scholarly journals Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jayaprakash Chinnappan ◽  
Akilandeswari Ramu ◽  
Vidhya Rajalakshmi V. ◽  
Akil Kavya S.
Keyword(s):  
Survival Analysis ◽  
Mutation Analysis ◽  
Pathway Analysis ◽  
Target Genes ◽  
Target Selection ◽  
Data Sets ◽  
Ppi Network ◽  
Gene Target ◽  
Different Types ◽  
Direct Target

AbstractIntegrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

scholarly journals Identifification of Key Genes Involved in Colorectal Cancer: A Bioinformatics Analysis

2020 ◽  
Author(s):  
Dongxue Lu ◽  
Jing Yan ◽  
Zhiguang Sun ◽  
Xuan Li ◽  
Feng Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Receiver Operating Characteristic ◽  
Signaling Pathway ◽  
Operating Characteristic ◽  
Positive Control ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels

Abstract BackgroundColorectal cancer is the life-threatening tumor with both high prevalence and mortality worldwide. However, the molecular mechanism behind it remains unknown. Methods: Herein, the potential prognostic candidate biomarkers for colorectal cancer were tested by Bioinformatical analysis combined with the CRC clinical samples. Three data sets (GSE32323, GSE44076 and GSE43078) were collected from the gene expression omnibus (GEO). The limma and clusterProfifiler packages were used to identify differentially expressed genes (DEGs) and conduct functional enrichment analysis, respectively. To retrieve Interacting Genes (STRING) database, protein–protein interaction (PPI) network was built up using Search Tool, and Cytoscape was applied to carry out the module analysis. Subsequently, the online tool GEPIA was employed to conduct overall survival analysis (http://gepia.cancer-pku.cn/index.html), and the Oncomine database was used to analyze prognostic candidate biomarkers. Finally, 4 key hub genes were selected for validation of their expression levels in 9 patients newly diagnosed with CRC via reverse transcription‑quantitative PCR (RT‑qPCR). To evaluate the accuracy of prediction, time-dependent receiver operating characteristic (ROC) was applied.ResultsIn total, 547 DEGs got identifified, inclusive of 475 downregulated and 72 upregulated genes with a signifificant enrichment in the cellular response to hypoxia, the positive control of ERK1 and ERK2 cascade and the positive control of apoptotic process. The enhanced pathways were Pathways in cancer,PI3K-Akt signaling pathway,cGMP-PKG signaling pathway. Through the extraction of critical modules from the PPI network, 10 hub genes got removed. These 10 hub genes are all up-regulated genes and are highly expressed in colorectal cancer. Survival analysis shows that only CCNB1 and CCNA2 are associated with the survival prediction of colorectal cancer. Moreover, consistence is show between the TCGA data sets and the expression levels of the 4 hub genes. Receiver Operating Characteristic(ROC) curves showed that all CCNB1,CCNA2,AURKA and BUB1B have potential predictive value.Briely, new hub genes identifified can shed light on the underlying mechanism behind CRC carcinogenesis and development, which is coducive to detecting and treating CRC timely.

MicroRNAs and their role in hematological malignant diseases

2012 ◽  
Vol 153 (52) ◽  
pp. 2051-2059 ◽  
Author(s):  
Zsuzsanna Gaál ◽  
Éva Oláh
Keyword(s):  
Target Genes ◽  
Lymphoblastic Leukemia ◽  
Cancer Tissue ◽  
Altered Expression ◽  
Diagnosis And Prognosis ◽  
Oncologic Patients ◽  
Different Types ◽  
Non Coding Rnas ◽  
Success Of Treatment ◽  
Posttranscriptional Level

MicroRNAs are a class of small non-coding RNAs regulating gene expression at posttranscriptional level. Their target genes include numerous regulators of cell cycle, cell proliferation as well as apoptosis. Therefore, they are implicated in the initiation and progression of cancer, tissue invasion and metastasis formation as well. MicroRNA profiles supply much information about both the origin and the differentiation state of tumours. MicroRNAs also have a key role during haemopoiesis. An altered expression level of those have often been observed in different types of leukemia. There are successful attempts to apply microRNAs in the diagnosis and prognosis of acute lymphoblastic leukemia and acute myeloid leukemia. Measurement of the expression levels may help to predict the success of treatment with different kinds of chemotherapeutic drugs. MicroRNAs are also regarded as promising therapeutic targets, and can contribute to a more personalized therapeutic approach in haemato-oncologic patients. Orv. Hetil., 2012, 153, 2051–2059.

