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Fan W, Chen X, Li R, Zheng R, Wang Y, Guo Y. A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database. Biochem Genet 2023; 61:138-150. [PMID: 35761155 DOI: 10.1007/s10528-022-10232-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/18/2022] [Indexed: 01/24/2023]
Abstract
This study explored prognostic genes of ovarian cancer and built a prognostic model based on these genes to predict patient's survival, which is of great significance for improving treatment of ovarian cancer. GSE26712 dataset was downloaded from Gene Expression Omnibus database as training set, while OV-AU dataset was downloaded from ICGC website as validation set. All genes in GSE26712 were analyzed by univariate Cox regression, Lasso regression, and multivariate Cox regression analyses. Then prognosis-related feature genes were screened to construct a multivariate risk model. Meanwhile, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed on samples in the high/low-risk groups using Gene Set Enrichment Analysis (GSEA) software. Finally, survival curve and receiver operating characteristic curve were drawn to verify the validity of the model. Ten feature genes related to prognosis of ovarian cancer were obtained: CMTM6, COLGALT1, F2R, GPR39, IGFBP3, RNF121, MTMR9, ORAI2, SNAI2, ZBTB16. GSEA enrichment analysis showed that there were notable differences in biological pathways such as gap junctions and homologous recombination between the high/low-risk groups. Through further verification of training set and validation set, the 10-gene prognostic model was found to be effective for the prognosis of ovarian cancer patients. In this study, we constructed a 10-gene prognostic model which predicted the prognosis of ovarian cancer patients well by integrating clinical prognostic parameters. It may have certain reference value for subsequent clinical treatment research of ovarian cancer patients and help in clinical treatment decision-making.
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Affiliation(s)
- Wei Fan
- Department of Gynecology, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou City, 730030, Gansu Province, China
| | - Xiaoyun Chen
- Department of Gynecology, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou City, 730030, Gansu Province, China
| | - Ruiping Li
- Department of Gynecology, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou City, 730030, Gansu Province, China
| | - Rongfang Zheng
- Department of Gynecology, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou City, 730030, Gansu Province, China
| | - Yunyun Wang
- Lanzhou University Second Hospital, Lanzhou City, 730030, Gansu Province, China
| | - Yuzhen Guo
- Department of Gynecology, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou City, 730030, Gansu Province, China.
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Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data. JOURNAL OF ONCOLOGY 2022; 2022:8024979. [PMID: 35378771 PMCID: PMC8976607 DOI: 10.1155/2022/8024979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 02/22/2022] [Indexed: 11/18/2022]
Abstract
Osteosarcoma is the most common primary malignancy in the musculoskeletal system. It is reported that copy number variation- (CNV-) derived lncRNAs contribute to the progression of osteosarcoma. However, whether CNV-derived lncRNAs affect the prognosis of osteosarcoma remains unclear. Here, we obtained osteosarcoma-related CNV data and gene expression profiles from The Cancer Genome Atlas (TCGA) database. CNV landscape analysis indicated that copy number amplification of lncRNAs was more frequent than deletion in osteosarcoma samples. Thirty-four CNV-lncRNAs with DNA-CNV frequencies greater than 30% and their corresponding 294 mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses revealed that these mRNAs were mainly enriched in olfaction, olfactory receptor activity, and olfactory transduction processes. Furthermore, we predicted that a total of 23 genes were cis-regulated by 16 CNV-lncRNAs, while 30 transcription factors (TFs) were trans-regulated by 5 CNV-lncRNAs. Through
-tests, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO), we constructed a CNV-related risk model including 3 lncRNAs (AC129492.1, PSMB1, and AC037459.4). The Kaplan-Meier (K-M) curves indicated that patients with high-risk scores showed poor prognoses. The areas under the receiver operating characteristic (ROC) curves (AUC) for predicting 3-, 5-, and 7-year overall survival (OS) were greater than 0.7, showing a satisfactory predictive efficiency. Gene set enrichment analysis (GSEA) revealed that the prognostic signature was intimately linked to skeletal system development, immune regulation, and inflammatory response. Collectively, our study developed a novel 3-CNV-lncRNA prognostic signature that would provide theoretical guidance for the clinical prognostic management of osteosarcoma.
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Bao J, Song Z, Song C, Wang Y, Li W, Mai W, Shi Q, Yu H, Ni L, Liu Y, Lu X, He C, Chen L, Qu G. Identification of Biomarkers for Osteosarcoma Based on Integration Strategy. Med Sci Monit 2020; 26:e920803. [PMID: 32173717 PMCID: PMC7101204 DOI: 10.12659/msm.920803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background Osteosarcoma (OS) is the most common primary malignant tumor of bone. The identification of novel biomarkers is necessary for the diagnosis and treatment of osteosarcoma. Material/Methods We obtained 11 paired fresh-frozen OS samples and normal controls from patients between September 2015 and February 2017. We used an integration strategy that analyzes next-generation sequencing data by bioinformatics methods based on the pathogenesis of osteosarcoma. Results One susceptibility lncRNA and 7 susceptibility genes regulated by the lncRNA for osteosarcoma were effectively identified, and real-time PCR and clinical index ALP data were used to test their effectiveness. Conclusions The results showed that the expression levels of the 7 genes were highly consistent in the training and test sample sets, especially between the expression value of the gene ALPL and the plasma detection value of its encoded protein ALP. In particular, both the expression of gene ALPL and the plasma detection values of protein ALP encoded by gene ALPL showed a high degree of consistency among different data types. The identified lncRNA and genes effectively classified the samples proved so that they could be used as potential biomarkers of osteosarcoma. Our strategy may also be helpful for the identification of biomarkers for other diseases.
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Affiliation(s)
- Junjie Bao
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Zhaona Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Chunyu Song
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Wei Mai
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Qingyu Shi
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Hongwei Yu
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Linying Ni
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Yishu Liu
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Xiaolin Lu
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Chuan He
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland)
| | - Guofan Qu
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (mainland)
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