Dataset Information

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

ABSTRACT: Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein-protein interaction networks. A total of 69, 43, and 64 leader genes were identified for stomach, liver, and colon cancers, respectively. Furthermore, literature reviews and experimental data including protein expression levels and independent datasets from other databases all verified their association with corresponding cancer types. These final leader genes were expected to be used as diagnostic biomarkers and targets for new treatment strategies. The procedure for identifying cancer leader genes could be expanded to open up a window into the mechanisms, early diagnosis, and treatment of other cancer types.

SUBMITTER: Chen H

PROVIDER: S-EPMC9548996 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

Chen Hongwei H Rong Zherou Z Ge Luanfeng L Yu Hongzheng H Li Chao C Xu Manyi M Zhang Zihan Z Lv Junjie J He Yuehan Y Li Wan W Chen Lina L

Frontiers in genetics 20220926

Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein-protein int ...[more]

PMID: 36226184

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Project description:BackgroundMany biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.ResultsUsing publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins.ConclusionUnderstanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.

Project description:The salivary microbiota is constantly swallowed and delivered to the digestive tract. These bacteria may be associated with gastrointestinal diseases. This case-control study examined the salivary microbiota in patients with digestive tract cancer (DTC) and evaluated their differential distribution based on the cancer sites. We collected saliva samples from 59 patients with cancer in any part of the digestive tract (tongue/pharynx, esophagus, stomach, and large intestine) and from 118 age- and sex-matched control subjects. There was no significant difference in periodontal status between DTC patients and control subjects (P = 0.72). We examined the bacterial diversity and composition in saliva by 16S ribosomal RNA gene sequencing. Salivary bacterial diversity in DTC patients was significantly higher than that in control subjects [number of operational taxonomic units (OTUs), P = 0.02; Shannon index, P < 0.01; Chao1, P = 0.04]. Eleven differentially abundant OTUs in DTC patients were identified using the linear discriminant analysis effect size (LEfSe) method. Based on the cancer sites, the diversity of salivary bacteria was especially higher in tongue/pharyngeal or esophageal cancer patients than in control subjects. Among the 11 differentially abundant OTUs in DTC patients, an OTU corresponding to Porphyromonas gingivalis was more abundant in the saliva of all groups of DTC patients compared to that in control subjects, and an OTU corresponding to Corynebacterium species was more abundant in all groups other than gastric cancer patients (P < 0.01). In addition, the relative abundances of OTUs corresponding to Fusobacterium nucleatum, Streptococcus parasanguinis II, and Neisseria species were significantly higher in tongue/pharyngeal cancer patients compared to their abundances in control subjects (P < 0.01). The relative abundance of an OTU corresponding to the Neisseria species was also significantly higher in gastric cancer patients and that of an OTU corresponding to Actinomyces odontolyticus was significantly higher in colorectal cancer patients (P < 0.01). These results suggest that the salivary microbiota might be associated with various digestive tract cancers.

Dataset Information

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

Publications

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

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