A region-resolved mucosa proteome of the human stomach

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

Observations from the Proteomics Bench

Many challenges in proteomics result from the high-throughput nature of the experiments. This paper first presents pre-analytical problems, which still occur, although the call for standardization in omics has been ongoing for many years. This article also discusses aspects that affect bioinformatic analysis based on three sets of reference data measured with different orbitrap instruments. Despite continuous advances in mass spectrometer technology as well as analysis software, data-set-wise quality control is still necessary, and decoy-based estimation, although challenged by modern instruments, should be utilized. We draw attention to the fact that numerous young researchers perceive proteomics as a mature, readily applicable technology. However, it is important to emphasize that the maximum potential of the technology can only be realized by an educated handling of its limitations.

Remodeling of the gastric environment in Helicobacter pylori-induced atrophic gastritis

Helicobacter pylori colonization of the human stomach is a strong risk factor for gastric cancer. To investigate H. pylori-induced gastric molecular alterations, we used a Mongolian gerbil model of gastric carcinogenesis. Histologic evaluation revealed varying levels of atrophic gastritis (a premalignant condition characterized by parietal and chief cell loss) in H. pylori-infected animals, and transcriptional profiling revealed a loss of markers for these cell types. We then assessed the spatial distribution and relative abundance of proteins in the gastric tissues using imaging mass spectrometry and liquid chromatography with tandem mass spectrometry. We detected striking differences in the protein content of corpus and antrum tissues. Four hundred ninety-two proteins were preferentially localized to the corpus in uninfected animals. The abundance of 91 of these proteins was reduced in H. pylori-infected corpus tissues exhibiting atrophic gastritis compared with infected corpus tissues exhibiting non-atrophic gastritis or uninfected corpus tissues; these included numerous proteins with metabolic functions. Fifty proteins localized to the corpus in uninfected animals were diffusely delocalized throughout the stomach in infected tissues with atrophic gastritis; these included numerous proteins with roles in protein processing. The corresponding alterations were not detected in animals infected with a H. pylori ∆cagT mutant (lacking Cag type IV secretion system activity). These results indicate that H. pylori can cause loss of proteins normally localized to the gastric corpus as well as diffuse delocalization of corpus-specific proteins, resulting in marked changes in the normal gastric molecular partitioning into distinct corpus and antrum regions.IMPORTANCEA normal stomach is organized into distinct regions known as the corpus and antrum, which have different functions, cell types, and gland architectures. Previous studies have primarily used histologic methods to differentiate these regions and detect H. pylori-induced alterations leading to stomach cancer. In this study, we investigated H. pylori-induced gastric molecular alterations in a Mongolian gerbil model of carcinogenesis. We report the detection of numerous proteins that are preferentially localized to the gastric corpus but not the antrum in a normal stomach. We show that stomachs with H. pylori-induced atrophic gastritis (a precancerous condition characterized by the loss of specialized cell types) exhibit marked changes in the abundance and localization of proteins normally localized to the gastric corpus. These results provide new insights into H. pylori-induced gastric molecular alterations that are associated with the development of stomach cancer.

Allergen Stability in Food Allergy: A Clinician's Perspective

PURPOSE OF REVIEW

The globally rising food allergy prevalence is associated with the urgent need for new disease prevention methods, efficient treatment, and reliable risk assessment methods for characterization of food allergens. Due to inter-individual variations in the digestive system, food allergens are degraded to a different extent in each person. Food processing also influences allergen digestion.

RECENT FINDINGS

In this review, we provide an overview of the digestive system with focus on relevance for food allergy. Main food proteins causing allergic reactions are evaluated, and the combined role of food processing and digestion for allergen stability is highlighted. Finally, clinical implications of this knowledge are discussed. Recent literature shows that allergen digestibility is dependent on food processing, digestive conditions, and food matrix. Digestion affects proteins allergenicity. It is currently not possible to predict the immunogenicity of allergens solely based on protein stability.

