Comprehensive Analysis of Key mRNAs and lncRNAs in Osteosarcoma Response to Preoperative Chemotherapy with Prognostic Values

Mi Li , Wei-ting Cheng , Hao Li , Zhi Zhang , Xiao-li Lu , Si-si Deng , Jian Li , Cai-hong Yang

Current Medical Science ›› 2021, Vol. 41 ›› Issue (5) : 916 -929.

PDF
Current Medical Science ›› 2021, Vol. 41 ›› Issue (5) : 916 -929. DOI: 10.1007/s11596-021-2430-2
Article

Comprehensive Analysis of Key mRNAs and lncRNAs in Osteosarcoma Response to Preoperative Chemotherapy with Prognostic Values

Author information +
History +
PDF

Abstract

Objective

Osteosarcoma is one of the most common types of bone sarcoma with a poor prognosis. However, identifying the predictive factors that contribute to the response to neoadjuvant chemotherapy remains a significant challenge.

Methods

A public data series (GSE87437) was downloaded to identify differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between osteosarcoma patients that do and do not respond to preoperative chemotherapy. Subsequently, functional analysis of the transcriptome expression profile, regulatory networks of DEGs and DElncRNAs, competing endogenous RNAs (ceRNA) and protein-protein interaction networks were performed. Furthermore, the function, pathway, and survival analysis of hub genes was performed and drug and disease relationship prediction of DElncRNA was carried out.

Results

A total of 626 DEGs, 26 DElncRNAs, and 18 hub genes were identified. However, only one gene and two lncRNAs were found to be suitable as candidate gene and lncRNAs respectively.

Conclusion

The DEGs, hub genes, candidate gene, and candidate lncRNAs screened out in this context were considered as potential biomarkers for the response to neoadjuvant chemotherapy of osteosarcoma.

Keywords

osteosarcoma / preoperative chemotherapy / competing endogenous RNAs / differentially expressed genes / survival analysis

Cite this article

Download citation ▾
Mi Li, Wei-ting Cheng, Hao Li, Zhi Zhang, Xiao-li Lu, Si-si Deng, Jian Li, Cai-hong Yang. Comprehensive Analysis of Key mRNAs and lncRNAs in Osteosarcoma Response to Preoperative Chemotherapy with Prognostic Values. Current Medical Science, 2021, 41(5): 916-929 DOI:10.1007/s11596-021-2430-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

SiegelRL, MillerKD, JemalA. Cancer Statistics, 2020. CA Cancer J Clin, 2020, 70(1): 7-30

[2]

CloseAG, DreyzinA, MillerKD, et al.. Adolescent and young adult oncology-past, present, and future. CA Cancer J Clin, 2019, 69(6): 485-496

[3]

CromptonJG, OguraK, BernthalNM, et al.. Local Control of Soft Tissue and Bone Sarcomas. J Clin Oncol, 2018, 36(2): 111-117

[4]

LiM, JinX, GuoF, et al.. Integrative analyses of key genes and regulatory elements in fluoride-affected osteosarcoma. J Cell Biochem, 2019, 120(9): 15397-15409

[5]

LiM, JinX, LiH, et al.. Comprehensive Analysis of Key Genes and Regulatory Elements in Osteosarcoma Affected by Bone Matrix Mineral With Prognostic Values. Front Genet, 2020, 11: 533

[6]

LiM, JinX, LiH, et al.. Key genes with prognostic values in suppression of osteosarcoma metastasis using comprehensive analysis. BMC Cancer, 2020, 20(1): 65

[7]

VellaS, TavantiE, HattingerCM, et al.. Targeting CDKs with Roscovitine Increases Sensitivity to DNA Damaging Drugs of Human Osteosarcoma Cells. PLoS One, 2016, 11(11): e0166233

[8]

CloughE, BarrettT. The Gene Expression Omnibus Database. Methods Mol Biol, 2016, 1418: 93-110

[9]

SubramanianA, TamayoP, MoothaVK, et al.. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA, 2005, 102(43): 15545-15550

[10]

GeSX, SonEW, YaoR. iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics, 2018, 19(1): 534

[11]

ZhouG, XiaJ. Using OmicsNet for Network Integration and 3D Visualization. Curr Protoc Bioinformatics, 2019, 65(1): e69

[12]

CasperJ, ZweigAS, VillarrealC, et al.. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res, 2018, 46(D1): D762-d769

[13]

HuH, MiaoYR, JiaLH, et al.. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res, 2019, 47(D1): D33-d38

[14]

SmootME, OnoK, RuscheinskiJ, et al.. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 2011, 27(3): 431-432

[15]

FanCN, MaL, LiuN. Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer. J Transl Med, 2018, 16(1): 264

[16]

MaL, CaoJ, LiuL, et al.. LncBook: a curated knowledgebase of human long non-coding RNAs. Nucleic Acids Res, 2019, 47(D1): D128-d134

[17]

StichtC, De La TorreC, ParveenA, et al.. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One, 2018, 13(10): e0206239

[18]

SzklarczykD, MorrisJH, CookH, et al.. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res, 2017, 45(D1): D362-d368

[19]

