ACP-DRL: an anticancer peptides recognition method based on deep representation learning.

Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.

Large-Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis.

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients.

cfOmics: a cell-free multi-Omics database for diseases.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

SARS-CoV-2-infected hiPSC-derived cardiomyocytes reveal dynamic changes in the COVID-19 hearts.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has had an enormous impact on our societies. Moreover, the disease's extensive and sustained symptoms are now becoming a nonnegligible medical challenge. In this respect, data indicate that heart failure is one of the most common readmission diagnoses among COVID-19 patients. METHODS: In this study, we used human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes to develop an in vitro model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and studied the dynamic changes occurring in cardiomyocytes after SARS-CoV-2 infection. RESULTS: To this end, we have created an effective time series SARS-CoV-2 infection model exhibiting different functional patterns of up- and downregulated proteins, and demonstrating that SARS-CoV-2 mainly affects (i) the lipid and the energy metabolism of hiPSC-derived cardiomyocytes during the early infection stage, and (ii) the DNA repair ability of cardiomyocytes during the late infection stage. By analyzing the proteome changes occurring at different infection timepoints, we were able to observe that the simulated disease (COVID-19) course developed rapidly, and that each of the studied timepoints was characterized by a distinct protein expression pattern. CONCLUSIONS: Our findings highlight the importance of early detection and personalized treatment based on the disease stage. Finally, by combing the proteomics data with virus-host interaction network analysis, we were able to identify several potential drug targets for the disease.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.

SUMMARY: A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research. AVAILABILITY AND IMPLEMENTATION: COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

[Deciphering the dynamic characteristics of non-neuronal cells in dorsal root ganglion of rat at different developmental stage based on single cell transcriptome data].

Dorsal root ganglia (DRG) is an essential part of the peripheral nervous system and the hub of the peripheral sensory afferent. The dynamic changes of neuronal cells and their gene expression during the development of dorsal root ganglion have been studied through single-cell RNAseq analysis, while the dynamic changes of non-neuronal cells have not been systematically studied. Using single cell RNA sequencing technology, we conducted a research on the non-neuronal cells in the dorsal root ganglia of rats at different developmental stage. In this study, primary cell suspension was obtained from using the dorsal root ganglions (DRGs, L4-L5) of ten 7-day-old rats and three 3-month-old rats. The 10×Genomics platform was used for single cell dissociation and RNA sequencing. Twenty cell subsets were acquired through cluster dimension reduction analysis, and the marker genes of different types of cells in DRG were identified according to previous researches about DRG single cell transcriptome sequencing. In order to find out the non-neuronal cell subsets with significant differences at different development stage, the cells were classified into different cell types according to markers collected from previous researches. We performed pseudotime analysis of 4 types Schwann cells. It was found that subtype Ⅱ Schwann cells emerged firstly, and then were subtype Ⅲ Schwann cells and subtype Ⅳ Schwann cells, while subtype Ⅰ Schwann cells existed during the whole development procedure. Pseudotime analysis indicated the essential genes influencing cell fate of different subtypes of Schwann cell in DRG, such as Ntrk2 and Pmp2, which affected cell fate of Schwann cells during the development period. GO analysis of differential expressed genes showed that the up-regulated genes, such as Cst3 and Spp1, were closely related to biological process of tissue homeostasis and multi-multicellular organism process. The down regulated key genes, such as Col3a1 and Col4a1, had close relationship with the progress of extracellular structure organization and negative regulation of cell adhesion. This suggested that the expression of genes enhancing cell homestasis increased, while the expression of related genes regulating ECM-receptor interaction pathway decreased during the development. The discovery provided valuable information and brand-new perspectives for the study on the physical and developmental mechanism of Schwann cell as well as the non-neuronal cell changes in DRG at different developmental stage. The differential gene expression results provided crucial references for the mechanism of somatosensory maturation during development.

Magnetron sputtering system for depositing boron carbide film use as neutron detection.

