bims-meluca 2023-12-03 papers

Sci Rep. 2023 Nov 28. 13(1): 20948

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors.

Helen Pasternack, Mirjam Polzer, Timo Gemoll, Christiane Kümpers, Thorben Sauer, Pamela Lazar-Karsten, Sofie Hinrichs, Sabine Bohnet, Sven Perner, Franz Friedrich Dressler, Jutta Kirfel.

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

DOI: https://doi.org/10.1038/s41598-023-47767-5

Medicine (Baltimore). 2023 Nov 24. 102(47): e36267

Identification of metabolism-related gene signature in lung adenocarcinoma.

Ning Wang, Hui Wang.

AIM: Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.
METHODS: TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.
RESULTS: A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.
CONCLUSION: In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

DOI: https://doi.org/10.1097/MD.0000000000036267

Adv Mater. 2023 Nov 28. e2309094

A PD-L1-targeting Regulator for Metabolic Reprogramming to Enhance Glutamine Inhibition-mediated Synergistic Antitumor Metabolic And Immune Therapy.

Xiao-Kang Jin, Shi-Man Zhang, Jun-Long Liang, Shun-Kang Zhang, You-Teng Qin, Qian-Xiao Huang, Chuan-Jun Liu, Xian-Zheng Zhang.

Inhibition of glutamine metabolism in tumor cells can cause metabolic compensation-mediated glycolysis enhancement and PD-L1 upregulation-induced immune evasion, significantly limiting the therapeutic efficacy of glutamine inhibitors. Here, inspired by the specific binding of receptor and ligand, a PD-L1-targeting metabolism and immune regulator (PMIR) was constructed by decorating the glutaminase inhibitor (BPTES)-loading zeolitic imidazolate framework (ZIF) with PD-L1-targeting peptides for regulating the metabolism within the tumor microenvironment (TME) to improve immunotherapy. At tumor sites, PMIR inhibited glutamine metabolism of tumor cells for elevating glutamine levels within the TME to improve the function of immune cells. Ingeniously, the accompanying PD-L1 upregulation on tumor cells caused self-amplifying accumulation of PMIR through PD-L1 targeting, while also blocking PD-L1, which had the effects of converting enemies into friends. Meanwhile, PMIR exactly offset the compensatory glycolysis, while disrupting the redox homeostasis in tumor cells via the cooperation of components of the ZIF and BPTES. These together caused immunogenic cell death of tumor cells and relieved PD-L1-mediated immune evasion, further reshaping the immunosuppressive TME and evoking robust immune responses to effectively suppress bilateral tumor progression and metastasis. This work proposed a rational strategy to surmount the obstacles in glutamine inhibition for boosting existing clinical treatments. This article is protected by copyright. All rights reserved.

Keywords: Drug delivering; Glutamine inhibition; Metabolic reprogramming; Self-amplifying PD-L1 targeting; Tumor immunotherapy

DOI: https://doi.org/10.1002/adma.202309094

J Cell Mol Med. 2023 Nov 27.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma.

Qineng Luo, Xing Li, Zixing Meng, Hao Rong, Yanguo Li, Guofang Zhao, Huangkai Zhu, Lvjun Cen, Qi Liao.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

Keywords: DNA methylation; gene expression; hypoxia; lung adenocarcinoma; multi-omics; prognostic model; somatic mutation

DOI: https://doi.org/10.1111/jcmm.18032