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Proteomics and Its Applications in Cancers 2.0

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 15300

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Dr. Stanislav Naryzhny

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1. Institute of Biomedical Chemistry, Pogodinskaya 10, 119121 Moscow, Russia
2. B. P. Konstantinov Petersburg Nuclear Physics Institute, National Research Center “Kurchatov Institute”, Leningrad Region, 188300 Gatchina, Russia
Interests: proteomics; cancer; biomarkers; extracellular vesicles; glioblastoma; haptoglobin; plasma; proteoforms; 2DE; databases
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Dear Colleagues,

It is known that the vast majority of pathological changes in the functioning of cells, tissues, and organs are accompanied by a deviation from the physiological protein profile of a normal healthy organism. In modern conditions, the analysis and prediction of such changes come to the fore in the creation of preclinical screening protocols (i.e., the determination of hidden and latent protein "precursors" of the disease, as well as the assessment of the effectiveness of the applied therapy methods). The search, determination, separation, quantitative, and qualitative identification of protein molecules that play a role in providing sensitivity or directly in the formation of a disease are the main tasks of proteomics. The most popular is the comparative analysis of proteomes in the field of cancer research. Here, much should be expected from proteomics and systems approaches, which could allow the development of detection of cancerous and precancerous conditions based on the analysis of not one, but a group of markers. The determination of the proteomic profile by mass spectrometry or immunodetection allows the identification of individual protein forms (proteoforms) and the observation of changes in their number and composition in health and disease, as well as under the influence of various factors. The detection of changes in proteoform profiles associated with pathology enables the selection of the most characteristic markers of the diseases and their use for inclusion in the developed barcodes, which represent powerful tools for monitoring the course of cancer and the efficacy and safety of therapeutic agents. However, the analysis of large data sets obtained in various conditions (for example, normal, inflammation, and cancer) and in different tissues and organs is now coming to the fore. Thus, there is a need for standardized processes for storing and retrieving data obtained using different methods and by different researchers; that is, in addition to the development of technologies and the acquisition of new data, there is a need for their organization, formatting, and analysis, which is critical for answering clinical questions in cancer research. Additionally, here, bioinformatics plays a decisive role. In this Special Issue, we would like to gather contributions reporting very recent advances in the proteomics study of cancer. We invite you to participate in this Special Issue.

Dr. Stanislav Naryzhny
Guest Editor

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Keywords

proteomics
cancer
proteome
biomarkers
drug target
proteoforms
post-translational modifications
mass spectrometry
bioinformatics
2DE
database
plasma
tissue

Published Papers (10 papers)

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Editorial

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3 pages, 387 KiB

Open AccessEditorial

Proteomics and Its Applications in Cancers 2.0

by Stanislav Naryzhny

Int. J. Mol. Sci. 2024, 25(8), 4447; https://doi.org/10.3390/ijms25084447 - 18 Apr 2024

Viewed by 156

Abstract

Considering the success of our previous Special Issue (SI) “Proteomics and Its Applications in Cancers”, we aimed to attract more publications where cancer proteomics is involved [...] Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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Research

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13 pages, 3350 KiB

Open AccessArticle

Epitomics: Analysis of Plasma C9 Epitope Heterogeneity in the Plasma of Lung Cancer Patients and Control Subjects

by Ilona Tornyi, Jozsef Lazar, Aladar Pettko-Szandtner, Eva Hunyadi-Gulyas and Laszlo Takacs

Int. J. Mol. Sci. 2023, 24(18), 14359; https://doi.org/10.3390/ijms241814359 - 21 Sep 2023