Boron carbide (B4C) films used as neutron conversion layers were investigated in this paper to replace the traditional 3He detectors due to their shortage. A magnetron sputtering system was developed for depositing large-size B4C films with the 1500 × 400 mm2 uniform-area. B4C films at the micron scale were deposited on aluminum (Al), float glass (SiO2), and silicon (Si) substrates with an inserting adhesion layer. The key characteristics, including surface morphology, thickness nonuniformity, purity, and neutron efficiency of B4C films, were characterized using atomic force microscopy, scanning electron microscopy, grazing incidence x-ray reflectivity, x-ray photoelectron spectroscopy, and neutron radiation metrology. The experimental results indicate that the deposition thickness nonuniformity across a 1500 × 400 mm2 area was better than ±3%. The stoichiometric ratio of boron atoms and carbon atoms (B/C) is 5.18, with 6 at. % O and 0.79 at. % N concentrations. The measured neutron detection efficiency of a 3 µm 10B4C film for 25 meV neutrons was 3.3 ± 0.3(sys)%, which is close to the simulated results (3.4%). The results show that the B4C neutron conversion layer is a promising substitute for 3He for neutron detection in the future.

Dynamic atlas of immune cells reveals multiple functional features of macrophages associated with progression of pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high mortality rate and unclarified aetiology. Immune response is elaborately regulated during the progression of IPF, but immune cells subsets are complicated which has not been detailed described during IPF progression. Therefore, in the current study, we sought to investigate the role of immune regulation by elaborately characterize the heterogeneous of immune cells during the progression of IPF. To this end, we performed single-cell profiling of lung immune cells isolated from four stages of bleomycin-induced pulmonary fibrosis-a classical mouse model that mimics human IPF. The results revealed distinct components of immune cells in different phases of pulmonary fibrosis and close communication between macrophages and other immune cells along with pulmonary fibrosis progression. Enriched signals of SPP1, CCL5 and CXCL2 were found between macrophages and other immune cells. The more detailed definition of the subpopulations of macrophages defined alveolar macrophages (AMs) and monocyte-derived macrophages (mo-Macs)-the two major types of primary lung macrophages-exhibited the highest heterogeneity and dynamic changes in expression of profibrotic genes during disease progression. Our analysis suggested that Gpnmb and Trem2 were both upregulated in macrophages and may play important roles in pulmonary fibrosis progression. Additionally, the metabolic status of AMs and mo-Macs varied with disease progression. In line with the published data on human IPF, macrophages in the mouse model shared some features regarding gene expression and metabolic status with that of macrophages in IPF patients. Our study provides new insights into the pathological features of profibrotic macrophages in the lung that will facilitate the identification of new targets for disease intervention and treatment of IPF.

Research Progress of α-Glucosidase Inhibitors Produced by Microorganisms and Their Applications.

Based on the easy cultivation of microorganisms and their short cycle time, research on α-glucosidase inhibitors (α-GIs) of microbial origin is receiving extensive attention. Raw materials used in food production, such as cereals, dairy products, fruits, and vegetables, contain various bioactive components, like flavonoids, polyphenols, and alkaloids. Fermentation with specific bacterial strains enhances the nutritional value of these raw materials and enables the creation of hypoglycemic products rich in diverse active ingredients. Additionally, conventional food processing often results in significant byproduct generation, causing resource wastage and environmental issues. However, using bacterial strains to ferment these byproducts into α-GIs presents an innovative solution. This review describes the microbial-derived α-GIs that have been identified. Moreover, the production of α-GIs using industrial food raw materials and processing byproducts as a medium in fermentation is summarized. It is worth analyzing the selection of strains and raw materials, the separation and identification of key compounds, and fermentation broth research methods. Notably, the innovative ideas in this field are described as well. This review will provide theoretical guidance for the development of microbial-derived hypoglycemic foods.

PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing.

Summary: De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies. In recent years, neural network models for de novo peptide sequencing have manifested a remarkable ability to accommodate various data sources and outperformed conventional peptide identification tools. However, the excellent model is computationally expensive, taking up to 1 week to process about 400 000 spectrums. This article presents PGPointNovo, a novel neural network-based tool for parallel de novo peptide sequencing. PGPointNovo uses data parallelization technology to accelerate training and inference and optimizes the training obstacles caused by large batch sizes. The results of extensive experiments conducted on multiple datasets of different sizes demonstrate that compared with PointNovo the excellent neural network-based de novo peptide sequencing tool, PGPointNovo, accelerates de novo peptide sequencing by up to 7.35× without precision or recall compromises. Availability and implementation: The source code and the parameter settings are available at https://github.com/shallFun4Learning/PGPointNovo. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

[An antibacterial peptides recognition method based on BERT and Text-CNN].

