Tumor relevant protein functional interactions identified using bipartite graph analyses

Abstract An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Divya Lakshmi Venkatraman, Deepshika Pulimamidi, Harsh G. Shukla, Shubhada R. Hegde
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6cee77b5d1654b368370000e93496267
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6cee77b5d1654b368370000e93496267
record_format	dspace
spelling	oai:doaj.org-article:6cee77b5d1654b368370000e934962672021-11-08T10:50:55ZTumor relevant protein functional interactions identified using bipartite graph analyses10.1038/s41598-021-00879-22045-2322https://doaj.org/article/6cee77b5d1654b368370000e934962672021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00879-2https://doaj.org/toc/2045-2322Abstract An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.Divya Lakshmi VenkatramanDeepshika PulimamidiHarsh G. ShuklaShubhada R. HegdeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Divya Lakshmi Venkatraman Deepshika Pulimamidi Harsh G. Shukla Shubhada R. Hegde Tumor relevant protein functional interactions identified using bipartite graph analyses
description	Abstract An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.
format	article
author	Divya Lakshmi Venkatraman Deepshika Pulimamidi Harsh G. Shukla Shubhada R. Hegde
author_facet	Divya Lakshmi Venkatraman Deepshika Pulimamidi Harsh G. Shukla Shubhada R. Hegde
author_sort	Divya Lakshmi Venkatraman
title	Tumor relevant protein functional interactions identified using bipartite graph analyses
title_short	Tumor relevant protein functional interactions identified using bipartite graph analyses
title_full	Tumor relevant protein functional interactions identified using bipartite graph analyses
title_fullStr	Tumor relevant protein functional interactions identified using bipartite graph analyses
title_full_unstemmed	Tumor relevant protein functional interactions identified using bipartite graph analyses
title_sort	tumor relevant protein functional interactions identified using bipartite graph analyses
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6cee77b5d1654b368370000e93496267
work_keys_str_mv	AT divyalakshmivenkatraman tumorrelevantproteinfunctionalinteractionsidentifiedusingbipartitegraphanalyses AT deepshikapulimamidi tumorrelevantproteinfunctionalinteractionsidentifiedusingbipartitegraphanalyses AT harshgshukla tumorrelevantproteinfunctionalinteractionsidentifiedusingbipartitegraphanalyses AT shubhadarhegde tumorrelevantproteinfunctionalinteractionsidentifiedusingbipartitegraphanalyses
_version_	1718442625115095040

Tumor relevant protein functional interactions identified using bipartite graph analyses

Ejemplares similares