Bioinformatics integrated analysis to investigate candidate biomarkers and associated metabolites in osteosarcoma

Identification of Differentially Expressed Intronic Transcripts in Osteosarcoma

Over the past decade; the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed that they play a major role in the development of various diseases; including cancer. Intronic transcripts are one of the most fascinating lncRNAs that are located within intron regions of protein-coding genes, which have the advantage of encoding micropeptides. There have been several studies looking at intronic transcript expression profiles in cancer; but almost none in osteosarcoma. To overcome this problem; we have investigated differentially expressed intronic transcripts between osteosarcoma and normal bone tissues. The results highlighted that NRG1-IT1; FGF14-IT1; and HAO2-IT1 were downregulated; whereas ER3-IT1; SND1-IT1; ANKRD44-IT1; AGAP1-IT1; DIP2A-IT1; LMO7DN-IT1; SLIT2-IT1; RNF216-IT1; and TCF7L1-IT1 were upregulated in osteosarcoma tissues compared to normal bone tissues. Furthermore, we identified if the transcripts encode micropeptides and the transcripts' locations in a cell.

Bioinformatics analysis of the key genes in osteosarcoma metastasis and immune invasion

BACKGROUND

This study sought to identify potential key genes for osteosarcoma metastasis and analyze their immune infiltration patterns using bioinformatic methods.

METHODS

We obtained transcriptomic data related to osteosarcoma and osteosarcoma with metastasis from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) and The Gene Expression Omnibus (GEO) databases and identified the differentially expressed genes (DEGs). We also identified potential key genes for osteosarcoma metastasis by a protein-protein interaction network analysis, and we conducted a Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the core genes for prognosis, immune cell infiltration, and drug sensitivity, and the risk prediction and prognosis models of metastasis were constructed.

RESULTS

By comparing the transcriptome data of osteosarcomas without metastasis and those with metastasis, a total of 19 core DEGs were identified, and the GO and KEGG analyses revealed an association between these DEGs and the regulation of cell division, secretory granule lumen, the Ras-associated protein 1 (Rap1) signaling pathway, and the mitogen-activated protein kinase (MAPK) signaling pathway. Compared with other immune cells, macrophage infiltration was predominant in osteosarcoma samples with metastatic osteosarcoma, and insulin-like growth factors-1 (IGF1) and myelocytomatosis protein 2 (MYC2) genes were predicted to more than 50 targeted therapeutic agents. A metastasis prediction model with 5 genes [i.e., ecotropic viral integration site 2B (EVI2B), CCAAT/enhancer binding protein (CEBPA), lymphocyte cytosolic protein 2 (LCP2), selectin L (SELL), and Niemann-Pick disease, type C2A (NPC2A)], and a prognostic model with 4 genes [i.e., insulin-like growth factors-2 (IGF2), cathepsin O (CTSO), Niemann-Pick disease, type C2 (NPC2), and amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein (APBB1IP)] were developed.

CONCLUSIONS

We constructed a metastasis prediction model with 5 genes (i.e., EVI2B, CEBPA, LCP2, SELL, and NPC2A), and a prognostic model with 4 genes (i.e., IGF2, CTSO, NPC2, and APBB1IP) that may be potential biomarkers for osteosarcoma metastasis. Macrophages are the predominant immune infiltrating cells in osteosarcoma metastasis and may provide a new direction for the treatment of osteosarcoma.