Top

Investigational New Drugs

Published in:

18-04-2022 | osteosarcoma | Review

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

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

Published in: Investigational New Drugs | Issue 4/2022

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.

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

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

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

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

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

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

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

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

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 33:1–22CrossRef

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) 64:31–35CrossRef

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

Title: Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients
Authors: Kangsong Tian
Wei Qi
Qian Yan
Ming Lv
Delei Song, M.M.
Publication date: 18-04-2022
Publisher: Springer US
Keywords: osteosarcoma
Osteosarcoma
Published in: Investigational New Drugs / Issue 4/2022
Print ISSN: 0167-6997
Electronic ISSN: 1573-0646
DOI: https://doi.org/10.1007/s10637-022-01228-4

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 4/2022

A novel alectinib-sensitive CTNND1-ALK fusion in a lung adenocarcinoma patient: a case report

Synergistic activities of the histone deacetylase inhibitors with conventional cytotoxic chemotherapies in angiosarcomas

The efficacy and safety of immune checkpoint inhibitor plus chemotherapy in patients with advanced non-small-cell lung cancer: a meta-analysis

A lung adenocarcinoma patient harboring MET c. 3028 + 2 T >A variant sensitive to crizotinib treatment

NSC305787, a pharmacological ezrin inhibitor, exhibits antineoplastic activity in pancreatic cancer cells

Eftozanermin alfa (ABBV-621) monotherapy in patients with previously treated solid tumors: findings of a phase 1, first-in-human study

Keynote webinar | Spotlight on antibody–drug conjugates in cancer