One carbon metabolism in human lung cancer

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells

Background

The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.

Method

Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.

Results

Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.

Conclusion

The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Serine synthesis and catabolism in starved lung cancer and primary bronchial epithelial cells

Serine and glycine give rise to important building blocks in proliferating cells. Both amino acids are either synthesized de novo or taken up from the extracellular space. In lung cancer, serine synthesis gene expression is variable, yet, expression of the initial enzyme, phosphoglycerate dehydrogenase (PHGDH), was found to be associated with poor prognosis. While the contribution of de novo synthesis to serine pools has been shown to be enhanced by serine starvation, the impact of glucose deprivation, a commonly found condition in solid cancers is poorly understood. Here, we utilized a stable isotopic tracing approach to assess serine and glycine de novo synthesis and uptake in different lung cancer cell lines and normal bronchial epithelial cells in variable serine, glycine, and glucose conditions. Under low glucose supplementation (0.2 mM, 3-5% of normal plasma levels), serine de novo synthesis was maintained or even activated. As previously reported, also gluconeogenesis supplied carbons from glutamine to serine and glycine under these conditions. Unexpectedly, low glucose treatment consistently enhanced serine to glycine conversion, along with an up-regulation of the mitochondrial one-carbon metabolism enzymes, serine hydroxymethyltransferase (SHMT2) and methylenetetrahydrofolate dehydrogenase (MTHFD2). The relative contribution of de novo synthesis greatly increased in low serine/glycine conditions. In bronchial epithelial cells, adaptations occurred in a similar fashion as in cancer cells, but serine synthesis and serine to glycine conversion, as assessed by label enrichments and gene expression levels, were generally lower than in (PHGDH positive) cancer cells. In summary, we found a variable contribution of glucose or non-glucose carbon sources to serine and glycine and a high adaptability of the downstream one-carbon metabolism pathway to variable glucose supply.

Developing technologies and areas of interest in lung cancer screening adjuncts

Lung cancer remains the leading cause of cancer mortality. Screening guidelines have been implemented in the past decade to aid in earlier detection of at-risk groups. Nevertheless, computed tomography (CT) scans, the principal screening modality in use today, are still low yield, with 3.6% of lung cancer confirmed amongst 39.1% of lesions detected over a 3-year period. They also carry relatively high false positive rates, between 9% and 27%, which can bear unnecessary financial and emotional costs to patients. As such, research efforts have been dedicated to the development of lung cancer screening adjuncts to improve detection reliability. We herein review several emerging technologies in this specific arena and their efficacy. These include plasma markers (microDNA, DNA methylation, and tumor-associated antibodies), breath/sputum biomarkers [volatile organic compounds (VOCs) and exhaled breath condensate (EBC)], proteomics, metabolomics, and machine learning, such as radiomics technology. We find that, across the board, they offer promising results in terms of non-invasive diagnostics, genetic sequencing for higher-risk individuals, and accessibility for a diverse cohort of patients. While these screening adjuncts are unlikely to completely replace the current standard of care at the moment, continued research into these technologies is crucial to improve and personalize the identification, treatment, and outcome of lung cancer patients in the near future.

