Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.

Mass spectrometry analysis of phosphotyrosine-containing proteins.

Tyrosine phosphorylation is a crucial posttranslational modification that is involved in various aspects of cell biology and often has functions in cancers. It is necessary not only to identify the specific phosphorylation sites but also to quantify their phosphorylation levels under specific pathophysiological conditions. Because of its high sensitivity and accuracy, mass spectrometry (MS) has been widely used to identify endogenous and synthetic phosphotyrosine proteins/peptides across a range of biological systems. However, phosphotyrosine-containing proteins occur in extremely low abundance and they degrade easily, severely challenging the application of MS. This review highlights the advances in both quantitative analysis procedures and enrichment approaches to tyrosine phosphorylation before MS analysis and reviews the differences among phosphorylation, sulfation, and nitration of tyrosine residues in proteins. In-depth insights into tyrosine phosphorylation in a wide variety of biological systems will offer a deep understanding of how signal transduction regulates cellular physiology and the development of tyrosine phosphorylation-related drugs as cancer therapeutics.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas.

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

The comparison between 2DE-MS and bottom-up LC-MS demands high-end techniques for both technologies.

In contrast to bottom-up LC-MS only 2DE-MS can separate and detect a huge number of human protein species. Kwiatkowski et al. (in this issue) established parameters to estimate the amount of protein speciation for each human protein. Proteins identified in 2DE-MS approaches showed more protein speciation than in bottom-up LC-MS. The authors state that protein speciation is likely to increase the chance of proteins to be determined in 2-DE/MS, though admitting that low-sensitivity 2DE-MS methods were used in this study. In agreement with Kwiatkowski et al., we are convinced that the difference between 2DE-MS and bottom-up LC-MS will disappear, if high-resolution 2DE is combined with identification by a high-sensitivity LC-Orbitrap-MS. Meta-analysis of proteomic data is surely a promising tool, though the technological progress in 2DE and MS has to reach a plateau to enable useful comparisons.

Quantitative proteomics reveals a broad-spectrum antiviral property of ivermectin, benefiting for COVID-19 treatment.

Viruses such as human cytomegalovirus (HCMV), human papillomavirus (HPV), Epstein-Barr virus (EBV), human immunodeficiency virus (HIV), and coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) represent a great burden to human health worldwide. FDA-approved anti-parasite drug ivermectin is also an antibacterial, antiviral, and anticancer agent, which offers more potentiality to improve global public health, and it can effectively inhibit the replication of SARS-CoV-2 in vitro. This study sought to identify ivermectin-related virus infection pathway alterations in human ovarian cancer cells. Stable isotope labeling by amino acids in cell culture (SILAC) quantitative proteomics was used to analyze human ovarian cancer cells TOV-21G treated with and without ivermectin (20 µmol/L) for 24 h, which identified 4447 ivermectin-related proteins in ovarian cancer cells. Pathway network analysis revealed four statistically significant antiviral pathways, including HCMV, HPV, EBV, and HIV1 infection pathways. Interestingly, compared with the reported 284 SARS-CoV-2/COVID-19-related genes from GencLip3, we identified 52 SARS-CoV-2/COVID-19-related protein alterations when treated with and without ivermectin. Protein-protein network (PPI) was constructed based on the interactions between 284 SARS-CoV-2/COVID-19-related genes and between 52 SARS-CoV-2/COVID-19-related proteins regulated by ivermectin. Molecular complex detection analysis of PPI network identified three hub modules, including cytokines and growth factor family, MAP kinase and G-protein family, and HLA class proteins. Gene Ontology analysis revealed 10 statistically significant cellular components, 13 molecular functions, and 11 biological processes. These findings demonstrate the broad-spectrum antiviral property of ivermectin benefiting for COVID-19 treatment in the context of predictive, preventive, and personalized medicine in virus-related diseases.

MASS SPECTROMETRY-BASED MITOCHONDRIAL PROTEOMICS IN HUMAN OVARIAN CANCERS.

The prominent characteristics of mitochondria are highly dynamic and regulatory, which have crucial roles in cell metabolism, biosynthetic, senescence, apoptosis, and signaling pathways. Mitochondrial dysfunction might lead to multiple serious diseases, including cancer. Therefore, identification of mitochondrial proteins in cancer could provide a global view of tumorigenesis and progression. Mass spectrometry-based quantitative mitochondrial proteomics fulfils this task by enabling systems-wide, accurate, and quantitative analysis of mitochondrial protein abundance, and mitochondrial protein posttranslational modifications (PTMs). Multiple quantitative proteomics techniques, including isotope-coded affinity tag, stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantification, tandem mass tags, and label-free quantification, in combination with different PTM-peptide enrichment methods such as TiO2 enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides, increase flexibility for researchers to study mitochondrial proteomes. This article reviews isolation and purification of mitochondria, quantitative mitochondrial proteomics, quantitative mitochondrial phosphoproteomics, mitochondrial protein-involved signaling pathway networks, mitochondrial phosphoprotein-involved signaling pathway networks, integration of mitochondrial proteomic and phosphoproteomic data with whole tissue proteomic and transcriptomic data and clinical information in ovarian cancers (OC) to in-depth understand its molecular mechanisms, and discover effective mitochondrial biomarkers and therapeutic targets for predictive, preventive, and personalized treatment of OC. This proof-of-principle model about OC mitochondrial proteomics is easily implementable to other cancer types. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

