The association between the Dietary Inflammatory Index (DII) and breast cancer risk has been widely reported in recent years, but there is still controversy about whether a pro-inflammatory diet is a risk factor for breast cancer. We conducted a meta-analysis to investigate the relationship between the DII and breast cancer risk in pre-menopausal and post-menopausal women. We comprehensively searched PubMed, Embase and the Cochrane Library in January 2021 to identify articles reporting an association between the DII and breast cancer risk. A pooled analysis was conducted with 14 studies covering 312,885 participants. Overall, women in the most pro-inflammatory diet category were at greater risk for breast cancer than those in the most anti-inflammatory category (relative risk [RR]=1.37, 95% confidence interval [CI] 1.17-1.60, P<0.001). This association was strong in both pre-menopausal women (RR=1.87, 95% CI 1.17-2.99, P=0.001) and post-menopausal women (RR=1.23, 95% CI 1.08-1.40, P<0.001). Thus, a strong and independent association was observed between a pro-inflammatory diet (assessed using the DII score) and breast cancer risk, irrespective of menopausal status. Further studies will be required to determine the relationship between a pro-inflammatory diet and different subtypes of breast cancer.
Introduction. Serine hydroxymethyltransferase 2 (SHMT2) has a critical role in serine-glycine metabolism to drive cancer cell proliferation. Yet, the function of SHMT2 in tumorigenesis, especially in human colorectal cancer (CRC) progression, remains largely unclear. Materials and Methods. CRC and paired normal samples were collected in the Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, and assessed by real-time polymerase chain reaction (qPCR) analysis, western blot (WB), and immunohistochemistry (IHC). Moreover, SHMT2 expression in human CRC cells was identified by qPCR and WB. The CRC cell proliferation, migration, and invasion after SHMT2 knockdown were explored through in vitro and in vivo assays. mRNA-seq assays were used to investigate the underlying mechanisms behind the SHMT2 function. Results. It was found that SHMT2 mRNA and protein were overexpressed in CRC tissue compared to the levels in normal mucosa. Positive expression of SHMT2 was significantly correlated with TNM stage and lymph node metastasis, and elevated expression of SHMT2 resulted as an independent prognostic factor in patients with CRC. SHMT2 knockdown impaired the proliferation of CRC in vitro and in vivo and induced cell cycle arrest by regulating UHRF1 expression. Conclusion. Taken together, our findings reveal that UHRF1 is a novel target gene of SHMT2, which can be used as a potential therapeutic strategy for CRC therapy.
Objectives Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct. Methods The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR). Results Eighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues. Conclusions An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.
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