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      Baseline differences in metabolic profiles of patients with lung squamous cell carcinoma responding or not responding to treatment with nanoparticle albumin-bound paclitaxel (nab-paclitaxel)




            Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) is a preparation widely used in chemotherapy for cancers. However, only some patients benefit from this treatment. Therefore, identifying which patients will respond to nab-paclitaxel therapy is crucial.


            A cohort of 32 patients with lung squamous cell carcinoma (LUSC) treated with nab-paclitaxel were enrolled in this study. Plasma samples were collected before chemotherapy and used to perform metabolomic and lipidomic analyses. Tumor response to two cycles of chemotherapy was evaluated. Metabolites differentially present among populations were screened and analyzed.


            According to the RECIST criteria, one-third of patients had a significant response to nab-paclitaxel, whereas one-fifth showed no discernible benefit. According to the criteria of variable importance in projection >1 and fold change >2, we identified 61, 81 and 54 differential metabolites between the progressive disease (PD) vs partial response (PR), PD vs stable disease (SD), and SD vs PR groups, respectively. Moreover, we used three variation in logistic regression models and ROC diagnostic curves to identify optimal metabolites for stratifying patients with differing chemotherapeutic responses. The PD vs SD, SD vs PR, and PD vs PR groups were well separated on the basis of cis-9,10-epoxystearic acid/octapentaenoic acid (AUC 0.9330), salicyluric acid/DG (18:1/20:5/0:0) (AUC 1.0000) and D-glyceric acid/9,12-octadecadienoic acid (AUC 1.0000), respectively.


            The baseline metabolic profiles significantly differed between responder and non-responder patients with LUSC treated with nab-paclitaxel. These differential metabolites have the potential to predict the outcomes of patients with LUSC before chemotherapy.

            Main article text

            1. INTRODUCTION

            Non-small cell lung cancer (NSCLC), accounting for 80% of diagnosed lung cancers, is among the most lethal cancers (5-year survival rate <10% for late stage NSCLC) [1, 2]. Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) is formed through binding of the classic chemotherapy agent paclitaxel to albumin, thus enabling the formation of nanoparticles. This form of paclitaxel is widely used in chemotherapy for lung cancer, breast cancer and other malignant tumors, because of its substantial efficacy, and low toxicity and adverse effects. In contrast to unbound, solvent-based paclitaxel, this treatment does not require anti-allergy treatment before administration [3, 4].

            Previous studies have demonstrated that nab-paclitaxel combined with carboplatin elicits a better response than solvent-based paclitaxel with carboplatin in patients with lung squamous cell carcinoma (LUSC) [5, 6]. However, not all patients benefit from nab-paclitaxel treatment, and the clinical response to nab-paclitaxel is often unpredictable. Therefore, identifying whether patients are likely to respond to nab-paclitaxel is critical to facilitate more precise and targeted treatment regimes. In recent years, metabolomics has shown great potential in predicting individual differences in drug metabolism, efficacy and toxicity, and has been suggested to aid in providing individualized treatments for patients with various diseases [7, 8]. This approach has also been used for cancer therapy.

            In this study, the utility of metabolomics was explored to discover biomarkers associated with the response to nab-paclitaxel chemotherapy in patients with NSCLC. To discover effective biomarkers for predicting and stratifying likely chemotherapy outcomes, we classified patients with differing responses and compared differences in their baseline metabolic characteristics.

            2. METHODS

            2.1 Study participants

            From June 2019 to December 2019, 32 patients at the cancer center of Wuhan Union Hospital (Wuhan, Hubei, China) were initially enrolled, all of whom were confirmed to have LUSC and were being treated with nab-paclitaxel-based chemotherapy. Heparin anticoagulated plasma samples from each participant were collected before treatment and frozen at −80°C until metabolic analysis. Seven patients were excluded from this study because of a lack of efficacy of nab-paclitaxel treatment.

            The tumor response to chemotherapy after two cycles of treatment was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines and classified into progressive disease (PD), partial response (PR) or stable disease (SD). The study protocol was approved by the Ethics Committee of the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, and written informed consent was obtained from all participants.