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.

Integrated analysis of circulating and tissue proteomes reveals that fibronectin 1 is a potential biomarker in papillary thyroid cancer.

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid cancer, but 20% of cases are indeterminate (i.e., cannot be accurately diagnosed) based on preoperative cytology, which might lead to surgical removal of a normal thyroid gland. To address this concern, we performed an in-depth analysis of the serum proteomes of 26 PTC patients and 23 healthy controls using antibody microarrays and data-independent acquisition mass spectrometry (DIA-MS). We identified a total of 1091 serum proteins spanning 10-12 orders of magnitude. 166 differentially expressed proteins were identified that participate in complement activation, coagulation cascades, and platelet degranulation pathways. Furthermore, the analysis of serum proteomes before and after surgery indicated that the expression of proteins such as lactate dehydrogenase A and olfactory receptor family 52 subfamily B member 4, which participate in fibrin clot formation and extracellular matrix-receptor interaction pathways, were changed. Further analysis of the proteomes of PTC and neighboring tissues revealed integrin-mediated pathways with possible crosstalk between the tissue and circulating compartments. Among these cross-talk proteins, circulating fibronectin 1 (FN1), gelsolin (GSN) and UDP-glucose 4-epimerase (GALE) were indicated as promising biomarkers for PTC identification and validated in an independent cohort. In differentiating between patients with benign nodules or PTC, FN1 produced the best ELISA result (sensitivity = 96.89%, specificity = 91.67%). Overall, our results present proteomic landscapes of PTC before and after surgery as well as the crosstalk between tissue and the circulatory system, which is valuable to understand PTC pathology and improve PTC diagnostics in the future.

Using ATCLSTM-Kcr to predict and generate the human lysine crotonylation database.

Lysine crotonylation (Kcr) is an evolutionarily conserved protein post-translational modifications, which plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation, telomere maintenance, inflammation, and cancer. Tandem mass spectrometry (LC-MS/MS) has been used to identify the global Kcr profiling of human, at the same time, many computing methods have been developed to predict Kcr sites without high experiment cost. Deep learning network solves the problem of manual feature design and selection in traditional machine learning (NLP), especially the algorithms in natural language processing which treated peptides as sentences, thus can extract more in-depth information and obtain higher accuracy. In this work, we establish a Kcr prediction model named ATCLSTM-Kcr which use self-attention mechanism combined with NLP method to highlight the important features and further capture the internal correlation of the features, to realize the feature enhancement and noise reduction modules of the model. Independent tests have proved that ATCLSTM-Kcr has better accuracy and robustness than similar prediction tools. Then, we design pipeline to generate MS-based benchmark dataset to avoid the false negatives caused by MS-detectability and improve the sensitivity of Kcr prediction. Finally, we develop a Human Lysine Crotonylation Database (HLCD) which using ATCLSTM-Kcr and the two representative deep learning models to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. HLCD provides an integrated platform for human Kcr sites prediction and screening through multiple prediction scores and conditions, and can be accessed on the website:www.urimarker.com/HLCD/. SIGNIFICANCE: Lysine crotonylation (Kcr) plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation and cancer. To better elucidate the molecular mechanisms of crotonylation and reduce the high experimental cost, we establish a deep learning Kcr prediction model and solve the problem of false negatives caused by the detectability of mass spectrometry (MS). Finally, we develop a Human Lysine Crotonylation Database to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. Our work provides a convenient platform for human Kcr sites prediction and screening through multiple prediction scores and conditions.

Proteomic profiling of IgA nephropathy reveals distinct molecular prognostic subtypes.

