Gene Ontology: A Resource for Analysis and Interpretation of Alzheimer’s Disease Data

Kramarz, Barbara; Lovering, Ruth C.

doi:10.15586/alzheimersdisease.2019.ch2

Cited by 15 publications

(17 citation statements)

References 74 publications

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“…To identify HSPA family members-related signaling pathways that were differently activated in colon cancer, we conducted Gene ontology (GO) Analysis ( 14 ). As illustrated in Figure 6 , ECM-receptor interaction, heparin binding, glycosaminoglycan binding, extracellular matrix structural constituent, mitochondrial inner membrane, mitochondrial protein complex, collagen-containing extracellular matrix, mitochondrial translation, and extracellular matrix organization were potential signaling pathways regulated by HSPA1A ( Figures 6A, B ).…”

Section: Resultsmentioning

confidence: 99%

Upregulation of HSPA1A/HSPA1B/HSPA7 and Downregulation of HSPA9 Were Related to Poor Survival in Colon Cancer

et al. 2021

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The human HSP70 family is a type of heat shock protein (HSP), consisting of 13 members encoded by the HSPA genes. HSPs play important roles in regulating cellular responses and functions during carcinogenesis, but their relationship with colon cancer is unclear. In our study, we found that the expressions of HSPA1B, HSPA4, HSPA5, HSPA6, HSPA8, HSPA9, HSPA13, and HSPA14 were significantly increased, while those of HSPA1A, HSPA2, HSPA7, and HSPA12B were significantly decreased in colon cancer tissues. The expression of HSPA gene family members was associated with some clinicopathological characteristics, including age, gender, TNM stage, pathological stage, and CEA level. Furthermore, the Kaplan–Meier method and Cox regression analysis showed that high HSPA1A, HSPA1B, and HSPA7 expressions were related to unfavorable survival, and high HSPA9 was associated with favorable survival. The relationships between HSPA1A and HSPA9 expression and survival were validated in the GEO dataset, and the HSPA1A and HSPA9 protein expression differences between colon cancer tissues and normal tissues were validated in the UALCAN database. Methylation of HSPA1A and HSPA9 was also analyzed, and it was found that the methylation of the HSPA1A promoter was significantly increased, and the methylation of the HSPA9 promoter was significantly decreased in colon cancer tissues. Increasing the methylation level of the HSPA1A gene and decreasing the methylation level of HSPA9 were related to favorable prognosis. The expression difference of HSPA1A/HSPA1B/HSPA7/HSPA9 was verified in colon cancer cell lines and colonic epithelial cells. Gene ontology analysis was used to screen signal pathways related to HSPA1A-, HSPA1B-, HSPA7-, and HSPA9- high phenotype. In summary, the increased expressions of HSPA1A1, HSPA1B, and HSPA7 were associated with poor prognosis, while that of HSPA9 was related to favorable prognosis for colon cancer patients.

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Section: Resultsmentioning

confidence: 99%

Upregulation of HSPA1A/HSPA1B/HSPA7 and Downregulation of HSPA9 Were Related to Poor Survival in Colon Cancer

et al. 2021

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“…GO is a tool for the unification of biology, which collects structured, defined, and controlled vocabulary for large scale of gene annotation, including biological processes (BP), cellular component (CC), and molecular function (MF) ( Kramarz and Lovering, 2019 ). KEGG database classifies correlating gene sets into their respective pathways.…”

Section: Methodsmentioning

confidence: 99%

RNA Sequencing Reveals LINC00167 as a Potential Diagnosis Biomarker for Primary Osteoarthritis: A Multi-Stage Study

et al. 2021

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ObjectivesGiven the roles played by lncRNA in human diseases and the high incidence of OA, this study investigated the pivotal pathways involved in the disease and identified potential biomarkers for OA diagnosis.MethodsWe first performed an exploration of RNA-sequencing in peripheral blood leukocytes from six subjects (3 OA and 3 healthy controls). Promising candidate lncRNAs were evaluated in first stage validation using a GEO dataset (GSE114007) of 38 subjects (20 OA and 18 healthy controls), followed by a second stage validation using quantitative PCR analysis with 101 subjects (67 OA and 34 controls). The third stage investigated the potential value of validated lncRNA in the early diagnosis of OA in peripheral blood leukocytes from a total of 120 participants (60 cases and 60 controls).ResultsThe dataset identified a total of 1,380 up-regulated and 719 down-regulated mRNAs and 5,743 up-regulated and 7,384 down-regulated lncRNAs. The up-regulated DEGs were mainly enriched in the extracellular matrix, while the down-regulated DEGs were mainly enriched in the IL-17 and wnt signaling pathways. 18 overlapping candidate lncRNAs survived after first-stage validation. 3 hub lncRNAs were selected for the second validation stage and qualified in an external sample, and lncRNA LINC00167 was further confirmed with a similar result (down-expressed in both stages). Receiver operating characteristic analysis showed that LINC00167 can distinguish OA cases from healthy controls with a high area under the curve of 0.879 (95%CI: 0.819, 0.938; P < 0.001), with a sensitivity of 80.7% and specificity of 83.5%.ConclusionThe expression profile of OA was identified and critical pathways were elucidated by an integrated approach to RNA-seq from easily accessible blood. LINC00167 may serve as a potential early diagnosis marker for OA in clinical practice. The detailed mechanism of action of this lncRNA requires further elucidation in future studies.

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“…Our study contributes to a conceptual model (Figure 4) guiding specialists in adopting existing software platforms or building new ones along with a software development team. We represent the conceptual model using an ontology given its wide adoption [60][61][62][63][64] and description power [65]. In the ESPIM ontology, the Person class represents observers (specialists) and participants (target users).…”

Section: Espim: Ontology-based Conceptual Modelmentioning

confidence: 99%

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

Cunha¹,

Rodrigues²,

Zaine³

et al. 2021

J Med Internet Res

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Background Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. Objective The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). Methods We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method’s conceptual model, support to 8 real case studies, and postdesign interviews. Results The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists’ target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. Conclusions The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology–based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.

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Gene Ontology: A Resource for Analysis and Interpretation of Alzheimer’s Disease Data

Cited by 15 publications

References 74 publications

Upregulation of HSPA1A/HSPA1B/HSPA7 and Downregulation of HSPA9 Were Related to Poor Survival in Colon Cancer

Upregulation of HSPA1A/HSPA1B/HSPA7 and Downregulation of HSPA9 Were Related to Poor Survival in Colon Cancer

RNA Sequencing Reveals LINC00167 as a Potential Diagnosis Biomarker for Primary Osteoarthritis: A Multi-Stage Study

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

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