Ribosome Heterogeneity in Development and Disease

The functional ribosome is composed of ∼80 ribosome proteins. With the intensity-based absolute quantification (iBAQ) value, we calculate the stoichiometry ratio of each ribosome protein. We analyze the ribosome ratio-omics (Ribosome R ), which reflects the holistic signature of ribosome composition, in various biological samples with distinct functions, developmental stages, and pathological outcomes. The Ribosome R reveals significant ribosome heterogeneity among different tissues of fat, spleen, liver, kidney, heart, and skeletal muscles. During tissue development, testes at various stages of spermatogenesis show distinct Ribosome R signatures. During in vitro neuronal maturation, the Ribosome R changes reveal functional association with certain molecular aspects of neurodevelopment. Regarding ribosome heterogeneity associated with pathological conditions, the Ribosome R signature of gastric tumors is functionally linked to pathways associated with tumorigenesis. Moreover, the Ribosome R undergoes dynamic changes in macrophages following immune challenges. Taken together, with the examination of a broad spectrum of biological samples, the Ribosome R barcode reveals ribosome heterogeneity and specialization in cell function, development, and disease.

One-Sentence Summary

Ratio-omics signature of ribosome deciphers functionally relevant heterogeneity in development and disease.

Strategies adopted by gastric pathogen Helicobacter pylori for a mature biofilm formation: Antimicrobial peptides as a visionary treatment

Enormous efforts in recent past two decades to eradicate the pathogen that has been prevalent in half of the world's population have been problematic. The biofilm formed by Helicobacter pylori provides resistance towards innate immune cells, various combinatorial antibiotics, and human antimicrobial peptides, despite the fact that these all are potent enough to eradicate it in vitro. Biofilm provides the opportunity to secrete various virulence factors that strengthen the interaction between host and pathogen helping in evading the innate immune system and ultimately leading to persistence. To our knowledge, this review is the first of its kind to explain briefly the journey of H. pylori starting with the chemotaxis, the mechanism for selecting the site for colonization, the stress faced by the pathogen, and various adaptations to evade these stress conditions by forming biofilm and the morphological changes acquired by the pathogen in mature biofilm. Furthermore, we have explained the human GI tract antimicrobial peptides and the reason behind the failure of these AMPs, and how encapsulation of Pexiganan-A(MSI-78A) in a chitosan microsphere increases the efficiency of eradication.

Comprehensive proteogenomic characterization of early duodenal cancer reveals the carcinogenesis tracks of different subtypes

The subtypes of duodenal cancer (DC) are complicated and the carcinogenesis process is not well characterized. We present comprehensive characterization of 438 samples from 156 DC patients, covering 2 major and 5 rare subtypes. Proteogenomics reveals LYN amplification at the chromosome 8q gain functioned in the transmit from intraepithelial neoplasia phase to infiltration tumor phase via MAPK signaling, and illustrates the DST mutation improves mTOR signaling in the duodenal adenocarcinoma stage. Proteome-based analysis elucidates stage-specific molecular characterizations and carcinogenesis tracks, and defines the cancer-driving waves of the adenocarcinoma and Brunner's gland subtypes. The drug-targetable alanyl-tRNA synthetase (AARS1) in the high tumor mutation burden/immune infiltration is significantly enhanced in DC progression, and catalyzes the lysine-alanylation of poly-ADP-ribose polymerases (PARP1), which decreases the apoptosis of cancer cells, eventually promoting cell proliferation and tumorigenesis. We assess the proteogenomic landscape of early DC, and provide insights into the molecular features corresponding therapeutic targets.

Integrative proteogenomic characterization of early esophageal cancer

Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.

