Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

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Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

Current Gene Therapy ◽

10.2174/1566523220666201208093325 ◽

2020 ◽

Vol 20 ◽

Author(s):

Hongwei Zhang ◽

Steven Wang ◽

Tao Huang

Keyword(s):

Feature Selection ◽

Expression Analysis ◽

Hypersensitivity Pneumonitis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Support Vector ◽

Svm Classifier ◽

Clinical Tool ◽

Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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Exploring the Potential of Capsaicin Against Cancer Metastasis Based on TGF-β Signaling Modulation Through Module-based Network Pharmacology Approach

Current Drug Discovery Technologies ◽

10.2174/1570163816666190515104041 ◽

2020 ◽

Vol 17 (5) ◽

pp. 647-660 ◽

Cited By ~ 1

Author(s):

Shivananda Kandagalla ◽

Sharath Belenahalli Shekarappa ◽

Gollapalli Pavan ◽

Umme Hani ◽

Manjunatha Hanumanthappa

Keyword(s):

Amino Acid ◽

Amino Acid Composition ◽

Signaling Pathway ◽

Acid Composition ◽

Protein Interaction ◽

Cancer Metastasis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Mode Analysis ◽

Network Pharmacology

Background: Capsaicin is an active alkaloid /principal component of red pepper responsible for the pungency of chili pepper. Capsaicin by changing the intracellular redox homeostasis regulate a variety of signaling pathways ultimately producing a divergent cellular outcome. Several reports showed the potential of capsaicin against cancer metastasis, however unexplored molecular mechanism is still an active part of the research. Several growth factors have a critical role during cancer metastasis among them TGF- β signaling play a vital role. Methods: The present study aimed at analyzing capsaicin modulation of TGF-β signaling using network pharmacology approach. The chemical and protein interaction data of capsaicin was curated and abstracted using STITCH4.0, PubChem and ChEMBL database. Further, the compiled data set was subjected to the pathway and functional enrichment analysis using Protein Analysis THrough Evolutionary Relationship (PANTHER) and, Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Meanwhile, the pattern of amino acid composition across the capsaicin targets was analyzed using the EMBOSS Pepstat tool. Capsaicin targets involved in TGF- β were identified and their Protein-Protein Interaction (PPI) network constructed using STRING v10 and Cytoscape (v 3.2.1). From the above-constructed network, the clusters were mined using the MCODE clustering algorithm and finally binding affinity of capsaicin with its targets involved in TGF-β signaling pathway was analyzed using Autodock Vina. Results: The analysis explored capsaicin targets and, their associated functional and pathway annotations. Besides, the analysis also provides a detailed distinct pattern of amino acid composition across the capsaicin targets. The capsaicin targets described as MAPK14, JUN, SMAD3, MAPK3, MAPK1 and MYC involved in TGF-β signaling pathway through pathway enrichment analysis. The binding mode analysis of capsaicin with its targets has shown high affinity with MAPK3, MAPK1, JUN and MYC. Conclusion: The study explores the potential of capsaicin as a potent modulator of TGF-β signaling pathway during cancer metastasis and proposes new methodology and mechanism of action of capsaicin against TGF- β signaling pathway.

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Cancer Bioinformatics for Updating Anticancer Drug Developments and Personalized Therapeutics

Reviews on Recent Clinical Trials ◽

10.2174/1574887112666170209161444 ◽

2017 ◽

Vol 12 (2) ◽

pp. 101-110 ◽

Cited By ~ 4

Author(s):

Da-Yong Lu ◽

Rong-Xin Qu ◽

Ting-Ren Lu ◽

Hong-Ying Wu

Keyword(s):

Anticancer Drug ◽

Cancer Bioinformatics

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HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

Current Bioinformatics ◽

10.2174/1574893613666180402141107 ◽

2019 ◽

Vol 14 (2) ◽

pp. 148-156

Author(s):

Nighat Noureen ◽

Sahar Fazal ◽

Muhammad Abdul Qadir ◽

Muhammad Tanvir Afzal

Keyword(s):

Hierarchical Clustering ◽

Real Data ◽

Cell Types ◽

Computational Scheme ◽

Data Set ◽

Chromatin States ◽

Functional Regions ◽

Visualization Strategy ◽

Hidden States ◽

Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

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Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

Current Bioinformatics ◽

10.2174/1574893614666190204152500 ◽

2019 ◽

Vol 14 (7) ◽

pp. 591-601 ◽

Cited By ~ 1

Author(s):

Aravind K. Konda ◽

Parasappa R. Sabale ◽

Khela R. Soren ◽

Shanmugavadivel P. Subramaniam ◽

Pallavi Singh ◽

...

Keyword(s):

Expression Pattern ◽

Gene Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Wrky Transcription Factor ◽

Functional Enrichment ◽

Differentially Expressed ◽

Callose Deposition ◽

Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

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Identification of potential biomarkers for abdominal pain in IBS patients by bioinformatics approach

BMC Gastroenterology ◽

10.1186/s12876-021-01626-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhongyuan Lin ◽

Yimin Wang ◽

Shiqing Lin ◽

Decheng Liu ◽

Guohui Mo ◽

...

Keyword(s):

Gene Expression ◽

Irritable Bowel Syndrome ◽

Abdominal Pain ◽

Gastrointestinal Disease ◽

Rectal Mucosa ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Irritable Bowel ◽

Potential Biomarkers

Abstract Background Irritable bowel syndrome (IBS) is the most common functional gastrointestinal disease characterized by chronic abdominal discomfort and pain. The mechanisms of abdominal pain, as a relevant symptom, in IBS are still unclear. We aimed to explore the key genes and neurobiological changes specially involved in abdominal pain in IBS. Methods Gene expression data (GSE36701) was downloaded from Gene Expression Omnibus database. Fifty-three rectal mucosa samples from 27 irritable bowel syndrome with diarrhea (IBS-D) patients and 40 samples from 21 healthy volunteers as controls were included. Differentially expressed genes (DEGs) between two groups were identified using the GEO2R online tool. Functional enrichment analysis of DEGs was performed on the DAVID database. Then a protein–protein interaction network was constructed and visualized using STRING database and Cytoscape. Results The microarray analysis demonstrated a subset of genes (CCKBR, CCL13, ACPP, BDKRB2, GRPR, SLC1A2, NPFF, P2RX4, TRPA1, CCKBR, TLX2, MRGPRX3, PAX2, CXCR1) specially involved in pain transmission. Among these genes, we identified GRPR, NPFF and TRPA1 genes as potential biomarkers for irritating abdominal pain of IBS patients. Conclusions Overexpression of certain pain-related genes (GRPR, NPFF and TRPA1) may contribute to chronic visceral hypersensitivity, therefore be partly responsible for recurrent abdominal pain or discomfort in IBS patients. Several synapses modification and biological process of psychological distress may be risk factors of IBS.

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Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Experimental & Molecular Medicine ◽

10.1038/s12276-021-00612-z ◽

2021 ◽

Author(s):

Anders Berglund ◽

Ryan M. Putney ◽

Imene Hamaidi ◽

Sungjune Kim

Keyword(s):

Patient Outcome ◽

Immune Responses ◽

Cancer Cells ◽

Immune Evasion ◽

Epigenetic Regulation ◽

Current Evidence ◽

Immune Recognition ◽

Bioinformatics Tools ◽

Visualization Techniques ◽

Cancer Bioinformatics

AbstractCancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

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