Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

Energy metabolism: a new target for gastric cancer treatment

Gastric cancer is the fifth most common malignancy worldwide having the fourth highest mortality rate. Energy metabolism is key and closely linked to tumour development. Most important in the reprogramming of cancer metabolism is the Warburg effect, which suggests that tumour cells will utilise glycolysis even with normal oxygen levels. Various molecules exert their effects by acting on enzymes in the glycolytic pathway, integral to glycolysis. Second, mitochondrial abnormalities in the reprogramming of energy metabolism, with consequences for glutamine metabolism, the tricarboxylic acid cycle and oxidative phosphorylation, abnormal fatty acid oxidation and plasma lipoprotein metabolism are important components of tumour metabolism. Third, inflammation-induced oxidative stress is a danger signal for cancer. Fourth, patterns of signalling pathways involve all aspects of metabolic transduction, and many clinical drugs exert their anticancer effects through energy metabolic signalling. This review summarises research on energy metabolism genes, enzymes and proteins and transduction pathways associated with gastric cancer, and discusses the mechanisms affecting their effects on postoperative treatment resistance and prognoses of gastric cancer. We believe that an in-depth understanding of energy metabolism reprogramming will aid the diagnosis and subsequent treatment of gastric cancer.

Sijunzi decoction ameliorates gastric precancerous lesions via regulating oxidative phosphorylation based on proteomics and metabolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Sijunzi decoction (SJZD), a traditional Chinese medicine formula, is commonly used in clinical practice for the treatment of gastric precancerous lesions (GPL). However, the mechanism of gastric protection is not fully understood.

AIMS OF THE STUDY

The purpose of this study was to systematically evaluate the efficacy of SJZD in blocking the development of GPL and to reveal the underlying mechanism.

METHODS

First, we established a rat model of GPL, which was induced by N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) combined with an irregular diet and 40% ethanol. The efficacy of SJZD was evaluated based on pathological sections and serum biochemical indices. Then, the pharmacodynamic mechanism of SJZD was revealed by quantitative proteomics based on stable isotope dimethyl labeling. At the same time, the pharmacodynamic mechanism was verified by quantitative metabolomics. In addition, the anti-gastritis effect of SJZD was confirmed by a serum pharmacology method in a cell model, and the functional mechanism was further verified.

RESULTS

We demonstrated that SJZD could block the development of GPL in the animal model. Proteomics and metabolomics revealed that SJZD blocks GPL development by regulating oxidative phosphorylation (OXPHOS). In addition, the serum pharmacology results showed that SJZD-containing serum (SJZD-CS) could inhibit apoptosis in MNNG-induced GES-1 cells. OXPHOS inhibitors could significantly reduce the protective effect of SJZD-CS.

CONCLUSION

SJZD effectively ameliorates GPL, and proteomics and metabolomics revealed that its protective effects are closely related to OXPHOS.

Identification of plasma proteomic signatures associated with the progression of cardia gastric cancer and precancerous lesions

Objective

Considering that there are no effective biomarkers for the screening of cardia gastric cancer (CGC), we developed a noninvasive diagnostic approach, employing data-independent acquisition (DIA) proteomics to identify candidate protein markers.

Methods

Plasma samples were obtained from 40 subjects, 10 each for CGC, cardia high-grade dysplasia (CHGD), cardia low-grade dysplasia (CLGD), and healthy controls. Proteomic profiles were obtained through liquid chromatography-mass spectrometry (LC-MS/MS-based DIA proteomics. Candidate plasma proteins were identified by weighted gene co-expression network analysis (WGCNA) combined with machine learning and further validated by the Human Protein Atlas (HPA) database. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the biomarker panel.

Results

There was a clear distinction in proteomic features among CGC, CHGD, CLGD, and the healthy controls. According to the WGCNA, we found 42 positively associated and 164 inversely associated proteins related to CGC progression and demonstrated several canonical cancer-associated pathways. Combined with the results from random forests, LASSO regression, and immunohistochemical results from the HPA database, we identified three candidate proteins (GSTP1, CSRP1, and LY6G6F) that could together distinguish CLGD (AUC = 0.91), CHGD (AUC = 0.99) and CGC (AUC = 0.98) from healthy controls with excellent accuracy.

