Biology subjects

Yi, X.

Publications and source records attributed to Yi, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

An immune-lncRNA risk model to predict prognosis for patients with head and neck squamous cell carcinoma

Backgroundlong noncoding RNA (lncRNA) are closely correlated with the occurrence and development of tumors. The purpose of this research is to construct a risk model of immune-related lncRNAs that can be used for predicting the survival and prognosis of head and neck squamous cell carcinoma (HNSCC) patients and further exploring the immune function of immune-lncRNAs. Methodsobtained detailed transcriptional data and clinical information for 545 cases of HNSCC from The Cancer Genome Atlas (TCGA). Combined lncRNAs and the immune gene sets from the Molecular Signatures Database (MSigDB) were employed to construct co{square}expression networks, the significant correlation of immune-lncRNAs obtained from the results of Cox regression analysis were used to build the model. Survival analysis and QRT-PCR were used to confirm the expression of immune-lncRNAs and optimize the risk model. Then single-cell RNA Sequencing (scRNA-seq) dataset analysis of laryngeal squamous cell carcinoma (LSCC), co-expression analysis, correlation analysis and immunohistochemistry were used to explore the immune function of immune-lncRNAs. ResultsA total of 13 immune-lncRNAs including AC104083.1AC004148.2AL357033.4AC116914.2AC024075.1AC008115.3PCED1B-AS1RAB11B-AS1AC004687.1 AL450992.2EP300-AS1AC024075.2AC136475.2 were identified to construct the risk model. In this model, low-risk patients had higher overall survival (OS, P=1.923e-12, hazard ratio=2.104, 95% CI: 1.622-2.730). AC004687.1 and PCED1B-AS1 were overexpressed in T cells and were closely related to the genes encoding major histocompatibility complex II (MHC II) molecules. And the results of QRT-PCR and immunohistochemistry showed the expression significant positively correlated with HLA-DR which is one of the MHC II classical molecules. Conclusionthe immune-associated risk model constructed by 13 lncRNAs has good prognostic value for HNSCC. AC004687.1 and PCED1B-AS1 are significant correlating to clinical characteristics and MHC II, we provide a new insight into the immune function of lncRNAs.

bioinformatics↗

bioRxiv2022

Assessing base-resolution DNA mechanics on the genome scale

Intrinsic DNA properties such as bending play a crucial role in diverse biological systems. A recent advantage in the high-throughput method called loop-seq makes it possible to determine bendability of hundred thousand 50-bp DNA duplexes in one experiment. However, its still infeasible to assess whole sequence bendability in large genomes such as human, which needs thousands of loop-seq experiments. Here we introduce BendNet - a neural network to accurately predict the intrinsic DNA bending at base-resolution by only given DNA sequences. BendNet can increase the resolution of experimental results, and can predict DNA bendability for any new given sequences in high accuracy. We applied BendNet to the human genome and observed high-stiffness regions located at both transcriptional start sites and transcriptional end sites. Such stiffness patterns are different for coding and non-coding genes, which matches distinct nucleosome occupancy patterns. As expected, most transcription factors (TFs) bind in DNA of low bendability. In contrast, we observed an unusually high bendability within binding elements of specific TFs such as EBF1 and regulators of genome folding such as CTCF. These factors either co-bind or compete with nucleosomes to carry out their functions. More interestingly, CTCF binding regions exhibit the highest bendability than other DNA elements, implying their potential role in trapping and holding the CTCF in the exact locations to make sure CTCF as stable anchor in loop extrusion process. Our work provides a tool to assess DNA bendability for large-scale DNA sequences and expands our understanding on DNA mechanics in chromatin regulation and genome folding.

genomics↗

bioRxiv2022

ccnWheat: A Database for Comparing Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named ccnWheat. ccnWheat is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathway, gene family, protein-protein interactions, microRNA (miRNA), and several lines of epigenome data are integrated in this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using ccnWheat, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CArG specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. ccnWheat is useful for investigating co-expression networks and conducting other associated analyses, and thus facilitates comparative and functional genomic studies in wheat. ccnWheat is freely available at http://bioinformatics.cau.edu.cn/ccnWheat.

bioinformatics↗

bioRxiv2021

Genome assembly of the JD17 soybean provides a new reference genome for Comparative genomics

Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromsome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with three published soybeans (WM82, ZH13 and W05), which identified five large inversions and two large translocations specific to JD17, 20,984 - 46,912 PAVs spanning 13.1 - 46.9 Mb in size, and 5 - 53 large PAV clusters larger than 500kb. 1,695,741 - 3,664,629 SNPs and 446,689 - 800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation (SNF) genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research.

genomics↗

bioRxiv2021

PySOFI: an open source Python package for SOFI

Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution (SR) without requirement of sophisticated imaging instruments. An open source package for SOFI algorithm is needed to support not only the utilization of SOFI, but also the community adoption and participation for further development of SOFI. In this work, we developed PySOFI, an open source python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve and extend the algorithm. We provide a complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of PySOFI, illustrate how to use each functional module, and demonstrate how to extend the PySOFI package with additional modules. We expect PySOFI to facilitate efficient adoption, testing, modification, dissemination and prototyping of new SOFI-relevant algorithms.

biophysics↗

bioRxiv2021

A new single-cell level R-index for EGFR-TKI resistance and survival prediction in LUAD

EGFR-TKIs achieved excellent efficacy in EGFR-mutated patients. Unfortunately, most patients would inevitably develop progressive disease within a median of 10 to 14 months. Predicting the resistance probability remains a challenge. Therefore, we created an R-index model trained by single-cell RNA data with the OCLR algorithm. This model can be applied to estimate the level of EGFR-TKI resistance in cell line and xenograft mice models and predict prognosis in multiple cohorts. Comparing the high and the low R-index group, we found that the glycolysis pathway and KRAS up-regulation pathway were related to resistance, and MDSC was the leading cause of immunosuppression in the tumor microenvironment. These results are consistent with previous studies indicating that the R-index provides an insight into resistance status and a new way to explore resistance mechanisms and clinical treatment by the combination of Glucose metabolism-targeted or MDSC-targeted therapies. This is the first quantification method of EGFR-TKI resistance based on single-cell sequencing data solving the problem of the mixed resistance state of tumor cells and helping explore transcriptome characteristics of drug-resistant cell populations.

bioinformatics↗

bioRxiv2021

Rapid Ensemble Measurement of Protein Diffusion, Probe Blinking and Photobleaching Dynamics in the Complex Cellular Space

We present a fluorescence fluctuation image correlation analysis method that can rapidly and simultaneously measure the diffusion coefficient, photoblinking rates, and fraction of diffusing particles of fluorescent molecules in cells. Unlike other image correlation techniques, we demonstrated that our method could be applied irrespective of a non-uniformly distributed, immobile blinking fluorophore population. This allows us to measure blinking and transport dynamics in complex cell morphologies, a benefit for a range of super-resolution fluorescence imaging approaches that rely on probe emission blinking. Furthermore, we showed that our technique could be applied without directly accounting for photobleaching. We successfully employed our technique on several simulations with realistic EMCCD noise and photobleaching models, as well as on Dronpa-C12 labeled beta-actin in living NIH/3T3 and HeLa cells. We found that the diffusion coefficients measured using our method were consistent with previous literature values. We further found that photoblinking rates measured in the live HeLa cells varied as expected with changing excitation power.

biophysics↗

bioRxiv2021

METTL9 regulates N1-histidine methylation of zinc transporters to promote tumor growth

Methyltransferase like 9 (Mettl9) is a member of the methyltransferase like protein family which is characterized by the presence of binding domains for S-adenosyl methionine, (SAM), a co-substrate for methylation reactions. Despite METTL9 is predicted to be a methyltransferase, its enzymatic activity, substrate specificities and biological functions are still poorly characterized. In this study, we revealed a tumor-promoting role for METTL9. We found that deletion of Mettl9 in tumor cells suppresses tumor growth and elicits potent anti-tumor immunity. Mechanistically, METTL9 is a N1-histidine methyltransferase which methylates the histidine residues of a x-His-x-His (xHxH) motif on the substrates. This motif is found extensively in zinc transporter families SLC39s and SLC30s, particularly in SLC39A7. Deletion of Mettl9 impairs cytoplasmic zinc homeostasis, resulting in an altered gene expression program with increased endoplasmic reticulum (ER) stress and reduced cell cycle. Mutation of key METTL9 catalyzed methylhistidine residues of SLC39A7 impairs cytoplasmic zinc homeostasis and affects cell growth as well. Notably, METTL9 expression is increased in some cancer types and its higher expression is associated with worse clinical outcomes, particularly in liver and pancreatic cancer. In summary, our work identified METTL9 as a potential new oncogene and its mediated methylation is of regulatory importance. Identifying selective and potent small-molecule inhibitors of METTL9 could thus represent novel therapeutic strategy for anti-proliferative cancer drugs.