SUMOylation of TUFT1 is essential for gastric cancer progression through AKT/mTOR signaling pathway activation

Tuftelin (TUFT1) is highly expressed in various tumor types and promotes tumor growth and metastasis by activating AKT and other core signaling pathways. However, the effects of post-translational modifications of TUFT1 on its oncogenic function remain unexplored. In this study, we found that TUFT1 was SUMOylated at K79. SUMOylation deficiency significantly impaired the ability of TUFT1 to promote the proliferation, migration, and invasion of gastric cancer (GC) cells by blocking AKT/mTOR signaling pathway activation. SUMOylation of TUFT1 is mediated by the E3 SUMO ligase tripartite motif-containing protein 27 (TRIM27), and these two proteins regulate the malignant behavior of GC cells and AKT activation in the same pathway. TUFT1 binds to TRIM27 through its N-terminus, and decreased binding affinity of TUFT1 to TRIM27 significantly impairs its oncogenic effect. In addition, data collected from GC clinical samples indicated that the combined detection of TUFT1 and TRIM27 expression reflected tumor malignancy and patient survival with higher precision. In addition, we proved that SUMOylated TUFT1 is not only an upstream signal for AKT activation but also directly activates mTOR by forming a complex with Rab GTPase activating protein 1, which further inhibits Rab GTPases and promotes the perinuclear accumulation of mTORC1. Altogether, these data indicate that SUMOylated TUFT1 is the active form that affects GC progression through the AKT/mTOR signaling pathway and might be a promising therapeutic target or biomarker for GC progression.

Mirror proteases of Ac-Trypsin and Ac-LysargiNase precisely improve novel event identifications in Mycolicibacterium smegmatis MC2 155 by proteogenomic analysis

Accurate identification of novel peptides remains challenging because of the lack of evaluation criteria in large-scale proteogenomic studies. Mirror proteases of trypsin and lysargiNase can generate complementary b/y ion series, providing the opportunity to efficiently assess authentic novel peptides in experiments other than filter potential targets by different false discovery rates (FDRs) ranking. In this study, a pair of in-house developed acetylated mirror proteases, Ac-Trypsin and Ac-LysargiNase, were used in Mycolicibacterium smegmatis MC² 155 for proteogenomic analysis. The mirror proteases accurately identified 368 novel peptides, exhibiting 75–80% b and y ion coverages against 65–68% y or b ion coverages of Ac-Trypsin (38.9% b and 68.3% y) or Ac-LysargiNase (65.5% b and 39.6% y) as annotated peptides from M. smegmatis MC² 155. The complementary b and y ion series largely increased the reliability of overlapped sequences derived from novel peptides. Among these novel peptides, 311 peptides were annotated in other public M. smegmatis strains, and 57 novel peptides with more continuous b and y pairs were obtained for further analysis after spectral quality assessment. This enabled mirror proteases to successfully correct six annotated proteins' N-termini and detect 17 new coding open reading frames (ORFs). We believe that mirror proteases will be an effective strategy for novel peptide detection in both prokaryotic and eukaryotic proteogenomics.

Microbial Proteins in Stomach Biopsies Associated with Gastritis, Ulcer, and Gastric Cancer

(1) Background: Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide. Helicobacter pylori infection is a major risk factor, but other microbial species may also be involved. In the context of an earlier proteomics study of serum and biopsies of patients with gastroduodenal diseases, we explored here a simplified microbiome in these biopsies (H. pylori, Acinetobacter baumannii, Escherichia coli, Fusobacterium nucleatum, Bacteroides fragilis) on the protein level. (2) Methods: A cohort of 75 patients was divided into groups with respect to the findings of the normal gastric mucosa (NGM) and gastroduodenal disorders such as gastritis, ulcer, and gastric cancer (GC). The H. pylori infection status was determined. The protein expression analysis of the biopsy samples was carried out using high-definition mass spectrometry of the tryptic digest (label-free data-independent quantification and statistical analysis). (3) Results: The total of 304 bacterial protein matches were detected based on two or more peptide hits. Significantly regulated microbial proteins like virulence factor type IV secretion system protein CagE from H. pylori were found with more abundance in gastritis than in GC or NGM. This finding could reflect the increased microbial involvement in mucosa inflammation in line with current hypotheses. Abundant proteins across species were heat shock proteins and elongation factors. (4) Conclusions: Next to the bulk of human proteins, a number of species-specific bacterial proteins were detected in stomach biopsies of patients with gastroduodenal diseases, some of which, like those expressed by the cag pathogenicity island, may provide gateways to disease prevention without antibacterial intervention in order to reduce antibiotic resistance.