MASS SPECTROMETRY-BASED PERSONALIZED DRUG THERAPY.

Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost-effective, and easy to be used high-throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS-based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix-assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI-TOF-MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS-based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS-based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

How many proteins can be identified in a 2DE gel spot within an analysis of a complex human cancer tissue proteome?

Two-dimensional gel electrophoresis (2DE) in proteomics is traditionally assumed to contain only one or two proteins in each 2DE spot. However, 2DE resolution is being complemented by the rapid development of high sensitivity mass spectrometers. Here we compared MALDI-MS, LC-Q-TOF MS and LC-Orbitrap Velos MS for the identification of proteins within one spot. With LC-Orbitrap Velos MS each Coomassie Blue-stained 2DE spot contained an average of at least 42 and 63 proteins/spot in an analysis of a human glioblastoma proteome and a human pituitary adenoma proteome, respectively, if a single gel spot was analyzed. If a pool of three matched gel spots was analyzed this number further increased up to an average of 230 and 118 proteins/spot for glioblastoma and pituitary adenoma proteome, respectively. Multiple proteins per spot confirm the necessity of isotopic labeling in large-scale quantification of different protein species in a proteome. Furthermore, a protein abundance analysis revealed that most of the identified proteins in each analyzed 2DE spot were low-abundance proteins. Many proteins were present in several of the analyzed spots showing the ability of 2DE-MS to separate at the protein species level. Therefore, 2DE coupled with high-sensitivity LC-MS has a clearly higher sensitivity as expected until now to detect, identify and quantify low abundance proteins in a complex human proteome with an estimated resolution of about 500 000 protein species. This clearly exceeds the resolution power of bottom-up LC-MS investigations.

The lncRNA SNHG3 regulates energy metabolism of ovarian cancer by an analysis of mitochondrial proteomes.

BACKGROUND: Malignant tumors are heterogeneous diseases characterized by different metabolic phenotypes. These were revealed by Warburg effect and reverse Warburg effect phenotypes. However, the molecular mechanism remains largely unknown. METHODS: Isobaric tag for relative and absolute quantification (iTRAQ) proteomics was used to identify mitochondrial differentially expressed proteins (DEPs) of ovarian cancers relative to controls, followed by bioinformatic analysis. The molecular profiling of long non-coding RNAs (lncRNAs) was also investigated by searching the dataset of the Cancer Genome Atlas (TCGA) consisting of 419 ovarian cancer patients. RESULTS: A total of 1198 mitochondrial DEPs were identified by iTRAQ quantitative proteomics. Bioinformatic analysis of those DEPs showed that cancer cells exhibited an increased dependence on Kreb's cycle and oxidative phosphorylation, with some related upregulated proteins. Moreover, TCGA analysis showed lncRNA SNHG3 was not only related to ovarian cancer survival, but also energy metabolism. Interestingly, integrated analysis of the results of GSEA analysis and Starbase 2.0 found that SNHG3 was related to energy metabolism by regulating miRNAs and EIF4AIII, and those molecules had target sites with PKM, PDHB, IDH2, and UQCRH in the glycolysis, Kreb's cycle, and oxidative phosphorylation (OXPHOS) pathways. Furthermore, SNHG3 might be associated with drug resistance. CONCLUSION: The results derived from TCGA data and mitochondrial DEPs data are consistent with the Warburg and reverse Warburg effects that cancer cells mainly rely on glycolysis and oxidative phosphorylation to produce energy. Also, this integrated lncRNA-miRNA-mRNA and lncRNA-binding protein-mRNA signatures might have important merit for insights into molecular mechanisms and clinical implications in ovarian cancer.

Mass spectrometry analysis of nitrotyrosine-containing proteins.

