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Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients

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Abstract

Background

Glycolysis and tumor immunity were interrelated. In present study, we aimed to construct a prognostic model based on glycolysis-immune-related genes (GIGs) of osteosarcoma (OS) patients.

Methods

The mRNA expression data of OS patients were downloaded from GEO and TARGET databases. The hub genes were screened from 305 differentially expressed genes by univariate cox regression analysis and used to further establish a prognostic Risk Score. The independence of the Risk Score prognostic prediction model based on five genes was tested by multivariate Cox regression analysis. Finally, CIBERSORT and LM22 feature matrix were used to estimate the differences in immune infiltration of OS patients.

Results

A total of 141 OS patients’ mRNA expression data and 296 glycolysis-associated genes were analyzed. Based on these 296 genes, all patients could be divided into two clusters: high glycolysis state and low glycolysis state. In the group with high glycolysis status, patients had low immune scores, indicating that glycolysis status was negatively correlated with immune function. The OS patients with high glycolysis and low immunity had the worst prognosis. Next, the Risk Score was constructed by 5 GIGs, including RAI14, MAF, CLEC5A, TIAL1 and CENPJ. Moreover, the Risk Score was shown to be an independent prognostic model, and high Risk Score patients had a greater risk of death.

Conclusions

The Risk Score based on GIG could predict the prognosis of OS patients.

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Availability of data and materials:

The datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) repository and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET, https://ocg.cancer.gov/programs/target) repository.

References

  1. Ren C, Liu J, Zheng B, Yan P, Sun Y, Yue B (2019) The circular RNA circ-ITCH acts as a tumour suppressor in osteosarcoma via regulating miR-22. Artif Cells Nanomed Biotechnol 47:3359–3367. http://doi.org.10.1080/21691401.2019.1649273

  2. He M, Shen P, Qiu C, Wang J (2019) miR-627-3p inhibits osteosarcoma cell proliferation and metastasis by targeting PTN. Aging (Albany NY) 11:5744–5756. http://doi.org.10.18632/aging.102157

  3. Huang C, Wang Q, Ma S, Sun Y, Vadamootoo AS, Jin C (2019) A four serum-miRNA panel serves as a potential diagnostic biomarker of osteosarcoma. Int J Clin Oncol 24:976–982. http://doi.org.10.1007/s10147-019-01433-x

  4. Lv S, Guan M (2018) miRNA-1284, a regulator of HMGB1, inhibits cell proliferation and migration in osteosarcoma. Biosci Rep 38:http://doi.org.10.1042/BSR20171675

  5. Warburg O (1956) On the origin of cancer cells. Science 123:309–314. http://doi.org.10.1126/science.123.3191.309

  6. Teicher BA, Linehan WM, Helman LJ (2012) Targeting cancer metabolism. Clin Cancer Res 18:5537–5545. http://doi.org.10.1158/1078-0432.CCR-12-2587

  7. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029–1033. http://doi.org.10.1126/science.1160809

  8. Wan J, Liu Y, Long F, Tian J, Zhang C (2021) circPVT1 promotes osteosarcoma glycolysis and metastasis by sponging miR-423-5p to activate Wnt5a/Ror2 signaling. Cancer Sci 112:1707–1722. http://doi.org.10.1111/cas.14787

  9. Han X, Yang Y, Sun Y, Qin L, Yang Y (2018) LncRNA TUG1 affects cell viability by regulating glycolysis in osteosarcoma cells. Gene 674:87–92. http://doi.org.10.1016/j.gene.2018.06.085

  10. Sottnik JL, Lori JC, Rose BJ, Thamm DH (2011) Glycolysis inhibition by 2-deoxy-D-glucose reverts the metastatic phenotype in vitro and in vivo. Clin Exp Metastasis 28:865–875. http://doi.org.10.1007/s10585-011-9417-5

  11. Jiang Z, Liu Z, Li M, Chen C, Wang X (2019) Increased glycolysis correlates with elevated immune activity in tumor immune microenvironment. EBioMedicine 42:431–442. http://doi.org.10.1016/j.ebiom.2019.03.068

  12. Chang CH, Qiu J, O’Sullivan D, Buck MD, Noguchi T, Curtis JD et al (2015) Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progression. Cell 162:1229–1241. http://doi.org.10.1016/j.cell.2015.08.016

  13. Bi J, Bi F, Pan X, Yang Q (2021) Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment. J Transl Med 19:382. http://doi.org.10.1186/s12967-021-03057-0

  14. Yang M, Ma X, Wang Z, Zhang T, Hua Y, Cai Z (2021) Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients. Aging (Albany NY) 13:12896–12918. http://doi.org.10.18632/aging.202958

  15. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. http://doi.org.10.1093/nar/gkv007

  16. Friedman J, Hastie T, Tibshirani R (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33:1–22

    Article  Google Scholar 

  17. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. http://doi.org.10.1038/nmeth.3337

