Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (cell-free DNA) shows relatively higher coverage at TFBS due to the protection by TF from degradation by nucleases and short fragments of cfDNA are enriched in TFBS. However, there are still great difficulties in the noninvasive identification of TFBSs from experimental techniques. In this study, we propose a deep learning-based approach that can noninvasively predict TFBSs of cfDNA by learning sequence information from known TFBSs through convolutional neural networks. Under the addition of long short-term memory, our model achieved an area under the curve of 84%. Based on this model to predict cfDNA, we found consistent motifs in cfDNA fragments and lower coverage occurred upstream and downstream of these cfDNA fragments, which is consistent with a previous study. We also found that the binding sites of the same TF differ in different cell lines. TF-specific target genes were detected from cfDNA and were enriched in cancer-related pathways. In summary, our method of locating TFBSs from plasma has the potential to reflect the intrinsic regulatory mechanism from a noninvasive perspective and provide technical guidance for dynamic monitoring of disease in clinical practice.

Spatial transcriptomics: recent developments and insights in respiratory research.

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Extracellular RNA profiles in non-small cell lung cancer plasma.

Background: Non-small cell lung cancer (NSCLC) has a high mortality rate and poor prognosis. The early detection of high-risk patients is essential to improve patient prognosis. Thus, the identification of a non-invasive, non-radiative, convenient, and fast diagnostic approach should be a top priority in NSCLC research. Circulating extracellular RNAs (exRNAs) in the plasma are potential biomarkers for NSCLC. Methods: We used RNA-sequencing (RNA-seq) technology to explore the NSCLC-related RNAs, especially the circular RNAs (circRNAs). The circRNA-targeted micro RNAs (miRNAs) were predicted using 3 circRNA databases [i.e., the Cancer-Specific CircRNA Database (CSCD), circBank, and Circular RNA Interactome]. The circRNA-miRNA-messenger RNA (mRNA) network was constructed using Cytoscape V3.8.0 (Cytoscape Consortium, San Diego, CA, USA). The expression levels of some differentially expressed genes were validated by a quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Results: The results showed that the RNA biotypes of the mitochondrial ribosomal RNAs (mt-rRNAs) and mitochondrial transfer RNAs (mt-tRNAs) were upregulated in the NSCLC plasma. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms of the differentially expressed transcripts of NSCLC included oxidative phosphorylation, proton transmembrane transport, and the response to oxidative stress. Additionally, the qRT-PCR validation indicated that hsa_circ_0000722 had significantly higher expression in the NSCLC plasma than the control plasma, but hsa_circ_0006156 did not differ between the NSCLC plasma and the control plasma. The expression levels of miR-324-5p and miR-326 were higher in the NSCLC plasma than the control plasma. Conclusions: In this study, an exRNA-sequencing strategy was used to identify the expression of NSCLC-specific transcription factors in clinical plasma samples, and hsa_circ_0000722 and hsa-miR-324-5p were identified as potential biomarkers in NSCLC.

Single-cell multi-omics sequencing and its application in tumor heterogeneity.

In recent years, the emergence and development of single-cell sequencing technologies have provided unprecedented opportunities to analyze deoxyribonucleic acid, ribonucleic acid and proteins at single-cell resolution. The advancements and reduced costs of high-throughput technologies allow for parallel sequencing of multiple molecular layers from a single cell, providing a comprehensive insight into the biological state and behavioral mechanisms of cells through the integration of genomics, transcriptomics, epigenomics and proteomics information. Researchers are actively working to further improve the cost-effectiveness, stability and high-throughput capabilities of single-cell multi-omics sequencing technologies and exploring their potential in precision medicine through clinical diagnostics. This review aims to survey the cutting-edge advancements in single-cell multi-omics sequencing, summarizing the representative technologies and their applications in profiling complex diseases, with a particular focus on tumors.

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Spatial Transcriptome Profiling of Mouse Hippocampal Single Cell Microzone in Parkinson's Disease.

