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Xiong Z, Li W, Luo X, Lin Y, Huang W, Zhang S. Seven bacterial response-related genes are biomarkers for colon cancer. BMC Bioinformatics 2023; 24:103. [PMID: 36941538 PMCID: PMC10026208 DOI: 10.1186/s12859-023-05204-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/23/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.
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Affiliation(s)
- Zuming Xiong
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Wenxin Li
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xiangrong Luo
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Yirong Lin
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Wei Huang
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Sen Zhang
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
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van Bilsen JHM, van den Brink W, van den Hoek AM, Dulos R, Caspers MPM, Kleemann R, Wopereis S, Verschuren L. Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue. Front Physiol 2021; 12:703370. [PMID: 34858196 PMCID: PMC8631400 DOI: 10.3389/fphys.2021.703370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/18/2021] [Indexed: 01/12/2023] Open
Abstract
Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications, it is crucial to gain a deeper understanding of the inflammatory dynamics and to monitor them on the individual level. To this end, blood-based biomarkers reflecting the tissue-specific inflammatory dynamics would be of great value. Here, we describe an in silico approach to select candidate biomarkers for tissue-specific inflammation by using a priori mechanistic knowledge from pathways and tissue-derived molecules. The workflow resulted in a list of candidate markers, in part consisting of literature confirmed biomarkers as well as a set of novel, more innovative biomarkers that reflect inflammation in the liver and adipose tissue. The first step of biomarker verification was on murine tissue gene-level by inducing hepatic inflammation and adipose tissue inflammation through a high-fat diet. Our data showed that in silico predicted hepatic markers had a strong correlation to hepatic inflammation in the absence of a relation to adipose tissue inflammation, while others had a strong correlation to adipose tissue inflammation in the absence of a relation to liver inflammation. Secondly, we evaluated the human translational value by performing a curation step in the literature using studies that describe the regulation of the markers in human, which identified 9 hepatic (such as Serum Amyloid A, Haptoglobin, and Interleukin 18 Binding Protein) and 2 adipose (Resistin and MMP-9) inflammatory biomarkers at the highest level of confirmation. Here, we identified and pre-clinically verified a set of in silico predicted biomarkers for liver and adipose tissue inflammation which can be of great value to study future development of therapeutic/lifestyle interventions to combat metabolic inflammatory complications.
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Affiliation(s)
- Jolanda H M van Bilsen
- Department of Risk Assessment for Products in Development, The Netherlands Organization for Applied Scientific Research (TNO), Utrecht, Netherlands
| | - Willem van den Brink
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Anita M van den Hoek
- Department of Metabolic Health Research, The Netherlands Organization for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Remon Dulos
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Martien P M Caspers
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Robert Kleemann
- Department of Metabolic Health Research, The Netherlands Organization for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Suzan Wopereis
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Lars Verschuren
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
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van Bilsen JHM, Dulos R, van Stee MF, Meima MY, Rouhani Rankouhi T, Neergaard Jacobsen L, Staudt Kvistgaard A, Garthoff JA, Knippels LMJ, Knipping K, Houben GF, Verschuren L, Meijerink M, Krishnan S. Seeking Windows of Opportunity to Shape Lifelong Immune Health: A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation. Front Immunol 2020; 11:644. [PMID: 32362896 PMCID: PMC7182036 DOI: 10.3389/fimmu.2020.00644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/20/2020] [Indexed: 01/01/2023] Open
Abstract
A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into prophylactic or therapeutic approaches, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. Six early life immune causal networks, each compromising a different time period in early life (the 1st, 2nd, 3rd trimester of gestations, birth, newborn, and infant period), were generated. Thereto information was extracted and structured from early life literature using the automated text mining and machine learning tool: Integrated Network and Dynamical Reasoning Assembler (INDRA). The tool identified relevant entities (e.g., genes/proteins/metabolites/processes/diseases), extracted causal relationships among these entities, and assembled them into early life-immune causal networks. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools (GO/GO-SLIM), inferred missing relationships and added expert knowledge to generate information-dense early life immune networks. Analysis of the six early life immune networks by PageRank, not only confirmed the central role of the "commonly used immune markers" (e.g., chemokines, interleukins, IFN, TNF, TGFB, and other immune activation regulators (e.g., CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious candidates (e.g., CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP, and IFNG), and also genes that were only described in certain early life period(s). Concluding, here we describe a network-based approach that provides a science-based and systematical method to explore the functional development of the early life immune system through time. This systems approach aids the generation of a testing strategy for the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development.
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Affiliation(s)
| | - Remon Dulos
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Mariël F van Stee
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Marie Y Meima
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | | | | | | | | | - Léon M J Knippels
- Danone Nutricia Research, Utrecht, Netherlands.,Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Karen Knipping
- Danone Nutricia Research, Utrecht, Netherlands.,Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Geert F Houben
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Lars Verschuren
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Marjolein Meijerink
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Shaji Krishnan
- Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
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Meijerink M, van den Broek TJ, Dulos R, Garthoff J, Knippels L, Knipping K, Harthoorn L, Houben G, Verschuren L, van Bilsen J. Network-Based Selection of Candidate Markers and Assays to Assess the Impact of Oral Immune Interventions on Gut Functions. Front Immunol 2019; 10:2672. [PMID: 31798593 PMCID: PMC6863931 DOI: 10.3389/fimmu.2019.02672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 10/29/2019] [Indexed: 12/05/2022] Open
Abstract
To assess the safety and efficacy of oral immune interventions, it is important and required by regulation to assess the impact of those interventions not only on the immune system, but also on other organs such as the gut as the porte d'entrée. Despite clear indications that the immune system interacts with several physiological functions of the gut, it is still unknown which pathways and molecules are crucial to assessing the impact of nutritional immune interventions on gut functioning. Here we used a network-based systems biology approach to clarify the molecular relationships between immune system and gut functioning and to identify crucial biomarkers to assess effects on gut functions upon nutritional immune interventions. First, the different gut functionalities were categorized based on literature and EFSA guidance documents. Moreover, an overview of the current assays and methods to measure gut function was generated. Secondly, gut-function related biological processes and adverse events were selected and subsequently linked to the physiological functions of the GI tract. Thirdly, database terms and annotations from the Gene ontology database and the Comparative Toxicogenomics Database (CTD) related to the previously selected gut-function related processes were selected. Next, database terms and annotations were used to identify the pathways and genes involved in those gut functionalities. In parallel, information from CTD was used to identify immune disease related genes. The resulting lists of both gut and immune function genes showed an overlap of 753 genes out of 1,296 gut-function related genes indicating the close gut-immune relationship. Using bioinformatics enrichment tools DAVID and Panther, the identified gut-immune markers were predicted to be involved in motility, barrier function, the digestion and absorption of vitamins and fat, regulation of the digestive system and gastric acid, and protection from injurious or allergenic material. Concluding, here we provide a promising systems biology approach to identify genes that help to clarify the relationships between immune system and gut functioning, with the aim to identify candidate biomarkers to monitor nutritional immune intervention assays for safety and efficacy in the general population. This knowledge helps to optimize future study designs to predict effects of nutritional immune intervention on gut functionalities.
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Affiliation(s)
| | | | | | | | - Léon Knippels
- Danone Nutricia Research, Utrecht, Netherlands.,Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Karen Knipping
- Danone Nutricia Research, Utrecht, Netherlands.,Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
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