Nanotrap-enabled quantification of KRAS-induced peptide hydroxylation in blood for cancer early detection

Circulating peptide is a potential source of biomarkers for cancer detection.However,the existence of large molecular weight proteins in plasma have a disastrous effect on circulating peptides isolating and detecting.Herein,nanotrap fractionation following by mass spectrometry have been applied to quantify the levels of bradykinin (BK) and hydroxylated bradykinin (Hyp-BK) as a relative measure of KRAS-regulated prolyl-4-hydroxylase alpha-1 (P4HA1) which may serve as early diagnosis marker for pancreatic ductal adenocarcinoma (PDAC).We found that P4HA1 can be upregulated by KRASG12v,which is a PDAC driver mutation,using HPNE/KRAS and HPNE cells.And we revealed that P4HA1 is overexpressed in PDAC tumors,compared to normal and inflamed pancreatic tissues.RNA interference revealed that P4HA1 activity was primarily responsible for Hyp-BK production.Mass spectrometry analysis revealed that plasma Hyp-BK/BK ratio was higher in PDAC than pancreatitis patients and healthy controls,while the area under the receiver operating characteristic (ROC) curve (AUC) is 0.8209 (95%Cl,0.7269-0.9149).The Hyp-BK/BK association with PDAC was reproduced in another cohort,where this ratio was found to increase with advancing tumor stage.These novel findings paved the way for wider applications of Nanotrap coupled mass spectrometry as a powerful tool for revealing biosignatures from plasma.

A Public Health Perspective on Preventing and Controlling the Spread of Coronavirus Disease 2019

On March 11,2020,the World Health Organization(WHO)formally declared coronavirus disease 2019(COVID-19)a worldwide pandemic as the virus spreads rapidly with new cases and deaths rising exponentially in many countries.As of March 12,there were 125,048 confirmed cases and 4,613 deaths,and the numbers are still surging affecting 118 countries(1).Now we know that regional efforts to contain individual outbreaks have failed.The next phase of epidemic control is mitigation,and China has implemented multiple effective measurements such as mandatory citywide lockdowns to isolate and block the spread since January 2020.Recent data has shown evidence of controlling the epidemic(2).

Smoking, smoking cessation, and survival after cancer diagnosis in 128,423 patients across cancer types

Dear Editor,Given the systemic and substantial health consequences of smoking and the significant cancer risk from smoking[1,2],it is expected that cancer patients would quit smoking after cancer diagnosis.However,the smoking rate among cancer survivors is only slightly lower than that among the general population[3,4],and 64%of smokers diagnosed with cancer continued to smoke even after they learned they had cancer[5].In addition,some former smokers may resume smoking after surviving cancer[6].A number of previous studies have investigated the association between smoking and clinical outcomes in cancer patients[1,7–10].Nevertheless,most studies were conducted in smoking-related cancers,and evidence for non-smoking-related cancers is limited.There is an urgent need for more convincing evidence showing the harms of smoking and the benefits of smoking cessation to promote smoking cessation in cancer patients and survivors.We systematically studied the associations of smoking status,smoking intensity,age at initiation,and smoking cessation at cancer diagnosis with all-cause mortality in a prospective cohort of 128,423 cancer patients across 23 cancer types from the MD Anderson Cancer Patients and Survivors Cohort.

Body mass index and survival after cancer diagnosis:A pan-cancer cohort study of 114430 patients with cancer

The recommendation encouraging patients with cancer to keep a normal body mass index(BMI)is largely extrapolated from data on risk of developing cancer.We tested the prospective association between peri-diagnostic(within 1 year post-diagnosis)BMI and all-cause mortality in patients with incident cancers.During 7.2 years of follow-up,42%(48,340)of the 114430 patients with cancer died.Spline analysis revealed that compared with a BMI of 22.5,a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types.A BMI higher than 22.5 was associated with reduced all-cause mortality,while a non-linear association was observed;the lowest risk was found at a BMI of 29.6–34.2,and the risk started to return to and above unity at very high BMI values.The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias,confounding by smoking and comorbidities,and reserve causality.

Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated Time Series Models—Worldwide,2020

Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention evaluation.Methods:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in China.Based on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the United States,Italy and Republic of Korea.The intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real trends.Results:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May 19.Temporary abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting points.Conclusion:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.