Implications of MTHFD2 expression in renal cell carcinoma aggressiveness

Renal cell carcinoma (RCC) is the most common type of cancer in kidney and is often diagnosed in advanced stages. Until now, there is no reliable biomarker to assess tumor prognosis during histopathological diagnosis. The Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) overexpression has been suggested as prognostic indicator for RCC, however, its protein profile needs to be clarified. This study investigated the MTHFD2 expression in different RCC cohorts, associating it with tumor characteristics and prognostic factors. Gene expression comparisons between non-neoplastic (NN) and tumor samples, as well as patients' survival analysis, were assessed using KM-Plotter tool. MTHFD2 protein pattern was evaluated in 117 RCC by immunohistochemistry and associations with prognosis, clinical and pathological data were investigated. The tumors exhibited higher MTHFD2 transcript levels than NN, being even higher in the metastatic group. Opposite gene expression patterns were found among clear cell renal cell carcinoma (ccRCC) and pappilary renal cell carcinoma (pRCC) subtypes, showing higher and lower expressions compared to NN samples respectively. Overexpression was associated with shorter overall survival for ccRCC and pRCC subtypes, and shorter recurrence-free survival for pRCC. The immunolabeling profile varied according to tumor subtypes, with lower intensity and expression scores in ccRCC compared to pRCC and to chromophobe renal cell carcinoma (chRCC). MTHFD2 protein expression was associated with larger tumors and higher Fuhrman grades. Although prognostic value of protein immunostaining was not confirmed, patients with higher MTHFD2 tended to have lower survival rates in the pRCC group. The results highlight MTHFD2 different patterns according to RCC histological subtypes, revealing marked variations at both the genetic and protein levels. The mRNA indicated tumor prognosis, and greater expression in the tumor samples. Although MTHFD2 immunolabeling suggests tumor aggressiveness, it needs to be validated in other cohorts as potential prognostic factor.

A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer

Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.

Total coumarins of Pileostegia tomentella induces cell death in SCLC by reprogramming metabolic patterns, possibly through attenuating β-catenin/AMPK/SIRT1

BACKGROUND

Small-cell lung cancer (SCLC) is a high malignant and high energy-consuming type of lung cancer. Total coumarins of Pileostegia tomentella (TCPT) from a traditional folk medicine of Yao minority, is a potential anti-cancer mixture against SCLC, but the pharmacological and molecular mechanism of TCPT remains largely unknown.

METHODS

Screening of viability inhibition of TCPT among 7 cell lines were conducted by using CCK-8 assays. Anti-proliferative activities of TCPT in SCLC were observed by using colony formation and flow cytometry assays. Morphological changes were observed by transmission electron microscope and Mito-Tracker staining. High Throughput RNA-seq analysis and bio-informatics analysis were applied to find potential targeted biological and signaling pathways affected by TCPT. The mRNA expression of DEGs and protein expression of signalling proteins and metabolic enzymes were verified by qPCR and Western blot assays. Activity of rate-limiting enzymes and metabolite level were detected by corresponding enzyme activity and metabolites kits. Xenograft nude mice model of SCLC was established to observe the in vivo inhibition, metabolism reprogramming and mechanism of TCPT.

RESULTS

TCPT treatment shows the best inhibition in SCLC cell line H1688 rather than other 5 lung cancer cell lines. Ultrastructural investigation indicates TCPT induces mitochondria damage such as cytoplasm shrinkage, ridges concentration and early sight of autolysosome, as well as decrease of membrane potential. Results of RNA-seq combined bio-informatics analysis find out changes of metabolism progression affected the most by TCPT in SCLC cells, and these changes might be regulated by β-catenin/AMPK/SIRT1 axis. TCPT might mainly decline the activity and expression of rate-limiting enzymes, OGDH, PDHE1, and LDHA/B to reprogram aerobic oxidation pattern, resulting in reduction of ATP production in SCLC cells. Xenograft nude mice model demonstrates TCPT could induce cell death and inhibit growth in vivo. Assimilate to the results of in vitro model, TCPT reprograms metabolism by decreasing the activity and expression of rate-limiting enzymes (OGDH, PDHE1, and LDHA/B), and attenuates the expression of β-catenin, p-β-catenin, AMPK and SIRT1 accordance with in vitro data.

CONCLUSION

Our results demonstrated TCPT induces cell death of SCLC by reprograming metabolic patterns, possibly through attenuating master metabolic pathway axis β-catenin/AMPK/SIRT1.