            2.2 Targeted metabolic/lipidomic detection and statistical analysis

            Metabolomic testing and subsequent statistical analysis were performed as previously reported [9]. In brief, two aliquots of plasma collected before chemotherapy were pre-treated with two methods to extract either hydrophilic or hydrophobic compounds. The metabolomes and lipidomes of these treated samples were then analyzed through ultra-performance liquid chromatography-tandem mass spectrometry by using a database of more than 3000 endogenous molecules. The results were analyzed with multivariate statistical methods, pathway enrichment analysis and Spearman correlation analysis. The orthogonal partial least squares discriminant analysis (OPLS-DA) model was used to predict the efficacy of nab-paclitaxel, on the basis of metabolic data. Detailed information is provided in the Supplementary File .

            3. RESULTS

            3.1 Baseline data for the study population

            The clinical characteristics of the study participants are displayed in Table 1 . The average age of these individuals was approximately 63 years. Most patients were men (84%). Four-fifths of the participants were smokers or ex-smokers. More than half had advanced-stage lung cancer. One in five participants did not respond to nab-paclitaxel chemotherapy (PD), 32% showed a significant decrease in tumor volume (PR), and 48% showed minimal changes (SD) according to the RECIST criteria.

            Table 1 |

            Characteristics of enrolled patients with LUSC.

            Patient characteristics
            Age, years63.1±5.9
            Sex (male/female)21:4
            Smoking history
            (smoking/former smoking/never smoking)
            TNM stage (III: IV)10:15
            RECIST 2.0 (PD:PR:SD)5:8:12

            Footnotes: Age and BMI are expressed as mean±S.D.

            3.2 Identification of differential metabolites among chemotherapy response groups

            We used a comprehensive platform to determine metabolomes and lipidomes. The platform’s database contains more than 3000 characteristic compounds, 1141 of which were detected in the present study, including 332 types of endogenous metabolites and 809 types of lipids ( Table 2 and Supplementary File 1 ).

            Table 2 |

            Compound database and detected compounds.

            CategoryDatabaseDetectedDifferential metabolites
            PD vs PRPD vs SDSD vs PR
            MetabolomicsAmino acids220+97652
            Alcohols and amines100+9///
            Benzene and substituted derivatives150+26/11
            Bile acids (BA)35+14651
            Carboxylic acids and derivatives80+2633/
            Heterocyclic compounds50+24/1/
            Hormones and related compounds40+7///
            Organic acids and derivatives250+681064
            Coenzymes and vitamins50+11//1
            LipidomicsCholesteryl esters (CE)20+15///
            Ceramides (Cer)80+20///
            Sphingomyelin (SM)30/1/
            Coenzyme Q1021///
            Free fatty acids (FFA)221//
            Carnitines (CAR)38///
            Lysophosphatidic acids (LPA)560+2///
            Lysophosphatidylcholines (LPC)261/1
            Lysophosphatidylethanolamine (LPE)171/2
            Phosphatidic acid (PA)3///
            Phosphatidylcholine (PC)109213
            Phosphatidylethanolamine (PE)63/56
            Other phosphatidyl compounds24///
            Monoglycerides (MG)370+2///
            Diglycerides (DG)52176
            Triglycerides (TG)199173418
            Other lipids350+1771135
            Total 3000+ 1141 61 81 54

            The characteristics of metabolites potentially associated with chemotherapy response were determined with the OPLS-DA score map. On the basis of the 1141 detected compounds, the OPLS-DA score plot indicated a clear separation of the PD vs PR, PD vs SD, and SD vs PR groups ( Figure 1a ). We constructed volcano plots of all compounds detected in all groups ( Figure 1b ), in which the ordinate indicates the variable importance in the projection value of the OPLS-DA score chart, and the abscissa indicates the logarithmic transformation of the fold change. On the basis of the criteria of variable importance in the projection >1 and fold change >2, we identified 61, 81 and 54 differential metabolites between the PD vs PR, PD vs SD, and SD vs PR groups, respectively. Red dots indicate up-regulation of metabolites, and green dots indicate down-regulation. A list of all differential metabolites among groups is shown in Supplementary File 2 .

            Figure 1 |

            (a) OPLS-DA score map. (b) Volcano plot.

