RNF149 Promotes HCC Progression through Its E3 Ubiquitin Ligase Activity.

Hepatocellular carcinoma (HCC) accounts for over 80% of cases among liver cancer, with high incidence and poor prognosis. Thus, it is of valuable clinical significance for discovery of potential biomarkers and drug targets for HCC. In this study, based on the proteomic profiling data of paired early-stage HCC samples, we found that RNF149 was strikingly upregulated in tumor tissues and correlated with poor prognosis in HCC patients, which was further validated by IHC staining experiments of an independent HCC cohort. Consistently, overexpression of RNF149 significantly promoted cell proliferation, migration, and invasion of HCC cells. We further proved that RNF149 stimulated HCC progression via its E3 ubiquitin ligase activity, and identified DNAJC25 as its new substrate. In addition, bioinformatics analysis showed that high expression of RNF149 was correlated with immunosuppressive tumor microenvironment (TME), indicating its potential role in immune regulation of HCC. These results suggest that RNF149 could exert protumor functions in HCC in dependence of its E3 ubiquitin ligase activity, and might be a potential prognostic marker and therapeutic target for HCC treatment.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78.

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

In-Depth Serum Proteomics Reveals the Trajectory of Hallmarks of Cancer in Hepatitis B Virus-Related Liver Diseases.

Hepatocellular carcinoma (HCC) is a prevalent cancer in China, with chronic hepatitis B (CHB) and liver cirrhosis (LC) being high-risk factors for developing HCC. Here, we determined the serum proteomes (762 proteins) of 125 healthy controls and Hepatitis B virus-infected CHB, LC, and HCC patients and constructed the first cancerous trajectory of liver diseases. The results not only reveal that the majority of altered biological processes were involved in the hallmarks of cancer (inflammation, metastasis, metabolism, vasculature, and coagulation) but also identify potential therapeutic targets in cancerous pathways (i.e., IL17 signaling pathway). Notably, the biomarker panels for detecting HCC in CHB and LC high-risk populations were further developed using machine learning in two cohorts comprised of 200 samples (discovery cohort = 125 and validation cohort = 75). The protein signatures significantly improved the area under the receiver operating characteristic curve of HCC (CHB discovery and validation cohort = 0.953 and 0.891, respectively; LC discovery and validation cohort = 0.966 and 0.818, respectively) compared to using the traditional biomarker, alpha-fetoprotein, alone. Finally, selected biomarkers were validated with parallel reaction monitoring mass spectrometry in an additional cohort (n = 120). Altogether, our results provide fundamental insights into the continuous changes of cancer biology processes in liver diseases and identify candidate protein targets for early detection and intervention.

High-affinity SOAT1 ligands remodeled cholesterol metabolism program to inhibit tumor growth.

BACKGROUND: Although cholesterol metabolism is a common pathway for the development of antitumor drugs, there are no specific targets and drugs for clinical use. Here, based on our previous study of sterol O-acyltransferase 1 (SOAT1) in hepatocelluar carcinoma, we sought to screen an effective targeted drug for precise treatment of hepatocelluar carcinoma and, from the perspective of cholesterol metabolism, clarify the relationship between cholesterol regulation and tumorigenesis and development. METHODS: In this study, we developed a virtual screening integrated affinity screening technology for target protein drug screening. A series of in vitro and in vivo experiments were used for drug activity verification. Multi-omics analysis and flow cytometry analysis were used to explore antitumor mechanisms. Comparative analysis of proteome and transcriptome combined with survival follow-up information of patients reveals the clinical therapeutic potential of screened drugs. RESULTS: We screened three compounds, nilotinib, ABT-737, and evacetrapib, that exhibited optimal binding with SOAT1. In particular, nilotinib displayed a high affinity for SOAT1 protein and significantly inhibited tumor activity both in vitro and in vivo. Multi-omics analysis and flow cytometry analysis indicated that SOAT1-targeting compounds reprogrammed the cholesterol metabolism in tumors and enhanced CD8+ T cells and neutrophils to suppress tumor growth. CONCLUSIONS: Taken together, we reported several high-affinity SOAT1 ligands and demonstrated their clinical potential in the precision therapy of liver cancer, and also reveal the potential antitumor mechanism of SOAT1-targeting compounds.

ExpressVis: a biologist-oriented interactive web server for exploring multi-omics data.

In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein-protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.

Anaphylactic Rare Saponins Separated from Panax notoginseng Saponin and a Proteomic Approach to Their Anaphylactic Mechanism.