Phosphorylation by IKKβ Promotes the Degradation of HMGCL via NEDD4 in Lung Cancer

Inflammation and metabolic reprogramming are hallmarks of cancer. How inflammation regulates cancer metabolism remains poorly understood. In this study, we found that 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL), the enzyme that catalyzes the catabolism of leucine and promotes the synthesis of ketone bodies, was downregulated in lung cancer. Downregulation of HMGCL was associated with a larger tumor size and a shorter overall survival time. In a functional study, overexpression of HMGCL increased the content of β-hydroxybutyrate (β-HB) and inhibited the tumorigenicity of lung cancer cells, and deletion of HMGCL promoted de novo tumorigenesis in KP (Kras^G12D;P53^f/f) mice. Mechanistically, tumor necrosis factor α (TNFα) treatment decreased the HMGCL protein level, and IKKβ interacted with HMGCL and phosphorylated it at Ser258, which destabilized HMGCL. Moreover, NEDD4 was identified as the E3 ligase for HMGCL and promoted its degradation. In addition, mutation of Ser258 to alanine inhibited the ubiquitination of HMGCL by NEDD4 and thus inhibited the anchorage-independent growth of lung cancer cells more efficiently than did wild-type HMGCL. In summary, this study demonstrated a link between TNFα-mediated inflammation and cancer metabolism.

Comprehensive analyses of one-carbon metabolism related genes and their association with prognosis, tumor microenvironment, chemotherapy resistance and immunotherapy in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a global public health concern. One-carbon (1C) metabolism plays a crucial role in the occurrence and development of multiple cancer types. However, there are limited studies investigating 1C metabolism in LUAD. This study aims to evaluate the prognostic value of 1C metabolism-related genes in LUAD and to explore the potential correlation of these genes with gene methylation, the tumor microenvironment, and immunotherapy. Methods: We identified 26 1C metabolism-related genes and performed a Kaplan-Meier and Cox regression analysis to evaluate the prognostic value of these genes. Consensus clustering was further performed to determine the 1C metabolism-related gene patterns in LUAD. The clinical and molecular characteristics of subgroups were investigated based on consensus clustering. CIBERSORT and ssGSEA algorithms were used to calculate the relative infiltration levels of multiple immune cell subsets. The relationship between 1C metabolism-related genes and drug sensitivity to immunotherapy was evaluated using the CellMiner database and IMvigor210 cohort, respectively. Results: The expression levels of 23 1C metabolism-related genes were significantly different between LUAD tumor tissues and normal tissues. Seventeen of these genes were related to prognosis. Two clusters (cluster 1 and cluster 2) were identified among 497 LUAD samples based on the expression of 7 prognosis-related genes. Distinct expression patterns were observed between the two clusters. Compared to cluster 2, cluster 1 was characterized by inferior overall survival (OS) (median OS = 41 vs. 60 months, p = 0.00031), increased tumor mutation burden (15.8 vs. 7.5 mut/Mb, p < 0.001), high expression of PD-1 (p < 0.001) and PD-L1 (p < 0.001), as well as enhanced immune infiltration. 1C metabolism-related genes were positively correlated with the expression of methylation enzymes, and a lower methylation level was observed in cluster 1 (p = 0.0062). Patients in cluster 1 were resistant to chemotherapy drugs including pemetrexed, gemcitabine, paclitaxel, etoposide, oxaliplatin, and carboplatin. The specific expression pattern of 1C metabolism-related genes was correlated with a better OS in patients treated with immunotherapy (median OS: 11.2 vs. 7.8 months, p = 0.0034). Conclusion: This study highlights that 1C metabolism is correlated with the prognosis of LUAD patients and immunotherapy efficacy. Our findings provide novel insights into the role of 1C metabolism in the occurrence, development, and treatment of LUAD, and can assist in guiding immunotherapy for LUAD patients.

