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. 2023 Mar 16:13:1156455.
doi: 10.3389/fonc.2023.1156455. eCollection 2023.

Cuproptosis signature and PLCD3 predicts immune infiltration and drug responses in osteosarcoma

Hai Hu  1 , Yuesong Yin  1 , Binbin Jiang  1 , Zhennan Feng  1 , Ting Cai  2 , Song Wu  1
Affiliations

Cuproptosis signature and PLCD3 predicts immune infiltration and drug responses in osteosarcoma

Hai Hu et al. Front Oncol. .

Abstract

Osteosarcoma (OS) is a cancer that is frequently found in children and adolescents and has made little improvement in terms of prognosis in recent years. A recently discovered type of programmed cell death called cuproptosis is mediated by copper ions and the tricarboxylic acid (TCA) cycle. The expression patterns, roles, and prognostic and predictive capabilities of the cuproptosis regulating genes were investigated in this work. TARGET and GEO provided transcriptional profiling of OS. To find different cuproptosis gene expression patterns, consensus clustering was used. To identify hub genes linked to cuproptosis, differential expression (DE) and weighted gene co-expression network analysis (WGCNA) were used. Cox regression and Random Survival Forest were used to build an evaluation model for prognosis. For various clusters/subgroups, GSVA, mRNAsi, and other immune infiltration experiments were carried out. The drug-responsive study was carried out by the Oncopredict algorithm. Cuproptosis genes displayed two unique patterns of expression, and high expression of FDX1 was associated with a poor outcome in OS patients. The TCA cycle and other tumor-promoting pathways were validated by the functional study, and activation of the cuproptosis genes may also be connected with immunosuppressive state. The robust survival prediction ability of a five-gene prognostic model was verified. This rating method also took stemness and immunosuppressive characteristics into account. Additionally, it can be associated with a higher sensitivity to medications that block PI3K/AKT/mTOR signaling as well as numerous chemoresistances. U2OS cell migration and proliferation may be encouraged by PLCD3. The relevance of PLCD3 in immunotherapy prediction was verified. The prognostic significance, expressing patterns, and functions of cuproptosis in OS were revealed in this work on a preliminary basis. The cuproptosis-related scoring model worked well for predicting prognosis and chemoresistance.

