Identifying metabolic reprogramming phenotypes with glycolysis-lipid metabolism discoordination and intercellular communication for lung adenocarcinoma metastasis

Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

Deciphering two decades of cellular reprogramming in cancer: A bibliometric analysis of evolving trends and research frontiers

Recent research has reevaluated the traditional view of cancer's linear progression and recurrence by introducing cellular reprogramming a process in which cancer cells can their state under certain conditions. This change is driven by a combination of genetic and epigenetic factors, with pivotal roles played by key genes, and pathways, notably Wnt and Notch. The complexity of cancer's behavior is further influenced by factors such as the epithelial-mesenchymal transition (EMT) and therapy-induced stress, both of which are significant contributors to cancer recurrence. In this context bibliometric analysis emerges as a crucial tool for evaluating the impacts and trends within scientific literature. Our study utilized bibliometrics to analysis the role of cellular reprogramming oncology over the past two decades, highlighting its potential to improve cancer treatment outcomes. In conducting this analysis, we searched for literature search on cellular reprogramming (CR) in the Web of Science database, covering the years 2002-2022. We employed visualization tools like Citespace, VOSviewer, and Bibliometrix to analyze the collected data resulting in a dataset of 3102 articles. The United States and China emerged as leading contributors to this field, with the University of Texas MD Anderson Cancer Center being the most prolific institution. Menendez was the most influential scholar in this research domain. Cancers was the journal with the most publications on this subject. The most local-cited document was the article titled "Hallmarks of Cancer: The Next Generation". A comprehensive analysis has been conducted based on keywords and cited references. In recent years, the research emphasis has shifted to "extracellular vesicles," "cancer therapy," and "cellular plasticity". Therefore, this analysis uses bibliometrics to chart cutting-edge progress in cancer's cellular reprogramming, aiding experts to quickly understand and innovate in this crucial area.

Enigmatic exosomal connection in lung cancer drug resistance

Lung cancer remains a significant global health concern with limited treatment options and poor prognosis, particularly in advanced stages. Small extracellular vesicles such as exosomes, secreted by cancer cells, play a pivotal role in mediating drug resistance in lung cancer. Exosomes have been found to facilitate intercellular communication by transferring various biomolecules between cancer cells and their microenvironment. Additionally, exosomes can transport signaling molecules promoting cancer cell survival and proliferation conferring resistance to chemotherapy. Moreover, exosomes can modulate the tumor microenvironment by inducing phenotypic changes hindering drug response. Understanding the role of exosomes in mediating drug resistance in lung cancer is crucial for developing novel therapeutic strategies and biomarkers to overcome treatment limitations. In this review, we summarize the current knowledge on conventional and emerging drug resistance mechanisms and the involvement of exosomes as well as exosome-mediated factors mediating drug resistance in lung cancer.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

BZW2 Modulates Lung Adenocarcinoma Progression through Glycolysis-Mediated IDH3G Lactylation Modification

Histone lactylation (Hla) is a metabolically stress-related histone modification that featured in specific gene expression regulation. However, the role of Hla in the pathogenesis of lung adenocarcinoma (LUAD) remains unexplored. Through bioinformatics analysis, we found that BZW2 exhibited an elevated level of expression in LUAD tissues, which was associated with a poor prognosis. Flow cytometry and TUNEL assay were used to analyze the apoptosis of LUAD cells and tissues, respectively. The effect of the cell function experiment on the LUAD cell phenotype was analyzed. An XF 96 Extracellular Flux Analyzer measured the ECAR value, and kits were used to detect lactate production and glucose consumption. Animal experiments were performed for further verification. Cell experiments showed that BZW2 fostered the malignant progression of LUAD by promoting glycolysis-mediated lactate production and lactylation of IDH3G. In a compelling in vivo validation, the inhibition of Hla could suppress the malignant progression of LUAD. Knockdown of BZW2 combined with 2-DG treatment significantly repressed tumor growth in mice. BZW2 could regulate the progression of LUAD through glycolysis-mediated IDH3G lactylation, offering a theoretical basis for the targeted treatment of LUAD with glycolysis and Hla.

