bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2020‒10‒18
three papers selected by
Cristina Muñoz Pinedo
L’Institut d’Investigació Biomèdica de Bellvitge

  1. Lipids Health Dis. 2020 Oct 13. 19(1): 222
    Li J, Li Q, Su Z, Sun Q, Zhao Y, Feng T, Jiang J, Zhang F, Ma H.
      BACKGROUND: Lung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD.METHODS: In this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213.
    RESULTS: A total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result.
    CONCLUSIONS: In this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.
    Keywords:  Diagnosis; Hub genes; Lipid metabolism; Lung adenocarcinoma; Nomogram; Prognosis; Signature
  2. Lung Cancer. 2020 Oct 10. pii: S0169-5002(20)30641-3. [Epub ahead of print]150 44-52
    Wang L, Ruan M, Lei B, Yan H, Sun X, Chang C, Liu L, Xie W.
      OBJECTIVES: To investigate the potential of 2-deoxy-2(18F)fluoro-d-glucose (18F-FDG) combined positron emission tomography and computed tomography (PET/CT) in predicting programmed cell death ligand-1 (PDL1) expression status in pulmonary lesions of advanced non-small-cell lung cancer (NSCLC).MATERIALS AND METHODS: This retrospective study includes 133 untreated stage IIIB-IV NSCLC patients who underwent pulmonary lesion biopsy for PDL1 immunochemistry 1-4 weeks after 18F-FDG PET/CT scanning, randomly assigned to cohorts for modelling and validation of PDL1 expression predictors. Mean and maximum standard uptake values (pSUVmean and pSUVmax), metabolic tumour volume (pMTV), and total lesion glycolysis (pTLG) of primary lesions were determined. PDL1 expression in pulmonary lesions (pPDL1) was determined using tumour proportion score (TPS), and pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % were considered as pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively.
    RESULTS: pSUVmean and pSUVmax values were increased with the increase of pPDL1 levels, whereas pMTV and pTLG values were not associated with pPDL1 levels. In the modelling cohort, we found that pSUVmax rather than pSUVmean was an independent predictor for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, whereas pSUVmax < 14.4, 14.4-17.5, and > 17.5 were suggested as predictors for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (odds ratio: 4.82, 3.92, and 4.45, respectively; P = 0.002, 0.021, and 0.020, respectively). In the validation cohort, pSUVmax < 14.4, 14.4-17.5, and > 17.5 showed significantly high probabilities of being pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (P = 0.006). The accuracies of pSUVmax < 14.4, 14.4-17.5, and > 17.5 predicting pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively, in validation cohort, were 66.7 %, 75.8 %, and 84.8 %, respectively.
    CONCLUSION: pSUVmax on 18F-FDG PET/CT is a potential biomarker for pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % in untreated stage IIIB-IV NSCLC, and therefore may be helpful for determining immunotherapeutic strategy for advanced NSCLC.
    Keywords:  (18)F-FDG PET/CT; Immunotherapy; NSCLC; PDL1; SUVmax
  3. Cancers (Basel). 2020 Oct 11. pii: E2917. [Epub ahead of print]12(10):
    Tse SW, Tan CF, Park JE, Gnanasekaran J, Gupta N, Low JK, Yeoh KW, Chng WJ, Tay CY, McCarthy NE, Lim SK, Sze SK.
      Extracellular vesicles (EVs) mediate critical intercellular communication within healthy tissues, but are also exploited by tumour cells to promote angiogenesis, metastasis, and host immunosuppression under hypoxic stress. We hypothesize that hypoxic tumours synthesize hypoxia-sensitive proteins for packing into EVs to modulate their microenvironment for cancer progression. In the current report, we employed a heavy isotope pulse/trace quantitative proteomic approach to study hypoxia sensitive proteins in tumour-derived EVs protein. The results revealed that hypoxia stimulated cells to synthesize EVs proteins involved in enhancing tumour cell proliferation (NRSN2, WISP2, SPRX1, LCK), metastasis (GOLM1, STC1, MGAT5B), stemness (STC1, TMEM59), angiogenesis (ANGPTL4), and suppressing host immunity (CD70). In addition, functional clustering analyses revealed that tumour hypoxia was strongly associated with rapid synthesis and EV loading of lysosome-related hydrolases and membrane-trafficking proteins to enhance EVs secretion. Moreover, lung cancer-derived EVs were also enriched in signalling molecules capable of inducing epithelial-mesenchymal transition in recipient cancer cells to promote their migration and invasion. Together, these data indicate that lung-cancer-derived EVs can act as paracrine/autocrine mediators of tumorigenesis and metastasis in hypoxic microenvironments. Tumour EVs may, therefore, offer novel opportunities for useful biomarkers discovery and therapeutic targeting of different cancer types and at different stages according to microenvironmental conditions.
    Keywords:  epithelial–mesenchymal transition; extracellular vesicles; hypoxia; pulsed-SILAC; quantitative proteomics; tumorigenesis; tumour microenvironment