Keywords: cuproptosis; drug response; immune infiltration; machine learning; osteosarcoma; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Cuproptosis regulatory genes were expressed in distinct patterns in OS samples. (A, B) Expression distributions of cuproptosis regulatory genes in TARGET_OS (A) and GSE21257 (B) datasets. (C–E) K-M survival curve for FDX1 high- and low- expression subgroups in TARGET_OS (C, D) and GSE21257 datasets (E), Ovs, overall survival; DFS, disease-free survival. (F, G) Results of consensus clustering based on the expression of cuproptosis regulatory genes, (F) Consensus heatmap, (G) Item-Consensus plot. (H) Heatmap shows the expression of cuproptosis genes in distinct patterns.
Figure 2
Figure 2
OS samples in different Cu_Clusters exhibited distinct tumor biological characteristics. (A) GSVA analysis showed diverse enriched pathways in different Cu_Clusters. (B) Divergence in the mRNAsi index showed differences in stemness properties between Cu_ClusterA and Cu_ClusteB. (C–E) ESTIMATE analysis for the overall status of immune cell infiltration and stromal component samples in the TARGET_OS dataset. (F) ssGSEA for the infiltration analysis of 29 types of immune cells in different Cu_Clusters *P < 0.05; **P < 0.01.
Figure 3
Figure 3
Screening of cuproptosis-related genes by DE analysis and WGCNA. (A, B) Results of DE analysis in different Cu_Cluseters; (A) Volcano plot of DEGs; (B) Heatmap of DEGs. (C–F) WGCNA analysis for DEGs to identify gene module that was most correlated with Cu_Clusters; (G, H) Pathway enrichment analysis for hub genes obtained from WGCNA; (G) Molecular function analysis of WGCNA hub genes in GO (H) Pathway enrichment analysis of WGCNA hub genes in KEGG.
Figure 4
Figure 4
Construction and validation of CRP score model. (A, B) RSF model training and variable selection; (A) error rate trends as the number of trees increased when training RSF model; (B) Variable importance of selected features. (C) Correlation of expression in 5 genes that RSF selected to train CRP score model. (D–F) Time-dependent ROC curve to test the predictive ability of CRP score for OS patients in TARGET_OS train set (D), test set (E), as well as GSE21573 external validation set (F). (G, H) K-M curve of CRP scores high and low subgroups for patients’ overall survival in TARGET_OS (G) and GSE21573 (H) datasets.
Figure 5
Figure 5
Correlation analysis between CRP score and malignant biological behaviors. (A) GSVA analysis showed diverse enriched pathways between CRP_high and CRP_low subgroups. (B, C) mRNAsi index analysis revealed differences in stemness properties between CRP_high and CRP_low subgroups and a significant correlation between CRP_score and mRNAsi. (D, E) ESTIMATE analysis for the correlation between CRP_score and immune cell infiltration as well as a stromal component in samples of the TARGET_OS dataset. (F) ssGSEA for the infiltration analysis of 29 types of immune cells in CRP_high and CRP_low subgroups *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 6
Figure 6
(A) Pan-cancer expression pattern of model genes. (B) Pan-cancer SNP analysis on model genes. (C) Pan-cancer SNP landscape on model genes.
Figure 7
Figure 7
(A) The heterozygous CNV profiles (amplification and depletion) of model genes. (B) The homozygous CNV profiles (amplification and depletion) of model genes. (C) Pathway analysis related to PLCD3. (D) The miRNA regulation network of model genes.
Figure 8
Figure 8
The tumor-promoting role of PLCD3. (A) The relative RNA expression of PLCD3 in NC and three si-RNA groups by q-PCR assay. (B) Statistical analysis of the cell counts in NC and two si-RNA groups by Transwell assay. (C) Statistical analysis of the proliferation rate (EdU/DAPI) in NC and two si-RNA groups by EdU assay. (D) The cell counts in NC and two si-RNA groups by Transwell assay. (E) The proliferation rate (EdU/DAPI) in NC and two si-RNA groups by EdU assay. **, P<0.01; ***, P<0.001; ****, P<0.0001.
Figure 9
Figure 9
Immunotherapy prediction of PLCD3. (A) The expression of PLCD3 in responders and non-responders in immunotherapy cohorts. (B) Survival analysis was performed on the two groups regarding PLCD3 expression in immunotherapy cohorts. (C) The ROC curve of PLCD3 in predicting immunotherapy response in immunotherapy cohorts.
Figure 10
Figure 10
Immunotherapy prediction of PLCD3. (A) Regulator prioritization performed by TIDE. (B) Biomarker evaluation by TIDE. (C) Cytokine treatment prediction by TISMO. (D) Immunotherapy prediction by TISMO.
Figure 11
Figure 11
(A) The PLCD3 protein interaction network. (B) The PLCD3 illness network. (C) PLCD3’s pan-cancer immune infiltration pattern.
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References

    1. Mirabello L, Troisi R, Savage S. Osteosarcoma incidence and survival rates from 1973 to 2004: Data from the surveillance, epidemiology, and end results program. Cancer (2009) 115(7):1531–43. doi: 10.1002/cncr.24121 - DOI - PMC - PubMed
    1. Siegel R, Miller K, Fuchs H, Jemal A. Cancer statistics, 2021. CA: Cancer J Clin (2021) 71(1):7–33. doi: 10.3322/caac.21654 - DOI - PubMed
    1. Isakoff M, Bielack S, Meltzer P, Gorlick R. Osteosarcoma: Current treatment and a collaborative pathway to success. J Clin Oncol (2015) 33(27):3029–35. doi: 10.1200/JCO.2014.59.4895 - DOI - PMC - PubMed
    1. Li S, Liu F, Zheng K, Wang W, Qiu E, Pei Y, et al. . CircDOCK1 promotes the tumorigenesis and cisplatin resistance of osteogenic sarcoma via the miR-339-3p/IGF1R axis. Mol cancer (2021) 20(1):161. doi: 10.1186/s12943-021-01453-0 - DOI - PMC - PubMed
    1. Tang Z, Dong H, Li T, Wang N, Wei X, Wu H, et al. . The synergistic reducing drug resistance effect of cisplatin and ursolic acid on osteosarcoma through a multistep mechanism involving ferritinophagy. Oxid Med Cell longevity (2021) 2021:5192271. doi: 10.1155/2021/5192271 - DOI - PMC - PubMed