The hippocampus is an important part of the limbic system in the human brain that has essential roles in spatial navigation and cognitive functions. It is still unknown how gene expression changes in single-cell in different spatial locations of the hippocampus of Parkinson's disease. The purpose of this study was to analyze the gene expression features of single cells in different spatial locations of mouse hippocampus, and to explore the effects of gene expression regulation on learning and memory mechanisms. Here, we obtained 74 single-cell samples from different spatial locations in a mouse hippocampus through microdissection technology, and used single-cell RNA-sequencing and spatial transcriptome sequencing to visualize and quantify the single-cell transcriptome features of tissue sections. The results of differential expression analysis showed that the expression of Sv2b, Neurod6, Grp and Stk32b genes in a hippocampus single cell at different locations was significantly different, and the marker genes of CA1, CA3 and DG subregions were identified. The results of gene function enrichment analysis showed that the up-regulated differentially expressed genes Tubb2a, Eno1, Atp2b1, Plk2, Map4, Pex5l, Fibcd1 and Pdzd2 were mainly involved in neuron to neuron synapse, vesicle-mediated transport in synapse, calcium signaling pathway and neurodegenerative disease pathways, thus affecting learning and memory function. It revealed the transcriptome profile and heterogeneity of spatially located cells in the hippocampus of PD for the first time, and demonstrated that the impaired learning and memory ability of PD was affected by the synergistic effect of CA1 and CA3 subregions neuron genes. These results are crucial for understanding the pathological mechanism of the Parkinson's disease and making precise treatment plans.

Mutational profiling of circulating tumor DNA and clinical characteristics in lymphoma: Based on next generation sequencing.

Liquid biopsy has been experimented with to identify the mutation of lymphoma based on next-generation sequencing (NGS). We applied NGS analysis to circulating tumor DNA (ctDNA) in 20 lymphoma patients. Then, we compared treatment outcomes, and clinical characteristics among these patients, then investigated mutational profiling. Two independent cohorts of 241 patients with mature B cell lymphoma in Mature B-cell malignancies data set (MBN) data set and 50 diffuse large B-cell lymphoma (DLBCL) patients in DLBCL data set, were used to examine the association between gene mutations and prognosis. We found ctDNA positive group had significantly more relapsed/PD (7/12, 58.3%) and less CR/PR patients (1/12, 8.3%) compared to negative group (0, 0%) (5/8, 62.5%) (p < 0.001). Somatic alterations were identified in 12 of 20 patients and the total 11 mutations were: Ataxia telangiectasia mutated (ATM), TP53, BCL2, BTG2, CD28, EP300, IDH2, IRF8, JAK3, NOTCH1, and NRAS. ATM (S2168L) was found in SLL and TLBL for the first time. BTG2 (c.292_293del), CD28 (P119T), IRF8 (E74D) and NOTCH1 (c.4348 G > A) were newly detected in DLBCL, angioimmunoblastic T-cell lymphoma, primary central nervous system lymphoma, and BCL for the first time respectively. We also disclosed an unreported mutation EP300 (c.1058_1059insC) in DLBCL. Our cases implied ctDNA detection consistent with the FISH of tissue samples to some extent, speculating new molecular subtypes of DLBCL, finding some potential drug-resistant mutations, and suggesting disease recurrence. Moreover, in MBN and DLBCL datasets, patients with TP53 mutation had a significantly shorter OS (all p < 0.05) in both circulating free DNA and tumor tissue. The mutations (no SNP) of NOTCH1 (all p < 0.05) significantly contributed to worse OS in the two cohorts.

Analysis approaches for the identification and prediction of N6-methyladenosine sites.

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m6A sites, in particular full-transcriptome m6A. And individual m6A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m6A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m6A methylation, including the experimental detection of m6A methylation sites, techniques of data analysis, the way of predicting m6A methylation sites, m6A methylation databases, and detection of m6A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA-sequencing.

Circular RNAs (circRNAs) are differentially expressed in various cancer types. The present study aimed to investigate the expression and clinical implication of circRNAs in hepatocellular carcinoma (HCC) and to evaluate the potential of circRNAs as diagnostic biomarkers for HCC. CircRNA expression was profiled in 19 patients with HCC and 19 normal controls using ribosomal RNA-depleted RNAs. Differentially expressed circRNAs (DE-circRNAs) between HCC and controls were identified using CIRI2 and distinct circRNA expression signatures were screened. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to predict the potential functions of these DE-circRNAs and the circRNA-miRNA-mRNA regulatory networks were then constructed. Several DE-circRNAs were selected and confirmed by RT-qPCR. A total of 40 DE-circRNAs (27 upregulated and 13 downregulated) were identified between patients with HCC and controls. Functional annotation indicated that these DE-circRNAs were involved in cellular components, molecular functions and cancer-associated pathways related to HCC. These included pathways in cancer, TNF signaling pathway, hepatitis B, hepatitis C and hepatocyte differentiation. The circRNA-miRNA-mRNA regulatory network was generated based on 11 candidate circRNAs. Receiver operating characteristic curve analysis indicated that Homo sapiens (hsa)_circ_0073239, hsa_circ_007090, hsa_circ_0008304, hsa_circ_0017586, hsa_circ_0000369 and hsa_circ_0001181 may serve as potential biomarkers for HCC. Results from Cell Counting Kit-8 assay suggested that small interfering RNA targeting hsa_circ_0001181 reduced the proliferation of HepG2 cells, which implicated it as a potential therapeutic target for HCC. Therefore, in the present study, the differential expression pattern and important role of circRNAs in HCC were determined. The present results highlight the diagnostic potential of circRNAs in HCC and provide novel insight into the development of and treatment approaches for HCC.

RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and has extremely high morbidity and mortality. Although many existing studies have focused on the identification of biomarkers, little information has been uncovered regarding the PBMC RNA profile of HCC. We attempted to create a profile throughout using expression of peripheral blood mononuclear cell (PBMC) RNA using RNA-seq technology and compared the transcriptome between HCC patients and healthy controls. Seventeen patients and 17 matched healthy controls were included in this study, and PBMC RNA was sequenced from all samples. Sequencing data were analyzed using bioinformatics tools, and quantitative reverse transcription PCR (qRT-PCR) was used for selected validation of DEGs. A total of 1,578 dysregulated genes were found in the PBMC samples, including 1,334 upregulated genes and 244 downregulated genes. GO enrichment and KEGG studies revealed that HCC is closely linked to differentially expressed genes (DEGs) implicated in the immune response. Expression of 6 selected genes (SELENBP1, SLC4A1, SLC26A8, HSPA8P4, CALM1, and RPL7p24) was confirmed by qRT-PCR, and higher sensitivity and specificity were obtained by ROC analysis of the 6 genes. CALM1 was found to gradually decrease as tumors enlarged. Nearly the opposite expression modes were obtained when compared to tumor sequencing data. Immune cell populations exhibited significant differences between HCC and controls. These findings suggest a potential biomarker for the diagnosis of HCC. This study provides new perspectives for liver cancer development and possible future successful clinical diagnosis.

Plasma RNA sequencing of extracellular RNAs reveals potential biomarkers for non-small cell lung cancer.

BACKGROUND: Lung cancer is one of the most common malignancies, and it has extremely high incidence and mortality rates. Although there have been many studies focused on lung cancer biomarkers, few have reported the extracellular RNA profiles of lung cancer. In this study, we used RNA-seq technology to analyze extracellular RNAs in low volume peripheral blood plasma; we compared the differentially expressed genes from the plasma of non-small cell lung cancer (NSCLC) patients with that of healthy controls. METHODS: We used RNA-seq technology and bioinformatics to analyze the extracellular RNA (exRNA) sequences of 12 human plasma samples (500 µl per sample), 6 from NSCLC patients and 6 from healthy controls. Subsequently, we used gene ontology (GO) enrichment, KEGG analysis and coexpression experiments to compare the differentially expressed genes (DEGs) and identify tumor biomarkers that were highly correlated with NSCLC. These DEGs were further verified by quantitative PCR. RESULTS: Approximately 20 million clean reads were produced for each plasma sample; 50-80% of the reads aligned to the human references, and hundreds of thousands of reads were counted in each plasma sample. In addition, a total of 640 genes (368 upregulated and 272 downregulated) were differentially expressed between NSCLC plasma and normal plasma. Further, we identified 7 key DEGs that are highly correlated with lung tumorigenesis: COX1, COX2, COX3, ND1, ND2, ND4L, and ATP6. CONCLUSION: exRNA-seq from a small amount (400-500 µl) of plasma opens new possibilities for exploring lung cancer biomarkers in the plasma.

CiRS-126 inhibits proliferation of ovarian granulosa cells through targeting the miR-21-PDCD4-ROS axis in a polycystic ovarian syndrome model.

Polycystic ovarian syndrome (PCOS) is considered to be one of the most prevalent endocrine disorders affecting women of reproductive age. CiRS-126, an innovative circular microRNA, has previously been proven to be a promising miR-21 sponge. However, a proper understanding of the impact of ciRS-126 on PCOS is needed. Circular RNA (CiRS) profiles were initially evaluated in ovarian cortex samples obtained from 18 women with PCOS as well from 9 women without PCOS. Insulin-induced ovarian granulosa cells isolated from mice were utilized for the functional study. CiRS microarray analysis and quantitative real-time PCR indicated that ciRs-126 expression was downregulated while miR-21 expression was upregulated in PCOS samples and insulin-induced granulosa cells as compared with non-PCOS samples and non-insulin-induced granulosa cells. Furthermore, ectopic overexpression of ciRS-126 was associated with a reduction in proliferation and increased apoptosis in insulin-treated granulosa cells. Meanwhile, bioinformatic prediction and the results of the dual-luciferase reporter assay indicated the presence of consecutive binding in the ciRS-126-miR-21-programmed cell death protein 4 (PDCD4) axis. Moreover, overexpression of miR-21 blocked ciRS-126 repression of proliferation and triggered the death of insulin-induced granulosa cells. Excessive PDCD4 expression counteracted the influence of miR-21 on cell death and proliferation. The data indicated that PDCD4 played a regulatory role in ROS generation, which is reportedly involved in apoptosis. Therefore, ciRS-126 reduction in PCOS granulosa cells targeted the miR-21-PDCD4 axis to reduce proliferation and promote apoptosis. CiRS-126 shows potential as a promising predictor of clinical outcome as well as a therapeutic target in PCOS.

