Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome

Francisquini, Rodrigo; Berton, Rafael; Soares, Sandro G.; Pessotti, Dayelle S.; Camacho, Maurício F.; Andrade-Silva, Débora; Barcick, Uilla; Serrano, Solange M.T.; Chammas, Roger; Nascimento, Mariá C. V.; Zelanis, André

doi:10.1016/j.jprot.2020.104063

Cited by 6 publications

(7 citation statements)

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“…Indeed, changes in the interactome of secreted proteins in tumoral cells have been reported and the rearrangement in the tumoral interactome was correlated with the malignant transformation. 57 Overall, the recurrent presence of nuclear proteins in the secretomes reported herein may represent a protein signature of unconventional secretion, regardless of the cancer type. Although the functional implications of such a feature need further investigation, this pattern may be relevant for diagnostic/prognostic purposes as well as for addressing malignant transformation in each cancer type.…”

Section: Protein Relocation and Nuclear Proteins In The Secretome Of Tumoral Cellsmentioning

confidence: 71%

Bioinformatic reanalysis of public proteomics data reveals that nuclear proteins are recurrent in cancer secretomes

Morais

Zelanis

2021

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Proteins secreted by tumoral cells (cancer secretomes) have been continuously associated with cancer development and progression processes. In this context, secreted proteins contribute to the signaling mechanisms related to tumor growth and spreading and studies on tumor secretomes provide valuable clues on putative tumor biomarkers.Although the in vitro identification of intracellular proteins in cancer secretome studies has usually been associated with contamination derived from cell lysis or fetal bovine serum, accumulated evidence reports on intracellular proteins with moonlighting functions in the extracellular environment. In this study, we performed a systematic reanalysis of public proteomics data regarding different cancer secretomes, aiming to identify intracellular proteins potentially secreted by tumor cells via unconventional secretion pathways. We found a similar repertoire of unconventionally secreted proteins, including the recurrent identification of nuclear proteins secreted by different cancer cells. In addition, in some cancer types, immunohistochemical data were in line with proteomics identifications and suggested that nuclear proteins might relocate from the nucleus to the cytoplasm. Both the presence of nuclear proteins and the likely unconventional secretion of such proteins may comprise biological signatures of malignant transformation in distinct cancer types and may be targeted for further analysis aiming at the prognostic/ therapeutic value of such features.

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Section: Protein Relocation and Nuclear Proteins In The Secretome Of Tumoral Cellsmentioning

confidence: 71%

Bioinformatic reanalysis of public proteomics data reveals that nuclear proteins are recurrent in cancer secretomes

Morais

Zelanis

2021

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“…Likewise, although preserving some phenotypic features of their “normal” counterparts, tumoral cells are in many ways distinct—neoplastic cells display altered signaling circuits which eventually provide tumor supportive pathways. As cancer is a disease of (altered) pathways [71], altered secretomes may consequently result in modified protein‐protein interactions (interactomes) and, consequently, emerging interaction patterns could impact malignant transformation [72]. Hence, as new interaction partners are observed, these “new” interactomes might indicate functional network motifs, providing therapeutical opportunities for cancer treatment.…”

Section: Secretomes As “Signatures” Of Diseasementioning

confidence: 99%

Heterotypic communication as the promoter of phenotypic plasticity of cancer cells: The role of cancer secretomes

Zelanis,

Barcick,

Racorti

et al. 2023

Proteomics

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Cellular communication relies on signaling circuits whose statuses are mainly modulated by soluble biomolecules such as carbohydrates, lipids, proteins, and metabolites as well as extracellular vesicles (EVs). Therefore, the active secretion of such biomolecules is critical for both cell homeostasis and proper pathophysiological responses in a timely fashion. In this context, proteins are among the main modulators of such biological responses. Hence, profiling cell line secretomes may be an opportunity for the identification of “signatures” of specific cell types (i.e., stromal or metastatic cells) with important prognostic/therapeutic value. This review will focus on the biological implications of cell secretomes in the context of cancer, as well as their functional roles in shaping the tumoral microenvironment (TME) and communication status of participating cells.

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“…Proteins, represented by the graph nodes, may have quantitative attributes that correspond to the expression level of the protein in the network. In these networks, a community can be interpreted as a group of functionally related proteins (Francisquini et al, 2021) whose attributes have similar characteristics. For this type of application, analyzing only the adjacency affinities may not reveal relevant information.…”

Section: Related Workmentioning

confidence: 99%

“…A great number of applications consists of networks with community structure. Some examples are in internet of things (IoT) (Chen et al, 2021), protein-protein interactions (Francisquini et al, 2021) and COVID-19 related data (Francisquini et al, 2021). Anomaly detection in this type of network is a specially relevant task.…”

Section: Introductionmentioning

confidence: 99%

“…These networks model a wide range of applications as the attributes of the networks are not limited to time series. As a result, for example, networks representing biological processes can be considered by the proposed method Francisquini et al (2021). Moreover, this paper introduces a framework that considers the topological graph structure to describe the anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Community-based anomaly detection using spectral graph filtering

Francisquini¹,

Lorena²,

Nascimento³

2022

Preprint

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Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely studied research topic with applications in various domains. Despite a significant amount of anomaly detection frameworks, there is a dearth on the literature of methods that consider both attributed graphs and the community structure of the networks. This paper proposes a community-based anomaly detection algorithm using a spectral graph-based filter that includes the network community structure into the Laplacian matrix adopted as the basis for the Fourier transform. In addition, the choice of the cutoff frequency of the filter considers the number of communities found. In computational experiments, the proposed strategy, called SpecF, showed an outstanding performance in successfully identifying even discrete anomalies. SpecF is better than a baseline disregarding the community structure, especially for networks with a higher community overlapping. Additionally, we present a case study to validate the proposed method to study the dissemination of COVID-19 in the different districts of São José dos Campos, Brazil.

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Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome

Cited by 6 publications

References 50 publications

Bioinformatic reanalysis of public proteomics data reveals that nuclear proteins are recurrent in cancer secretomes

Bioinformatic reanalysis of public proteomics data reveals that nuclear proteins are recurrent in cancer secretomes

Heterotypic communication as the promoter of phenotypic plasticity of cancer cells: The role of cancer secretomes

Community-based anomaly detection using spectral graph filtering

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