Liver cancer heterogeneity modeled by in situ genome editing of hepatocytes

Mechanistic study and precision treatment of primary liver cancer (PLC) are hindered by marked heterogeneity, which is challenging to recapitulate in any given liver cancer mouse model. Here, we report the generation of 25 mouse models of PLC by in situ genome editing of hepatocytes recapitulating 25 single or combinations of human cancer driver genes. These mouse tumors represent major histopathological types of human PLCs and could be divided into three human-matched molecular subtypes based on transcriptomic and proteomic profiles. Phenotypical characterization identified subtype- or genotype-specific alterations in immune microenvironment, metabolic reprogramming, cell proliferation, and expression of drug targets. Furthermore, single-cell analysis and expression tracing revealed spatial and temporal dynamics in expression of pyruvate kinase M2 (Pkm2). Tumor-specific knockdown of Pkm2 by multiplexed genome editing reversed the Warburg effect and suppressed tumorigenesis in a genotype-specific manner. Our study provides mouse PLC models with defined genetic drivers and characterized phenotypical heterogeneity suitable for mechanistic investigation and preclinical testing.

Gastric Cancer Pre-Stage Detection and Early Diagnosis of Gastritis Using Serum Protein Signatures

Background: A gastric cancer (GC) diagnosis relies on histopathology. Endoscopy rates are increasing. Helicobacter pylori infection is a major GC risk factor. In an effort to elucidate abundant blood biomarkers, and potentially reduce the number of diagnostic surgical interventions, we investigated sera and biopsies from a cohort of 219 H. pylori positive and negative patients diagnosed with GC, gastritis, and ulcers. This allowed the comparative investigation of the different gastroduodenal diseases, and the exclusion of protein changes resulting from bacterial infection or inflammation of the gastric mucosa when searching for GC-dependent proteins. Methods: High-definition mass spectrometry-based expression analysis of tryptically digested proteins was performed, followed by multivariate statistical and network analyses for the different disease groups, with respect to H. pylori infection status. Significantly regulated proteins differing more than two-fold between groups were shortlisted, and their role in gastritis and GC discussed. Results: We present data of comparative protein analyses of biopsies and sera from patients suffering from mild to advanced gastritis, ulcers, and early to advanced GC, in conjunction with a wealth of metadata, clinical information, histopathological evaluation, and H. pylori infection status. We used samples from pre-malignant stages to extract prospective serum markers for early-stage GC, and present a 29-protein marker panel containing, amongst others, integrin β-6 and glutathione peroxidase. Furthermore, ten serum markers specific for advanced GC, independent of H. pylori infection, are provided. They include CRP, protein S100A9, and kallistatin. The majority of these proteins were previously discussed in the context of cancer or GC. In addition, we detected hypoalbuminemia and increased fibrinogen serum levels in gastritis. Conclusion: Two protein panels were suggested for the development of multiplex tests for GC serum diagnostics. For most of the elements contained in these panels, individual commercial tests are available. Thus, we envision the design of multi-protein assays, incorporating several to all of the panel members, in order to gain a level of specificity that cannot be achieved by testing a single protein alone. As their development and validation will take time, gastritis diagnosis based on the fibrinogen to albumin serum ratio may be a quick way forward. Its determination at the primary/secondary care level for early diagnosis could significantly reduce the number of referrals to endoscopy. Preventive measures are in high demand. The protein marker panels presented in this work will contribute to improved GC diagnostics, once they have been transferred from a research result to a practical tool.

Integrative proteomic characterization of trace FFPE samples in early-stage gastrointestinal cancer

Background

The surveillance and therapy of early-stage cancer would be better for patients’ prognosis. However, the extreme trace amount of tissue samples in different stages have limited in portraying the characterization of early-stage cancer. Therefore, we focused on and presented comprehensive proteomic and phosphoproproteomic profiling of the trace FFPE samples from early-stage gastrointestinal cancer, and then explored the potential biomarkers of early-stage gastrointestinal cancer.