Cited by 1 | Viewed by 1173

Abstract

The human proteome is more complex than the genetic code predicts it to be. Epitomics, or protein epitome profiling, is a tool for understanding sub-protein level variation. With the ultimate goal to explore C9 proteoforms and their relevance to lung cancer, here we report plasma C9 epitope-associated molecular heterogeneity in plasma samples of lung cancer patients and control subjects. We show three C9 epitopes (BSI0449, BSI0581, BSI0639) with markedly different association with lung cancer (“unaltered”, “upregulated” and “downregulated”). In order to exclude confounding effects, we show first that the three epitope-defining mAbs recognize C9 in purified form and in the natural context, in the human plasma. Then, we present data demonstrating the lack of major epitope interdependence or overlap. The next experiments represent a quest toward the understanding of the molecular basis of apparent disparate association with lung cancer. Using immunochemistry, SDS PAGE and LC-MS/MS technologies, we demonstrate that epitope-specific immunoprecipitates of plasma C9 seem identical regarding peptide sequence. However, we found epitope-specific posttranslational modification and coprecipitated protein composition differences with respect to control and lung cancer plasma. Epitope profiling enabled the classification of hypothetical C9 proteoforms through differential association with lung cancer. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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23 pages, 4282 KiB

Open AccessArticle

Uncharacterized Proteins CxORFx: Subinteractome Analysis and Prognostic Significance in Cancers

by Pavel Ershov, Evgeniy Yablokov, Yuri Mezentsev and Alexis Ivanov

Int. J. Mol. Sci. 2023, 24(12), 10190; https://doi.org/10.3390/ijms241210190 - 15 Jun 2023

Viewed by 1544

Abstract

Functions of about 10% of all the proteins and their associations with diseases are poorly annotated or not annotated at all. Among these proteins, there is a group of uncharacterized chromosome-specific open-reading frame genes (CxORFx) from the ‘Tdark’ category. The aim of the work was to reveal associations of CxORFx gene expression and ORF proteins’ subinteractomes with cancer-driven cellular processes and molecular pathways. We performed systems biology and bioinformatic analysis of 219 differentially expressed CxORFx genes in cancers, an estimation of prognostic significance of novel transcriptomic signatures and analysis of subinteractome composition using several web servers (GEPIA2, KMplotter, ROC-plotter, TIMER, cBioPortal, DepMap, EnrichR, PepPSy, cProSite, WebGestalt, CancerGeneNet, PathwAX II and FunCoup). The subinteractome of each ORF protein was revealed using ten different data sources on physical protein–protein interactions (PPIs) to obtain representative datasets for the exploration of possible cellular functions of ORF proteins through a spectrum of neighboring annotated protein partners. A total of 42 out of 219 presumably cancer-associated ORF proteins and 30 cancer-dependent binary PPIs were found. Additionally, a bibliometric analysis of 204 publications allowed us to retrieve biomedical terms related to ORF genes. In spite of recent progress in functional studies of ORF genes, the current investigations aim at finding out the prognostic value of CxORFx expression patterns in cancers. The results obtained expand the understanding of the possible functions of the poorly annotated CxORFx in the cancer context. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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14 pages, 4949 KiB

Open AccessArticle

p53 Affects Zeb1 Interactome of Breast Cancer Stem Cells

by Sergey E. Parfenyev, Sergey V. Shabelnikov, Elena N. Tolkunova, Nickolai A. Barlev and Alexey G. Mittenberg

Int. J. Mol. Sci. 2023, 24(12), 9806; https://doi.org/10.3390/ijms24129806 - 06 Jun 2023

Viewed by 1405

Abstract

P53 is a critical tumor suppressor that protects the integrity of genome and prevents cells from malignant transformation, including metastases. One of the driving forces behind the onset of metastases is the epithelial to mesenchymal transition (EMT) program. Zeb1 is one of the key transcription factors that govern EMT (TF-EMT). Therefore, the interaction and mutual influence of p53 and Zeb1 plays a critical role in carcinogenesis. Another important feature of tumors is their heterogeneity mediated by the presence of so-called cancer stem cells (CSCs). To this end, we have developed a novel fluorescent reporter-based approach to enrich the population of CSCs in MCF7 cells with inducible expression of Zeb1. Using these engineered cell lines, we studied the effect of p53 on Zeb1 interactomes isolated from both CSCs and regular cancer cells. By employing co-immunoprecipitations followed by mass spectrometry, we found that the composition of Zeb1 interactome was affected not only by the p53 status but also by the level of Oct4/Sox2 expression, indicating that stemness likely affects the specificity of Zeb1 interactions. This study, together with other proteomic studies of TF-EMT interactomes, provides a framework for future molecular analyses of biological functions of Zeb1 at all stages of oncogenesis. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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19 pages, 2523 KiB