Arginine Deprivation in SCLC: Mechanisms and Perspectives for Therapy

Arginine deprivation has gained increasing traction as a novel and safe antimetabolite strategy for the treatment of several hard-to-treat cancers characterised by a critical dependency on arginine. Small cell lung cancer (SCLC) displays marked arginine auxotrophy due to inactivation of the rate-limiting enzyme argininosuccinate synthetase 1 (ASS1), and as a consequence may be targeted with pegylated arginine deiminase or ADI-PEG20 (pegargiminase) and human recombinant pegylated arginases (rhArgPEG, BCT-100 and pegzilarginase). Although preclinical studies reveal that ASS1-deficient SCLC cell lines are highly sensitive to arginine-degrading enzymes, there is a clear disconnect with the clinic with minimal activity seen to date that may be due in part to patient selection. Recent studies have explored resistance mechanisms to arginine depletion focusing on tumor adaptation, such as ASS1 re-expression and autophagy, stromal cell inputs including macrophage infiltration, and tumor heterogeneity. Here, we explore how arginine deprivation may be combined strategically with novel agents to improve SCLC management by modulating resistance and increasing the efficacy of existing agents. Moreover, recent work has identified an intriguing role for targeting arginine in combination with PD-1/PD-L1 immune checkpoint inhibitors and clinical trials are in progress. Thus, future studies of arginine-depleting agents with chemoimmunotherapy, the current standard of care for SCLC, may lead to enhanced disease control and much needed improvements in long-term survival for patients.

ATF4/MYC Regulates MTHFD2 to Promote NSCLC Progression by Mediating Redox Homeostasis

Purpose. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) has been reported to be overexpressed in non-small-cell lung cancer (NSCLC) and to correlate with malignant proliferation. However, the mechanism of high MTHFD2 expression in NSCLC has not been clarified. Methods. qPCR, western blot, and immunofluorescence experiments were used to measure the expression of related mRNAs and proteins. Cell apoptosis was measured by flow cytometry and TUNEL assays. The CCK-8 assay was used to determine cell viability. Flow cytometry was used to analyze the cell cycle. ROS, H2O2, MDA, SOD, and NADPH/NADP+ were evaluated by relevant assay kits. Transfection of siRNA or vectors was used to downregulate or upregulate gene expression. Dual-luciferase reporter gene assays were used to evaluate the regulated relationship between MTHFD2 and ATF4 or MYC. Results. MTHFD2 was highly expressed in NSCLC cells. Knockdown of MTHFD2 inhibited proliferation and increased apoptosis. Furthermore, oxidative factors significantly increased, while antioxidant factors significantly decreased in NSCLC cells with MTHFD2 knockdown, indicating that MTHFD2 was involved in NSCLC progression through the redox pathway. Although MTHFD2 was downregulated with ATF4 silencing, the dual-luciferase reporter assay suggested that ATF4 did not directly mediate MTHFD2 transcription. Further studies revealed that MYC had a transcriptional effect on MTHFD2 and was also regulated by ATF4. PCR, and western blotting experiments with ATF4 knockdown and MYC overexpression as well as ATF4 overexpression and MYC knockdown proved that ATF4 stimulated MTHFD2 through MYC mediation. Conclusions. ATF4 promoted high expression of MTHFD2 in NSCLC dependent on MYC.

Regulation and Therapeutic Targeting of MTHFD2 and EZH2 in KRAS-Mutated Human Pulmonary Adenocarcinoma

Activating KRAS mutations occur in about 30% of pulmonary adenocarcinoma (AC) cases and the discovery of specific inhibitors of G12C-mutated KRAS has considerably improved the prognosis for a subgroup of about 14% of non-small cell lung cancer (NSCLC) patients. However, even in patients with a KRAS G12C mutation, the overall response rate only reaches about 40% and mutations other than G12C still cannot be targeted. Despite the fact that one-carbon metabolism (1CM) and epigenetic regulation are known to be dysregulated by aberrant KRAS activity, we still lack evidence that co-treatment with drugs that regulate these factors might ameliorate response rates and patient prognosis. In this study, we show a direct dependency of Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) and Enhancer of Zeste Homolog 2 (EZH2) expression on mutationally activated KRAS and their prognostic relevance in KRAS-mutated AC. We show that aberrant KRAS activity generates a vulnerability of AC cancer cell lines to both MTHFD2 and EZH2 inhibitors. Importantly, co-inhibition of both factors was synergistically effective and comparable to KRASG12C inhibition alone, paving the way for their use in a therapeutic approach for NSCLC cancer patients.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.