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. 2023 Jul 17;21(1):474.
doi: 10.1186/s12967-023-04333-x.

CHSY3 promotes proliferation and migration in gastric cancer and is associated with immune infiltration

Xinkun Huang #  1   2   3 , Yonghui Liu #  4 , Chenyu Qian  1   5   3 , Qicheng Shen  1   5   3 , Menglong Wu  1   2   3 , Bin Zhu  6   7 , Ying Feng  8   9   10
Affiliations

CHSY3 promotes proliferation and migration in gastric cancer and is associated with immune infiltration

Xinkun Huang et al. J Transl Med. .

Abstract

Background: The glycosyltransferase CHSY3 is a CHSY family member, yet its importance in the context of gastric cancer development remains incompletely understood. The present study was thus developed to explore the mechanistic importance of CHSY3 as a regulator of gastric cancer.

Methods: Expression of CHSY3 was verified by TCGA, GEO and HPA databases. Kaplan-Meier curve, ROC, univariate cox, multivariate cox, and nomogram models were used to verify the prognostic impact and predictive value of CHSY3. KEGG and GO methods were used to identify signaling pathways associated with CHSY3. TIDE and IPS scores were used to assess the immunotherapeutic value of CHSY3. WGCNA, Cytoscape constructs PPI networks and random forest models to identify key Hub genes. Finally, qRT-PCR and immunohistochemical staining were performed to verify CHSY3 expression in clinical specimens. The ability of CHSY3 to regulate tumor was further assessed by CCK-8 assay and cloning assay, EDU assay, migration assay, invasion assay, and xenograft tumor model analysis.

Results: The expression of CHSY3 was discovered to be abnormally upregulated in GC tissues through TCGA, GEO, and HPA databases, and the expression of CHSY3 was associated with poor prognosis in GC patients. Correlation analysis and Cox regression analysis revealed higher CHSY3 expression in higher T staging, an independent prognostic factor for GC. Moreover, elevated expression of CHSY3 was found to reduce the benefit of immunotherapy as assessed by the TIDE score and IPS score. Then, utilizing WGCNA, the PPI network constructed by Cytoscape, and random forest model, the Hub genes of COL5A2, POSTN, COL1A1, and FN1 associated with immunity were screened. Finally, the expression of CHSY3 in GC tissues was verified by qRT-PCR and immunohistochemical staining. Moreover, the expression of CHSY3 was further demonstrated by in vivo and in vitro experiments to promote the proliferation, migration, and invasive ability of GC.

Conclusions: The results of this study suggest that CHSY3 is an important regulator of gastric cancer progression, highlighting its promise as a therapeutic target for gastric cancer.

Keywords: CHSY3; Gastric cancer; IPS; Prognosis; TIDE.

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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