cancer biology↗

bioRxiv2020

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

bioinformatics↗

bioRxiv2020

Generation of two Multipotent Mesenchymal Progenitor Cell Lines Capable of Osteogenic, Mature Osteocyte, Adipogenic, and Chondrogenic Differentiation

Differentiation of multi-potent mesenchymal progenitor cells give rise to several tissue types including bone, cartilage, and adipose. In addition to the complication arising from the numerous spatial, temporal, and hormonal factors that regulate lineage allocation, targeting of these cells in vivo is challenging, making mesenchymal progenitor cell lines valuable tools to study both tissue development and the differentiated cell types. Mesenchymal stem cells (MSCs) can be isolated from humans and animals; however, obtaining homogenous, responsive cells in a reproducible fashion can be problematic. As such, we have developed two novel mesenchymal progenitor cell (MPC) lines, MPC1 and MPC2, which were generated from the bone marrow of male C57BL/6 mice. These cells were immortalized using the temperature sensitive large T-antigen, allowing for thermal control of proliferation and differentiation. Both MPC1 and MPC2 cell lines are capable of osteogenic, adipogenic, and chondrogenic differentiation. Under osteogenic conditions both cell lines formed discrete mineralized nodules, staining for alizarin red and alkaline phosphatase, while expressing high levels of osteogenic genes including Sost, Fgf23, and Dmp1. Sost and Dmp1 mRNA levels were drastically reduced with parathyroid hormone, thus recapitulating in vivo responses. MPC cells secreted both the intact (iFGF23) and C-terminal (cFGF23) forms of endocrine hormone FGF23, which was upregulated in the presence of 1,25 dihydroxy vitamin D (1,25D). In addition to osteogenic differentiation, both cell lines also rapidly entered the adipogenic lineage, expressing several adipose markers after only 4 days in adipogenic media. MPC cells were also capable of chondrogenic differentiation, displaying increased expression of common cartilage genes including aggrecan, sox9, and cartilage oligomeric matrix protein. With the ability to differentiate into multiple mesenchymal lineages and mimic in vivo responses of key regulatory genes/proteins, MPC cells are a valuable model to study factors that regulate mesenchymal lineage allocation as well as the mechanisms that dictate transcription, protein modification, and secretion of these factors.

cell biology↗

bioRxiv2020

Limited window for donation of convalescent plasma with high live-virus neutralizing antibodies for COVID-19 immunotherapy

The optimal timeframe for donating convalescent plasma to be used for COVID-19 immunotherapy is unknown. To address this important knowledge deficit, we determined in vitro live-virus neutralizing capacity and persistence of IgM and IgG antibody responses against the receptor-binding domain and S1 ectodomain of the SARS-CoV-2 spike glycoprotein in 540 convalescent plasma samples obtained from 175 COVID-19 plasma donors for up to 142 days post-symptom onset. Robust IgM, IgG, and viral neutralization responses to SARS-CoV-2 persist, in the aggregate, for at least 100 days post-symptom onset. However, a notable acceleration in decline in virus neutralization titers [≥]160, a value suitable for convalescent plasma therapy, was observed starting 60 days after first symptom onset. Together, these findings better define the optimal window for donating convalescent plasma useful for immunotherapy of COVID-19 patients and reveal important predictors of an ideal plasma donor, including age and COVID-19 disease severity score. One Sentence SummaryEvaluation of SARS-CoV-2 anti-spike protein IgM, IgG, and live-virus neutralizing titer profiles reveals that the optimal window for donating convalescent plasma for use in immunotherapy is within the first 60 days of symptom onset.

immunology↗

bioRxiv2020

Relationship between Anti-Spike Protein Antibody Titers and SARS-CoV-2 In Vitro Virus Neutralization in Convalescent Plasma

