Background Circular RNAs (circRNAs) are involved in diverse processes that drive cancer development. However, the expression landscape and mechanistic function of circRNAs in osteosarcoma (OS) remain to be studied. Methods Bioinformatic analysis and high-throughput RNA sequencing tools were employed to identify differentially expressed circRNAs between OS and adjacent noncancerous tissues. The expression level of circ_001422 in clinical specimens and cell lines was measured using qRT-PCR. The association of circ_001422 expression with the clinicopathologic features of 55 recruited patients with OS was analyzed. Loss- and gain-of-function experiments were conducted to explore the role of circ_001422 in OS cells. RNA immunoprecipitation, fluorescence in situ hybridization, bioinformatics database analysis, RNA pulldown assays, dual-luciferase reporter assays, mRNA sequencing, and rescue experiments were conducted to decipher the competitive endogenous RNA regulatory network controlled by circ_001422. Results We characterized a novel and abundant circRNA, circ_001422, that promoted OS progression. Circ_001422 expression was dramatically increased in OS cell lines and tissues compared with noncancerous samples. Higher circ_001422 expression correlated with more advanced clinical stage, larger tumor size, higher incidence of distant metastases and poorer overall survival in OS patients. Circ_001422 knockdown markedly repressed the proliferation and metastasis and promoted the apoptosis of OS cells in vivo and in vitro, whereas circ_001422 overexpression exerted the opposite effects. Mechanistically, competitive interactions between circ_001422 and miR-195-5p elevated FGF2 expression while also initiating PI3K/Akt signaling. These events enhanced the malignant characteristics of OS cells. Conclusions Circ_001422 accelerates OS tumorigenesis and metastasis by modulating the miR-195-5p/FGF2/PI3K/Akt axis, implying that circ_001422 can be therapeutically targeted to treat OS.
A number of studies have revealed that there is an increasing incidence of early-onset colorectal cancer (CRC) in young adults (before the age of 50 years) and a progressive decline in CRC among older patients, after the age of 50 years (late-onset CRC). However, the etiology of early-onset CRC is not fully understood. The aim of the present study was to identify key genes associated with the development of early-onset CRC through weighted gene co-expression network analysis (WGCNA). The GSE39582 dataset was downloaded from the Gene Expression Omnibus database, and the data profiles of tissues from patients diagnosed before the age of 50 years were selected. The top 10,000 genes with the highest variability were used to construct the WGCNA. Hub genes were identified from the modules associated with clinical traits using gene significance >0.2 and module membership >0.8 as the cutoff criteria. Gene Ontology and pathway analyses were subsequently performed on the hub genes and a protein-protein interaction network (PPI) was constructed. The diagnostic value of module hub genes with a degree score >5 in the PPI network was verified in samples from patients with CRC diagnosed before the age of 50 years obtained from The Cancer Genome Atlas. Eight co-expressed gene modules were identified in the WGCNA and two modules (blue and turquoise) were associated with the tumor-node-metastasis stage. A total of 140 module hub genes were identified and found to be enriched in 'mitochondrial large ribosomal subunit', 'structural constituent of ribosome', 'poly (A) RNA binding', 'collagen binding', 'protein ubiquitination' and 'ribosome pathway'. Twenty-six module hub genes were found to have a degree score >5 in the PPI network, seven of which [secreted protein acidic and cysteine rich (SPARC), decorin (DCN), fibrillin 1 (FBN1), WW domain containing transcription regulator 1 (WWTR1), transgelin (TAGLN), DEAD-box helicase 28 (DDX28) and cold shock domain containing C2 (CSDC2)], had good prognostic values for patients with early-onset CRC, but not late-onset CRC. Therefore, SPARC, DCN, FBN1, WWTR1, TAGLN, DDX28 and CSDC2 may contribute to the development of early-onset CRC and may serve as potential diagnostic biomarkers.
Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56 bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.
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