bims-meluca 2021-11-21 papers

Phytomedicine. 2021 Nov 05. pii: S0944-7113(21)00372-X. [Epub ahead of print] 153831

A method establishment and comparison of in vivo lung cancer model development platforms for evaluation of tumour metabolism and pharmaceutical efficacy.

BACKGROUND: Currently, the identification of accurate biomarkers for the diagnosis of patients with early-stage lung cancer remains difficult. Fortunately, metabolomics technology can be used to improve the detection of plasma metabolic biomarkers for lung cancer. In a previous study, we successfully utilised machine learning methods to identify significant metabolic markers for early-stage lung cancer diagnosis. However, a related research platform for the investigation of tumour metabolism and drug efficacy is still lacking.HYPOTHESIS/PURPOSE: A novel methodology for the comprehensive evaluation of the internal tumour-metabolic profile and drug evaluation needs to be established.
METHODS: The optimal location for tumour cell inoculation was identified in mouse chest for the non-traumatic orthotopic lung cancer mouse model. Microcomputed tomography (micro-CT) was applied to monitor lung tumour growth. Proscillaridin A (P.A) and cisplatin (CDDP) were utilised to verify the anti-lung cancer efficacy of the platform. The top five clinically valid biomarkers, including proline, L-kynurenine, spermidine, taurine and palmitoyl-L-carnitine, were selected as the evaluation indices to obtain a suitable lung cancer mouse model for clinical metabolomics research by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS).
RESULTS: The platform was successfully established, achieving 100% tumour development rate and 0% surgery mortality. P.A and CDDP had significant anti-lung cancer efficacy in the platform. Compared with the control group, four biomarkers in the orthotopic model and two biomarkers in the metastatic model had significantly higher abundance. Principal component analysis (PCA) showed a significant separation between the orthotopic/metastatic model and the control/subcutaneous/KRAS transgenic model. The platform was mainly involved in arginine and proline metabolism, tryptophan metabolism, and taurine and hypotaurine metabolism.
CONCLUSION: This study is the first to simulate clinical metabolomics by comparing the metabolic phenotype of plasma in different lung cancer mouse models. We found that the orthotopic model was the most suitable for tumour metabolism. Furthermore, the anti-tumour drug efficacy was verified in the platform. The platform can very well match the clinical reality, providing better lung cancer diagnosis and securing more precise evidence for drug evaluation in the future.

Keywords: Evaluation platform; Lung cancer; Monitoring internal pulmonary tumour growth; Tumour-metabolic profiling

DOI: https://doi.org/10.1016/j.phymed.2021.153831

Biochim Biophys Acta Mol Cell Biol Lipids. 2021 Nov 15. pii: S1388-1981(21)00210-9. [Epub ahead of print] 159082

Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues.

Eva Cífková, Radana Brumarová, Magdaléna Ovčačíková, Dana Dobešová, Kateřina Mičová, Aleš Kvasnička, Zuzana Vaňková, Jiří Šiller, Lukáš Sákra, David Friedecký, Michal Holčapek.

Lung cancer represents one of the leading worldwide causes of cancer death, but the pathobiochemistry of this disease is still not fully understood. Here we characterize the lipidomic and metabolomic profiles of the tumor and surrounding normal tissues for 23 patients with non-small cell lung cancer. In total, 500 molecular species were identified and quantified by a combination of the lipidomic shotgun tandem mass spectrometry (MS/MS) analysis and the targeted metabolomic approach using liquid chromatography (LC) - MS/MS. The statistical evaluation includes multivariate and univariate methods with the emphasis on paired statistical approaches. Our research revealed significant changes in several biochemical pathways related to the central carbon metabolism, acylcarnitines, dipeptides as well as the disruption in the lipid metabolism observed mainly for glycerophospholipids, sphingolipids, and cholesteryl esters.