Conclusions

The panel of protein biomarkers showed promising diagnostic potential for CGC and precancerous lesions. Further validation and a larger-scale study are warranted to assess its potential clinical applications, suggesting a potential avenue for CGC prevention in the future.

WIPF1 promotes gastric cancer progression by regulating PI3K/Akt signaling in a myocardin-dependent manner

Wiskott-Aldrich syndrome protein-interacting protein family member 1 (WIPF1) is associated with malignant tumor progression. However, molecular links between WIPF1 and gastric cancer (GC) remain elusive. The expression of WIPF1 was detected in GC tissues and cells. WIPF1 was overexpressed in GC tissues and cells and high expression of WIPF1 was an independent risk factor for a poor prognosis in patients with GC. Further experiments indicated that WIPF1 promoted the proliferation, invasion, and migration of GC cells in vivo and in vitro. WIPF1-regulated genes were closely related to cell proliferation and migration in GC, and silencing WIPF1 significantly repressed PI3K/AKT signaling pathway activation. WIPF1 was activated by myocardin (MYOCD) translation. Rescue experiments confirmed that MYOCD promotes the proliferation, invasion, and migration of GC cells in a WIPF1-dependent manner and activates the PI3K/AKT signaling pathway. MYOCD may transactivate WIPF1 and facilitate GC cell growth and metastasis by activating the PI3K/AKT signaling pathway.

A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis

BACKGROUND

Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.

METHODS

Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes.

RESULTS

Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages.

CONCLUSIONS

These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.

Proteomic signatures of infiltrative gastric cancer by proteomic and bioinformatic analysis

BACKGROUND

Proteomic signatures of Ming's infiltrative gastric cancer (IGC) remain unknown.

AIM

To elucidate the molecular characteristics of IGC at the proteomics level.

METHODS

Twelve pairs of IGC and adjacent normal tissues were collected and their proteomes were analyzed by high performance liquid chromatography tandem mass spectrometry. The identified peptides were sequenced de novo and matched against the SwissProt database using Maxquant software. The differentially expressed proteins (DEPs) were screened using |log2(Fold change)| > 1 and P-adj < 0.01 as the thresholds. The expression levels of selected proteins were verified by Western blotting. The interaction network of the DEPs was constructed with the STRING database and visualized using Cytoscape with cytoHubba software. The DEPs were functionally annotated using clusterProfiler, STRING and DAVID for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. P < 0.05 was considered statistically significant.

RESULTS

A total of 7361 DEPs were identified, of which 94 were significantly up-regulated and 223 were significantly down-regulated in IGC relative to normal gastric tissues. The top 10 up-regulated proteins were MRTO4, BOP1, PES1, WDR12, BRIX1, NOP2, POLR1C, NOC2L, MYBBP1A and TSR1, and the top 10 down-regulated proteins were NDUFS8, NDUFS6, NDUFA8, NDUFA5, NDUFC2, NDUFB8, NDUFB5, NDUFB9, UQCRC2 and UQCRC1. The up-regulated proteins were enriched for 9 biological processes including DNA replication, ribosome biogenesis and initiation of DNA replication, and the cellular component MCM complex. Among the down-regulated proteins, 17 biological processes were enriched, including glucose metabolism, pyruvic acid metabolism and fatty acid β-oxidation. In addition, the mitochondrial inner membrane, mitochondrial matrix and mitochondrial proton transport ATP synthase complex were among the 6 enriched cellular components, and 11 molecular functions including reduced nicotinamide adenine dinucleotide dehydrogenase activity, acyl-CoA dehydrogenase activity and nicotinamide adenine dinucleotide binding were also enriched. The significant KEGG pathways for the up-regulated proteins were DNA replication, cell cycle and mismatch repair, whereas 18 pathways including oxidative phosphorylation, fatty acid degradation and phenylalanine metabolism were significantly enriched among the down-regulated proteins.

CONCLUSION

The proteins involved in cell cycle regulation, DNA replication and mismatch repair, and metabolism were significantly altered in IGC, and the proteomic profile may enable the discovery of novel biomarkers.

Early detection of colorectal cancer based on circular DNA and common clinical detection indicators

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide, and it is the second leading cause of death from cancer in the world, accounting for approximately 9% of all cancer deaths. Early detection of CRC is urgently needed in clinical practice.

