Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets

Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55%) were clean reads. Five differentially expressed genes with an adjusted P value < 0.05 and |log⁡2(fold change)| > 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

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Metascoring and Gene Expression Profiling in Clinical Routine in Multiple Myeloma,

Blood ◽

10.1182/blood.v118.21.3940.3940 ◽

2011 ◽

Vol 118 (21) ◽

pp. 3940-3940

Author(s):

Tobias Meißner ◽

Anja Seckinger ◽

Thierry Rème ◽

Thomas Hielscher ◽

Thomas Möhler ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Prognostic Factors ◽

Risk Stratification ◽

Gene Expression Profiling ◽

Open Source ◽

Expression Profiling ◽

Target Genes ◽

Molecular Heterogeneity ◽

Clinical Routine

Abstract Abstract 3940 BACKGROUND. Multiple myeloma is characterized by molecular heterogeneity transmitting to survival ranging from several months to over 15 years. Gene expression profiling allows assessment of biological entities, risk, and targets. Its translation into clinical routine alongside conventional prognostic factors has been prevented by lack of appropriated reporting tools and the integration with other prognostic factors into a single risk stratification (metascoring). METHODS. We present here a non-commercial open source software-framework developed in the open source language R (GEP-report) containing a graphic user interphase based on Gtk2. Affymetrix microarray raw-data and a documentation-by-value strategy to directly apply thresholds and grouping-algorithms from a reference cohort of 262 myeloma patients are used. Gene expression-based and conventional prognostic factors are integrated within one risk-stratification (HM-metascore) and the final report is created as a PDF-file. RESULTS. The GEP-report comprises i) quality control, ii) test of sample identity, iii) biological classifications of multiple myeloma, iv) risk stratification, v) assessment of target-genes, and vi) integration of expression-based and clinical risk factors within one metascore. This HM-metascore sums over the weighted factors gene-expression based risk-assessment (UAMS-, IFM-score), proliferation, ISS-stage, t(4;14), and expression of prognostic target-genes (AURKA, IGF1R) for which clinical grade inhibitors exist. It delineates three significantly different groups of 13.1, 72.1 and 14.7% of patients with a 6-year survival of 89.3, 60.6 and 18.6%, respectively. CONCLUSION. GEP-reporting allows prospective assessment of target gene expression and integration of current prognostic factors within one risk stratification (metascoring), being customizable regarding novel parameters or other cancer entities. Disclosures: No relevant conflicts of interest to declare.

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