SLC41A3, as a member of the 41st family of solute carriers, participates in the transport of magnesium. The role of SLC41A3 in cancer prognosis and immune regulation has rarely been reported. This study was designed to analyze the expression status and prognostic significance of SLC41A3 in pan-cancers. The mRNA expression profiles of SLC41A3 were obtained from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), the Broad Institute Cancer Cell Line Encyclopedia (CCLE), and the International Cancer Genome Consortium (ICGC). The Cox regression and Kaplan-Meier analyses were used to evaluate the prognostic value of SLC41A3 in pan-cancer. Furthermore, the correlation between SLC41A3 expression and immune cells infiltration, immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI) were calculated using data form TCGA database. The results showed that the expression of SLC41A3 was down-regulated in kidney renal clear cell carcinoma (KIRC), and was associated with poor overall survival and tumor-specific mortality. Whereas, the expression of SLC41A3 was up-regulated in liver hepatocellular carcinoma (LIHC), and the results of Cox regression analysis revealed that SLC41A3 was an independent factor for LIHC prognosis. Meanwhile, a nomogram including SLC41A3 and stage was built and exhibited good predictive power for the overall survival of LIHC patients. Additionally, correlation analysis suggested a significant correlation between SLC41A3 and TMB, MSI, MMR, DNMT, and immune cells infiltration in various cancers. The overall survival and disease-specific survival analysis revealed that the combined SLC41A3 expression and immune cell score, TMB, and MSI were significantly associated with clinical outcomes in ACC, LIHC, and UVM patients. Therefore, we proposed that SLC41A3 may serve as a potential prognostic biomarker for cancer.
Background Lysine acetylation is a post-translational modification that regulates a diversity of biological processes, including cancer development. Methods Here, we performed the quantitative acetylproteomic analysis of three primary cervical cancer tissues and corresponding adjacent normal tissues by using the label-free proteomics approach. Results We identified a total of 928 lysine acetylation sites from 1547 proteins, in which 495 lysine acetylation sites corresponding to 296 proteins were quantified. Further, 41 differentially expressed lysine acetylation sites corresponding to 30 proteins were obtained in cervical cancer tissues compared with adjacent normal tissues (Fold change > 2 and P < 0.05), of which 1 was downregulated, 40 were upregulated. Moreover, 75 lysine acetylation sites corresponding to 58 proteins were specifically detected in cancer tissues or normal adjacent tissues. Motif-X analysis showed that kxxxkxxxk, GkL, AxxEk, kLxE, and kkxxxk are the most enriched motifs with over four-fold increases when compared with the background matches. KEGG analysis showed that proteins identified from differently and specifically expressed peptides may influence key pathways, such as Notch signaling pathway, viral carcinogenesis, RNA transport, and Jak-STAT, which play an important role in tumor progression. Furthermore, the acetylated levels of CREBBP and S100A9 in cervical cancer tissues were confirmed by immunoprecipitation (IP) and Western blot analysis. Conclusions Taken together, our data provide novel insights into the role of protein lysine acetylation in cervical carcinogenesis.
This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.
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