IgA nephropathy (IgAN) is a heterogeneous disease, which poses a series of challenges to accurate diagnosis and personalized therapy. Herein, we constructed a systematic quantitative proteome atlas from 59 IgAN and 19 normal control donors. Consensus sub-clustering of proteomic profiles divided IgAN into three subtypes (IgAN-C1, C2, and C3). IgAN-C2 had similar proteome expression patterns with normal control, while IgAN-C1/C3 exhibited higher level of complement activation, more severe mitochondrial injury, and significant extracellular matrix accumulation. Interestingly, the complement mitochondrial extracellular matrix (CME) pathway enrichment score achieved a high diagnostic power to distinguish IgAN-C2 from IgAN-C1/C3 (AUC>0.9). In addition, the proteins related to mesangial cells, endothelial cells, and tubular interstitial fibrosis were highly expressed in IgAN-C1/C3. Most critically, IgAN-C1/C3 had a worse prognosis compared to IgAN-C2 (30% eGFR decline, p = 0.02). Altogether, we proposed a molecular subtyping and prognostic system which could help to understand IgAN heterogeneity and improve the treatment in the clinic.

Identification of functional gene modules by integrating multi-omics data and known molecular interactions.

Multi-omics data integration has emerged as a promising approach to identify patient subgroups. However, in terms of grouping genes (or gene products) into co-expression modules, data integration methods suffer from two main drawbacks. First, most existing methods only consider genes or samples measured in all different datasets. Second, known molecular interactions (e.g., transcriptional regulatory interactions, protein-protein interactions and biological pathways) cannot be utilized to assist in module detection. Herein, we present a novel data integration framework, Correlation-based Local Approximation of Membership (CLAM), which provides two methodological innovations to address these limitations: 1) constructing a trans-omics neighborhood matrix by integrating multi-omics datasets and known molecular interactions, and 2) using a local approximation procedure to define gene modules from the matrix. Applying Correlation-based Local Approximation of Membership to human colorectal cancer (CRC) and mouse B-cell differentiation multi-omics data obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and ProteomeXchange database, we demonstrated its superior ability to recover biologically relevant modules and gene ontology (GO) terms. Further investigation of the colorectal cancer modules revealed numerous transcription factors and KEGG pathways that played crucial roles in colorectal cancer progression. Module-based survival analysis constructed four survival-related networks in which pairwise gene correlations were significantly correlated with colorectal cancer patient survival. Overall, the series of evaluations demonstrated the great potential of Correlation-based Local Approximation of Membership for identifying modular biomarkers for complex diseases. We implemented Correlation-based Local Approximation of Membership as a user-friendly application available at https://github.com/free1234hm/CLAM.

The Analysis of the Glycosyltransferase Gene Function From a Novel Granaticin Producer, Streptomyces Vilmorinianum. YP1.

Glycosylation is common among the synthesis of natural product and imparts the bioactivity for natural product. As for granaticin, a natural product with great bioactivity, glycosylation is an unusual sugar attachment and remains enigmatic. Orf14 in the gra cluster is the predicted glycosyltransferase but without being identified. Recently, we isolated and identified a novel granaticin producer Streptomyces vilmorinianum YP1. Orf14 gene in gra cluster of YP1 is knocked out and complemented. The instrumental analysis of the blue product synthesized by orf14-deficient mutant exhibits the none-granaticin detection and deglycosylated intermediates accumulation. The bioactivity and stability test suggests the weaker or none antibacterial activity and cytotoxicity of this blue product with greater ultraviolet stability and thermostability than granaticin and derivatives produced by YP1. All the result indicates that orf14 encodes glycosyltransferase and glycosylation played an important role in the bioactivity of granaticin. Meanwhile, the blue pigment, deglycosylated intermediates, has favorable processing characteristics. Our finding supplies the function of orf14 and glycosylation, but also indicates a promising candidate of edible blue pigment applicated in food industry.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

The ProteomeXchange consortium at 10 years: 2023 update.

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

[Precision oncology from a proteogenomics perspective].

Cancer is a heterogeneous disease with complex mechanisms that requires targeted precision medicine strategies. The growth of precision medicine is indispensable from the rapid development of genomics. However, genomics has certain limitations in molecular phenotype analysis, proteogenomics thus arose at the right time. Proteogenomics is the merging of proteomics and genomics. This review describes the limitations of genomic analysis and highlights the importance of proteogenomics to re-understand precision oncology from a proteogenomic perspective. In addition, the application of proteogenomics in precision oncology is briefly introduced, the related public data projects are described, and finally, the challenges that need to be addressed at this stage are proposed.