MiRNA-145 and Its Direct Downstream Targets in Digestive System Cancers: A Promising Therapeutic Target

2020 ◽  
Vol 26 ◽  
Author(s):  
Yini Ma ◽  
Xiu Cao ◽  
Guojuan Shi ◽  
Tianlu Shi
Keyword(s):  
Digestive System ◽  
Target Genes ◽  
Malignant Neoplasm ◽  
Vital Role ◽  
Diagnostic Biomarkers ◽  
Cellular Processes ◽  
Promising Therapeutic Target ◽  
Direct Target ◽  
Potential Strategy

: MicroRNAs (miRNAs) play a vital role in the onset and development of many diseases, including cancers. Emerging evidence shows that numerous miRNAs have the potential to be used as diagnostic biomarkers for cancers, and miRNA-based therapy may be a promising therapy for the treatment of malignant neoplasm. MicroRNA-145 (miR-145) has been considered to play certain roles in various cellular processes, such as proliferation, differentiation and apoptosis, via modulating expression of direct target genes. Recent reports show that miR-145 participates in the progression of digestive system cancers, and plays crucial and novel roles for cancer treatment. In this review, we summarize the recent knowledge concerning the function of miR-145 and its direct targets in digestive system cancers. We discuss the potential role of miR-145 as valuable biomarkers for digestive system cancers and how miR-145 regulates these digestive system cancers via different targets to explore the potential strategy of targeting miR-145.

scholarly journals The role of m6A modification in the biological functions and diseases

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiulin Jiang ◽  
Baiyang Liu ◽  
Zhi Nie ◽  
Lincan Duan ◽  
Qiuxia Xiong ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Rna Modification ◽  
Biological Functions ◽  
Rna Methylation ◽  
Downstream Target ◽  
Different Types ◽  
M6a Rna Methylation

AbstractN6-methyladenosine (m6A) is the most prevalent, abundant and conserved internal cotranscriptional modification in eukaryotic RNAs, especially within higher eukaryotic cells. m6A modification is modified by the m6A methyltransferases, or writers, such as METTL3/14/16, RBM15/15B, ZC3H3, VIRMA, CBLL1, WTAP, and KIAA1429, and, removed by the demethylases, or erasers, including FTO and ALKBH5. It is recognized by m6A-binding proteins YTHDF1/2/3, YTHDC1/2 IGF2BP1/2/3 and HNRNPA2B1, also known as “readers”. Recent studies have shown that m6A RNA modification plays essential role in both physiological and pathological conditions, especially in the initiation and progression of different types of human cancers. In this review, we discuss how m6A RNA methylation influences both the physiological and pathological progressions of hematopoietic, central nervous and reproductive systems. We will mainly focus on recent progress in identifying the biological functions and the underlying molecular mechanisms of m6A RNA methylation, its regulators and downstream target genes, during cancer progression in above systems. We propose that m6A RNA methylation process offer potential targets for cancer therapy in the future.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiying Chen ◽  
Jiahui Wei ◽  
Min Li ◽  
Yongjuan Zhao
Keyword(s):  
Functional Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Interaction Analysis ◽  
Early Stage ◽  
Gene Interaction ◽  
Gene Expression Omnibus ◽  
Circular Rnas ◽  
Cerna Network

Abstract Background This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). Methods The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA–miRNA–mRNA). Finally, the functional analysis and drug–gene interaction analysis of mRNAs in the ceRNA network was performed. Results A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA–miRNA and 674 miRNA–mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA–miRNA–mRNA network was constructed, containing 5 miRNA–circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA–mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. Conclusion Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

Molecular Mechanism of The Effect of Huanglian Jiedu Decoction On Ulcerative Colitis Based On Network Pharmacology And Molecular Docking

