Roles of Mitochondrial Serine Hydroxymethyltransferase 2 (SHMT2) in Human Carcinogenesis

Zeng, Yuanyuan; Zhang, Jie; Xu, Mengmeng; Chen, Fuxian; Zi, Ruidong; Yue, Jicheng; Zhang, Yanan; Chen, Nannan; Chin, Y. Eugene

doi:10.7150/jca.60170

Cited by 31 publications

(21 citation statements)

References 67 publications

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“…In the present study, the bioinformatic analysis revealed a clear increase in SHMT2 expression in human pRCC samples, which served as the most potent regulator of serine metabolism. In previous research, the SHMT2 gene was defined as an oncogene, which exerted its function in mitochondria ( 34 ), mediating the conversion of serine to glycine and tetrahydrofolate (THF) to N5, N10-methylated THF (MTHF). MTHF is the main transportation form of the one-carbon unit and contributes to the metabolism of proteins and nucleotides metabolism, which corresponds with tumour cell growth.…”

Section: Discussionmentioning

confidence: 99%

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SHMT2 regulates serine metabolism to promote the progression and immunosuppression of papillary renal cell carcinoma

et al. 2022

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Recent research has demonstrated the diverse relationship between tumour metabolism and the tumour microenvironment (TME), for example, abnormal serine metabolism. This study investigated the role of serine metabolism in papillary renal cell carcinoma (pRCC) focusing on the prognostic value and regulatory mechanisms. Gene expression profiles and clinical data of patients with pRCC were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Kaplan–Meier curves were used for survival analysis and consensus clustering for tumour serine metabolic signatures extraction. Functional analysis, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA), was applied to explore the biological characteristics. The gene set variation analysis (GSVA), single-sample GSEA (ssGSEA), and Estimation of Stromal and Immune cells in Malignant Tumour tissues using Expression data (ESTIMATE) methods were utilised to estimate the immune infiltration in the various subtypes. Five serine metabolic genes (SMGs) were used to classify patients with pRCC, with four clusters identified with diverse prognoses and immune features based on these survival-related SMGs. Further analysis of the best and worst clusters (B and D clusters) revealed variations in survival, clinical progression, oncogenic pathways, and TME, which included immune infiltration scores, immunosuppressive cell infiltration, and expression of immune checkpoints. In addition, SMGs, especially SHMT2, exacerbated the carcinogenesis and immunosuppressive cells in pRCC, thus promoting tumour proliferation. In conclusion, higher SHMT2 gene expression and higher serine metabolism in tumour cells are associated with poorer clinical outcomes in pRCC. SHMT2 is a potential novel target gene for targeted therapy and immunotherapy in pRCC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

SHMT2 regulates serine metabolism to promote the progression and immunosuppression of papillary renal cell carcinoma

et al. 2022

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“…However, drug resistance is a common problem, and the presence of CSCs is reported to be the main reason for drug resistance in cancer [ 16 , 17 ]. Serine hydroxymethyltransferase-2 ( SHMT2 ) is a key enzyme in one-carbon metabolism [ 18 ]. It converts serine to glycine, which provides the methyl group of the one-carbon pool required for DNA methylation and nucleotide biosynthesis [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

SHMT2 Induces Stemness and Progression of Head and Neck Cancer

Jin

Jung

Lim

et al. 2022

IJMS

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Various enzymes in the one-carbon metabolic pathway are closely related to the development of tumors, and they can all be potential targets for cancer therapy. Serine hydroxymethyltransferase2 (SHMT2), a key metabolic enzyme, is very important for the proliferation and growth of cancer cells. However, the function and mechanism of SHMT2 in head and neck cancer (HNC) are not clear. An analysis of The Cancer Genome Atlas (TCGA) data showed that the expression of SHMT2 was higher in tumor tissue than in normal tissue, and its expression was significantly associated with male sex, aggressive histological grade, lymph node metastasis, distant metastasis, advanced TNM stage, and lymphovascular invasion in HNC. SHMT2 knockdown in FADU and SNU1041 cell lines significantly inhibited cell proliferation, colony formation, migration, and invasion. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using TCGA data revealed that SHMT2 was closely related to cancer stem cell regulation and maintenance. Furthermore, we found that silencing SHMT2 inhibited the expression of stemness markers and tumor spheroid formation compared with a control group. On the contrary, stemness markers were significantly increased after SHMT2 overexpression in HEP-2 cells. Interestingly, we found that knocking down SHMT2 reduced the expression of genes related to the Notch and Wnt pathways. Finally, silencing SHMT2 significantly reduced tumor growth and decreased stemness markers in a xenograft model. Taken together, our study suggests that targeting SHMT2 may play an important role in inhibiting HNC progression.

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“…Among these 11 genes, SHMT2, encoded by SHMT2 , is an important enzyme related to carbon metabolism in OSCC, as it catalyzes the conversion of serine to glycine [ 42 ]. SHMT2 level is discovered to be up-regulated in OSCC tissues, which has been linked to a poor prognosis; the increased expression predicts the worse the pathological state of the tumor [ 43 ].…”

Section: Disscussionmentioning

confidence: 99%

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

Zhang

Qin

et al. 2022

BMC Med Genomics

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Background Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. Methods This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. Results The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan–Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making. Conclusion In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

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Roles of Mitochondrial Serine Hydroxymethyltransferase 2 (SHMT2) in Human Carcinogenesis

Cited by 31 publications

References 67 publications

SHMT2 regulates serine metabolism to promote the progression and immunosuppression of papillary renal cell carcinoma

SHMT2 regulates serine metabolism to promote the progression and immunosuppression of papillary renal cell carcinoma

SHMT2 Induces Stemness and Progression of Head and Neck Cancer

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

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