Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

Jia, Yutao; Liu, Yang; Han, Zhihua; Tian, Rong

doi:10.7717/peerj.11496

Cited by 20 publications

(15 citation statements)

References 39 publications

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“…Then, we performed a receiver operating characteristic (ROC) analysis using TCGA database to evaluate the diagnostic value of hub genes ( Jia et al, 2021 ). Usually, the AUC value > 0.85 showed a good diagnostic value for lung cancer.…”

Section: Methodsmentioning

confidence: 99%

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Liu

Liao

et al. 2022

PeerJ

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Background Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis. Methods In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues. Results A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues. Conclusions The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

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Section: Methodsmentioning

confidence: 99%

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Liu

Liao

et al. 2022

PeerJ

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“…The rise of throughput sequencing technology helps clarify disease-causing genes, explore disease pathogenesis, develop biomarkers, and profoundly change our understanding of biology and human diversity 5 . Researchers have developed many statistical models that use genomic data to accurately predict whether the prognostic risk of cancer patients is high or low 6 – 10 .…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have developed many statistical models that use genomic data to accurately predict whether the prognostic risk of cancer patients is high or low 6 – 10 . Many researchers have screened multiple biomarkers related to OS by mining gene expression data 5 . Gene signature can contain more than one single gene with a unique characteristic pattern of gene expression resulting from an altered or unaltered biological process or pathogenic medical condition 11 .…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Yang

Zhang

Luo

et al. 2022

Sci Rep

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Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

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“…Recent studies have found that METTL7A affected the survival ability and osteogenic differentiation of BMSCs by affecting the methylation status of some related genes (Lee et al, 2021). METTL7A is also a tumor suppressor gene with decreased expression in osteosarcoma (Jia et al, 2021). Moreover, these gene expressions were detected by real-time RT-PCR and western blot.…”

Section: Discussionmentioning

confidence: 99%

“…An existing study indicates that miR-6807-5p mainly involved in the inflammatory process by downregulating drug metabolism cytochrome P450 (Kugler et al, 2020). In addition, miR-6807-5p was also one of the biomarkers of gastric cancer (Iwasaki et al, 2019). However, the role of miR-6807-5p in odontogenic differentiation of DPSCs has not been elucidated.…”

Section: Introductionmentioning

confidence: 99%

Table_1.DOCX

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Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

Cited by 20 publications

References 39 publications

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Table_1.DOCX

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