  18. Zhao W, Wu L, Zhao A, Zhang M, Tian Q, Shen Y et al (2020) A nomogram for predicting survival in patients with de novo metastatic breast cancer: a population-based study. BMC Cancer 20:982. http://doi.org.10.1186/s12885-020-07449-1

  19. Sun Y, Deng C, Zhang Z, Ma X, Zhou F, Liu X (2021) Novel nomogram for predicting the progression of osteoarthritis based on 3D-MRI bone shape: data from the FNIH OA biomarkers consortium. BMC Musculoskelet Disord 22:782. http://doi.org.10.1186/s12891-021-04620-y

  20. Mirabello L, Zhu B, Koster R, Karlins E, Dean M, Yeager M et al (2020) Frequency of Pathogenic Germline Variants in Cancer-Susceptibility Genes in Patients With Osteosarcoma. JAMA Oncol 6:724–734. http://doi.org.10.1001/jamaoncol.2020.0197

  21. Yang E, Wang X, Gong Z, Yu M, Wu H, Zhang D (2020) Exosome-mediated metabolic reprogramming: the emerging role in tumor microenvironment remodeling and its influence on cancer progression. Signal Transduct Target Ther 5:242. http://doi.org.10.1038/s41392-020-00359-5

  22. Zheng XM, Xu CW, Wang F (2018) MiR-33b inhibits osteosarcoma cell proliferation through suppression of glycolysis by targeting Lactate Dehydrogenase A (LDHA). Cell Mol Biol. (Noisy-le-grand) 64:31–35

    Article  CAS  Google Scholar 

  23. Wen JF, Jiang YQ, Li C, Dai XK, Wu T, Yin WZ (2020) LncRNA-SARCC sensitizes osteosarcoma to cisplatin through the miR-143-mediated glycolysis inhibition by targeting Hexokinase 2. Cancer Biomark 28:231–246. http://doi.org.10.3233/CBM-191181

  24. Xiao Y, Zhang H, Du G, Meng X, Wu T, Zhou Q et al (2020) RAI14 Is a Prognostic Biomarker and Correlated With Immune Cell Infiltrates in Gastric Cancer. Technol Cancer Res Treat 19:1533033820970684. http://doi.org.10.1177/1533033820970684

  25. Janiszewska J, Bodnar M, Paczkowska J, Ustaszewski A, Smialek MJ, Szylberg L et al (2021) Loss of the MAF Transcription Factor in Laryngeal Squamous Cell Carcinoma. Biomolecules 11:http://doi.org.10.3390/biom11071035

  26. Hurt EM, Wiestner A, Rosenwald A, Shaffer AL, Campo E, Grogan T et al (2004) Overexpression of c-maf is a frequent oncogenic event in multiple myeloma that promotes proliferation and pathological interactions with bone marrow stroma. Cancer Cell 5:191–199. http://doi.org.10.1016/s1535-6108(04)00019-4

  27. Wang Q, Shi M, Sun S, Zhou Q, Ding L, Jiang C et al (2020) CLEC5A promotes the proliferation of gastric cancer cells by activating the PI3K/AKT/mTOR pathway. Biochem Biophys Res Commun 524:656–662. http://doi.org.10.1016/j.bbrc.2019.10.122

  28. Li Y, Wang X, Vural S, Mishra NK, Cowan KH, Guda C (2015) Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers. PLoS One 10:e0119383. http://doi.org.10.1371/journal.pone.0119383

  29. Izquierdo JM, Alcalde J, Carrascoso I, Reyes R, Ludena MD (2011) Knockdown of T-cell intracellular antigens triggers cell proliferation, invasion and tumour growth. Biochem J 435:337–344. http://doi.org.10.1042/BJ20101030

  30. Hayes C, Donohoe CL, Davern M, Donlon NE (2021) The oncogenic and clinical implications of lactate induced immunosuppression in the tumour microenvironment. Cancer Lett 500:75–86. http://doi.org.10.1016/j.canlet.2020.12.021

  31. Chen Y, Zhao B, Wang X (2020) Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma. BMC Cancer 20:1022. http://doi.org.10.1186/s12885-020-07536-3

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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    1. Department of West hospital orthopaedic trauma, ZiBo Central Hospital, No. 54 Gongqingtuan West Road, Zhangdian District, 255036, Zibo, Shandong, P. R. China

      Kangsong Tian, Wei Qi, Qian Yan, Ming Lv & Delei Song M.M.

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    Kangsong Tian and Wei Qi contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kangsong Tian, Wei Qi, Qian Yan and Ming Lv. The first draft of the manuscript was written by Kangsong Tian, Wei Qi and Delei Song and Delei Song commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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    Correspondence to Delei Song M.M..

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    Kangsong Tian and Wei Qi contributed equally to this study.

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    Tian, K., Qi, W., Yan, Q. et al. Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients. Invest New Drugs 40, 818–830 (2022). https://doi.org/10.1007/s10637-022-01228-4

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