Objective To investigate the clinical value of serum metabonomic profile of pancreatic cancer using nuclear magnetic resonance (NMR)-based metabonomics.Methods The retrospective case-control study was adopted.The clinical data of 23 patients with pancreatic cancer (PC group) and 16 healthy volunteers (control group) who were admitted to the Fujian Medical University Union Hospital between December 2013 and December 2014 were collected.The serum of the 2 groups was measured by 1H NMR spectroscopy.Multivariate statistical analyses were performed to identify the characteristic metabolites in the 2 groups,including principal component analysis (PCA),partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA).Observation indicators included:(1) multivariate statistical analysis of serum metabonomic profile,results of PCA,PLS-DA and OPLS-DA,(2) screening of metabolites.Measurement data with normal distribution were presented as x ± s.The comparison between groups was evaluated with the t test.The count data were analyzed using the chi-square test.Results (1) The multivariate statistical analysis of serum metabonomic profile:results of PCA showed that expression rates of principal component 1 (PC1) and principal component 2 (PC2) to original data were 54.9％ and 23.5％,with both cumulative contribution rate of 78.4％.Results of PLS-DA showed that the separative trend between PC group and control group was appeared,and variance of X and Y matrixes and predictive value were 0.254,0.816 and 0.385.Results of OPLS-DA showed that the differences of samples between the 2 groups were further increased,and differential metabolites were screened according to the distinction of scores between the 2 groups,value of R2X,R2Y and Q2 was 0.254,0.816 and 0.433.(2) Screening of metabolites:35 serum metabolites were detected in the 2 groups.Compared with the control group,levels of 3-hydroxybuyarate,citrate,formate,glutamate,isoleucine,methionine and phenylalanine in the PC group were elevated (r =0.524,0.511,0.656,0.566,0.503,0.498,0.648,P ＜0.05),and levels of 3-methylhistidine,alanine,glutamine,LDL and VLDL in the PC group were decreased (r =-0.607,-0.508,-0.560,-0.568,-0.559,P ＜ 0.05).Conclusions Compared with healthy controls,several amino acids,citrate and lipoproteins demonstrate the metabolic differences in the serum of patients with pancreatic cancer.NMR based metabonomic profile technology can distinguish the difference of serum metabolites between patients with pancreatic cancer and healthy controls.NMR based metabonomic technology may be a promising method for the diagnosis of pancreatic cancer.

An increasing number of studies suggested that the tumor microenvironment exerts a substantial influence on the pathophysiology of pancreatic cancer. As a crucial component of the tumor microenvironment,the tumor stroma plays a pivotal role in the occurrence,development,and chemotherapy resistance of pancreatic cancer. By serving as a proxy for the interaction between tumor cells and the microenvironment,the tumor stroma ratio(TSR) has emerged as a focal point of investigation in recent years. At present,numerous studies show that a low TSR is a protective factor for the prognosis of resectable pancreatic cancer. Additionally, patients with a low TSR are more suitable for the gemcitabine and albumin-bound paclitaxel chemotherapy regimen. But these researches are not conclusive, and there is still a gap between guiding precision treatment. Further research and exploration are required. Integration of artificial intelligence deep learning models into traditional pathological and imaging assessments facilitates precise evaluation of the TSR. It can also enable stratification and precision treatment of pancreatic cancer patients based on this index.

An increasing number of studies suggested that the tumor microenvironment exerts a substantial influence on the pathophysiology of pancreatic cancer. As a crucial component of the tumor microenvironment,the tumor stroma plays a pivotal role in the occurrence,development,and chemotherapy resistance of pancreatic cancer. By serving as a proxy for the interaction between tumor cells and the microenvironment,the tumor stroma ratio(TSR) has emerged as a focal point of investigation in recent years. At present,numerous studies show that a low TSR is a protective factor for the prognosis of resectable pancreatic cancer. Additionally, patients with a low TSR are more suitable for the gemcitabine and albumin-bound paclitaxel chemotherapy regimen. But these researches are not conclusive, and there is still a gap between guiding precision treatment. Further research and exploration are required. Integration of artificial intelligence deep learning models into traditional pathological and imaging assessments facilitates precise evaluation of the TSR. It can also enable stratification and precision treatment of pancreatic cancer patients based on this index.