Oxidative stress plays important roles in a wide range of diseases such as cancer, inflammatory disease, neurodegenerative disorders, etc. Tyrosine nitration in a protein is a chemically stable oxidative modification, and a marker of oxidative injuries. Mass spectrometry (MS) is a key technique to identify nitrotyrosine-containing proteins and nitrotyrosine sites in endogenous and synthetic nitroproteins and nitropeptides. However, in vivo nitrotyrosine-containing proteins occur with extreme low-abundance to severely challenge the use of MS to identify in vivo nitroproteins and nitrotyrosine sites. A preferential enrichment of nitroproteins and/or nitropeptides is necessary before MS analysis. Current enrichment methods include immuno-affinity techniques, chemical derivation of the nitro group plus target isolations, followed with tandem mass spectrometry analysis. This article reviews the MS techniques and pertinent before-MS enrichment techniques for the identification of nitrotyrosine-containing proteins. This article reviews future trends in the field of nitroproteomics, including quantitative nitroproteomics, systems biological networks of nitroproteins, and structural biology study of tyrosine nitration to completely clarify the biological functions of tyrosine nitration.

Proteomic and functional profiles of a follicle-stimulating hormone positive human nonfunctional pituitary adenoma.

Nonfunctional pituitary adenoma (NFPA) is highly heterogeneous with different hormone-expressed subtypes in NFPA tissues including follicle-stimulating hormone (FSH) positive, luteinizing hormone-positive, FSH/luteinizing hormone-positive, and negative types. To analyze in-depth the variations in the proteomes among different NFPA subtypes for our long-term goal to clarify molecular mechanisms of NFPA and to detect tumor biomarker for personalized medicine practice, a reference map of proteome of a human FSH-expressed NFPA tissue was described here. 2DE and PDQuest image analysis were used to array each protein. MALDI-TOF PMF and human Swiss-Prot databases with MASCOT search were used to identify each protein. A good 2DE pattern with high level of between-gel reproducibility was attained with an average positional deviation 1.98 ± 0.75 mm in the IEF direction and 1.62 ± 0.68 mm in the SDS-PAGE direction. Approximately 1200 protein spots were 2DE-detected and 192 redundant proteins that were contained in 141 protein spots were PMF-identified, representing 107 nonredundant proteins. Those proteins were located in cytoplasm, nucleus, plasma membrane, extracellular space, and so on, and those functioned in transmembrane receptor, ion channel, transcription/translation regulator, transporter, enzyme, phosphatase, kinase, and so on. Several important pathway networks were characterized from those identified proteins with DAVID and Ingenuity Pathway Analysis systems, including gluconeogenesis and glycolysis, mitochondrial dysfunction, oxidative stress, cell-cycle alteration, MAPKsignaling system, immune response, TP53-signaling, VEGF-signaling, and inflammation signaling pathways. Those resulting data contribute to a functional profile of the proteome of a human FSH-positive NFPA tissue, and will serve as a reference for the heterogeneity analysis of NFPA proteomes.

Identification of the proteomic variations of invasive relative to non-invasive non-functional pituitary adenomas.

The incomplete surgery section of invasive non-functional pituitary adenomas (NFPAs) carries the increased risks of complications and requires adjuvant radiotherapy and medications. It is necessary to clarify the molecular mechanisms and markers of invasiveness to guide the management of NFPA patients. The study aimed to proteomic variations of invasive and non-invasive NFPAs and sought the protein markers for invasive NFPAs. Invasive (n = 4) and non-invasive (n = 4) NFPA tissues were analyzed (n = 3-5/each tissue) with 2DE and PDQuest software. Twenty-four high-resolution 2DE gels were quantitatively compared to determine differentially expressed proteins (DEPs) between invasive and non-invasive NFPAs. Approximately 1200 protein spots were detected in each 2DE map, and 103 differential spots (64 upregulated and 39 downregulated) were identified. Among those 103 differential spots, 57 DEPs (30 upregulated and 27 downregulated) were characterized with peptide mass fingerprint and MS/MS. Gene-ontology (GO) and ingenuity pathway analyses of those DEPs revealed pathway networks including mitochondrial dysfunction, oxidative stress, mitogen-activated protein kinase signaling abnormality, TR/RXR activation, proteolysis abnormality, ketogenesis and ketolysis, cyclin-dependent kinase C signaling abnormality, and amyloid processing that were significantly associated with invasive characteristics of invasive NFPA. Those data demonstrate that proteomic variations exist between invasive and non-invasive NFPAs. 2DE-based comparative proteomics is an effective approach to identify proteomic variations and pathway network variations. Those findings will serve as a basis to understand the molecular mechanisms of invasive NFPAs and to discover protein markers to effectively manage patients with invasive NFPAs.

Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine.

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach.

Background: DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC. Method: Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified. Results: A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC. Conclusions: This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach.

Relevance: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. Results: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation. Conclusions: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

Identification of stromal differentially expressed proteins in the colon carcinoma by quantitative proteomics.