SETD2 deficiency accelerates sphingomyelin accumulation and promotes the development of renal cancer

Patients with polycystic kidney disease (PKD) encounter a high risk of clear cell renal cell carcinoma (ccRCC), a malignant tumor with dysregulated lipid metabolism. SET domain-containing 2 (SETD2) has been identified as an important tumor suppressor and an immunosuppressor in ccRCC. However, the role of SETD2 in ccRCC generation in PKD remains largely unexplored. Herein, we perform metabolomics, lipidomics, transcriptomics and proteomics within SETD2 loss induced PKD-ccRCC transition mouse model. Our analyses show that SETD2 loss causes extensive metabolic reprogramming events that eventually results in enhanced sphingomyelin biosynthesis and tumorigenesis. Clinical ccRCC patient specimens further confirm the abnormal metabolic reprogramming and sphingomyelin accumulation. Tumor symptom caused by Setd2 knockout is relieved by myriocin, a selective inhibitor of serine-palmitoyl-transferase and sphingomyelin biosynthesis. Our results reveal that SETD2 deficiency promotes large-scale metabolic reprogramming and sphingomyelin biosynthesis during PKD-ccRCC transition. This study introduces high-quality multi-omics resources and uncovers a regulatory mechanism of SETD2 on lipid metabolism during tumorigenesis.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Transcriptome data analysis of primary cardiomyopathies reveals perturbations in arachidonic acid metabolism

Introduction

Cardiomyopathies are complex heart diseases with significant prevalence around the world. Among these, primary forms are the major contributors to heart failure and sudden cardiac death. As a high-energy demanding engine, the heart utilizes fatty acids, glucose, amino acid, lactate and ketone bodies for energy to meet its requirement. However, continuous myocardial stress and cardiomyopathies drive towards metabolic impairment that advances heart failure (HF) pathogenesis. So far, metabolic profile correlation across different cardiomyopathies remains poorly understood.

Methods

In this study, we systematically explore metabolic differences amongst primary cardiomyopathies. By assessing the metabolic gene expression of all primary cardiomyopathies, we highlight the significantly shared and distinct metabolic pathways that may represent specialized adaptations to unique cellular demands. We utilized publicly available RNA-seq datasets to profile global changes in the above diseases (|log2FC| ≥ 0.28 and BH adjusted p-val 0.1) and performed gene set analysis (GSA) using the PAGE statistics on KEGG pathways.

Results

Our analysis demonstrates that genes in arachidonic acid metabolism (AA) are significantly perturbed across cardiomyopathies. In particular, the arachidonic acid metabolism gene PLA2G2A interacts with fibroblast marker genes and can potentially influence fibrosis during cardiomyopathy.

Conclusion

The profound significance of AA metabolism within the cardiovascular system renders it a key player in modulating the phenotypes of cardiomyopathies.

Comprehensive analysis of the significance of METTL7A gene in the prognosis of lung adenocarcinoma

Background

The most common subtype of lung cancer, called lung adenocarcinoma (LUAD), is also the largest cause of cancer death in the world. The aim of this study was to determine the importance of the METTL7A gene in the prognosis of patients with LUAD.

Methods

This particular study used a total of four different LUAD datasets, namely TCGA-LUAD, GSE32863, GSE31210 and GSE13213. Using RT-qPCR, we were able to determine METTL7A expression levels in clinical samples. Univariate and multivariate Cox regression analyses were used to identify factors with independent effects on prognosis in patients with LUAD, and nomograms were designed to predict survival in these patients. Using gene set variation analysis (GSVA), we investigated differences in enriched pathways between METTL7A high and low expression groups. Microenvironmental cell population counter (MCP-counter) and single-sample gene set enrichment analysis (ssGSEA) methods were used to study immune infiltration in LUAD samples. Using the ESTIMATE technique, we were able to determine the immune score, stromal score, and estimated score for each LUAD patient. A competing endogenous RNA network, also known as ceRNA, was established with the help of the Cytoscape program.

Results

We detected that METTL7A was down-regulated in pan-cancer, including LUAD. The survival study indicates that METTL7A was a protective factor in the prognosis of LUAD. The univariate and multivariate Cox regression analyses revealed that METTL7A was a robust independent prognostic indicator in survival prediction. Through the use of GSVA, several immune-related pathways were shown to be enriched in both the high-expression and low-expression groups of METTL7A. Analysis of the tumor microenvironment revealed that the immune microenvironment of the group with low expression was suppressed, which may be connected to the poor prognosis. To explore the ceRNA regulatory mechanism of METTL7A, we finally constructed a regulatory network containing 1 mRNA, 2 miRNAs, and 5 long non-coding RNAs (lncRNAs).

Conclusion

In conclusion, we presented METTL7A as a potential and promising prognostic indicator of LUAD. This biomarker has the potential to offer us with a comprehensive perspective of the prediction of prognosis and treatment for LUAD patients.