Development of “Biosearch System” for biobank management and storage of disease associated genetic information

Karim, Sajjad; Al-Kharraz, Muna; Mirza, Zeenat; Noureldin, Hend; Abusamara, Heba; Alganmi, Nofe; Merdad, Adnan; Jastaniah, Saddig D.; Kumar, Sudhir; Rasool, Mahmood; Abuzenadah, Adel M.; Al‐Qahtani, Mohammed

doi:10.1016/j.jksus.2021.101760

Cited by 2 publications

(3 citation statements)

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“…We used a comprehensive set of functional annotation tools, such as the QIAGEN Ingenuity Pathway Analysis (IPA knowledgebase v84978992, QIAGEN, USA) and WEB-based Gene SeT AnaLysis Toolkit (WebGestalt 2019, https://www.webgestalt.org/, accessed on 2 January 2023), to investigate and understand the biological meaning of long-list significant DEGs [1,25,26]. We explored gene ontologies, enriched and canonical pathways, upstream regulators, disease and functions, and the networks associated with BC.…”

Section: Functional Pathway and Gene Set Enrichment Analysismentioning

confidence: 99%

“…Breast cancer (BC) is the most common cause of cancer death in women, with 1 in 8 cancer cases, and its incidence has increased significantly despite the preventive and curative approaches utilized in recent years [ 1 , 2 ]. In 2020, the International Agency for Research on Cancer (IARC) and World Health Organization (WHO) reported 2.26 million new BC cases and 684,996 global cases of BC mortality in females, surpassing lung cancer with 2.20 million new cases.…”

Section: Introductionmentioning

confidence: 99%

“…Microarray-platform-based assays, including the Affymetrix GeneChip Human Genome U133 Plus 2.0 array (Affymetrix, Santa Clara, CA, USA), have the ability to measure thousands of gene expressions simultaneously for each data point (sample) [ 8 , 9 ]. Expression profiling to check for variability in gene expression is an important factor influencing the precision and accuracy of clinical decisions in the diagnosis of BC [ 1 , 8 , 10 , 11 , 12 , 13 ]. Despite the large-scale, high-dimensional, and highly redundant type of microarray data, with numerous tools to identify genes that are differentially expressed across cancer/disease phenotypes, the interpretation of results and follow-up analysis are quite challenging.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

Mirza

Ansari

Iqbal

et al. 2023

Cancers

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Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

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Section: Functional Pathway and Gene Set Enrichment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%