Metabolic enzyme ACSL3 is a prognostic biomarker and correlates with anticancer effectiveness of statins in non-small cell lung cancer

Dysregulation of genes involved in the long-chain fatty acid transport in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive lethal malignancy with limited options for treatment and a 5-year survival rate of 11% in the United States. As for other types of tumors, such as colorectal cancer, aberrant de novo lipid synthesis and reprogrammed lipid metabolism have been suggested to be associated with PDAC development and progression.

AIM

To identify the possible involvement of lipid metabolism in PDAC by analyzing in tumoral and non-tumoral tissues the expression level of the most relevant genes involved in the long-chain fatty acid (FA) import into cell.

METHODS

A gene expression analysis of FASN, CD36, SLC27A1, SLC27A2, SLC27A3, SLC27A4, SLC27A5, ACSL1, and ACSL3 was performed by qRT-PCR in 24 tumoral PDAC tissues and 11 samples from non-tumoral pancreatic tissues obtained via fine needle aspiration or via surgical resection. The genes were considered significantly dysregulated between the groups when the p value was < 0.05 and the fold change (FC) was ≤ 0.5 and ≥ 2.

RESULTS

We found that three FA transporters and two long-chain acyl-CoA synthetases genes were significantly upregulated in the PDAC tissue compared to the non-tumoral tissue: SLC27A2 (FC = 5.66; P = 0.033), SLC27A3 (FC = 2.68; P = 0.040), SLC27A4 (FC = 3.13; P = 0.033), ACSL1 (FC = 4.10; P < 0.001), and ACSL3 (FC = 2.67; P = 0.012). We further investigated any possible association between the levels of the analyzed mRNAs and the specific characteristics of the tumors, including the anatomic location, the lymph node involvement, and the presence of metastasis. A significant difference in the expression of SLC27A3 (FC = 3.28; P = 0.040) was found comparing patients with and without lymph nodes involvement with an overexpression of this transcript in 17 patients presenting tumoral cells in the lymph nodes.

CONCLUSION

Despite the low number of patients analyzed, these preliminary results seem to be promising. Addressing lipid metabolism through a broad strategy could be a beneficial way to treat this malignancy. Future in vitro and in vivo studies on these genes may offer important insights into the mechanisms linking PDAC with the long-chain FA import pathway.

Targeting ACSLs to modulate ferroptosis and cancer immunity

Five acyl-CoA synthetase long-chain family members (ACSLs) are responsible for catalyzing diverse long-chain fatty acids (LCFAs) into LCFA-acyl-coenzyme A (CoA) for their subsequent metabolism, including fatty acid oxidation (FAO), lipid synthesis, and protein acylation. In this review, we focus on ACSLs and their LCFA substrates and introduce their involvement in regulation of cancer proliferation, metastasis, and therapeutic resistance. Along with the recognition of the decisive role of ACSL4 in ferroptosis - an immunogenic cell death (ICD) initiated by lipid peroxidation - we review the functions of ACSLs on regulating ferroptosis sensitivity. Last, we discuss the current understanding of ACSL on the antitumor immune response. We emphasize the necessity to explore the functions of immune cells expressing ACSLs for developing novel strategies to augment immunotherapy by targeting ACSL.

Polyamine Pathway Inhibitor DENSPM Suppresses Lipid Metabolism in Pheochromocytoma Cell Line

Pheochromocytomas (PCCs) are tumors arising from chromaffin cells in the adrenal medulla, and paragangliomas (PGLs) are tumors derived from extra-adrenal sympathetic or parasympathetic paraganglia; these tumors are collectively referred to as PPGL cancer. Treatment for PPGL primarily involves surgical removal of the tumor, and only limited options are available for treatment of the disease once it becomes metastatic. Human carriers of the heterozygous mutations in the succinate dehydrogenase subunit B (SDHB) gene are susceptible to the development of PPGL. A physiologically relevant PCC patient-derived cell line hPheo1 was developed, and SDHB_KD cells carrying a stable short hairpin knockdown of SDHB were derived from it. An untargeted metabolomic approach uncovered an overactive polyamine pathway in the SDHB_KD cells that was subsequently fully validated in a large set of human SDHB-mutant PPGL tumor samples. We previously reported that treatment with the polyamine metabolism inhibitor N1,N11-diethylnorspermine (DENSPM) drastically inhibited growth of these PCC-derived cells in culture as well as in xenograft mouse models. Here we explored the mechanisms underlying DENSPM action in hPheo1 and SDHB_KD cells. Specifically, by performing an RNAseq analysis, we have identified gene expression changes associated with DENSPM treatment that broadly interfere with all aspects of lipid metabolism, including fatty acid (FA) synthesis, desaturation, and import/uptake. Furthermore, by performing an untargeted lipidomic liquid chromatography-mass spectrometry (LC/MS)-based analysis we uncovered specific groups of lipids that are dramatically reduced as a result of DENSPM treatment. Specifically, the bulk of plasmanyl ether lipid species that have been recently reported as the major determinants of cancer cell fate are notably decreased. In summary, this work suggests an intersection between active polyamine and lipid pathways in PCC cells.

