bims-meluca 2024-07-21 papers

on Metabolism of non-small cell lung carcinoma

Issue of 2024–07–21
six papers selected by
the Muñoz-Pinedo/Nadal (PReTT) lab, L’Institut d’Investigació Biomèdica de Bellvitge

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells.
Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy.
Value of prognostic nutritional index and controlling nutritional status score for advanced non-small cell lung cancer patients receiving PD-1 inhibitors.
PRMT1 promotes Warburg effect by regulating the PKM2/PKM1 ratio in non-small cell lung cancer.
LncRNA FEZF1-AS1 facilitates cisplatin resistance in non-small cell lung cancer through modulating the miR-32-5p-glutaminase axis.
Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Sci Rep. 2024 Jul 17. 14(1): 16561

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells.

Yajing Chu, Dianlong Ge, Jijuan Zhou, Yue Liu, Xiangxue Zheng, Wenting Liu, Li Ke, Yan Lu, Yannan Chu.

  Characteristic volatile organic compounds (VOCs) are anticipated to be used for the identification of lung cancer cells. However, to date, consistent biomarkers of VOCs in lung cancer cells have not been obtained through direct comparison between cancer and healthy groups. In this study, we regulated the glycolysis, a common metabolic process in cancer cells, and employed solid phase microextraction gas chromatography mass spectrometry (SPME-GC-MS) combined with untargeted analysis to identify the characteristic VOCs shared by cancer cells. The VOCs released by three types of lung cancer cells (A549, PC-9, NCI-H460) and one normal lung epithelial cell (BEAS-2B) were detected using SPME-GC-MS, both in their resting state and after treatment with glycolysis inhibitors (2-Deoxy-D-glucose, 2-DG/3-Bromopyruvic acid, 3-BrPA). Untargeted analysis methods were employed to compare the VOC profiles between each type of cancer cell and normal cells before and after glycolysis regulation. Our findings revealed that compared to normal cells, the three types of lung cancer cells exhibited three common differential VOCs in their resting state: ethyl propionate, acetoin, and 3-decen-5-one. Furthermore, under glycolysis control, a single common differential VOC-acetoin was identified. Notably, acetoin levels increased by 2.60-3.29-fold in all three lung cancer cell lines upon the application of glycolysis inhibitors while remaining relatively stable in normal cells. To further elucidate the formation mechanism of acetoin, we investigated its production by blocking glutaminolysis. This interdisciplinary approach combining metabolic biochemistry with MS analysis through interventional synthetic VOCs holds great potential for revolutionizing the identification of lung cancer cells and paving the way for novel cytological examination techniques.

Keywords:  Cytological examination; Glycolysis regulation; Lung cancer; SPME–GC–MS; VOCs

DOI:  https://doi.org/10.1038/s41598-024-67379-x
World J Clin Cases. 2024 Jul 16. 12(20): 4091-4107

Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy.

Yu-Ning Li, Jia-Lin Su, Shu-Hua Tan, Xing-Long Chen, Tian-Li Cheng, Zhou Jiang, Yong-Zhong Luo, Le-Meng Zhang.

   BACKGROUND: Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments.
AIM: To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients.
METHODS: In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established.
RESULTS: First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy.
CONCLUSION: The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.

Keywords:  Chemoimmunotherapy; Machine learning; Metabolomics; Neutrophil extracellular traps; Non-small cell lung cancer

DOI:  https://doi.org/10.12998/wjcc.v12.i20.4091
Am J Cancer Res. 2024 ;14(6): 2894-2904

Value of prognostic nutritional index and controlling nutritional status score for advanced non-small cell lung cancer patients receiving PD-1 inhibitors.

Sicong Jiang, Xuefeng Wang, Yuxuan Xing, Jiahao Wu, Xiya Yuan.

   OBJECTIVE: To explore the value of preoperative prognostic nutritional index (PNI) and controlling nutritional status (CONUT) score in predicting response and prognosis of patients with advanced non-small cell lung cancer (NSCLC) receiving programmed cell death protein 1 (PD-1) inhibitors.
METHODS: A retrospective study was conducted in patients who received PD-1 inhibitors for advanced NSCLC. Patients were assigned by immunotherapy effects into response (partial and complete response, pCR) group (n=52) and non-response (non-pCR) group (n=132). The pathological and clinical data were collected for statistical analysis of factors influencing the immunotherapeutic response. The diagnostic value of PNI and CONUT score for response was assessed. The overall survival (OS) was observed over a 3-year follow-up. COX regression analysis was performed to identify risk factors affecting the survival. The effects of different PNI and CONUT scores on the survival were observed.
RESULTS: Multivariate regression analysis showed that, the tumor-node-metastasis (TNM) stage (P=0.001), PNI (P<0.001), and CONUT score (P<0.001) were associated with response. The non-pCR group had a higher 3-year mortality rate and a shorter 3-year OS than the pCR group (P<0.001). COX regression analysis showed that low PNI and high CONUT score were risk factors for poor prognosis. Further analysis showed that patients with low PNI and high CONUT score had lower 3-year survival rates (P=0.005, P<0.001).
CONCLUSION: High TNM stage, PNI<50, and CONUT score ≥5 are risk factors for poor response in patients with advanced NSCLC receiving PD-1 inhibitors, and low PNI and high CONUT score suggest poor prognosis.