Newly emerged pathogens such as SARS-CoV-2 highlight the urgent need for assays that detect levels of neutralizing antibodies that may be protective. We studied the relationship between anti-spike ectodomain (ECD) and anti-receptor binding domain (RBD) IgG titers, and SARS-CoV-2 virus neutralization (VN) titers generated by two different in vitro assays using convalescent plasma samples obtained from 68 COVID-19 patients, including 13 who donated plasma multiple times. Only 23% (16/68) of donors had been hospitalized. We also studied 16 samples from subjects found to have anti-spike protein IgG during surveillance screening of asymptomatic individuals. We report a strong positive correlation between both plasma anti-RBD and anti-ECD IgG titers, and in vitro VN titer. Anti-RBD plasma IgG correlated slightly better than anti-ECD IgG titer with VN titer. The probability of a VN titer [≥]160 was 80% or greater with anti-RBD or anti-ECD titers of [≥]1:1350. Thirty-seven percent (25/68) of convalescent plasma donors lacked VN titers [≥]160, the FDA-recommended level for convalescent plasma used for COVID-19 treatment. Dyspnea, hospitalization, and disease severity were significantly associated with higher VN titer. Frequent donation of convalescent plasma did not significantly decrease either VN or IgG titers. Analysis of 2,814 asymptomatic adults found 27 individuals with anti-RBD or anti-ECD IgG titers of [≥]1:1350, and evidence of VN [≥]1:160. Taken together, we conclude that anti-RBD or anti-ECD IgG titers can serve as a surrogate for VN titers to identify suitable plasma donors. Plasma anti-RBD or anti-ECD titer of [≥]1:1350 may provide critical information about protection against COVID-19 disease.

immunology↗

bioRxiv2020

BatchServer: a web server for batch effect evaluation, visualization and correction

Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualizion of batch effects. We demonstate its application in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

bioinformatics↗

bioRxiv2020

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry

A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called "DIA tensor" (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data.

bioinformatics↗

bioRxiv2020

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

bioinformatics↗

bioRxiv2019

Mechanical Suppression of Breast Cancer Cell Invasion and Paracrine Signaling Requires Nucleo-Cytoskeletal Connectivity

Exercise benefits the musculoskeletal system and reduces the effects of cancer. The beneficial effects of exercise are multifactorial, where metabolic changes and tissue adaptation influence outcomes. Mechanical signals, a principal component of exercise, are anabolic to the musculoskeletal system and restrict cancer progression. We examined the mechanisms through which cancer cells sense and respond to mechanical signals. Low-magnitude, high-frequency signals were applied to human breast cancer cells in the form of low-intensity vibration (LIV). LIV decreased invasion through matrix and impaired secretion of osteolytic factors PTHLH, IL-11, and RANKL. Furthermore, paracrine signals from mechanically stimulated cancer cells, reduced osteoclast differentiation resorptive capacity. Physically disconnecting the nucleus by knockdown of SUN1 and SUN2 impaired the ability of LIV to suppress invasion and production of osteolytic factors. LIV also increased cell stiffness; an effect dependent on an intact LINC complex. These data show that mechanical signals alter the metastatic potential of human breast cancer cells, where the nucleus serves as a mechanosensory apparatus to alter cell structure and intercellular signaling.

cancer biology↗

bioRxiv2019

PulseDIA: in-depth data independent acquisition mass spectrometry using enhanced gas phase fractionation

An inherent bottleneck of data independent acquisition (DIA) analysis by Orbitrap-based mass spectrometers is the relatively large window width due to the relatively slow scanning rate compared to TOF. Here we present a novel gas phase separation and MS acquisition method called PulseDIA-MS, which improves the specificity and sensitivity of Orbitrap-based DIA analysis. This is achieved by dividing the ordinary DIA-MS analysis covering the entire mass range into multiple injections for DIA-MS analyses with complementary windows. Using standard HeLa digests, the PulseDIA method identified 69,530 peptide precursors from 9,337 protein groups with ten MS injections of 30 min LC gradient. The PulseDIA scheme containing two complementary windows led to the highest gain of peptide and protein identifications per time unit compared to the conventional 30 min DIA method. We further applied the method to profile the proteome of 18 cholangiocarcinoma (CCA) tissue samples (benign and malignant) from nine patients. PulseDIA identified 7,796 protein groups in these CCA samples, with 14% increase of protein identifications, compared to the conventional DIA method. The missing value for protein matrix dropped by 7% with PulseDIA acquisition. 681 proteins were significantly dysregulated in tumorous CCA samples. Together, we presented and benchmarked an alternative DIA method with higher sensitivity and lower missing rate.

cancer biology↗

bioRxiv2019

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

biochemistry↗