Novel cancer subtyping method based on patient-specific gene regulatory network.
Ontology highlight
ABSTRACT:
SUBMITTER: Nakazawa MA
PROVIDER: S-EPMC8654869 | biostudies-literature |
REPOSITORIES: biostudies-literature
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2012-07-04 | E-GEOD-35809 | biostudies-arrayexpress
Project description:Cryptosporidium meleagridis is a common cause of cryptosporidiosis in avian hosts and the third most common species involved in human cryptosporidiosis. Sequencing of the highly polymorphic 60-kDa glycoprotein (gp60) gene is a frequently used tool for investigation of the genetic diversity and transmission dynamics of Cryptosporidium. However, few studies have included gp60 sequencing of C. meleagridis. One explanation may be that the gp60 primers currently in use are based on Cryptosporidium hominis and Cryptosporidium parvum sequence data, potentially limiting successful amplification of the C. meleagridis gp60 gene. We therefore aimed to design primers for better gp60 subtyping of C. meleagridis. Initially, ∼1,440 bp of the gp60 locus of seven C. meleagridis isolates were amplified using primers flanking the open reading frame. The obtained sequence data (∼1,250 bp) were used to design primers for a nested PCR targeting C. meleagridis. Twenty isolates (16 from human and 4 from poultry) previously identified as C. meleagridis by analysis of small subunit (SSU) rRNA genes were investigated. Amplicons of the expected size (∼900 bp) were obtained from all 20 isolates. The subsequent sequence analysis identified 3 subtype families and 10 different subtypes. The most common subtype family, IIIb, was identified in 12 isolates, represented by 6 subtypes, 4 new and 2 previously reported. Subtype family IIIe was found in 3 isolates represented by 3 novel, distinct subtypes. Finally, IIIgA31G3R1 was found in 1 human isolate and 4 poultry isolates, all originating from a previously reported C. meleagridis outbreak at a Swedish organic farm.
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Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort.
2012-07-05 | GSE35809 | GEO
Project description:Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. One critical unmet challenge is that molecular disease subtypes characterized by relevant clinical differences, such as survival, are difficult to differentiate. With the advancement of multi-omics technologies, subtyping methods have shifted toward data integration in order to differentiate among subtypes from a holistic perspective that takes into consideration phenomena at multiple levels. However, these integrative methods are still limited by their statistical assumption and their sensitivity to noise. In addition, they are unable to predict the risk scores of patients using multi-omics data. Here, we present a novel approach named Subtyping via Consensus Factor Analysis (SCFA) that can efficiently remove noisy signals from consistent molecular patterns in order to reliably identify cancer subtypes and accurately predict risk scores of patients. In an extensive analysis of 7,973 samples related to 30 cancers that are available at The Cancer Genome Atlas (TCGA), we demonstrate that SCFA outperforms state-of-the-art approaches in discovering novel subtypes with significantly different survival profiles. We also demonstrate that SCFA is able to predict risk scores that are highly correlated with true patient survival and vital status. More importantly, the accuracy of subtype discovery and risk prediction improves when more data types are integrated into the analysis. The SCFA software and TCGA data packages will be available on Bioconductor.
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