Technical progress in circulating tumor DNA analysis using next generation sequencing.

Circulating tumor DNA (ctDNA) is tumor-derived, fragmented DNA that circulates freely in body fluids, predominantly in the peripheral blood. Recently, ctDNA analysis has been suggested as a complement to tissue biopsy in the detection and treatment of cancer. Genetic and epigenetic information specific to tumor cells, including single nucleotide variations, copy number variations, and modified methylation patterns, can be detected in ctDNA. Importantly, mutations in heterogenous tumors that could impart therapeutic resistance could be identified in ctDNA, which would aid in cancer diagnosis, prognosis, and real-time monitoring, and inform treatment with targeted therapies. However, ctDNA is still not a routinely used method for this purpose, because its detection techniques lack adequate sensitivity for reliable use in scientific studies and clinical trials. This review provides an up-to-date summary of ctDNA mutation detection methods based on next generation sequencing, highlighting their advantages and limitations, and focusing in particular on several optimized library preparation methods for improved sensitivity and specificity of ctDNA detection.

Expression And Biological Interaction Network Of RHOC For Hepatic Carcinoma With Metastasis In PBMC Samples.

OBJECTIVES: Hepatic carcinoma with metastasis remains incurable, and clinical diagnostic methods lacked adequate sensitivity and specificity. Therefore, seeking effectively diagnostic biomarkers is still essential for it. RHOC was reported to be linked to metastasis of hepatic carcinoma. However, almost all of the studies used tissues as detection samples, which was not ideal for clinical course minoring. Therefore, here, it was aimed to use PBMC samples that were not only easily accessible but also minimally invasive to determine the expression and biological interaction network of RHOC for hepatic carcinoma with metastasis. METHODS: PBMC samples were isolated. Then, RNA-seq was performed to identify the DEGs between hepatic carcinoma with metastasis and hepatic carcinoma with solitary tumor. Subsequently, q-RT-PCR was used to verify the expression level of RHOC. Finally, bioinformatic analysis was used to present the biological interaction network of RHOC for hepatic carcinoma with metastasis in PBMC samples. RESULTS: The results of both RNA-seq and q-RT-PCR showed that the expression level of RHOC was significantly higher in the PBMC samples of hepatic carcinoma with metastasis than in those of hepatic carcinoma with solitary tumor. By using variety of bioinformatic analysis platforms, in PBMCs, 18 co-expression genes with RHOC were identified and their interaction network showed that MYL9 and RHOC had the highest edge evidence, and were involved in some cell migration-related pathways. CONCLUSION: Our results indicated that RHOC in PBMCs could be potentially minimally invasive indicators for the diagnosis and clinical course supervision of hepatic carcinoma with metastasis, and its biological interaction network determined based on bioinformatic methods would lay a foundation for further study of the role of RHOC in tumor invasion and metastasis.

A vertical flow microarray chip based on SERS nanotags for rapid and ultrasensitive quantification of α-fetoprotein and carcinoembryonic antigen.

A vertical flow microarray chip is described that uses core-shell SERS nanotags as tags for ultrasensitive quantification of the tumor markers α-fetoprotein (AFP) and carcinoembryonic antigen (CEA) by detecting the intensity of the specific Raman bands at 592 cm-1. The nanotags warrant high sensitivity, and the use of porous nitrocellulose warrants a high surface-to-volume ratio. The linear dynamic ranges are 0.1 ng mL-1 - 10 µg mL-1 for both AFP and CEA, and the limits of detection) are 0.27 pg mL-1 and 0.96 pg mL-1, respectively. Quantification is rapid and can be performed without preconcentration. Graphical abstract Schematic representation of a vertical flow microarray chip using AuNBA@Ag SERS nanotags (where NBA stands for Nile blue A) as labels for rapid, ultrasensitive, and simultaneous detection of tumor biomarkers CEA and AFP.

Rapid and Ultrasensitive Quantification of Multiplex Respiratory Tract Infection Pathogen via Lateral Flow Microarray based on SERS Nanotags.

