Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling

Alahdal, Murad1,2,3; Sun, Deshun1; Duan, Li1; Ouyang, Hongwei2; Wang, Manyi1; Xiong, Jianyi1; Wang, Daping1,4

2021-02-01

10.1111/cas.14832

CANCER SCIENCE   影响因子和分区

1347-9032

1349-7006

In this study, a new mathematical model was established and validated to forecast and define sensitive targets in the kynurenine pathway (Kynp) in pancreatic adenocarcinoma (PDAC). Using the Panc-1 cell line, genetic profiles of Kynp molecules were tested. qPCR data were implemented in the algorithm programming (fmincon and lsqnonlin function) to estimate 35 parameters of Kynp variables by Matlab 2017b. All tested parameters were defined as non-negative and bounded. Then, based on experimental data, the function of the fmincon equation was employed to estimate the approximate range of each parameter. These calculations were confirmed by qPCR and Western blot. The correlation coefficient (R) between model simulation and experimental data (72 hours, in intervals of 6 hours) of every variable was >0.988. The analysis of reliability and predictive accuracy depending on qPCR and Western blot data showed high predictive accuracy of the model; R was >0.988. Using the model calculations, kynurenine (x3, a6), GPR35 (x4, a8), NF-k beta p105 (x7, a16), and NF-k beta p65 (x8, a18) were recognized as sensitive targets in the Kynp. These predicted targets were confirmed by testing gene and protein expression responses. Therefore, this study provides new interdisciplinary evidence for Kynp-sensitive targets in the treatment of PDAC.

kynurenine pathway mathematical modeling Matlab 2017b pancreatic adenocarcinoma sensitive targets

SCI

英语

通讯

National Natural Science Foundation of China[81972116,81972085,81772394] ; Key Program of Natural Science Foundation of Guangdong Province[2018B0303110003] ; Shenzhen Peacock Project[KQTD20170331100838136] ; Shenzhen Science and Technology Projects["JCYJ20170817172023838","JCYJ20170306092215436","JCYJ20170412150609690","JCYJ20170413161649437","JCYJ20170413161800287"]

Oncology

Oncology

WOS:000619852900001

WILEY

CLINICAL MEDICINE

Web of Science

1.Shenzhen Univ, Guangdong Prov Res Ctr Artificial Intelligence &, Hosp 1,Hlth Sci Ctr,Shenzhen Peoples Hosp 2, Shenzhen Key Lab Tissue Engn,Shenzhen Lab Digital, Shenzhen, Peoples R China
2.Zhejiang Univ, Sch Med, Dr Li Dak Sum & Yip Yio Chin Ctr Stem Cells & Reg, Hangzhou, Peoples R China
3.Hodeidah Univ, Dept Med Labs, Fac Med, Al Hudaydah, Yemen
4.Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen, Peoples R China

Alahdal, Murad,Sun, Deshun,Duan, Li,et al. Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling[J]. CANCER SCIENCE,2021,112(4).

Alahdal, Murad.,Sun, Deshun.,Duan, Li.,Ouyang, Hongwei.,Wang, Manyi.,...&Wang, Daping.(2021).Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling.CANCER SCIENCE,112(4).

Alahdal, Murad,et al."Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling".CANCER SCIENCE 112.4(2021).