ArticleScientific reports2017
Understanding cancer complexome using networks, spectral graph theory and multilayer framework.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Spectral-Entropy Network Analysis of Multidimensional Poverty: An Explainable AI Framework for Complex Socioeconomic Systems.Entropy (Basel, Switzerland) · 2026Article
- BDDN: bayesian dynamic differential network analysis in cancer proteomics.BMC bioinformatics · 2026Article
- Controllability and heterogeneity of river networks using spectral graph theory approach.Scientific reports · 2025Article
- Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks.PloS one · 2024Article
- CNVs in 8q24.3 do not influence gene co-expression in breast cancer subtypes.Frontiers in genetics · 2023Article
- Role of mitochondrial genetic interactions in determining adaptation to high altitude human population.Scientific reports · 2022Article
- Network approach to understand biological systems: From single to multilayer networks.Journal of biosciences · 2022Article
- Review
- Nucleotide-based genetic networks: Methods and applications.Journal of biosciences · 2022Article
- Classifying diseases by using biological features to identify potential nosological models.Scientific reports · 2021Article
- Cancer: An unknown territory; rethinking before going ahead.Genes & diseases · 2021Review
- Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy.NPJ systems biology and applications · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework.Scientific reports · 2020Article
- The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.Frontiers in aging neuroscience · 2020Article
- Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients.PloS one · 2019Article
- Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects.Scientific reports · 2018Article
- Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.Scientific reports · 2018Article
- Clustering multilayer omics data using MuNCut.BMC genomics · 2018Article
- Network spectra for drug-target identification in complex diseases: new guns against old foes.Applied network science · 2018Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.