Respiratory tract infections (RTIs) are severe acute infectious diseases, which require the timely and accurate identification of the pathogens involved so that the individual treatment plan can be selected, including optimized use of antibiotics. However, high throughput and ultrasensitive quantification of multiple nucleic acids is a challenge in a point of care testing (POCT) device. Methods: Herein, we developed a 2×3 microarray on a lateral flow strip with surface enhanced Raman scattering (SERS) nanotags encoding the nucleic acids of 11 common RTI pathogens. On account of the signal magnification of encoded SERS nanotags in addition to the high surface area to volume ratio of the nitrocellulose (NC) membrane, rapid quantification of the 11 pathogens with a broad linear dynamic range (LDR) and ultra-high sensitivity was achieved on one lateral flow microarray. Results: The limit of detection (LOD) for influenza A, parainfluenza 1, parainfluenza 3, respiratory syncytial virus, coxiella burnetii, legionella pneumophila, influenza B, parainfluenza 2, adenovirus, chlamydophila pneumoniae, and mycoplasma pneumoniae were calculated to be 0.031 pM, 0.030 pM, 0.038 pM, 0.038 pM, 0.040 pM, 0.039 pM, 0.035 pM, 0.032 pM, 0.040 pM, 0.039 pM, and 0.041 pM, respectively. The LDR of measurement of the target nucleic acids of the eleven RTI pathogens were 1 pM-50 nM, which span 5 orders of magnitude. Conclusions: We anticipate this novel approach could be widely adopted in the early and precise diagnosis of RTI and other diseases.

MiR-486-5p Serves as a Good Biomarker in Nonsmall Cell Lung Cancer and Suppresses Cell Growth With the Involvement of a Target PIK3R1.

MicroRNAs are a class of noncoding RNAs that can be involved in the regulation of gene expression in cancers, including lung cancer. Our previous research has shown that miR-486-5p is one of the most downregulated microRNAs in tissue and serum samples of lung cancer as a good diagnostic biomarker. The objective of this study is to investigate the roles of miR-486-5p in the progression of lung cancer. In this study, miR-486-5p was further validated to be significantly downregulated in additional nonsmall cell lung cancer (NSCLC) tissue, serum, and cell samples by quantitative reverse transcription-polymerase chain reaction (RT-PCR), and the expression level of miR-486-5p was significantly associated with clinical phenotype of NSCLC. The PIK3R1 gene was confirmed to be a direct target of miR-486-5p by dual-luciferase reporter assay, and the expression level of miR-486-5p was inversely correlated with that of PIK3R1 in tumor tissues (r = -0.774, p < 0.01). Overexpressed miR-486-5p effectively inhibited cell proliferation and invasion and successfully induced apoptosis in vitro. PIK3R1 was involved in the suppression of miR-486-5p on cell growth. It can be concluded that miR-486-5p may act as a tumor suppressor contributing to the progression of NSCLC, and miR-486-5p would be a diagnostic and prognostic biomarker and a potential therapeutic target for lung cancer.

Analysis of genome-wide in cell free DNA methylation: progress and prospect.

DNA methylation is an important epigenetic marker that affects gene expression. Cell-free DNA methylation detection is a promising approach as abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. This review summarizes the existing literature and reviews on the detection methods based on next generation sequencing for DNA methylation. The review also discusses the feasibility of detecting cfDNA methylation and the latest progress.

Vertical flow assays based on core-shell SERS nanotags for multiplex prostate cancer biomarker detection.

Rapid, simultaneous, and sensitive quantification of multiplex prostate biomarkers plays an important role in early diagnosis, especially for obese men and patients. Herein, a surface-enhanced Raman scattering (SERS)-based vertical flow assay (VFA) is presented for simultaneous detection of multiplex prostate cancer biomarkers, such as prostate-specific antigen (PSA), carcinoembryonic antigen (CEA), and alpha-fetoprotein (AFP) on a single test spot. In practice, Raman dyes (RDs) encoded core-shell SERS nanotags instead of conventional gold colloids used in the colorimetric assay are employed in the sensing membrane of SERS based VFAs for multiplex protein detection. Because of the enhanced Raman signal of the core-shell nanostructure and the high surface area to volume ratio (SVR) of the porous sensing membrane, this proposed biosensor shows a wide linear dynamic range (LDR) with detection limits of 0.37, 0.43, and 0.26 pg mL-1 for PSA, CEA, and AFP, respectively, suggesting that this approach can be a good candidate in point of care testing (POCT) for rapid and sensitive biomarker detection and has a huge potential in multiplex analysis and cancer diagnosis.