In recent years, many traditional Chinese medicine injections based on Panax notoginseng saponin (PNS) have been reported to cause anaphylaxis. Previous studies on the anaphylactic saponins of PNS and their mechanism are inadequate. In this study, potential anaphylactic saponins were obtained by the separation of PNS and preparation of each individual component through comprehensive techniques, such as liquid chromatography, preparative chromatography, HPLC, NMR, and MS. The anaphylactic abilities of these saponins were tested using RBL-2H3 cells via a ß-hexosaminidase release rate test. The results for the mechanism of anaphylaxis were obtained by a proteomic analysis using RBL-2H3 cells. The results indicate that, among all the saponins prepared, gypenoside LXXV and notoginsenoside T5 showed strong anaphylactic abilities and notoginsenoside ST-4 and ginsenoside Rk3 showed weak anaphylactic abilities. These 4 saponins can induce anaphylaxis via direct stimulation of effector cells. The gene oncology enrichment analysis results showed that, among these saponins, only gypenoside LXXV was related to organelles of the endoplasmic reticulum and Golgi apparatus and biological processes in response to organic cyclic compounds. Four proteins in RBL-2H3 cells with the accession numbers A0A0G2JWQ0, D3ZL85, D4A5G8, and Q8K3F0 were identified as crucial proteins in the anaphylactic process. This research will help traditional Chinese medicine injection manufacturers strengthen their quality control and ensure the safety of anaphylactic saponins.

ZNF498 promotes hepatocellular carcinogenesis by suppressing p53-mediated apoptosis and ferroptosis via the attenuation of p53 Ser46 phosphorylation.

BACKGROUND: Dysfunctional p53 signaling is one of the major causes of hepatocellular carcinoma (HCC) tumorigenesis and development, but the mechanisms underlying p53 inactivation in HCC have not been fully clarified. The role of Krüppel-associated box (KRAB)-type zinc-finger protein ZNF498 in tumorigenesis and the underlying mechanisms are poorly understood. METHODS: Clinical HCC samples were used to assess the association of ZNF498 expression with clinicopathological characteristics and patient outcomes. A mouse model in which HCC was induced by diethylnitrosamine (DEN) was used to explore the role of ZNF498 in HCC initiation and progression. ZNF498 overexpression and knockdown HCC cell lines were employed to examine the effects of ZNF498 on cellular proliferation, apoptosis, ferroptosis and tumor growth. Western blotting, immunoprecipitation, qPCR, luciferase assays and flow cytometry were also conducted to determine the underlying mechanisms related to ZNF498 function. RESULTS: ZNF498 was found to be highly expressed in HCC, and increased ZNF498 expression was positively correlated with advanced pathological grade and poor survival in HCC patients. Furthermore, ZNF498 promoted DEN-induced hepatocarcinogenesis and progression in mice. Mechanistically, ZNF498 directly interacted with p53 and suppressed p53 transcriptional activation by inhibiting p53 Ser46 phosphorylation. ZNF498 competed with p53INP1 for p53 binding and suppressed PKCδ- and p53INP1-mediated p53 Ser46 phosphorylation. In addition, functional assays revealed that ZNF498 promoted liver cancer cell growth in vivo and in vitro in a p53-dependent manner. Moreover, ZNF498 inhibited p53-mediated apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation. CONCLUSIONS: Our results strongly suggest that ZNF498 suppresses apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation in hepatocellular carcinogenesis, revealing a novel ZNF498-PKCδ-p53INP1-p53 axis in HCC cells that would enrich the non-mutation p53-inactivating mechanisms in HCC.

Phosphoproteomics Reveals Novel Targets and Phosphoprotein Networks in Cell Cycle Mediated by Dsk1 Kinase.

As the ortholog of human SR protein kinase 1 in fission yeast Schizosaccharomyces pombe, Dsk1 specifically phosphorylates SR proteins (serine/arginine-rich proteins) and promotes splicing of nonconsensus introns. The SRPK (SR protein-specific kinase) family performs highly conserved functions in eukaryotic cells including cell proliferation, differentiation, development, and apoptosis. Although Dsk1 was originally identified as a mitotic regulator, its specific targets involved in cell cycle have yet been unexplored. In this study, using a phosphoproteomics approach, we examined differential protein phosphorylation between wild-type cells and dsk1-deletion mutants. We found reduced phosphorylation of 149 peptides corresponding to 133 proteins in the dsk1-null cells. These proteins are involved in various cellular processes, including cytoskeleton organization and signal transduction, and specifically enriched in multiple steps of cell cycle control. Further, targeted MS analyses and in vitro biochemical assays established Cdr2 protein kinase and kinesin motor Klp9 as novel substrates of Dsk1, which function in cell size control for mitotic entry and in chromosome segregation for mitotic exit, respectively. The phosphoprotein networks mediated by Dsk1 reveal, for the first time, the molecular links connecting Dsk1 to mitotic phase transition, sister-chromatid segregation, and cytokinesis, providing further evidence of Dsk1's diverse influence on cell cycle progression and regulation.

[Research progress of feature selection and machine learning methods for mass spectrometry-based protein biomarker discovery].

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine.

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.

PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis.

SUMMARY: As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. AVAILABILITY AND IMPLEMENTATION: PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data.

Summary: Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data. Availability and implementation: PANDA-view is freely available at https://sourceforge.net/projects/panda-view/. Supplementary information: Supplementary data are available at Bioinformatics online.