Methods

In this study, a quantitative proteomic method with chromatography with mass spectrometry (LC-MS/MS) was used to analyse the proteomic difference between the trace early-stage esophageal squamous cell carcinoma (EESCC) and early-stage duodenum adenocarcinoma cancer (EDAC).

Results

We identified ~ 6000 proteins and > 10,000 phosphosites in single trace FFPE samples. Comparative analysis disclosed the diverse proteomic features of tumor tissues compared with paired normal tissue of EESCC and EDAC, and revealed the difference of EESCC and EDAC was derived from their origin normal tissue. The distinct separation of EESCC and EDAC illustrated the functions of cell cycle (RB1 T373, EGFR T693) in EESCC, and the positive impacts of apoptosis, metabolic processes (MTOR and MTOR S1261) in EDAC. Furthermore, we deconvoluted the immune infiltration of early-stage gastrointestinal cancer, in which higher immune cell signatures were detected in EDAC, and showed the specific cytokines in EESCC and EDAC. We performed kinases-substates relationship analysis and elucidated the specific proteomic kinase characterization of EESCC and EDAC, and proposed the medicative effects and corresponding drugs for EESCC and EDAC at the clinic.

Conclusion

We disclosed the specific immune characterization of the early-stage gastrointestinal cancer, and presented potential makers of EESCC (EGFR, PDGFRB, CDK4, WEE1) and EDAC (MTOR, MAP2K1, MAPK3). This study represents a major stepping stone towards investigating the carcinogenesis mechanism of gastrointestinal cancer, and providing a rich resource for medicative strategy in the clinic.

Graphical Abstract

Application of individualized differential expression analysis in human cancer proteome

Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

Proteomic profiling identifies signatures associated with progression of precancerous gastric lesions and risk of early gastric cancer

Background

Molecular features underlining the multistage progression of gastric lesions and development of early gastric cancer (GC) are poorly understood, restricting the ability to GC prevention and management.

Methods

We portrayed proteomic landscape and explored proteomic signatures associated with progression of gastric lesions and risk of early GC. Tissue proteomic profiling was conducted for a total of 324 subjects. A case-control study was performed in the discovery stage (n=169) based on populations from Linqu, a known high-risk area for GC in China. We then conducted two-stage validation, including a cohort study from Linqu (n = 56), with prospective follow-up for progression of gastric lesions (280–473 days), and an independent case-control study from Beijing (n = 99).

Findings

There was a clear distinction in proteomic features for precancerous gastric lesions and GC. We derived four molecular subtypes of gastric lesions and identified subtype-S4 with the highest progression risk. We found 104 positively-associated and 113 inversely-associated proteins for early GC, with APOA1BP, PGC, HPX and DDT associated with the risk of gastric lesion progression. Integrating these proteomic signatures, the ability to predict progression of gastric lesions was significantly strengthened (areas-under-the-curve=0.88 (95%CI: 0.78–0.99) vs. 0.56 (0.36–0.76), Delong's P = 0.002). Immunohistochemistry assays and examination at mRNA level validated the findings for four proteins.

Interpretation

We defined proteomic signatures for progression of gastric lesions and risk of early GC, which may have translational significance for identifying particularly high-risk population and detecting GC at an early stage, improving potential for targeted GC prevention.

Funding

The funders are listed in the Acknowledgement.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Proteomics reveals the potential mechanism of Mrps35 controlling Listeria monocytogenes intracellular proliferation in macrophages

Macrophages are sentinels in the organism which can resist and destroy various bacteria through direct phagocytosis. Here, we reported that expression level of mitochondrial ribosomal protein S35 (Mrps35) continued to decrease over infection time after Listeria monocytogenes (L. monocytogenes) infected macrophages. Our results indicated that knockdown Mrps35 increased the load of L. monocytogenes in macrophages. This result supported that Mrps35 played the crucial roles in L. monocytogenes infection. Moreover, we performed the comprehensive proteomics to analyze the differentially expressed protein of wild type and Mrps35 Knockdown Raw264.7 cells by L. monocytogenes infection over 6 h. Based on the results of mass spectrometry, we presented a wide variety of hypotheses about the mechanism of Mrps35 controlling the L. monocytogenes intracellular proliferation. Among them, experiments confirmed that Mrps35 and 60S ribosomal protein L22-like 1 (Rpl22l1) were a functional correlation or potentially a compensatory mechanism during L. monocytogenes infection. This study provided new insights into understanding that L. monocytogenes infection changed the basic synthesis or metabolism-related proteins of host cells.