Keywords:  Non-small cell lung cancer; PD-1 inhibitor; controlling nutritional status score; correlation; prognosis; prognostic nutritional index; response

DOI:  https://doi.org/10.62347/XQHL4852
Cell Death Dis. 2024 Jul 15. 15(7): 504

PRMT1 promotes Warburg effect by regulating the PKM2/PKM1 ratio in non-small cell lung cancer.

Lu Peng, Yujiao Zhao, Jiang Tan, Jingyao Hou, Xin Jin, Dong-Xu Liu, Baiqu Huang, Jun Lu.

Abnormal epigenetic modifications are involved in the regulation of Warburg effect in tumor cells. Protein arginine methyltransferases (PRMTs) mediate arginine methylation and have critical functions in cellular responses. PRMTs are deregulated in a variety of cancers, but their precise roles in Warburg effect in cancer is largely unknown. Experiments from the current study showed that PRMT1 was highly expressed under conditions of glucose sufficiency. PRMT1 induced an increase in the PKM2/PKM1 ratio through upregulation of PTBP1, in turn, promoting aerobic glycolysis in non-small cell lung cancer (NSCLC). The PRMT1 level in p53-deficient and p53-mutated NSCLC remained relatively unchanged while the expression was reduced in p53 wild-type NSCLC under conditions of glucose insufficiency. Notably, p53 activation under glucose-deficient conditions could suppress USP7 and further accelerate the polyubiquitin-dependent degradation of PRMT1. Melatonin, a hormone that inhibits glucose intake, markedly suppressed cell proliferation of p53 wild-type NSCLC, while a combination of melatonin and the USP7 inhibitor P5091 enhanced the anticancer activity in p53-deficient NSCLC. Our collective findings support a role of PRMT1 in the regulation of Warburg effect in NSCLC. Moreover, combination treatment with melatonin and the USP7 inhibitor showed good efficacy, providing a rationale for the development of PRMT1-based therapy to improve p53-deficient NSCLC outcomes.

DOI: https://doi.org/10.1038/s41419-024-06898-x
Am J Cancer Res. 2024 ;14(6): 3153-3170

LncRNA FEZF1-AS1 facilitates cisplatin resistance in non-small cell lung cancer through modulating the miR-32-5p-glutaminase axis.

Wei Lin, Wei-Chun Wu, Zhi Liang, Jian-Hao Zhang, Shi-Peng Fang.

  Non-small cell lung cancer (NSCLC) is one of the prevalent malignancies. Cisplatin (CDDP) is a conventional chemotherapeutic agent against NSCLC. However, inherent and acquired chemoresistance limited the effectiveness of cisplatin in treatment of NSCLC. This study aimed to investigate the roles and underlying mechanisms of lncRNA-FEZF1-AS1 in mediating cisplatin sensitivity in NSCLC. We found that FEZF1-AS1 levels were significantly higher in lung cancer patients and cell lines. Blocking FEZF1-AS1 sensitized lung cancer cells to cisplatin. Additionally, both glutamine metabolism and FEZF1-AS1 were significantly elevated in cisplatin resistant NSCLC cell lines, A549/CDDP R and SK-MES-1 CDDP/R. Analysis using bioinformatics, RNA pull-down assay and luciferase assay demonstrated that FEZF1-AS1 sponged miR-32-5p, which acted as a tumor suppressor in NSCLC. Glutaminase (GLS), a key enzyme in the glutamine metabolism, was predicted and validated as the direct target of miR-32-5p in NSCLC cells. Inhibiting glutamine metabolism or reducing glutamine supply effectively resensitized cisplatin-resistant cells. Furthermore, restoring miR-32-5p in FEZF1-AS1-overexpressing cisplatin resistant cells successfully overcame FEZF1-AS1-mediated cisplatin resistance by targeting GLS. These findings were further supported by in vivo xenograft mice experiments. This study uncovered the roles and molecular mechanisms of lncRNA FEZF1-AS1 in mediating cisplatin resistance in NSCLC, specifically through modulating the miR-32-5p-GLS axis, providing support for the development of new therapeutic approaches against chemoresistant lung cancer.

Keywords:  GLS; Non-small cell lung cancer; cisplatin resistance; glutamine metabolism; lncRNA-FEZF1-AS1; miR-32a-5p

DOI:  https://doi.org/10.62347/WUKN6549
Hum Mol Genet. 2024 Jul 16. pii: ddae110. [Epub ahead of print]

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Bo Dong, Mengyao Wang, Kaixiu Li, Zuwei Li, Lunxu Liu, Shensi Shen.

  Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

Keywords:  Mendelian randomization; Plasma proteometabolome; lung cancer; single-nucleotide polymorphisms

DOI:  https://doi.org/10.1093/hmg/ddae110