Tumor microenvironment plays very important roles in the carcinogenesis. A variety of stromal cells in the microenvironment have been modified to support the unique needs of the malignant state. This study was to discover stromal differentially expressed proteins (DEPs) that were involved in colon carcinoma carcinogenesis. Laser capture microdissection (LCM) was captured and isolated the stromal cells from colon adenocarcinoma (CAC) and non-neoplastic colon mucosa (NNCM) tissues, respectively. Seventy DEPs were identified between the pooled LCM-enriched CAC and NNCM stroma samples by iTRAQ-based quantitative proteomics. Gene Ontology (GO) relationship analysis revealed that DEPs were hierarchically grouped into 10 clusters, and were involved in multiple biological functions that were altered during carcinogenesis, including extracellular matrix organization, cytoskeleton, transport, metabolism, inflammatory response, protein polymerization, and cell motility. Pathway network analysis revealed 6 networks and 56 network eligible proteins with Ingenuity pathway analysis. Four significant networks functioned in digestive system development and its function, inflammatory disease, and developmental disorder. Eight DEPs (DCN, FN1, PKM2, HSP90B1, S100A9, MYH9, TUBB, and YWHAZ) were validated by Western blotting, and four DEPs (DCN, FN1, PKM2, and HSP90B1) were validated by immunohistochemical analysis. It is the first report of stromal DEPs between CAC and NNCM tissues. It will be helpful to recognize the roles of stromas in the colon carcinoma microenvironment, and improve the understanding of carcinogenesis in colon carcinoma. The present data suggest that DCN, FN1, PKM2, HSP90B1, S100A9, MYH9, TUBB, and YWHAZ might be the potential targets for colon cancer prevention and therapy.

Mapping the interactome of overexpressed RAF kinase inhibitor protein in a gastric cancer cell line.

BACKGROUND: Gastric cancer (GC) is a threat to human health with increasing incidence and mortality worldwide. Down-regulation or absence of RAF kinase inhibitor protein (RKIP) was associated with the occurrence, differentiation, invasion, and metastasis of GC. This study aims to investigate the molecular mechanisms and biological functions of RKIP in the GC biology. METHODS: The fusion expression plasmid pcDNA3.1-RKIP-3xFLAG was transfected into SGC7901 cells, the RKIP fusion proteins were purified with anti-flag M2 magnetic beads, and the RKIP-interacting proteins were identified with tandem mass spectrometry (MS/MS), and were analyzed with bioinformatics tools. Western blot and co-immunoprecipitation were used to confirm the interaction complex. RESULTS: A total of 72 RKIP-interacting proteins were identified by MS/MS. Those proteins play roles in enzyme metabolism, molecular chaperoning, biological oxidation, cytoskeleton organization, signal transduction, and enzymolysis. Three RKIP-interaction protein network diagrams were constructed with Michigan Molecular Interactions, functional linage network, and Predictome analysis to address the molecular pathways of the functional activity of RKIP. The MS/MS-characterized components of the existing interaction complex (RKIP, HSP90, 14-3-3ε, and keratin 8) were confirmed by Western blot analysis and co-immunoprecipitation. CONCLUSION: This study is the first discovery of the interaction of RKIP with HSP90, 14-3-3, and keratin. The present data would provide insight into the molecular mechanisms of how RKIP inhibits the occurrence and development of GC.

[Effect of DJ-1 siRNA on biological behavior of human lung squamous carcinoma SK-MES-1 cells].

OBJECTIVE: RNA interference technology (siRNA) was used to inhibit the expression of DJ-1 gene in lung squamous cell carcinoma SK-MES-1 cells, and the cell biological behaviors were investigated to explore the function of DJ-1 gene. METHODS: A targeted DJ-1 siRNA lentiviral vector with a green fluorescent protein (GFP) as a reporter was constructed. The constructed DJ-1 siRNA and control-siRNA vectors were infected into SK-MES-1 cells as experimental (DJ-1 siRNA) and control (Control siRNA) groups, respectively. The DJ-1 protein expression was determined by Western blot. The cell proliferation capability was measured with methyl thiazolyl tetrazolium (MTT). The cell cycle was analyzed by flow cytometry. The capability of cell migration was determined by Transwell method. RESULTS: Compared with control-siRNA and blank-control groups, the protein expression of DJ-1 gene was down-regulated, the capability of cell proliferation was obviously inhibited (P<0.01), the cell cycle was arrested with increased number of G1- and G2-phase cells and reduced number of S-phase cells, and the capability of cell migration was significantly decreased (P<0.01) in the DJ-1 siRNA-infected cells. CONCLUSION: DJ-1 gene might play a role in promoting cell proliferation and cell migration capability in vitro in lung cancer SK-MES-1 cells.