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Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma
Published on: April 12, 2019
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Identification of Biomarkers for Osteosarcoma Based on Integration Strategy
Junjie Bao1, Zhaona Song2, Chunyu Song1
1Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland).
Summary
Researchers identified a novel long non-coding RNA (lncRNA) and seven genes that show high consistency in osteosarcoma (OS) samples. These findings suggest potential new biomarkers for diagnosing and treating this bone cancer.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- Osteosarcoma (OS) is the most common primary bone malignancy.
- Novel biomarkers are crucial for effective OS diagnosis and treatment.
Purpose of the Study:
- To identify novel susceptibility long non-coding RNAs (lncRNAs) and genes associated with osteosarcoma.
- To validate the potential of these identified molecules as diagnostic and therapeutic biomarkers.
Main Methods:
- Integration strategy analyzing next-generation sequencing data using bioinformatics.
- Validation using real-time polymerase chain reaction (PCR) and alkaline phosphatase (ALP) clinical data.
- Analysis of 11 paired fresh-frozen OS samples and normal controls.
Main Results:
- One susceptibility lncRNA and seven susceptibility genes regulated by the lncRNA were identified.
- Expression levels of the seven genes showed high consistency across training and test sample sets.
- Gene ALPL expression and its encoded protein ALP plasma levels demonstrated high consistency.
Conclusions:
- The identified lncRNA and genes effectively classified osteosarcoma samples, indicating their potential as biomarkers.
- The expression of gene ALPL and plasma levels of protein ALP are highly consistent across data types.
- The bioinformatics strategy may aid in identifying biomarkers for other diseases.