Keywords: Lipidomics; Lung cancer; Mass spectrometry; Metabolism; Metabolomics

DOI: https://doi.org/10.1016/j.bbalip.2021.159082

J Thorac Dis. 2021 Oct;13(10): 5691-5700

Pyruvate dehydrogenase E1α represents a reliable prognostic predictor for patients with non-small cell lung cancer resected via curative operation.

Ryuichi Ito, Masakazu Yashiro, Takuma Tsukioka, Nobuhiro Izumi, Hiroaki Komatsu, Hidetoshi Inoue, Yurie Yamamoto, Noritoshi Nishiyama.

Background: Lung cancer is associated with a high morbidity and mortality rate worldwide; however, no reliable and independent prognostic predictor for non-small cell lung cancer (NSCLC) after curative surgery is available. Glucose metabolism is correlated with cancer cell proliferation. Pyruvate dehydrogenase E1α (PDH-E1α) catalyzes the conversion of pyruvate to acetyl-CoA and promotes aerobic glucose metabolism. In this study, we examined the relationship between PDH-E1α expression and clinicopathological factors associated with NSCLC to identify a reliable prognostic predictor of NSCLC after curative surgery.Methods: A total of 445 patients with NSCLC who underwent curative resection were enrolled in this study. PDH-E1α expression was evaluated via immunohistochemistry. We analyzed the correlation between PDH-E1α expression and clinicopathological features of the patients.
Results: In total, 248 (56%) of the 445 patients with NSCLC were PDH-E1α-positive, and 197 patients were PDH-E1α-negative. PDH-E1α positivity was significantly correlated with the presence of adenocarcinoma (P<0.001) compared to the PDH-E1α-negative group. Patients with NSCLC showing PDH-E1α-negative expression had a significantly poorer overall survival rate (P=0.007) than those showing PDH-E1α-positive expression, especially at stage II. Patients with PDH-E1α negative expression also showed a poorer disease-free survival rate (P=0.02). Multivariate analysis revealed that PDH-E1α negativity (P=0.037) and male sex (P<0.001) were significantly correlated with a poor overall survival.
Conclusions: PDH-E1α may represent a reliable prognostic predictor for NSCLC in patients that have recently undergone curative resection, especially at stage II.

Keywords: Pyruvate dehydrogenase (PDH); curative resection; non-small cell lung cancer (NSCLC); predictive marker

DOI: https://doi.org/10.21037/jtd-21-1463

Front Oncol. 2021 ;11 752036

Glucose Metabolism Reprogramming of Primary Tumor and the Liver Is Associated With Disease-Free Survival in Patients With Early NSCLC.

Hongpei Tan, Mengtian Ma, Jing Huang, Wenhao Zhu, Shuo Hu, Kai Zheng, Pengfei Rong.

Purpose: Tumor promote disease progression by reprogramming their metabolism and that of distal organs, so it is of great clinical significance to study the changes in glucose metabolism at different tumor stages and their effect on glucose metabolism in other organs.Methods: A retrospective single-centre study was conducted on 253 NSCLC (non-small cell lung cancer) patients with negative lymph nodes and no distant metastasis. According to the AJCC criteria, the patients were divided into different groups based on tumor size: stage IA, less than 3 cm (group 1, n = 121); stage IB, greater than 3-4 cm (group 2, n = 64); stage IIA, greater than 4-5 cm (group 3, n = 36); and stage IIB, greater than 5-7 cm (group 4, n = 32). All of the patients underwent baseline 18F-FDG PET/CT scans, and the primary lesion SUVmax (maximum standardized uptake value), liver SUVmean (mean standardized uptake value), spleen SUVmean, TLR (Tumor-to-liver SUV ratio) and TSR (Tumor-to-spleen SUV ratio) were included in the study, combined with clinical examination indicators to evaluate DFS (disease free survival).
Results: In NSCLC patients, with the increase in the maximum diameter of the tumor, the SUVmax of the primary lesion gradually increased, and the SUVmean of the liver gradually decreased. The primary lesion SUVmax, liver SUVmean, TLR and TSR were related to disease recurrence or death. The best predictive parameters were different when the tumor size differed. SUVmax had the highest efficiency when the tumor size was less than 4 cm (AUC:0.707 (95% CI, 0.430-0.984) tumor size < 3 cm), (AUC:0.726 (95% CI, 0.539-0.912) tumor size 3-4 cm), liver SUVmean had the highest efficiency when the tumor size was 4-5 cm (AUC:0.712 (95% CI, 0.535-0.889)), and TLR had the highest efficiency when the tumor size was 5-7 cm [AUC:0.925 (95%CI, 0.820-1.000)].
Conclusions: In patients with early NSCLC, glucose metabolism reprogramming occurs in the primary lesion and liver. With the increase in tumor size, different metabolic parameters should be selected to evaluate the prognosis of patients.