Overexpressed NEDD8 as a potential therapeutic target in esophageal squamous cell carcinoma

OBJECTIVE

The hyperactivated neddylation pathway plays an important role in tumorigenesis and is emerging as a promising anticancer target. We aimed to study whether NEDD8 (neural precursor cell expressed, developmentally down-regulated 8) might serve as a therapeutic target in esophageal squamous cell carcinoma (ESCC).

METHODS

The clinical relevance of NEDD8 expression was evaluated by using The Cancer Genome Atlas (TCGA) database and tissue arrays. NEDD8-knockdown ESCC cells generated with the CRISPR/Cas9 system were used to explore the anticancer effects and mechanisms. Quantitative proteomic analysis was used to examine the variations in NEDD8 knockdown-induced biological pathways. The cell cycle and apoptosis were assessed with fluorescence activated cell sorting. A subcutaneous-transplantation mouse tumor model was established to investigate the anticancer potential of NEDD8 silencing in vivo.

RESULTS

NEDD8 was upregulated at both the mRNA and protein expression levels in ESCC, and NEDD8 overexpression was associated with poorer overall patient survival (mRNA level: P = 0.028, protein level: P = 0.026, log-rank test). Downregulation of NEDD8 significantly suppressed tumor growth both in vitro and in vivo. Quantitative proteomic analysis revealed that downregulation of NEDD8 induced cell cycle arrest, DNA damage, and apoptosis in ESCC cells. Mechanistic studies demonstrated that NEDD8 knockdown led to the accumulation of cullin-RING E3 ubiquitin ligases (CRLs) substrates through inactivation of CRLs, thus suppressing the malignant phenotype by inducing cell cycle arrest and apoptosis in ESCC. Rescue experiments demonstrated that the induction of apoptosis after NEDD8 silencing was attenuated by DR5 knockdown.

CONCLUSIONS

Our study elucidated the anti-ESCC effects and underlying mechanisms of NEDD8 knockdown, and validated NEDD8 as a potential target for ESCC therapy.

Proteome-wide Analysis Reveals Substrates of E3 Ligase RNF146 Targeted for Degradation

Specific E3 ligases target tumor suppressors for degradation. Inhibition of such E3 ligases may be an important approach to cancer treatment. RNF146 is a RING domain and PARylation-dependent E3 ligase that functions as an activator of the β-catenin/Wnt and YAP/Hippo pathways by targeting the degradation of several tumor suppressors. Tankyrases 1 and 2 (TNKS1/2) are the only known poly-ADP-ribosyltransferases that require RNF146 to degrade their substrates. However, systematic identification of RNF146 substrates have not yet been performed. To uncover substrates of RNF146 that are targeted for degradation, we generated RNF146 knockout cells and TNKS1/2-double knockout cells and performed proteome profiling with label-free quantification as well as transcriptome analysis. We identified 160 potential substrates of RNF146, which included many known substrates of RNF146 and TNKS1/2 and 122 potential TNKS-independent substrates of RNF146. In addition, we validated OTU domain-containing protein 5 and Protein mono-ADP-ribosyltransferase PARP10 as TNKS1/2-independent substrates of RNF146 and SARDH as a novel substrate of TNKS1/2 and RNF146. Our study is the first proteome-wide analysis of potential RNF146 substrates. Together, these findings not only demonstrate that proteome profiling can be a useful general approach for the systemic identification of substrates of E3 ligases but also reveal new substrates of RNF146, which provides a resource for further functional studies.

