Aims: Cysteine persulfidation (also called sulfhydration or sulfuration) has emerged as a potential redox mechanism to regulate protein functions and diverse biological processes in hydrogen sulfide (H 2 S) signaling. Due to its intrinsically unstable nature, working with this modification has proven to be challenging. Although methodological progress has expanded the inventory of persulfidated proteins, there is a continued need to develop methods that can directly and unequivocally identify persulfidated cysteine residues in complex proteomes. Results: A quantitative chemoproteomic method termed as low-pH quantitative thiol reactivity profiling (QTRP) was developed to enable direct site-specific mapping and reactivity profiling of proteomic persulfides and thiols in parallel. The method was first applied to cell lysates treated with NaHS, resulting in the identification of overall 1547 persulfidated sites on 994 proteins. Structural analysis uncovered unique consensus motifs that might define this distinct type of modification. Moreover, the method was extended to profile endogenous protein persulfides in cells expressing H 2 S-generating enzyme, mouse tissues, and human serum, which led to additional insights into mechanistic, structural, and functional features of persulfidation events, particularly on human serum albumin. Innovation and Conclusion: Low-pH QTRP represents the first method that enables direct and unbiased proteomic mapping of cysteine persulfidation. Our method allows to generate the most comprehensive inventory of persulfidated targets of NaHS so far and to perform the first analysis of in vivo persulfidation events, providing a valuable tool to dissect the biological functions of this important modification. Antioxid. Redox Signal. 33, 1061-1076.

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SOCS3 as an important negative regulator of IL6/JAK/STAT3 signaling pathway may be early critical determinants of carcinogenesis. This study aimed to explore the aberrant promoter methylation of SOCS3 gene in circulating DNA as a noninvasive biomarker for screening hepatocellular carcinoma (HCC) high-risk individuals and for prognosis of HCC patients after partial hepatectomy. We detected its methylation status in 116 liver tissues and 326 plasma specimens of different hepatic diseases and healthy subjects, and its mRNA and protein expression in tissues. Higher methylation rate was remarkably detected in HCC (47.92%), compared with corresponding non-tumor (25.0%), liver cirrhosis (LC) (10.0%), benign liver diseases (0%) and normal liver tissues (0%) (all P < 0.05). SOCS3 mRNA level was significantly lower in methylated HCC tissues (P < 0.05). The expressions of SOCS3 and pSTAT3 were affected by methylation status. Correlation and consistency of SOCS3 methylation were found between cancer tissue and corresponding plasma (P < 0.001, κ = 0.747). The detection rate of plasma for HCC reached 73.91%, with no false positive error. SOCS3 methylation status both in tissue and plasma was significantly associated with AFP400, tumor size, tumor differentiation, LC, metastasis and recurrence (all P < 0.05). Patients with SOCS3 methylation were followed up a markedly poorer prognosis than those unmethylated for disease-free survival (P < 0.05). These data indicate that methylation status of SOCS3 in plasma cell-free DNA can correctly reflect that in tissue DNA and be used as a noninvasive potential biomarker for chronic liver disease monitoring, predicting the degree of malignancy and poor prognosis of HCC.

A formal risk assessment for identifying high-risk patients is essential in clinical practice and promoted in guidelines for the management of anterior acute myocardial infarction. In this study, we sought to evaluate the performance of different machine learning models in predicting the 1-year mortality rate of anterior ST-segment elevation myocardial infarction (STEMI) patients and to compare the utility of these models to the conventional Global Registry of Acute Coronary Events (GRACE) risk scores. We enrolled all of the patients aged >18 years with discharge diagnoses of anterior STEMI in the Western China Hospital, Sichuan University, from January 2011 to January 2017. A total of 1244 patients were included in this study. The mean patient age was 63.8±12.9 years, and the proportion of males was 78.4%. The majority (75.18%) received revascularization therapy. In the prediction of the 1-year mortality rate, the areas under the curve (AUCs) of the receiver operating characteristic curves (ROCs) of the six models ranged from 0.709 to 0.942. Among all models, XGBoost achieved the highest accuracy (92%), specificity (99%) and f1 score (0.72) for predictions with the full variable model. After feature selection, XGBoost still obtained the highest accuracy (93%), specificity (99%) and f1 score (0.73). In conclusion, machine learning algorithms can accurately predict the rate of death after a 1-year follow-up of anterior STEMI, especially the XGBoost model.

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Background: Most hepatocellular carcinoma (HCC) patients have undergone a progression from chronic hepatitis, then liver cirrhosis (LC), and finally to carcinoma. The objective of this study was to elucidate risk factors to predict HCC development for cirrhosis patients.Methods: Multiple methylated specific PCR (MSP) was applied to determine methylation status of heparocarcinogenesis-related genes in 396 tissue and plasma specimens and multivariate cox model was used to analyze the relationship between risk variables and HCC development among cirrhosis patients, followed up in a median period of 30 months.Results: Among 105 LC cases, HCC incidence rate at 30 months was 30.48% (32/105), which were statistically associated with patients' age and aberrant methylation of p16, SFRP, and LINE1 (p<0.05). Receiver operating characteristic (ROC) curve showed the overall predictive accuracy reached the highest (90.7%) if the four risk variables were concurrent to predict HCC development. Moreover, along with the growth of age from 0-40, 40-55, to 55-70 years or the increased number of aberrantly-methylated gene from 0-1 to 2-3, the HCC incidence rate of cirrhosis patients rised from 10.00%, 12.28% to 82.14% and 17.44% to 89.47%, separately. Thus, based on combined analysis with diverse age and number of aberrantly-methylated gene, 105 cases were divided into five groups and computed their respective HCC incidecne rate to categorize them into different risk groups. Of note, A significant lifting of HCC incidence rate in the high-risk group (40-55 years coupled with 2-3 aberrantly-methylated genes, 55-70 years coupled with 0-1 aberrantly-methylated gene, 55-70 years coupled with 2-3 aberrantly-methylated genes; n=33) was observed compared with the low-risk group (0-40 years coupled with 0-1 aberrantly-methylated gene, 40-55 years coupled with 0-1 aberrantly-methylated gene; (n=72) (p<0.01).Conclusions: Ultimately, high-risk cirrhosis patients with 55-over years or 2-3 aberrantly-methylated genes should be paid more attention to be regularly screened with HCC development.

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