in patients with BC.

Long non‑coding RNAs as diagnostic and prognostic biomarkers for colorectal cancer (Review).

Colorectal cancer (CRC) ranks as the 3rd most common cancer globally and is the 2nd leading cause of cancer-related death. Owing to the lack of specific early symptoms and the limitations of existing early diagnostic methods, most patients with CRC are diagnosed at advanced stages. To overcome these challenges, researchers have increasingly focused on molecular biomarkers, with particular interest in long non-coding RNAs (lncRNAs). These non-protein-coding RNAs, which exceed 200 nucleotides in length, play critical roles in the development and progression of CRC. The stability and detectability of lncRNAs in the circulatory system make them promising candidate biomarkers. The analysis of circulating lncRNAs in peripheral blood represents a potential option for minimally invasive diagnostic tests based on liquid biopsy samples. The present review aimed to evaluate the efficacy of lncRNAs with altered expression levels in peripheral blood as diagnostic markers for CRC. Additionally, the clinical significance of lncRNAs as prognostic markers for this disease were summarized.

Analytical and clinical validation of a NGS panel in detecting targetable variants from ctDNA of metastatic NSCLC patients.

Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for noninvasive cancer diagnostics, particularly in the context of metastatic non-small-cell lung cancer (NSCLC). Detecting targetable variants through ctDNA analysis offers the potential to guide treatment decisions, especially in cases where tissue samples are insufficient or unavailable. In this study, we developed and validated a next-generation sequencing panel targeting 101 cancer-related genes (101-test) to detect somatic variants in ctDNA from a large cohort of Chinese patients with metastatic NSCLC. The performance of the 101-test was assessed by evaluating its limit of detection (LOD), accuracy, and precision in identifying molecular variants. Additionally, the concordance between ctDNA and tissue samples for detecting targetable variants was analyzed in 904 patients. The 101-test demonstrated a LOD of 0.38% for single-nucleotide variants (SNVs), 0.33% for insertions and deletions (InDels), and 0.33% for fusions. Sensitivity was 98.3% for SNVs, 100% for InDels, and 100% for fusions when compared to digital droplet PCR (ddPCR)/breakpoint PCR reference methods. The by-variant sensitivity for somatic variants was 97.5%, with a specificity of 99.9% between tumor-only and tumor-normal analyses. In a real-world cohort, the concordance between ctDNA and tissue samples for identifying targetable variants was 72.2% (457/633). Notably, the EGFR S768I variant, recently recommended by clinical guidelines, achieved an 80% concordance rate. Furthermore, 4.3% of patients (27/633) with targetable variants were identified exclusively through ctDNA testing. The ctDNA-based 101-test is a highly sensitive and specific tool for detecting targetable variants in metastatic NSCLC, particularly in cases with insufficient tissue samples. The findings support the use of ctDNA testing as a reliable and complementary method to traditional tissue-based molecular analysis, enhancing the precision of treatment strategies for NSCLC patients.

Yap methylation-induced FGL1 expression suppresses anti-tumor immunity and promotes tumor progression in KRAS-driven lung adenocarcinoma.

Despite significant strides in lung cancer immunotherapy, the response rates for Kirsten rat sarcoma viral oncogene homolog (KRAS)-driven lung adenocarcinoma (LUAD) patients remain limited. Fibrinogen-like protein 1 (FGL1) is a newly identified immune checkpoint target, and the study of related resistance mechanisms is crucial for improving the treatment outcomes of LUAD patients. This study aimed to elucidate the potential mechanism by which FGL1 regulates the tumor microenvironment in KRAS-mutated cancer. The expression levels of FGL1 and SET1 histone methyltransferase (SET1A) in lung cancer were assessed using public databases and clinical samples. Lentiviruses were constructed for transduction to overexpress or silence FGL1 in lung cancer cells and mouse models. The effects of FGL1 and Yes-associated protein (Yap) on the immunoreactivity of cytotoxic T cells in tumor tissues were evaluated using immunofluorescence staining and flow cytometry. Chromatin immunoprecipitation and dual luciferase reporter assays were used to study the SET1A-directed transcriptional program. Upregulation of FGL1 expression in KRAS-mutated cancer was inversely correlated with the infiltration of CD8+ T cells. Mechanistically, KRAS activated extracellular signal-regulated kinase 1/2 (ERK1/2), which subsequently phosphorylated SET1A and increased its stability and nuclear localization. SET1A-mediated methylation of Yap led to Yap sequestration in the nucleus, thereby promoting Yap-induced transcription of FGL1 and immune evasion in KRAS-driven LUAD. Notably, dual blockade of programmed cell death-1 (PD-1) and FGL1 further increased the therapeutic efficacy of anti-PD-1 immunotherapy in LUAD patients. FGL1 could be used as a diagnostic biomarker of KRAS-mutated lung cancer, and targeting the Yap-FGL1 axis could increase the efficacy of anti-PD-1 immunotherapy.

