Approaches to uncovering cancer diagnostic and prognostic molecular signatures.
Ontology highlight
ABSTRACT:
SUBMITTER: Hong S
PROVIDER: S-EPMC4905187 | biostudies-other | 2014 Apr-Jun
REPOSITORIES: biostudies-other
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Project description:BACKGROUND: Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers. RESULTS: We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable. CONCLUSIONS: Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add to traditional approaches? Our findings indicate there is value in the way in which different subspaces of the patient sample are captured differently among the various methods, highlighting the possibility of 'combination' classifiers capable of identifying which patients will be more accurately classified by one particular method over another. We have seen this clearly for the first time because of our in-depth analysis at the level of individual patients.
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Project description:BACKGROUND:Gastric cancer (GC) is the fifth leading cause of cancer-related deaths worldwide. As an effective and easily performed method, microscopy-based Lauren classification has been widely accepted by gastrointestinal surgeons and pathologists for GC subtyping, but molecular characteristics of different Lauren subtypes were poorly revealed. METHODS:GSE62254 was used as a derivation cohort, and GSE15459 was used as a validation cohort. The difference between diffuse and intestinal GC on the gene expression level was measured. Gene ontology (GO) enrichment analysis was performed for both subgroups. Hierarchical clustering and heatmap exhibition were also performed. Kaplan-Meier plot and Cox proportional hazards model were used to evaluate survival grouped by the given genes or hierarchical clusters. RESULTS:A total of 4598 genes were found differentially expressed between diffuse and intestinal GC. Immunity- and cell adhesion-related GOs were enriched for diffuse GC, whereas DNA repair- and cell cycle-related GOs were enriched for intestinal GC. We proposed a 40-gene signature (?2=30.71, P<.001) that exhibits better discrimination for prognosis than Lauren classification (?2=12.11, P=.002). FRZB [RR (95% CI)=1.824 (1.115-2.986), P=.017] and EFEMP1 [RR (95% CI)=1.537 (0.969-2.437), P=.067] were identified as independent prognostic factors only in diffuse GC but not in intestinal GC patients. KRT23 [RR (95% CI)=1.616 (0.938-2.785), P=.083] was identified as an independent prognostic factor only in intestinal GC patients but not in diffuse GC patients. Similar results were achieved in the validation cohort. CONCLUSION:We found that GCs with different Lauren classifications had different molecular characteristics and identified FRZB, EFEMP1, and KRT23 as subtype-specific prognostic factors for GC patients.
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2014-03-13 | E-GEOD-39603 | biostudies-arrayexpress
Project description:Genome wide DNA methylation profiling of normal and Prostate cancer samples. The Illumina GoldenGate Methylation Cancer Panel I with 1,505 CpG sites selected from 807 genes was used to obtain DNA methylation profiles. Samples included 24 normals and 87 tumors.
2014-03-13 | GSE39603 | GEO
Project description:Recent prostate-specific antigen-based screening trials indicate an urgent need for novel and noninvasive biomarker identification strategies to improve the prediction of prostate cancer behavior. Noncoding microRNAs (miRNA) in the serum and plasma have been shown to have potential as noninvasive markers for physiologic and pathologic conditions. To identify serum miRNAs that diagnose and correlate with the prognosis of prostate cancer, we developed a multiplex quantitative reverse transcription PCR method involving the purification of multiplex PCR products followed by uniplex analysis on a microfluidics chip to evaluate 384 human miRNAs. Using Dgcr8 and Dicer knockout (small RNA-deficient) mouse ES cells as the benchmark, we confirmed the validity of our technique and uncovered a considerable lack of accuracy in previously published methods. Profiling 48 sera from healthy men and untreated prostate cancer patients with differing CAPRA scores, we identified miRNA signatures that allow us to diagnose cancer patients and correlate with a prognosis. These serum signatures include oncogenic and tumor-suppressive miRNAs, suggesting functional roles in prostate cancer progression.
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Project description:Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality globally and is the third most prevalent cancer. The effective treatment of CRC is challenged by tumor drug resistance, underscoring the urgent need for novel therapeutic strategies. It is well recognized that epigenetic modifications, particularly DNA methylation, significantly contribute to the initiation and advancement of CRC. Tumors frequently exhibit hypermethylation of specific gene promoters alongside a general hypomethylation of genomic DNA. In normal tissues, gene promoters, particularly those of tumor suppressor genes, are typically unmethylated, whereas they are often hypermethylated in cancerous tissues. Previous studies have identified the methylation status of the CpG island within the SLC30A10 gene as a reliable biomarker for CRC. Notably, a hypermethylated phenotype is inversely correlated with SLC30A10 expression. However, the precise functional implications of SLC30A10 suppression in the context of CRC progression remain to be elucidated.). The investigation of molecular pathways involved in metal metabolism in CRC has not been conducted previously at the transcriptomic level. Furthermore, the relationship between the mutational characteristics of tumors—such as individual mutations, mutational burden, and microsatellite instability—and the activity of genes related to metal metabolic pathways has not been explored. This project aims to be the first to examine the antitumor potential of modulating the transport systems for manganese and zinc ions in CRC. The molecular function of SLC30A10 is to export zinc (Zn) and manganese (Mn) ions from the cell. Additionally, SLC30A10 plays a crucial role in mitigating Mn-induced cytotoxicity and facilitates Zn transport to early endosomes while promoting endosomal recirculation to avert Zn toxicity. Our findings indicate that overexpression of SLC30A10 is correlated with the knockout of its functional antagonist, SLC39A14 in the HuTu80 and HCT-15 cell lines.
2024-09-15 | GSE276849 | GEO
Project description:Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.
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Project description:BackgroundColorectal cancer (CRC) is a prevalent malignancy with diverse molecular characteristics. The NGS-based approach enhances our comprehension of genomic landscape of CRC and may guide future advancements in precision oncology for CRC patients.MethodIn this research, we conducted an analysis using Next-Generation Sequencing (NGS) on samples collected from 111 individuals who had been diagnosed with CRC. We identified somatic and germline mutations and structural variants across the tumor genomes through comprehensive genomic profiling. Furthermore, we investigated the landscape of driver mutations and their potential clinical implications.ResultsOur findings underscore the intricate heterogeneity of genetic alterations within CRC. Notably, BRAF, ARID2, KMT2C, and GNAQ were associated with CRC prognosis. Patients harboring BRAF, ARID2, or KMT2C mutations exhibited shorter progression-free survival (PFS), whereas those with BRAF, ARID2, or GNAQ mutations experienced worse overall survival (OS). We unveiled 80 co-occurring and three mutually exclusive significant gene pairs, enriched primarily in pathways such as TP53, HIPPO, RTK/RAS, NOTCH, WNT, TGF-Beta, MYC, and PI3K. Notably, co-mutations of BRAF/ALK, BRAF/NOTCH2, BRAF/CREBBP, and BRAF/FAT1 correlated with worse PFS. Furthermore, germline AR mutations were identified in 37 (33.33%) CRC patients, and carriers of these variants displayed diminished PFS and OS. Decreased AR protein expression was observed in cases with AR germline mutations. A four-gene mutation signature was established, incorporating the aforementioned prognostic genes, which emerged as an independent prognostic determinant in CRC via univariate and multivariate Cox regression analyses. Noteworthy BRAF and ARID2 protein expression decreases detected in patients with their respective mutations.ConclusionThe integration of our analyses furnishes crucial insights into CRC's molecular characteristics, drug responsiveness, and the construction of a four-gene mutation signature for predicting CRC prognosis.
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