AIM

To build a multi-parameter diagnostic model for early detection of CRC.

METHODS

Total 59 colorectal polyps (CRP) groups, and 101 CRC patients (38 early-stage CRC and 63 advanced CRC) for model establishment. In addition, 30 CRP groups, and 62 CRC patients (30 early-stage CRC and 32 advanced CRC) were separately included to validate the model. 51 commonly used clinical detection indicators and the 4 extrachromosomal circular DNA markers NDUFB7, CAMK1D, PIK3CD and PSEN2 that we screened earlier. Four multi-parameter joint analysis methods: binary logistic regression analysis, discriminant analysis, classification tree and neural network to establish a multi-parameter joint diagnosis model.

RESULTS

Neural network included carcinoembryonic antigen (CEA), ischemia-modified albumin (IMA), sialic acid (SA), PIK3CD and lipoprotein a (LPa) was chosen as the optimal multi-parameter combined auxiliary diagnosis model to distinguish CRP and CRC group, when it differentiated 59 CRP and 101 CRC, its overall accuracy was 90.8%, its area under the curve (AUC) was 0.959 (0.934, 0.985), and the sensitivity and specificity were 91.5% and 82.2%, respectively. After validation, when distinguishing based on 30 CRP and 62 CRC patients, the AUC was 0.965 (0.930-1.000), and its sensitivity and specificity were 66.1% and 70.0%. When distinguishing based on 30 CRP and 32 early-stage CRC patients, the AUC was 0.960 (0.916-1.000), with a sensitivity and specificity of 87.5% and 90.0%, distinguishing based on 30 CRP and 30 advanced CRC patients, the AUC was 0.970 (0.936-1.000), with a sensitivity and specificity of 96.7% and 86.7%.

CONCLUSION

We built a multi-parameter neural network diagnostic model included CEA, IMA, SA, PIK3CD and LPa for early detection of CRC, compared to the conventional CEA, it showed significant improvement.

Characteristics of the Immune Cell Infiltration Landscape in Gastric Cancer to Assistant Immunotherapy

Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients.

Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response.

Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits.

Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.

Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning

Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.

Proteomics and transcriptomics jointly identify the key role of oxidative phosphorylation in fluoride-induced myocardial mitochondrial dysfunction in rats

The regulation of mitochondrial function, which is dominated by oxidative phosphorylation (OXPHOs), is important in fluoride induced cardiovascular disease. Based on the previous study of fluoride-induced mitochondrial structure and membrane potential abnormalities, this study integrated ITRAQ protein quantification and RNA-Seq methods to analyze the sequencing data of rat myocardial tissue under fluoride exposure (0, 30, 60 and 90 mg/L). A total of 22 differentially expressed genes associated with the OXPHOs pathway were screened by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) co-enrichment analysis, and were localizated by Interaction Network and calculated inter-genes and inter-omics correlations by Pearson correlation. In general, fluoride exposure can down-regulate genes related OXPHOs, particularly affecting the assembly of the complex I including Ndufa10, resulting in abnormal mitochondrial ATP synthesis and reduced myocardial energy supply. Most importantly, this study shows that the enriched information from the proteomics can explain the change process of energy production, but the specific molecules involved in energy supply cannot be obtained via transcriptomics information alone. Based on the results of transcriptional and protein analysis, our findings contribute to an innovative understanding of the pathways and molecular changes of myocardial injury induced by fluorosis.

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

The aim of the present study was to explore the underlying mechanisms involved in gastric cancer (GC) formation using data-independent acquisition (DIA) quantitative proteomics analysis. We identified the differences in protein expression and related functions involved in biological metabolic processes in GC. Totally, 745 differentially expressed proteins (DEPs) were found in GC tissues vs. gastric normal tissues. Despite enormous complexity in the details of the underlying regulatory network, we find that clusters of proteins from the DEPs were mainly involved in 38 pathways. All of the identified DEPs involved in oxidative phosphorylation were down-regulated. Moreover, GC possesses significantly altered biological metabolic processes, such as NADH dehydrogenase complex assembly and tricarboxylic acid cycle, which is mostly consistent with that in KEGG analysis. Furthermore the higher expression of UQCRQ, NDUFB7 and UQCRC2 were positively correlated with a better prognosis, implicating these proteins may as novel candidate diagnostic and prognostic biomarkers.