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC). The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs. The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1. To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.

Circulating exosomal protein EFEMP1 and SERPINC1 as diagnostic biomarkers for epithelial ovarian cancer

ObjectivesCaner-derived exosomes, containing diverse nucleic acids and proteins, are being exploited in diagnostic biomarker development. This study aims to screen and identify the altered exosomal proteins between epithelial ovarian cancer (EOC) patient and healthy volunteers, and to evaluate their diagnostic accuracy for EOC. MethodsExosomes were separate by ultracentrifugation, and then subjected to TEM, qNano, and western blot for identification. Exosomal EFEMP1 and SERPINC1 were selected by MS/MS analysis, validated by ELISA in a cohort with 163 healthy donors, 183 EOC patients and 30 patients with benign ovarian tumors. ResultsMS/MS analyses identified a total of 207 differential exosomal proteins, including the 122 up-regulated and 85 down-regulated. Exosomal EFEMP1 and SERPINC1 were significantly upregulated in EOC patients compared with those in healthy donors as well as in the benign patients, possessing favorable diagnostic efficiency. The area under the curves (AUCs) were 0.8071, 0.8211, respectively. They also exerted rather high early diagnostic efficiency, as well as the potential to distinguish the malignant patients from the benign individuals. Besides, exosomal SERPINC1 was associated with coagulation index and LE-DVT (lower extremity deep venous thrombosis) in EOC patients. ConclusionsExosomal EFEMP1 and SERPINC1 are upregulated and serve as the promising diagnostic biomarkers for EOC.

Identification and validation of diagnostic and prognostic biomarkers in prostate cancer based on WGCNA

BackgroundProstate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.MethodsThe datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.ResultsThe blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.ConclusionsIn summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.

Serum exosomal miR-200c is a potential diagnostic biomarker for breast cancer

Background Breast cancer (BC) is one of the most common malignancies in women. Exosomes are widely found in body fluids and carry microRNAs (miRNAs) that reflect the biological properties of the parental cells. Our study aimed to investigate the differential expression of miR-200c in BC serum exosomes and its diagnostic value. Methodology miRNA profiles in culture supernatant exosomes of normal mammary epithelial cells MCF-10A and BC cells (MCF-7, MDA-MB-231, MCF-7 Taxol) were examined by miRNA deep sequencing to screen for significantly differentially expressed miRNAs; Transmission electron microscopy (TEM), Nanoparticle tracking analysis (NTA), and Western blot were used to identify exosomes; qPCR was used to detect the expression level of miR-200c in cellular exosomes and serum exosomes; The efficacy of individual and combined tests of each indicator to diagnose BC was evaluated using receiver operating characteristic (ROC) curves. Results We identified typical exosome features by TEM, NTA and Western blot, indicating successful exosome extraction. Then our miRNA sequencing results and qRT-PCR experiments showed that miR-200c was significantly down-regulated in BC cell exosomes. In addition, we divided the clinical serum samples into two cohorts according to region, and in independent cohort I, the serum exosomal miR-200c levels of BC patients were significantly lower than those of healthy controls. In cohort II, serum exosomal miR-200c expression was significantly lower in the BC group than in the control and benign breast disease (BBD) groups, whereas miR-200c expression in the BBD group was not statistically different from that in the control group. ROC analyses in both independent cohorts confirmed that serum exosomal miR-200c could differentiate between patients with and without BC disease and could be used as an early diagnostic marker for BC disease. Conclusion Serum exosome miR-200c can be used as a potential biomarker for the diagnosis of BC, and combined with conventional serum diagnostic markers AFP, CA125 and CA153 can help to improve diagnostic efficiency.