            The differential metabolites were displayed in three heatmaps ( Supplementary Figures 1–3 ). Notably, lipids accounted for a considerable proportion of the detected metabolites. Specifically, we identified 34, 53 and 41 differential lipid types (such as triglycerides [TG], diglycerides [DG], and phosphatidylcholine or phosphatidylethanolamine), which accounted for 56%, 65% and 76% of all differential metabolites between the PD vs PR, PD vs SD, and SD vs PR groups, respectively. In addition, various forms of TG were detected according to their chain lengths ( Supplementary File 2 ) and accounted for approximately half the differential lipids. For example, 17 TG in the PR group were up-regulated an average of 3.2 fold that in the PD group. Similarly, 34 types of TG were up-regulated in the SD group vs the PD group (average 2.5 fold difference). These data suggested that patients with lower TG levels may be more likely to respond poorly to nab-paclitaxel chemotherapy. However, this result must be validated by larger-scale metabolomic studies.

            3.3 KEGG pathway analysis of the chemotherapy response groups

            The KEGG database, the predominant public database for integrated metabolic pathways, helps researchers contextualize studies on genes, protein expression and metabolite concentrations, and provides a powerful tool for in vivo metabolism analysis and metabolic network research. We analyzed three differential metabolite comparison groups: PD vs PR, PD vs SD, and SD vs PR. The annotation results of the significant differential metabolites were classified according to KEGG pathways ( Figure 2a–c ). Among organismal system pathways, vitamin digestion and absorption, thermogenesis, regulation of lipolysis in adipocytes, fat digestion and absorption and cholesterol metabolism were important pathways differing among the three comparison groups in KEGG functional enrichment analysis. The proportions of annotated metabolites to total metabolites all exceeded 40%. This analysis clearly showed that differential lipids played key roles in these enriched pathways.

            Figure 2 |

            KEGG enrichment analysis of patients with NSCLC.

            (a-c) Differential metabolites classified in biological pathways (the ordinate shows the name of the KEGG metabolic pathway, and the abscissa shows the number of metabolites annotated with the pathway and proportion with respect to the total number of metabolites annotated). (d-f) Statistics of KEGG pathways (bubble color represents adjusted p values, and bubble size represents counts).

            Nearly all differential metabolites could be classified into metabolic pathways (annotation proportion >87%). In particular, glycerolipid metabolism was identified as an important metabolic pathway differing among groups, and the insulin resistance pathway was enriched in all comparisons between groups. The between-group differences in the above enrichment pathways were statistically significant ( Figure 2d–f ).

            3.4 Identification of predictive efficacy of differential metabolites

            As described above, we identified 61, 81 and 54 differential metabolites between the PD vs PR, PD vs SD, and SD vs PR groups, respectively. A Venn diagram was constructed to demonstrate the relationships among groups of differential metabolites ( Figure 3a ). The intersection of the diagram indicated that the organic acid 3,4,5-trimethoxycinnamic acid was present in three groups of differential metabolites.

            Figure 3 |

            Differential metabolites among groups.

            On the basis of the differential metabolites, we used a logistic regression model and ROC diagnostic curve to identify the optimal metabolites for stratification of patients with differing chemotherapeutic responses. The Akaike information criterion was used to screen the best metabolite combination, with lower values indicating better model fit. The PD vs SD, SD vs PR, and PD vs PR groups were well separated by cis-9,10-epoxystearic acid/octapentaenoic acid (AUC 0.9330), salicyluric acid/DG (18:1/20:5/0:0) (AUC 1.0000) and D-glyceric acid/9,12-octadecadienoic acid (AUC 1.0000), respectively ( Figure 3b ). Specifically, cis-9,10-epoxystearic acid and octapentaenoic acid were up-regulated in the PD group vs the SD group (p<0.05). Similarly, salicyluric acid/DG (18:1/20:5/0:0) was up-regulated in the SD group vs the PR group (p<0.05). D-glyceric acid levels were significantly higher in the PR group than the PD group (p<0.01); however 9,12-octadecadienoic acid levels were non-significantly lower in the PR group ( Figure 3c ).

            4. DISCUSSION

            Albumin-bound chemotherapeutic agents, a class of drugs used to treat cancer, have many advantages such as higher efficacy and lower toxicity than their solvent-based counterparts [10, 11]. Nab-paclitaxel, a nanoparticle conjugate of paclitaxel and human albumin, was the first clinically successful albumin-bound chemotherapeutic agent. Nab-paclitaxel has shown efficacy in lung cancer, pancreatic cancer and breast cancer [10, 12, 13]. Langer has found that nab-paclitaxel combined with carboplatin, as a first-line treatment for advanced NSCLC, is superior to the traditional solvent-based paclitaxel combined with carboplatin, and has higher safety [14]. These findings might be explained by higher administration concentrations of nab-paclitaxel and higher serum concentrations of free paclitaxel enhancing anti-tumor activity [15, 16]. However, not all patients respond to nab-paclitaxel, and biomarkers predicting which patients benefit most from its this treatment remain lacking [17].