Keywords: DFS = disease-free survival; FDG (18F-fluorodeoxyglucose)-PET/CT; Liver glucose metabolism; NSCLC; metabolism reprogramming; splenic glucose metabolism

DOI: https://doi.org/10.3389/fonc.2021.752036

Front Nutr. 2021 ;8 743475

Association Between the Nutritional Risk and the Survival Rate in Newly Diagnosed GIST Patients.

Ping'an Ding, Honghai Guo, Peigang Yang, Chenyu Sun, Yuan Tian, Yang Liu, Yong Li, Qun Zhao.

Background: Currently, the incidence of gastrointestinal stromal tumors (GIST) is increasing rapidly worldwide. Malnutrition may increase the risk of perioperative complications and affect the prognosis of patients. However, previous studies on the nutritional status of GIST patients and its impact on prognosis are limited. Therefore, this study aims to explore the incidence of malnutrition in newly diagnosed GIST patients, the proportion of participants in need of nutritional intervention, and the relationship between nutritional status and overall survival (OS). Methods: We retrospectively analyzed the clinical data of GIST patients treated in our hospital from January 2014 to January 2018. Nutritional Risk Screening 2002 (NRS2002) and Patient-Generated Subjective Global Assessment (PG-SGA) were used to assess the nutritional status of all patients. This study was to investigate the clinical significance of PG-SGA by analyzing the relationship between PG-SGA score and OS. Results: A total of 1,268 newly diagnosed GIST patients were included in this study, of which 77.76% were at risk of malnutrition (NRS2002 score ≥ 3), and the incidence of malnutrition was 10.09% (PG-SGA score ≥ 4). Meanwhile, we found 2.29% of the patients required urgent nutritional support (PG-SGA score ≥ 9). Multivariate analysis showed that age (p = 0.013), BMI (p = 0.001), weight loss (p = 0.001), anemia (p = 0.005), pre-albumin (p = 0.010), albumin (p = 0.002), tumor location (p = 0.001), tumor size (p = 0.002), and NIH classification (p = 0.001) were risk factors for nutritional status. The prognosis was significantly in GIST patients with different PG-SGA score at admission (p < 0.05). Conclusion: This study suggested that malnutrition is common in newly diagnosed GIST patients, and the higher the PG-SGA score, the worse the clinical outcome.

Keywords: NRS2002; PG-SGA; gastrointestinal stromal tumors (GIST); nutrition status; prognosis; risk factor

DOI: https://doi.org/10.3389/fnut.2021.743475

BMC Cancer. 2021 Nov 17. 21(1): 1235

A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma.

Zhongjian Chen, Chenxi Yang, Zhenying Guo, Siyu Song, Yun Gao, Ding Wang, Weimin Mao, Junping Liu.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research.METHODS: A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets.
RESULTS: After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism.
CONCLUSIONS: To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.

Keywords: GC-MS; Malignant pleural mesothelioma; Metabolomics; Patient-derived xenograft; Ultrasound-guided biopsy

DOI: https://doi.org/10.1186/s12885-021-08980-5