Dataset Information

Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

ABSTRACT: In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the "large d, small n" feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfactory. A cost-effective remedy is to conduct integrative analysis of multiple heterogeneous datasets. In this article, we investigate composite penalization methods for estimation and marker selection in integrative analysis. The proposed methods use the minimax concave penalty (MCP) as the outer penalty. Under the homogeneity model, the ridge penalty is adopted as the inner penalty. Under the heterogeneity model, the Lasso penalty and MCP are adopted as the inner penalty. Effective computational algorithms based on coordinate descent are developed. Numerical studies, including simulation and analysis of practical cancer datasets, show satisfactory performance of the proposed methods.

SUBMITTER: Liu J

PROVIDER: S-EPMC3933169 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

Liu Jin J Huang Jian J Ma Shuangge S

Scandinavian journal of statistics, theory and applications 20140301 1

In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the "large <i>d</i>, small <i>n</i>" feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfac ...[more]

PMID: 24578589

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Project description:BackgroundKIF15 plays a vital role in many biological processes and has been reported to influence the occurrence and development of certain human cancers. However, there are few systematic evaluations on the role of KIF15 in human cancers, and the role of KIF15 in the diagnosis and prognosis of nasopharyngeal carcinoma (NPC) also remains unexplored. Therefore, this study aimed to conduct a pan-cancer analysis of KIF15 and evaluate its diagnostic and prognostic potential in NPC.MethodsThe expression pattern, prognostic value, molecular function, tumor mutation burden, microsatellite instability, and immune cell infiltration of KIF15 were examined based on public databases. Next, the diagnostic value of KIF15 in NPC was analyzed using the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Kaplan-Meier curves, Cox regression analyses, and nomograms were used to evaluate the effects of KIF15 expression on NPC prognosis. Finally, the effect of KIF15 on NPC was explored by in vitro experiments.ResultsThe expression of KIF15 was significantly upregulated in 20 out of 33 cancer types compared to adjacent normal tissue. Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analysis showed that KIF15 could participate in several cancer-related pathways. The increased expression level of KIF15 was correlated with worse clinical outcomes in many types of human cancers. Additionally, KIF15 expression was related to cancer infiltration of immune cells, tumor mutation burden, and microsatellite instability. In the analysis of NPC, KIF15 was significantly upregulated based on the GEO database and immunohistochemistry. A high expression of KIF15 was negatively associated with the prognosis of patients with NPC. A nomogram model integrating clinical characteristics and KIF15 expression was established, and it showed good predictive ability with an area under the curve value of 0.73. KIF15 knockdown significantly inhibited NPC cell proliferation and migration.ConclusionsOur findings revealed the important and functional role of KIF15 as an oncogene in pan-cancer. Moreover, high expression of KIF15 was found in NPC tissues, and was correlated with poor prognosis in NPC. KIF15 may serve as a potential therapeutic target in NPC treatment.

Project description:Pathway analysis of large-scale omics data assists us with the examination of the cumulative effects of multiple functionally related genes, which are difficult to detect using the traditional single gene/marker analysis. So far, most of the genomic studies have been conducted in a single domain, e.g., by genome-wide association studies (GWAS) or microarray gene expression investigation. A combined analysis of disease susceptibility genes across multiple platforms at the pathway level is an urgent need because it can reveal more reliable and more biologically important information.We performed an integrative pathway analysis of a GWAS dataset and a microarray gene expression dataset in prostate cancer. We obtained a comprehensive pathway annotation set from knowledge-based public resources, including KEGG pathways and the prostate cancer candidate gene set, and gene sets specifically defined based on cross-platform information. By leveraging on this pathway collection, we first searched for significant pathways in the GWAS dataset using four methods, which represent two broad groups of pathway analysis approaches. The significant pathways identified by each method varied greatly, but the results were more consistent within each method group than between groups. Next, we conducted a gene set enrichment analysis of the microarray gene expression data and found 13 pathways with cross-platform evidence, including "Fc gamma R-mediated phagocytosis" (P GWAS = 0.003, P expr < 0.001, and P combined = 6.18 × 10(-8)), "regulation of actin cytoskeleton" (P GWAS = 0.003, P expr = 0.009, and P combined = 3.34 × 10(-4)), and "Jak-STAT signaling pathway" (P GWAS = 0.001, P expr = 0.084, and P combined = 8.79 × 10(-4)).Our results provide evidence at both the genetic variation and expression levels that several key pathways might have been involved in the pathological development of prostate cancer. Our framework that employs gene expression data to facilitate pathway analysis of GWAS data is not only feasible but also much needed in studying complex disease.

Dataset Information

Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

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Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

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