Open AccessArticle

Nuclear High Mobility Group A2 (HMGA2) Interactome Revealed by Biotin Proximity Labeling

by Antoine Gaudreau-Lapierre, Thomas Klonisch, Hannah Nicolas, Thatchawan Thanasupawat, Laura Trinkle-Mulcahy and Sabine Hombach-Klonisch

Int. J. Mol. Sci. 2023, 24(4), 4246; https://doi.org/10.3390/ijms24044246 - 20 Feb 2023

Cited by 1 | Viewed by 2044

Abstract

The non-histone chromatin binding protein High Mobility Group AT-hook protein 2 (HMGA2) has important functions in chromatin remodeling, and genome maintenance and protection. Expression of HMGA2 is highest in embryonic stem cells, declines during cell differentiation and cell aging, but it is re-expressed in some cancers, where high HMGA2 expression frequently coincides with a poor prognosis. The nuclear functions of HMGA2 cannot be explained by binding to chromatin alone but involve complex interactions with other proteins that are incompletely understood. The present study used biotin proximity labeling, followed by proteomic analysis, to identify the nuclear interaction partners of HMGA2. We tested two different biotin ligase HMGA2 constructs (BioID2 and miniTurbo) with similar results, and identified known and new HMGA2 interaction partners, with functionalities mainly in chromatin biology. These HMGA2 biotin ligase fusion constructs offer exciting new possibilities for interactome discovery research, enabling the monitoring of nuclear HMGA2 interactomes during drug treatments. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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11 pages, 2860 KiB

Open AccessArticle

Dual Oxidase 2 (DUOX2) as a Proteomic Biomarker for Predicting Treatment Response to Chemoradiation Therapy for Locally Advanced Rectal Cancer: Using High-Throughput Proteomic Analysis and Machine Learning Algorithm

by Hyebin Lee, Han Suk Ryu, Hee Chul Park, Jeong Il Yu, Gyu Sang Yoo, Changhoon Choi, Heerim Nam, Jason Joon Bock Lee, In-Gu Do, Dohyun Han and Sang Yun Ha

Int. J. Mol. Sci. 2022, 23(21), 12923; https://doi.org/10.3390/ijms232112923 - 26 Oct 2022

Viewed by 1711

Abstract

High-throughput mass-spectrometry-based quantitative proteomic analysis was performed using formalin-fixed, paraffin-embedded (FFPE) biopsy samples obtained before treatment from 13 patients with locally advanced rectal cancer (LARC), who were treated with concurrent chemoradiation therapy (CCRT) followed by surgery. Patients were divided into complete responder (CR) and non-complete responder (nCR) groups. Immunohistochemical (IHC) staining of 79 independent FFPE tissue samples was performed to validate the predictive ability of proteomic biomarker candidates. A total of 3637 proteins were identified, and the expression of 498 proteins was confirmed at significantly different levels (differentially expressed proteins—DEPs) between two groups. In Gene Ontology enrichment analyses, DEPs enriched in biological processes in the CR group included proteins linked to cytoskeletal organization, immune response processes, and vesicle-associated protein transport processes, whereas DEPs in the nCR group were associated with biosynthesis, transcription, and translation processes. Dual oxidase 2 (DUOX2) was selected as the most predictive biomarker in machine learning algorithm analysis. Further IHC validation ultimately confirmed DUOX2 as a potential biomarker for predicting the response of nCR to CCRT. In conclusion, this study suggests that the treatment response to RT may be affected by the pre-treatment tumor microenvironment. DUOX2 is a potential biomarker for the early prediction of nCR after CCRT. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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18 pages, 5493 KiB

Open AccessArticle

WNT1 Inducible Signaling Pathway Protein 1 Is a Stroma-Specific Secreting Protein Inducing a Fibroblast Contraction and Carcinoma Cell Growth in the Human Prostate

by Kang-Shuo Chang, Syue-Ting Chen, Hsin-Ching Sung, Shu-Yuan Hsu, Wei-Yin Lin, Chen-Pang Hou, Yu-Hsiang Lin, Tsui-Hsia Feng, Ke-Hung Tsui and Horng-Heng Juang

Int. J. Mol. Sci. 2022, 23(19), 11437; https://doi.org/10.3390/ijms231911437 - 28 Sep 2022

