Approximately 450,000 cases of Non-Hodgkin’s lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin’s Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.
Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.
H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they emerge. Here, we characterize antigenic drift events observed in the hemagglutinin (HA) sequence of the pandemic H1N1 lineage from 2015–2019. We observed the substitutions S200P, K147N, and P154S, together with other mutations in structural, functional, and/or epitope regions in 2015–2019 HA protein sequences from the Mountain West region of the United States, the larger United States, Europe, and other Northern Hemisphere countries. We reconstructed multiple phylogenetic trees to track the relationships and spread of these mutations and tested for evidence of selection pressure on HA. We found that the prevalence of amino acid substitutions at positions 147, 154, 159, 200, and 233 significantly changed throughout the studied geographical regions between 2015 and 2019. We also found evidence of coevolution among a subset of these amino acid substitutions. The results from this study could be relevant for future epidemiological tracking and vaccine prediction efforts. Similar analyses in the future could identify additional sequence changes that could affect the pathogenicity and/or infectivity of this virus in its human host.
Approximately 450,000 cases of Non-Hodgkin’s lymphoma are diagnosed annually worldwide, resulting in ∼240,000 deaths. An augmented understanding of the common mechanisms of pathology among relatively large numbers of B-cell Non-Hodgkin’s Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large transcriptomic meta-analysis of available BCNHL RNA-sequencing data from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms across BCNHL subtypes. The study was limited to GEO’s publicly available human B-cell RNA-sequencing datasets that met our criteria, and limitations may include lack of diversity in ethnicities and age groups. We found ∼10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing lymphoma samples to non-diseased samples. Our findings include a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of extracellular matrix-associated proteins. Our drug prediction results yielded new candidates including ocriplasmin and collagenase. We also used a machine learning approach to identify the BCNHL biomarkers YES1, FERMT2, and FAM98B, novel biomarkers of high predictive fidelity. This meta-analysis validates existing knowledge while providing novel insights into the inner workings and mechanisms of B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics. No external funding was used for this study.
We report here the results of a transcriptomic meta-analysis of public data for B-cell Non-Hodgkin’s Lymphomas (BCNHL). In 2016, there were 461,000 cases of Non-Hodgkin’s lymphoma worldwide, resulting in 240,000 deaths that year. BCNHLs pose a significant disease burden worldwide, making up 85-90% of BCNHL cases. BCNHL subtypes include Burkitt’s lymphoma, marginal-zone B-cell lymphomas, follicular lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma. There is a growing need to understand the mechanisms of BCNHL pathology. The scientific community has spent a great deal of time and effort to identify the hallmarks of cancer. Since cancers of different subtypes can be considered parallel systems, we have performed a meta-analysis including as many BCNHL subtypes as possible to determine common underlying mechanisms. We performed a transcriptomic meta-analysis of publicly available RNA-sequencing data for BCNHL, consisting of 322 relevant samples across seven distinct studies in the NCBI Gene Expression Omnibus (GEO). To our knowledge, no BCNHL meta-analysis of this magnitude has previously been performed. We found ~10,400 significant differentially expressed genes (FDR p-value <= 0.05) and 33 significantly modulated pathways (FDR p-value <= 0.05). We observed potential common mechanisms for BCNHL’s differentially expressed genes and signaling pathways. Our findings include a significant class of proteoglycans not previously associated with lymphomas as well as significant upregulation of extracellular matrix-associated proteins. This meta-analysis offers fresh insights into the inner workings and mechanisms of B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics. Citation Format: Naomi Rapier-Sharman, Brett E. Pickett. Transcriptomic meta-analysis of non-Hodgkin’s B-cell lymphomas reveals reliance on pathways associated with extracellular matrix [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2709.
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