2021 ◽  
Author(s):  
Jing Yang ◽  
Chao-Tao Tang ◽  
Ruiri Jin ◽  
Bixia Liu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Signal Pathway ◽  
Intestinal Barrier Function ◽  
Network Pharmacology ◽  
Bioactive Components ◽  
Ppi Network ◽  
Active Components

Abstract Huanglian jiedu decoction (HLJDD) is a heat-clearing and detoxifying agent composed of four kinds of Chinese herbal medicine. Previous studies have shown that HLJDD can improve the inflammatory response of ulcerative colitis (UC) and maintain intestinal barrier function. However, its molecular mechanism is not completely clear. In this study, we verified the bioactive components (BCI) and potential targets of HLJDD in the treatment of UC by means of network pharmacology and molecular docking, and constructed the pharmacological network and PPI network. Then the core genes were enriched by GO and KEGG. Finally, the bioactive components were docked with the key targets to verify the binding ability between them. A total of 54 active components related to UC were identified. Ten genes are considered to be very important to PPI network. Functional analysis showed that these target genes were mainly involved in the regulation of cell response to different stimuli, IL-17 signal pathway and TNF signal pathway. The results of molecular docking showed that the active components of HLJDD had good affinity with Hub gene. This study systematically elucidates the "multi-component, multi-target, multi-pathway" mechanism of anti-UC with HLJDD for the first time, suggesting that HLJDD or its active components may be candidate drugs for the treatment of ulcerative colitis.

scholarly journals Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

2019 ◽  
Vol 78 (8) ◽  
pp. 1127-1134 ◽  
Author(s):  
Paul Martin ◽  
James Ding ◽  
Kate Duffus ◽  
Vasanthi Priyadarshini Gaddi ◽  
Amanda McGovern ◽  
...  
Keyword(s):  
Rheumatic Diseases ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Healthcare Providers ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Causal Genes ◽  
Additional Cell

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

scholarly journals Machine learning for improved data analysis of biological aerosol using the WIBS

2018 ◽  
Vol 11 (11) ◽  
pp. 6203-6230 ◽  
Author(s):  
Simon Ruske ◽  
David O. Topping ◽  
Virginia E. Foot ◽  
Andrew P. Morse ◽  
Martin W. Gallagher
Keyword(s):  
Ultraviolet Light ◽  
Fungal Spores ◽  
Laboratory Data ◽  
Misclassification Rate ◽  
Gradient Boosting ◽  
Classification Error ◽  
Data Sets ◽  
Data Preparation ◽  
Different Types ◽  
Selection Of

Abstract. Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will respond differently in the presence of ultraviolet light, potentially allowing for different types of biological aerosol to be discriminated. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has allowed for size, morphology and fluorescence measurements to be collected in real-time. However, it is unclear without studying instrument responses in the laboratory, the extent to which different types of particles can be discriminated. Collection of laboratory data is vital to validate any approach used to analyse data and ensure that the data available is utilized as effectively as possible. In this paper a variety of methodologies are tested on a range of particles collected in the laboratory. Hierarchical agglomerative clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting. Whilst HAC was able to effectively discriminate between reference narrow-size distribution PSL particles, yielding a classification error of only 1.8 %, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5 % and 24.2 %. Furthermore, there is a large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable to attain consistent results across the different sets of laboratory generated aerosol tested. The lowest classification errors were obtained using gradient boosting, where the misclassification rate was between 4.38 % and 5.42 %. The largest contribution to the error, in the case of the higher misclassification rate, was the pollen samples where 28.5 % of the samples were incorrectly classified as fungal spores. The technique was robust to changes in data preparation provided a fluorescent threshold was applied to the data. In the event that laboratory training data are unavailable, DBSCAN was found to be a potential alternative to HAC. In the case of one of the data sets where 22.9 % of the data were left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42 % on the classified data. These results could not be replicated for the other data set where 26.8 % of the data were not classified and a classification error of 13.8 % was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring a different selection of parameters depending on the preparation used. Further analysis will also be required to confirm our selection of the parameters when using this method on ambient data. There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely to improve on current discrimination between pollen, bacteria and fungal spores and even between different species, however the need for extensive laboratory data sets will grow as a result.

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