Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

Yuan, Fei; Zhang, Yuhang; Wan, Sibao; Wang, Shaopeng; Kong, Xiangyin

doi:10.1155/2015/623121

Cited by 11 publications

(10 citation statements)

References 149 publications

(138 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is important to note that ARHGDIA (Rho GDP-dissociation inhibitor 1) and PARK7 (Protein/nucleic acid deglycase DJ-1) are specifically upregulated in PDAC, and the increased expression of ARHGDIA and PARK7 can be considered as potential biomarkers of the disease. ARHGDIA-a specific regulator of Rho protein exchange reactions crucial for JNK pathway-was previously identified by a bioinformatic pipeline that searched for candidate genes related to pancreatic cancer using protein-protein interactions and a shortest path approach [180]. Also, in accordance with our results, PARK7 was found to be significantly elevated in PDAC [181], correlated with tumor invasion and worse patients' outcome, and responsible for promoting invasion and metastasis of pancreatic cancer cells [182].…”

Section: Discussionsupporting

confidence: 85%

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Oliveira

Freire

Cury

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 85%

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Oliveira

Freire

Cury

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

“…Obviously, these types of RWR genes are not closely related to MD. To exclude these genes, a permutation test [ 64 , 67 , 68 ] was utilized. First, 1,000 Ensembl ID sets (namely, S 1 , S 2 ,…, S 1000 ) were constructed, and each of the sets consisted of 106 randomly selected Ensembl IDs from the network.…”

Section: Methodsmentioning

confidence: 99%

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

Wang

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

show abstract

“…Several studies reported that proteins that can interact with each other are likely to share similar functions [ 19 , 20 , 21 , 22 , 32 , 33 , 34 , 35 , 36 ]. Currently, numerous public databases collect PPI information derived from various sources, such as Database of Interacting Proteins [ 37 ] and BioGRID [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

Lei

Zhang

et al. 2017

Genes

Self Cite

View full text Add to dashboard Cite

The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein–protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity–disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity–disease co-occurrence and provide useful insights for disease prevention and intervention.

show abstract

Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

Cited by 11 publications

References 149 publications

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

Contact Info

Product

Resources

About