            Personalized medicine is the selection of medicines for subgroups of patients or individual patients, with the aim of maximizing treatment efficacy and minimizing toxicity [18, 19]. Metabolomics is a method of analyzing the levels of metabolites in biological fluids or tissues, to predict the benefits and toxicity of drug interventions [20]. In past decades, metabolomics was used primarily for early screening and diagnosis of cancer [21]. However, studies are increasingly using metabolomics to analyze chemotherapy efficacy in patients with cancer [20, 22, 23]. For example, lipids and high-sensitivity C-reactive protein have been shown to be important predictive biomarkers of lung cancer risk [22, 24]. Moreover, glycodeoxycholic acid and glycocholic acid have been found to have excellent sensitivity and specificity in predicting the efficacy of anlotinib in patients with lung cancer. Targeted metabolomic analysis has also identified NG-dimethylarginine as the most promising biomarker for the prediction of proteinuria occurrence after anlotinib treatment [20]. Additionally, a study has demonstrated that metabolites in the citric acid cycle, glutamate metabolism and amino acid metabolism can be used to predict the efficacy of platinum-based chemotherapy in patients with NSCLC, with high sensitivity and specificity [23].

            Although metabolic alternations are recognized as typical hallmarks of cancer, changes in lipid metabolism remain poorly understood. Lipids are a class of molecules with various types of structures, including TG, DG, cholesterol, phospholipids, fatty acids and sphingolipids. These lipids are widely distributed in organelles and play key roles in the construction of all membranes [25]. In addition, lipids act as second messengers that transduce signals in various organelles. Moreover, lipids serve as important energy sources when nutrients are limited [26]. Dysregulation and reprogramming of lipid metabolism have been shown to lead to the progression of malignancy, and targeting the regulation of lipid metabolism has emerged as a novel anti-cancer strategy [27].

            Herein, we used both metabolic and lipidomic analysis to comprehensively screen for differential metabolites among patients with LUSC with differing treatment responses. The baseline levels of some amino acid, bile acid, nucleotide and organic acid compounds differed among patient groups with differing responses. Our lipidomic analysis indicated that certain types of plasma lipids, particularly TG and phosphatidylethanolamine, differ among PD, SD and PR populations. Interestingly, the levels of various TG species were markedly higher in in responders than non-responders. A recent clinical case report has indicated a close association between paclitaxel and serum TG levels [28]. Specifically, the patients developed severe hypertriglyceridemia after being administered paclitaxel and carboplatin, but their TG levels returned to normal after a change to gemcitabine and carboplatin. Paclitaxel is believed to induce high serum TG levels. Moreover, Wang et al. have reported that paclitaxel-based chemotherapy results in transient hypertriglyceridemia in patients with lung cancer and have suggested that serum TG must be monitored during paclitaxel-based chemotherapy [29]. Inci et al. have demonstrated that paclitaxel leads to hepatic steatosis in patients with cancer [30]. Therefore, paclitaxel-induced hypertriglyceridemia is frequently seen in clinical settings. However a systematic study of this phenomenon remains lacking. On the basis of our findings, we suggest that elevated serum TG may be a temporary response in patients with good therapeutic effects. Elevated TG levels might be necessary to provide energy for organisms to inhibit tumor growth. However, more detailed exploration is required to confirm this hypothesis.

            In the enriched pathway analysis, terpenoid backbone biosynthesis (pyruvate) and pentose and glucuronate interconversions (2-oxoglutarate and pyruvate) were found to significantly differ between responders and non-responders. Pyruvate, an antioxidant, attenuates paclitaxel-mediated DNA damage and sub-G1 cells [31]. Pyruvate metabolism is closely associated with cancer therapy, and inhibition of pyruvate dehydrogenase kinase has been shown to be a therapeutic anti-cancer strategy [32]. Aleshin et al. have found that the metabolism of 2-oxoglutarate affects chemotherapeutic efficiency in lung cancer via the p53/p21 axis and the tricarboxylic acid cycle [33]. In this study, we observed potential effects of 2-oxoglutarate and pyruvate on paclitaxel chemotherapy; however, their molecular mechanisms must be further verified and elucidated.