Proteomic Profiling of the Human Tissue and Biological Fluid Proteome

In-depth analysis of the human genome sequence has led to the annotation of approximately 20,000 human protein-coding genes. Although mass spectrometry (MS)-based workflows have made a great headway in achieving near genome-wide coverage, an equivalent complete map of the human proteome remains elusive. Delineating the spatial distribution of all human proteins at the organ, tissue, and cellular level can offer insight into health and disease and represents an excellent reference for the discovery of biomarkers and therapeutic targets. Here, we performed label-free liquid chromatography coupled to tandem MS (LC-MS/MS) to profile the normal human proteome. In total, we analyzed 117 samples from 46 normal tissues and organs at autopsy. Our high-resolution MS approach allowed for the quantification of 10,438 unique proteins. In order to expand our coverage of the human proteome, we combined our previously published biological fluid proteomic data from healthy individuals. We considered data from seven biological fluids, including urine, cerebrospinal fluid, synovial fluid, seminal plasma, sweat, cervical vaginal fluid, and nipple aspirate fluid. Overall, we generated tandem mass spectra corresponding to 13,028 unique human protein-coding genes. Although our analysis did not accomplish complete proteome coverage, it should be an important complementary resource for future biomarker discovery.

A high-stringency blueprint of the human proteome

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

Background

Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.

Methods

In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.

Results

Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.

Conclusion

The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of valuable data for both basic and clinical researchers. Accurate prediction for tumor and non-tumor samples, as well as the tumor types has become a key step for biological and medical research, such as biomarker discovery, diagnosis, and monitoring of diseases. The traditional MS-based classification strategy mainly depends on the identification and quantification results of MS data, which has some inherent limitations, such as the low identification rate of MS data. Here, a deep learning-based tumor classifier directly using MS raw data is proposed, which is independent of the identification and quantification results of MS data. The potential precursors with intensities and retention times from MS data as input is first detected and extracted. Then, a deep learning-based classifier is trained, which can accurately distinguish between the tumor and non-tumor samples. Finally, it is demonstrated the deep learning-based classifier has a good performance compared with other machine learning methods and may help researchers find the potential biomarkers which are likely to be missed by the traditional strategy.

NQO1 is a determinant for cellular sensitivity to anti-tumor agent Napabucasin

Napabucasin (NAPA) is thought to be a potent cancer stemness inhibitor in different types of cancer cell lines. While it has shown promising activity in early phase clinical trials, two recent phase III NAPA clinical trials failed to meet the primary endpoint of overall survival. The reason for the failure is not clear, but a possible way to revive the clinical trial is to stratify patients with biomarkers that could predict NAPA response. Here, we report the identification of NAD(P)H dehydrogenase 1 (NQO1) as a major determinant of NAPA efficacy. A proteomic profiling of cancer cell lines revealed that NQO1 abundance is negatively correlated with IC₅₀; in vitro assays showed that NAPA is a substrate for NQO1, which mediates the generation of ROS that leads to cell death. Furthermore, activation of an NQO1 transcription factor NRF2 by chemicals, including an FDA approved drug, can increase the NAPA cytotoxicity. Our findings suggest a potential use of NQO1 expression as a companion diagnostic test to identify patients in future NAPA trials and a combination strategy to expand the application of NAPA-based regimens for cancer therapy.

Review: Gastric malignancies: Basic aspects

Gastric cancer is the third deadliest cancer in the world, and the absolute number of cases is increasing every year due to aging and growing of high-risk populations. This disease is a consequence of the complex interaction of microbial agents, with environmental and host factors, resulting in the dysregulation of multiple oncogenic and tumor-suppressing signaling pathways. Despite the advances in our understanding of carcinogenesis, there are still reduced therapeutic options for patients with gastric cancer. In recent years, genomic analyses of gastric tumors have emphasized their molecular heterogeneity. The distinction of gastric cancer molecular subtypes may be a key to identify novel therapeutic targets, to predict patient outcome and response to therapy, and to guide early diagnosis strategies. In this review, we summarize the most recent updates on the relationship between microbial agents and gastric cancer, in particular, Helicobacter pylori, the non-H pylori microbiome, and Epstein-Barr virus. We also highlight the main advances made in the past year regarding the molecular characterization of gastric cancer, especially the signatures with potential clinical utility.