Viewed by 1598

Abstract

The WNT1 inducible signaling pathway protein 1 (WISP1), a member of the connective tissue growth factor family, plays a crucial role in several important cellular functions in a highly tissue-specific manner. Results of a RT-qPCR indicated that WISP1 expressed only in cells of the human prostate fibroblasts, HPrF and WPMY-1, but not the prostate carcinoma cells in vitro. Two major isoforms (WISP1v1 and WISP1v2) were identified in the HPrF cells determined by RT-PCR and immunoblot assays. The knock-down of a WISP1 blocked cell proliferation and contraction, while treating respectively with the conditioned medium from the ectopic WISP1v1- and WISPv2-overexpressed 293T cells enhanced the migration of HPrF cells. The TNFα induced WISP1 secretion and cell contraction while the knock-down of WISP1 attenuated these effects, although TNFα did not affect the proliferation of the HPrF cells. The ectopic overexpression of WISP1v1 but not WISP1v2 downregulated the N-myc downstream regulated 1 (NDRG1) while upregulating N-cadherin, slug, snail, and vimentin gene expressions which induced not only the cell proliferation and invasion in vitro but also tumor growth of prostate carcinoma cells in vivo. The results confirmed that WISP1 is a stroma-specific secreting protein, enhancing the cell migration and contraction of prostate fibroblasts, as well as the proliferation, invasion, and tumor growth of prostate carcinoma cells. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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15 pages, 1621 KiB

Open AccessArticle

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

by Ombretta Repetto, Laura Caggiari, Mariangela De Zorzi, Caterina Elia, Lara Mussolin, Salvatore Buffardi, Marta Pillon, Paola Muggeo, Tommaso Casini, Agostino Steffan, Christine Mauz-Körholz, Maurizio Mascarin and Valli De Re

Int. J. Mol. Sci. 2022, 23(17), 9911; https://doi.org/10.3390/ijms23179911 - 31 Aug 2022

Cited by 2 | Viewed by 1687

Abstract

Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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11 pages, 3194 KiB

Open AccessArticle

A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response

by Ehsan Irajizad, Johannes F. Fahrmann, James P. Long, Jody Vykoukal, Makoto Kobayashi, Michela Capello, Chuan-Yih Yu, Yining Cai, Fu Chung Hsiao, Nikul Patel, Soyoung Park, Qian Peng, Jennifer B. Dennison, Taketo Kato, Mei Chee Tai, Ayumu Taguchi, Humam Kadara, Ignacio I. Wistuba, Hiroyuki Katayama, Kim-Anh Do, Samir M. Hanash and Edwin J. Ostrin Show full author list Hide full author list

Int. J. Mol. Sci. 2022, 23(16), 8933; https://doi.org/10.3390/ijms23168933 - 11 Aug 2022

Cited by 1 | Viewed by 1871

Abstract

There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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Review

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14 pages, 2094 KiB

Open AccessReview

Quantitative Aspects of the Human Cell Proteome

by Stanislav Naryzhny

Int. J. Mol. Sci. 2023, 24(10), 8524; https://doi.org/10.3390/ijms24108524 - 10 May 2023

Cited by 1 | Viewed by 1297

Abstract

The number and identity of proteins and proteoforms presented in a single human cell (a cellular proteome) are fundamental biological questions. The answers can be found with sophisticated and sensitive proteomics methods, including advanced mass spectrometry (MS) coupled with separation by gel electrophoresis and chromatography. So far, bioinformatics and experimental approaches have been applied to quantitate the complexity of the human proteome. This review analyzed the quantitative information obtained from several large-scale panoramic experiments in which high-resolution mass spectrometry-based proteomics in combination with liquid chromatography or two-dimensional gel electrophoresis (2DE) were used to evaluate the cellular proteome. It is important that even though all these experiments were performed in different labs using different equipment and calculation algorithms, the main conclusion about the distribution of proteome components (proteins or proteoforms) was basically the same for all human tissues or cells. It follows Zipf’s law and has a formula N = A/x, where N is the number of proteoforms, A is a coefficient, and x is the limit of proteoform detection in terms of abundance. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Cancers 2.0)

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