BandettiniWP, KellmanP, ManciniC, et al.. MultiContrast Delayed Enhancement ^(MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson, 2012, 14(1): 83

[20]

LiaoY, WangJ, JaehnigEJ, et al.. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res, 2019, 47(W1): W199-w205

[21]

GoswamiCP, NakshatriH. PROGgeneV2: enhancements on the existing database. BMC Cancer, 2014, 14: 970

[22]

LiY, LiL, WangZ, et al.. LncMAP: Pan-cancer atlas of long noncoding RNA-mediated transcriptional network perturbations. Nucleic Acids Res, 2018, 46(3): 1113-1123

[23]

Carvalho-SilvaD, PierleoniA, PignatelliM, et al.. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res, 2019, 47(D1): D1056-d1065

[24]

TangZ, LiC, KangB, et al.. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017, 45(W1): W98-W102

[25]

WishartDS, FeunangYD, GuoAC, et al.. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res, 2018, 46(D1): D1074-D1082

[26]

MiwaS, TakeuchiA, ShiraiT, et al.. Prognostic value of radiological response to chemotherapy in patients with osteosarcoma. PLoS One, 2013, 8(7): e70015

[27]

ByunBH, KongCB, LimI, et al.. Early response monitoring to neoadjuvant chemotherapy in osteosarcoma using sequential 18F-FDG PET/CT and MRI. Eur J Nucl Med Mol Imaging, 2014, 41(8): 1553-1562

[28]

LauxCJ, BerzaczyG, WeberM, et al.. Tumour response of osteosarcoma to neoadjuvant chemotherapy evaluated by magnetic resonance imaging as prognostic factor for outcome. Int Orthop, 2015, 39(1): 97-104

[29]

ZhangH, GeJ, HongH, et al.. Genetic polymorphisms in ERCC1 and ERCC2 genes are associated with response to chemotherapy in osteosarcoma patients among Chinese population: a meta-analysis. World J Surg Oncol, 2017, 15(1): 75

[30]

JungSY, KwakJO, KimHW, et al.. Calcium sensing receptor forms complex with and is up-regulated by caveolin-1 in cultured human osteosarcoma (Saos-2) cells. Exp Mol Med, 2005, 37(2): 91-100

[31]

BernardiniG, LaschiM, SerchiT, et al.. Proteomics and phosphoproteomics provide insights into the mechanism of action of a novel pyrazolo[3,4-d] pyrimidine Src inhibitor in human osteosarcoma. Mol Biosyst, 2014, 10(6): 1305-1312

[32]

VillanuevaF, ArayaH, BricenoP, et al.. The cancer-related transcription factor RUNX2 modulates expression and secretion of the matricellular protein osteopontin in osteosarcoma cells to promote adhesion to endothelial pulmonary cells and lung metastasis. J Cell Physiol, 2019, 234(8): 13659-13679

[33]

MauriziG, VermaN, GadiA, et al.. Sox2 is required for tumor development and cancer cell proliferation in osteosarcoma. Oncogene, 2018, 37(33): 4626-4632

[34]

LiuH, ChenY, ZhouF, et al.. Sox9 regulates hyperexpression of Wnt1 and Fzd1 in human osteosarcoma tissues and cells. Int J Clin Exp Pathol, 2014, 7(8): 4795-805

[35]

MandelaP, YankovaM, ContiLH, et al.. Kalrn plays key roles within and outside of the nervous system. BMC Neurosci, 2012, 13: 136

[36]

DangM, WangZ, ZhangR, et al.. KALRN Rare and Common Variants and Susceptibility to Ischemic Stroke in Chinese Han Population. Neuromolecular Med, 2015, 17(3): 241-50

[37]

LiuHQ, ShuX, MaQ, et al.. Identifying specific miRNAs and associated mRNAs in CD44 and CD90 cancer stem cell subtypes in gastric cancer cell line SNU-5. Int J Clin Exp Pathol, 2020, 13(6): 1313-1323

[38]

NathA, LauEYT, LeeAM, et al.. Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes. Proc Natl Acad Sci U S A, 2019, 116(44): 22 020-22 029

[39]

VishnubalajiR, ShaathH, ElkordE, et al.. Long noncoding RNA (lncRNA) transcriptional landscape in breast cancer identifies LINC01614 as non-favorable prognostic biomarker regulated by TGFβ and focal adhesion kinase (FAK) signaling. Cell Death Discov, 2019, 5: 109

[40]

Di AgostinoS, ValentiF, SacconiA, et al.. Long Non-coding MIR205HG Depletes Hsa-miR-590-3p Leading to Unrestrained Proliferation in Head and Neck Squamous Cell Carcinoma. Theranostics, 2018, 8(7): 1850-1868

[41]

WanJ, DengD, WangX, et al.. LINC00491 as a new molecular marker can promote the proliferation, migration and invasion of colon adenocarcinoma cells. Onco Targets Ther, 2019, 12: 6471-6480

[42]

LiuJ, YaoY, HuZ, et al.. Transcriptional profiling of long-intergenic noncoding RNAs in lung squamous cell carcinoma and its value in diagnosis and prognosis. Mol Genet Genomic Med, 2019, 7(12): e994

AI Summary AI Mindmap
PDF

107

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/