Measuring rank robustness in scored protein interaction networks

Bozhilova, Lyuba V.; Whitmore, Alan V.; Wray, J.; Reinert, Gesine; Deane, Charlotte M.

doi:10.1101/502302

Cited by 2 publications

(1 citation statement)

References 40 publications

(27 reference statements)

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“…Currently, the STRING Frontiers in Genetics frontiersin.org database consists of 18,838 human proteins with 25, 914, 693 core network interactions. The highest confidence interaction score was set to 0.9, which reduces the number of false-positive interactions (Bozhilova et al, 2019). The molecular complex detection (MCODE) algorithm was used to perform network function module mining (Bader and Hogue, 2003).…”

Section: Function Enrichment and Protein-protein Interaction (Ppi) An...mentioning

confidence: 99%

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

Wang

Zhang

He³

et al. 2023

Front. Genet.

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Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and help understand its mechanism.Materials and methods: GSE30219, GSE31210, GSE37745, and GSE50081 datasets were downloaded from the GEO database and integrated into a dataset (GSE). The integrated dataset was divided into the training and test datasets. TCGA-NSCLC dataset was regarded as an independent verification dataset. Here, the limma R package was used to identify the differentially expression genes (DEGs). Importantly, the RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, a nomogram was built using the rms package.Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which were closely associated with cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes from 472 DEGs, and the robustness was confirmed in GSE test, entire GSE, and TCGA datasets. Samples in the low RiskScore group had a higher gene mutation score and lower immunoinfiltration status. Moreover, we found that the patients in the low RiskScore group were more sensitive to the four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification.Conclusion: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.

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Section: Function Enrichment and Protein-protein Interaction (Ppi) An...mentioning

confidence: 99%

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

Wang

Zhang

He³

et al. 2023

Front. Genet.

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Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

Klimm

Toledo

Monfeuga

et al. 2019

Preprint

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Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

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Measuring rank robustness in scored protein interaction networks

Cited by 2 publications

References 40 publications

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

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