            This study has several limitations. First, this was an exploratory experiment in a small group of participants; consequently, further verification in larger cohorts is required. Second, additional experiments are required to explore potential mechanisms that may explain our findings. Finally, tissue metabolomics will be an essential future direction to identify potential biomarkers derived from tumor tissue secretions.

            Supplementary Material

            Supplementary Material can be downloaded here


            The authors declare no conflicts of interest.


            1. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Piñeros M, et al.. Estimating the Global Cancer Incidence and Mortality in 2018: GLOBOCAN Sources and Methods. International Journal of Cancer. 2019. Vol. 144:1941–1953

            2. Song P, Yang D, Wang H, Cui X, Si X, Zhang X, et al.. Relationship Between Intestinal Flora Structure and Metabolite Analysis and Immunotherapy Efficacy in Chinese NSCLC patients. Thoracic Cancer. 2020. Vol. 11:1621–1632

            3. Tan H, Hu J, Liu S. Efficacy and Safety of Nanoparticle Albumin-Bound Paclitaxel in Non-Small Cell Lung Cancer: A Systematic review and Meta-Analysis. Artificial Cells, Nanomedicine and Biotechnology. 2019. Vol. 47:268–277

            4. Adrianzen Herrera D, Ashai N, Perez-Soler R, Cheng H. Nanoparticle Albumin Bound-Paclitaxel for Treatment of Advanced Non-Small Cell Lung Cancer: An Evaluation of the Clinical Evidence. Expert Opinion on Pharmacotherapy. 2019. Vol. 20:95–102

            5. Fujita T, Hiroishi T, Shikano K, Yanagisawa A, Hayama N, Amano H, et al.. The Safety and Efficacy of Treatment with Nab-paclitaxel and Carboplatin for Patients with Advanced Squamous Non-small Cell Lung Cancer Concurrent with Idiopathic Interstitial Pneumonias. Internal Medicine. 2018. Vol. 57:1827–1832

            6. Simon GR. nab-Paclitaxel for the Treatment of Advanced Squamous Non-Small-Cell Lung Cancer: A Comprehensive Update. Clinical Lung Cancer. 2014. Vol. 15:391–397

            7. Du Preez I, Loots DT. Novel Insights into the Pharmacometabonomics of First-Line Tuberculosis Drugs Relating to Metabolism, Mechanism of Action and Drug-Resistance. Drug Metabolism Reviews. 2018. Vol. 50:466–481

            8. Sun J, Beger RD, Schnackenberg LK. Metabolomics as a Tool for Personalizing Medicine: 2012 Update. Personalized Medicine. 2013. Vol. 10:149–161

            9. Cao P, Wu S, Guo W, Zhang Q, Gong W, Li Q, et al.. Precise Pathological Classification of Non-Small Cell Lung Adenocarcinoma and Squamous Carcinoma Based on an Integrated Platform of Targeted Metabolome and Lipidome. Metabolomics. 2021. Vol. 17:98

            10. Schettini F, Giuliano M, De Placido S, Arpino G. Nab-Paclitaxel for the Treatment of Triple-Negative Breast Cancer: Rationale, Clinical Data and Future Perspectives. Cancer Treatment Reviews. 2016. Vol. 50:129–141

            11. Hirsh V, Ko A, Pilot R, Renschler MF, Socinski MA. Weekly nab-Paclitaxel in Combination With Carboplatin as First-Line Therapy in Patients With Advanced Non-Small-Cell Lung Cancer: Analysis of Safety and Efficacy in Patients With Diabetes. Clinical Lung Cancer. 2016. Vol. 17:367–374

            12. Kim B, Seo B, Park S, Lee C, Kim JO, Oh KT, et al.. Albumin Nanoparticles with Synergistic Antitumor Efficacy against Metastatic Lung Cancers. Colloids and Surfaces B: Biointerfaces. 2017. Vol. 158:157–166

            13. Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, et al.. Increased Survival in Pancreatic Cancer with nab-Paclitaxel Plus Gemcitabine. The New England Journal of Medicine. 2013. Vol. 369:1691–1703

