Multi-omics data integration approach identifies potential biomarkers for Prostate cancer

Abstract

Background: Prostate cancer (PCa) is one of the most common malignancies, and many studies have shown that PCa has a poor prognosis, which varies across different ethnicities. This variability is caused by genetic diversity. High-throughput omics technologies have identified and shed some light on the mechanisms of its progression and finding new biomarkers. Still, a systems biology approach is needed for a holistic molecular perspective. In this study, we applied a multi-omics approach to data analysis using different publicly available omics data sets from diverse populations to better understand the PCa disease etiology. Methodology: Our study used multiple omic datasets which included genomic, transcriptomic and metabolomic datasets to better identify drivers for PCa. We first perform an individual omics analysis based on the standard pipeline for each dataset. Furthermore, we applied a novel multi-omics pathways algorithm to integrate all the individual omics datasets. This algorithm applies the p-values of enriched pathways from individual omics data types, which are then combined using the MiniMax statistic to prioritize pathways dysregulated in the omics datasets. Result: The single omics result indicated an association between up-regulated genes in RNA-Seq data and the metabolomics data. Glucose and pyruvate are the primary metabolites, and the associated pathways are glycolysis, gluconeogenesis, pyruvate kinase deficiency, and the Warburg effect pathway. Conclusion: From the interim result, the identified genes in RNA-Seq single omics analysis are linked with the significant pathways from the metabolomics analysis. The multi-omics pathway will eventually enable the identification of biomarkers shared amongst these different omics datasets to ease prostate cancer prognosis.

Computational identification of potential inhibitors targeting cdk1 in colorectal cancer

“…Its over expression has been linked to unrestricted cell proliferation, tumor development, and dysregulation associated with the initiation of many cancers including colorectal cancer ( Li et al, 2020 ; Sofi et al, 2022a ; Sofi et al, 2022b ). Gene expression has been associated with biomarkers in complex diseases like prostate cancer ( Chikwambi et al, 2023 ), epilepsy ( El Abed et al, 2023 ), malaria/COVID-19 ( Nzungize et al, 2022 ) and SARS-CoV-2 infection ( Nyamari et al, 2023 ). Several studies suggest that overexpression of CDK1, which is the hallmark event in many malignancies is associated with 5 = fluorouracil resistance through altered activation of Wnt/β-catechin signaling, mTOR, and p53 pathways leading to poor prognosis in CRC ( Zhu et al, 2019 ; Zhu et al, 2020a ; Zhu et al, 2020b ; Sofi et al, 2022a ).…”

Investigating antimicrobial resistance genes in Kenya, Uganda and Tanzania cattle using metagenomics

“…The shown ability to project AMR from genetic sequencing data is a significant leap in resistome surveillance. There have been efforts to model the evolution of viral pathogens like SARS-CoV-2 using genomic sequence data ( Awe et al, 2023 ), HIV-1 evolution in sub-Saharan Africa ( Obura et al, 2022 ), biomarker discovery ( Chikwambi et al, 2023 ; Nyamari et al, 2023 ; El Abed et al., 2023 ), malaria/CoVID-19 biomarker discovery ( Nzungize et al, 2022 ), analysis of RNA-seq and ChIP-seq data ( Ather et al, 2018 ), and Ebola Virus comparative genomics ( Oluwagbemi & Awe, 2018 ). Genomics and bioinformatics have been reported to be playing a key role in newborn screening ( Wesonga & Awe, 2022 ) and in agriculture.…”

Expression Level Analysis of ACE2 Receptor Gene in African-American and Non-African-American COVID-19 Patients

“…This is closely followed by Europe (9.6% of the world population) with 36.7% of cases and 28.7% of global cases and deaths respectively (2,374 deaths/million). Study reports have shown that next-generation sequences can be used to screen for disorders in newborns [9], for the study of pathogen evolution using African SARS-CoV-2 sequences [18], prostate cancer biomarker discovery [10], malaria/CoVID-19 biomarker discovery [58], SARS-CoV-2 variants classification [59], HIV-1 evolution in sub-Saharan Africa [60], analysis of RNA-seq and ChIP-seq data [61], and ebola virus comparative genomics [62]. We will be using next-generation sequencing data for our investigation in this study.…”