Fig. 1
Fig. 1
Expression and prognosis analysis of CHSY3 in the database. Expression of CHSY3 in pan-cancer from TCGA database (A). Relative expression levels of CHSY3 in 375 gastric cancer tissues and 32 normal tissues from TCGA database (B). Expression of CHSY3 in GSE66229 (N = 100, T = 300), GSE65801 (N = 32, T = 32), GSE63089 (N = 45, T = 45), GSE54129 (N = 21, T = 111), GSE51575 (N = 26, T = 26) datasets (C–G). Kaplan–Meier survival curve analysis of CHSY3 in GC from TCGA, GSE26901, GSE88433 and GSE66229 datasets (H–K). Immunohistochemical analysis of CHSY3 in gastric cancer by HPA database (L, M). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 2
Fig. 2
Association of CHSY3 expression with clinicopathologic parameters. Gender (A). Age (B). Stage (C). T (D). N (E). M (F). Analysis of KM survival curves in T1–2 patients (G). Analysis of KM survival curves in N0 patients (H). Analysis of KM survival curves in Stage I–II patients. I Analysis of KM survival curves in T3–4 patients (J). Analysis of KM survival curves in N1-3 patients. K Analysis of KM survival curves in Stage III–IV patients (L). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 3
Fig. 3
CHSY3 is an independent predictor of patient outcome in GC and the construction of nomogram model. Univariate and multivariate cox regression analysis in the TCGA (A, B) and GSE66229 datasets (C, D). Construction of a nomogram model of CHSY3 in TCGA (E) and GSE66229 (F) datasets. Calibration curves for the nomogram model for 1, 3, and 5 years (G–L)
Fig. 4
Fig. 4
Functional analysis of CHSY3. Volcano plots represent differential gene expression between different CHSY3 expression subgroups (A). KEGG analysis of differential genes of CHSY3 (B). GO analysis (C). GSEA analysis (D)
Fig. 5
Fig. 5
Analysis of CHSY3 expression on the benefits of immunotherapy. Correlation of IPS with CHSY3 expression (A–D). Correlation of CHSY3 expression with TIDE, dysfunction, exclusion, MSI in TCGA (E–H), GSE26901 (I–L), GSE84433 (M–P), GSE66229 (Q–T) datasets. Submap analysis of CHSY3 expression in relation to the sensitivity of anti-PD1 treatment and anti-CTLA4 treatment (U–X). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 6
Fig. 6
WGCNA analysis and construction of PPI network to identify Hub genes. Correlation of module signature genes with IPS and TIDE (A). Construction of PPI network to identify Hub genes (B). Expression of 10 Hub genes in GC tissues and paracancerous tissues in TCGA database (C). Univariate cox analysis of 10 Hub genes (D). Radar plot showing p-values of Kaplan–Meier survival curves for 10 Hub genes (E). Random Forests Identify Key Prognostic Genes (F, G). Scatter plot showing correlation of CHSY3 with FN1 (H), POSTN (I), COL1A1 (J), COL5A2 (K). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 7
Fig. 7
CHSY3 regulates GC cell proliferation, migration and invasion. qRT-PCR analysis of the relative expression of CHSY3 in 10 pairs of GC samples and normal gastric tissues (A). Immunohistochemical analysis of CHSY3 in 68 on GC tissue and normal tissue (B, C). KM curve analysis of 68 GC samples (D). Knockdown efficiency (E) and overexpression efficiency (F) of CHSY3 in GC cells. CCK-8 assay analysis (G, H). Cloning experiment analysis (I). EDU assay analysis (J). Migration assay analysis (K). Invasion assay analysis (L). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 8
Fig. 8
CHSY3 promotes tumor growth in nude mice. Tumor growth of mice implanted subcutaneously with GC cells that have undergone overexpression of CHSY3 (A). Immunohistochemistry revealed the expression of CHSY3 and Ki67 in subcutaneously transplanted tumors in mice (Scale: 40 μm) (B). Tumor volume and weight were measured to show tumor size (C, D). *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 9
Fig. 9
In vivo antitumor effects of CHSY3 Knockdown Combined with αPD-L1. Therapeutic regimen for tumor-bearing mice (A). Images of isolated tumours from MFC tumour-bearing mice (B). Tumor growth curve and tumor weight in MFC tumor-bearing mice (C, D). Immunohistochemistry of MFC tumors (scale: 40 μm) (E). *P < 0.05; **P < 0.01; ***P < 0.001
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References

    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. doi: 10.3322/caac.21660. - DOI - PubMed
    1. Yaghoobi M, Bijarchi R, Narod SA. Family history and the risk of gastric cancer. Br J Cancer. 2010;102(2):237–242. doi: 10.1038/sj.bjc.6605380. - DOI - PMC - PubMed
    1. Kim J, Cho YA, Choi WJ, Jeong SH. Gene-diet interactions in gastric cancer risk: a systematic review. World J Gastroenterol. 2014;20(28):9600–9610. doi: 10.3748/wjg.v20.i28.9600. - DOI - PMC - PubMed
    1. Keszei AP, Goldbohm RA, Schouten LJ, Jakszyn P, van den Brandt PA. Dietary N-nitroso compounds, endogenous nitrosation, and the risk of esophageal and gastric cancer subtypes in the Netherlands cohort study. Am J Clin Nutr. 2013;97(1):135–146. doi: 10.3945/ajcn.112.043885. - DOI - PubMed
    1. Moy KA, Fan Y, Wang R, Gao YT, Yu MC, Yuan JM. Alcohol and tobacco use in relation to gastric cancer: a prospective study of men in Shanghai, China. Cancer Epidemiol Biomark Prev. 2010;19(9):2287–2297. doi: 10.1158/1055-9965.EPI-10-0362. - DOI - PMC - PubMed

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