            14. Langer CJ, Hirsh V, Ko A, Renschler MF, Socinski MA. Weekly nab-Paclitaxel in Combination with Carboplatin as First-Line Therapy in Patients with Advanced Non-Small-Cell Lung Cancer: Analysis of Safety and Efficacy in Patients with Renal Impairment. Clinical Lung Cancer. 2015. Vol. 16:112–120

            15. Chen N, Li Y, Ye Y, Palmisano M, Chopra R, Zhou S, et al.. Pharmacokinetics and Pharmacodynamics of nab-Paclitaxel in Patients with Solid Tumors: Disposition Kinetics and Pharmacology Distinct from Solvent-Based Paclitaxel. Journal of Clinical Pharmacology. 2014. Vol. 54:1097–1107

            16. Gardner ER, Dahut WL, Scripture CD, Jones J, Aragon-Ching JB, Desai N, et al.. Randomized Crossover Pharmacokinetic Study of Solvent-Based Paclitaxel and nab-Paclitaxel. Clinical Cancer Research. 2008. Vol. 14:4200–4205

            17. Chatterjee M, Ben-Josef E, Robb R, Vedaie M, Seum S, Thirumoorthy K, et al.. Caveolae-Mediated Endocytosis Is Critical for Albumin Cellular Uptake and Response to Albumin-Bound Chemotherapy. Cancer Research. 2017. Vol. 77:5925–5937

            18. Everett JR. From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine. Frontiers in Pharmacology. 2016. Vol. 7:297

            19. Everett JR. Pharmacometabonomics in Humans: A New Tool for Personalized Medicine. Pharmacogenomics. 2015. Vol. 16:737–754

            20. Hu T, An Z, Sun Y, Wang X, Du P, Li P, et al.. Longitudinal Pharmacometabonomics for Predicting Malignant Tumor Patient Responses to Anlotinib Therapy: Phenotype, Efficacy, and Toxicity. Frontiers in Oncology. 2020. Vol. 10:548300

            21. Luo X, Liu J, Wang H, Lu H. Metabolomics Identified New Biomarkers for the Precise Diagnosis of Pancreatic Cancer and Associated Tissue Metastasis. Pharmacol Research. 2020. Vol. 156:104805

            22. Lyu Z, Li N, Chen S, Wang G, Tan F, Feng X, et al.. Risk Prediction Model for Lung Cancer Incorporating Metabolic Markers: Development and Internal Validation in a Chinese Population. Cancer Medicine. 2020. Vol. 9:3983–3994

            23. Peng F, Liu Y, He C, Kong Y, Ouyang Q, Xie X, et al.. Prediction of Platinum-Based Chemotherapy Efficacy in Lung Cancer Based on LC-MS Metabolomics Approach. Journal of Pharmaceutical and Biomedical Analysis. 2018. Vol. 154:95–101

            24. Lyu Z, Li N, Wang G, Feng X, Chen S, Su K, et al.. Independent and Joint Associations of Blood Lipids and Lipoproteins with Lung Cancer Risk in Chinese Males: A Prospective Cohort Study. International Journal of Cancer. 2019. Vol. 144:2972–2984

            25. Holthuis JC, Menon AK. Lipid Landscapes and Pipelines in Membrane Homeostasis. Nature. 2014. Vol. 510:48–57

            26. Efeyan A, Comb WC, Sabatini DM. Nutrient-Sensing Mechanisms and Pathways. Nature. 2015. Vol. 517:302–310

            27. Cheng C, Geng F, Cheng X, Guo D. Lipid Metabolism Reprogramming and its Potential Targets in Cancer. Cancer Communications (London). 2018. Vol. 38:27

            28. Koneshamoorthy A, Hulse D, Yuen Chong C, Krishnamurthy B, Ananda S, Hamblin PS. Massive Hypertriglyceridemia Associated with Paclitaxel; A Case Report. Gynecologic Oncology Reports. 2022. Vol. 42:101030

            29. Wang G, Su C, Yin T. Paclitaxel and Platinum-Based Chemotherapy Results in Transient Dyslipidemia in Cancer Patients. Molecular and Clinical Oncology. 2017. Vol. 6:261–265

            30. Inci F, Karatas F. Paclitaxel-Induced Hepatic Steatosis in Patients with Breast Cancer. Journal of BUON. 2019. Vol. 24:2355–2360

            31. Ramanathan B, Jan KY, Chen CH, Hour TC, Yu HJ, Pu YS. Resistance to Paclitaxel is Proportional to Cellular Total Antioxidant Capacity. Cancer Research. 2005. Vol. 65:8455–8460

