Network-Based Transcriptome Analysis Reveals FAM3C as a Novel Potential Biomarker for Glioblastoma.
Pablo Shimaoka ChagasHenrique Izumi Shimaoka ChagasSahar Emami NaeiniBidhan BhandariJules GouronTathiane M MaltaÉvila Lopes SallesLei P WangJack C YuBabak BabanPublished in: Journal of cellular biochemistry (2024)
Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a high mortality rate. The aim of the present study was to investigate the clinical significance of Family with Sequence Similarity 3, Member C, FAM3C, in GBM using bioinformatic-integrated analysis. First, we performed the transcriptomic integration analysis to assess the expression profile of FAM3C in GBM using several data sets (RNA-sequencing and scRNA-sequencing), which were obtained from TCGA and GEO databases. By using the STRING platform, we investigated FAM3C-coregulated genes to construct the protein-protein interaction network. Next, Metascape, Enrichr, and CIBERSORT databases were used. We found FAM3C high expression in GBM with poor survival rates. Further, we observed, via FAM3C coexpression network analysis, that FAM3C plays key roles in several hallmarks of cancer. Surprisingly, we also highlighted five FAM3C‑coregulated genes overexpressed in GBM. Specifically, we demonstrated the association between the high expression of FAM3C and the abundance of the different immune cells, which may markedly worsen GBM prognosis. For the first time, our findings suggest that FAM3C not only can be a new emerging biomarker with promising therapeutic values to GBM patients but also gave a new insight into a potential resource for future GBM studies.
Keyphrases
- network analysis
- protein protein
- single cell
- end stage renal disease
- small molecule
- big data
- ejection fraction
- mass spectrometry
- high throughput
- binding protein
- chronic kidney disease
- risk assessment
- risk factors
- climate change
- peritoneal dialysis
- transcription factor
- current status
- amino acid
- data analysis
- deep learning
- bioinformatics analysis