bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2020‒12‒27
two papers selected by
Cristina Muñoz Pinedo
L’Institut d’Investigació Biomèdica de Bellvitge

  1. PeerJ. 2020 ;8 e10320
      Background: Lung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression.Methods: The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used.
    Results: The six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms.
    Conclusions: Our study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.
    Keywords:   GEO; Lung adenocarcinoma; Metabolic signature; Overall survival; Prognostic model; TCGA
  2. Mol Cell. 2020 Dec 15. pii: S1097-2765(20)30827-3. [Epub ahead of print]
      In tumors, nutrient availability and metabolism are known to be important modulators of growth signaling. However, it remains elusive whether cancer cells that are growing out in the metastatic niche rely on the same nutrients and metabolic pathways to activate growth signaling as cancer cells within the primary tumor. We discovered that breast-cancer-derived lung metastases, but not the corresponding primary breast tumors, use the serine biosynthesis pathway to support mTORC1 growth signaling. Mechanistically, pyruvate uptake through Mct2 supported mTORC1 signaling by fueling serine biosynthesis-derived α-ketoglutarate production in breast-cancer-derived lung metastases. Consequently, expression of the serine biosynthesis enzyme PHGDH was required for sensitivity to the mTORC1 inhibitor rapamycin in breast-cancer-derived lung tumors, but not in primary breast tumors. In summary, we provide in vivo evidence that the metabolic and nutrient requirements to activate growth signaling differ between the lung metastatic niche and the primary breast cancer site.
    Keywords:  MCT2; PHGDH; breast cancer; lung environment; mTORC1; metastasis formation; pyruvate; serine biosynthesis; α-ketoglutarate