            32. Sradhanjali S, Reddy MM. Inhibition of Pyruvate Dehydrogenase Kinase as a Therapeutic Strategy against Cancer. Current Topics in Medicinal Chemistry. 2018. Vol. 18:444–453

            33. Aleshin VA, Zhou X, Krishnan S, Karlsson A, Bunik VI. Interplay Between Thiamine and p53/p21 Axes Affects Antiproliferative Action of Cisplatin in Lung Adenocarcinoma Cells by Changing Metabolism of 2-Oxoglutarate/Glutamate. Frontiers in Genetics. 2021. Vol. 12:658446

            Graphical abstract

            • A total of 1141 kinds of endogenous metabolites were detected in the serum of participants.

            • Between the progressive disease (PD) vs partial response (PR), PD vs stable disease (SD), and SD vs PR groups, 61, 81, and 54 differential metabolites were identified, respectively.

            • The PD vs SD, SD vs PR, and PD vs PR groups were well separated by the combination of cis-9,10-epoxystearic acid and octapentaenoic acid, salicyluric acid and DG (18:1/20:5/0:0), and D-glyceric acid and 9,12-octadecadienoic acid, respectively.

            In brief

            In this study, 32 patients with lung squamous cell carcinoma (LUSC) who were treated with nab-paclitaxel were included to assess their chemotherapy response. Metabolomics studies identified 61, 81, and 54 different metabolites between patients with progressive disease (PD) and partial remission (PR), PD and stable disease (SD), and SD and PR groups, respectively

            Author and article information

            Acta Materia Medica
            Compuscript (Ireland )
            29 September 2023
            : 2
            : 3
            : 347-356
            [a ]Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
            [b ]Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan 430022, China
            [c ]Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan, Hubei 442000, China
            [d ]Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
            [e ]Mater Research Institute–The University of Queensland, TRI, Brisbane, Queensland, Australia
            Author notes
            *Correspondence: zhangwkp@ 123456163.com (Y. Zhang)

            1Equal contribution to this work.

            Copyright © 2023 The Authors.

            Creative Commons Attribution 4.0 International License

            : 13 July 2023
            : 04 August 2023
            : 09 September 2023
            Page count
            Figures: 3, Tables: 2, References: 33, Pages: 10
            Funded by: National Key R&D Program of China
            Award ID: 2017YFC0909900 to Prof. Yu Zhang
            Funded by: National Natural Science Foundation of China
            Award ID: 81903901 to Dr. Peng Cao
            Funded by: Hubei Provincial Natural Science Foundation
            Award ID: Dr. Peng Cao
            Funded by: Dawning Program of Wuhan Knowledge Innovation Special Project
            Award ID: 2022020801020467 to Dr. Peng Cao
            Funded by: Open Project Funding of Hubei Key Laboratory of Biological Targeted Therapy
            Award ID: F016.02004.22001.079 to Dr. Peng Cao
            Funded by: Hubei University of Medicine
            Award ID: WDCM2023003 to Dr. Peng Cao
            This study was supported by the National Key R&D Program of China (2017YFC0909900 to Prof. Yu Zhang), the National Natural Science Foundation of China (grant No. 81903901 to Dr. Peng Cao), the Hubei Provincial Natural Science Foundation (Dr. Peng Cao), the Dawning Program of Wuhan Knowledge Innovation Special Project (grant No. 2022020801020467 to Dr. Peng Cao), Open Project Funding of Hubei Key Laboratory of Biological Targeted Therapy (F016.02004.22001.079 to Dr. Peng Cao) and the Open Project of Hubei Key Laboratory of Wudang Local Chinese Medicine Research (Hubei University of Medicine, grant No. WDCM2023003 to Dr. Peng Cao). Mitchell Sullivan is supported by an Advance Queensland Industry Research Fellowship and Mater Foundation (Equity Trustees and the L G McCallam Est and George Weaber Trusts). We sincerely thank Dr. Hui Zheng and Mr. Xi Zhan from Wuhan Metware Biotechnology Co., Ltd., for technical support.
            Short Communication

            Toxicology,Pathology,Biochemistry,Clinical chemistry,Pharmaceutical chemistry,Pharmacology & Pharmaceutical medicine
            lipidomics,metabolomics,lung cancer,biomarkers,paclitaxel


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