Lipid droplets provide metabolic flexibility for cancer progression

A hallmark of cancer cells is their remarkable ability to efficiently adapt to favorable and hostile environments. Due to a unique metabolic flexibility, tumor cells can grow even in the absence of extracellular nutrients or in stressful scenarios. To achieve this, cancer cells need large amounts of lipids to build membranes, synthesize lipid-derived molecules, and generate metabolic energy in the absence of other nutrients. Tumor cells potentiate strategies to obtain lipids from other cells, metabolic pathways to synthesize new lipids, and mechanisms for efficient storage, mobilization, and utilization of these lipids. Lipid droplets (LDs) are the organelles that collect and supply lipids in eukaryotes and it is increasingly recognized that the accumulation of LDs is a new hallmark of cancer cells. Furthermore, an active role of LD proteins in processes underlying tumorigenesis has been proposed. Here, by focusing on three major classes of LD-resident proteins (perilipins, lipases, and acyl-CoA synthetases), we provide an overview of the contribution of LDs to cancer progression and discuss the role of LD proteins during the proliferation, invasion, metastasis, apoptosis, and stemness of cancer cells.

Proteomic and metabolomic signatures of U87 glioblastoma cells treated with cisplatin and/or paclitaxel

BACKGROUND

Glioblastoma (GBM) is a primary malignancy of the central nervous system and is classified as a grade IV astrocytoma by the World Health Organization (WHO). Although GBM rarely metastasizes, its prognosis remains poor. Moreover, the standard treatment for GBM, temozolomide (TMZ), is associated with chemoresistance, which is a major factor behind GBM-related deaths. Investigating drugs with repurposing potential in the context of GBM is worthwhile to bypass lengthy bench-to-bedside research. The field of omics has garnered significant interest in scientific research because of its potential to delineate the intricate regulatory network underlying tumor development. In particular, proteomic and metabolomic analyses are powerful approaches for the investigation of metabolic enzymes and intermediate metabolites since they represent the functional end of the cancer phenotype.

METHODS

We chose two of the most widely prescribed anticancer drugs, cisplatin and paclitaxel. To our knowledge, the current literature lacks studies examining their effects on metabolic and proteomic alterations in GBM. We employed the mass spectrometry technological platform 'UHPLC-Q-TOF-MS/MS' to examine the changes in the proteome and metabolome profiles of the U87 cell line with defined concentrations of cisplatin and/or paclitaxel via an untargeted approach.

RESULTS

A total of 1,419 distinct proteins and 90 metabolites were generated, and subsequent analysis was performed. We observed that upon treatment with cisplatin (9.5 μM), U87 cells exhibited apparent efforts to cope with this exogenous stressor, understanding the effect of paclitaxel (5.3 μM) on altering the transport machinery of the cell, and how the combination of cisplatin and/or paclitaxel suggests potential interactions with promising benefits in GBM therapeutics.

CONCLUSION

Our research provides a detailed map of alterations in response to cisplatin and paclitaxel treatment, provides crucial insights into the molecular basis of their action, and paves the way for further research to identify molecular targets for this elusive malignancy.

Establishment of a prognostic signature based on fatty acid metabolism genes in HCC associated with hepatitis B

BACKGROUND

Hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) is one of the most common and deadly cancer and often accompanied by varying degrees of liver damage, leading to the dysfunction of fatty acid metabolism (FAM). This study aimed to investigate the relationship between FAM and HBV-associated HCC and identify FAM biomarkers for predicting the prognosis of HBV-associated HCC.

METHODS

Gene Set Enrichment Analysis (GSEA) was used to analyze the difference of FAM pathway between paired tumor and adjacent normal tissue samples in 58 HBV-associated HCC patients from the Gene Expression Omnibus (GEO) database. Next, 117 HBV-associated HCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to establish a prognostic signature based on 42 FAM genes. Then, the prognostic signature was validated in an external cohort consisting of 30 HBV-associated HCC patients. Finally, immune infiltration analysis was performed to evaluate the FAM-related immune cells in HBV-associated HCC.

RESULTS

As a result, FAM pathway was clearly downregulated in tumor tissue of HBV-associated HCC, and survival analysis demonstrated that 12 FAM genes were associated with the prognosis of HBV-associated HCC. Lasso-penalized Cox regression analysis identified and established a five-gene signature (ACADVL, ACAT1, ACSL3, ADH4 and ECI1), which showed effective discrimination and prediction for the prognosis of HBV-associated HCC both in the TCGA cohort and the validation cohort. Immune infiltration analysis showed that the high-risk group, identified by FAM signature, of HBV-associated HCC had a higher ratio of Tregs, which was associated with the prognosis.

CONCLUSIONS

Collectively, these findings suggest that there is a strong connection between FAM and HBV-associated HCC, indicating a potential therapeutic strategy targeting FAM to block the accumulation of Tregs into the tumor microenvironment of HBV-associated HCC.

Lipid metabolic vulnerabilities of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy worldwide, characterized by abnormal proliferation of malignant plasma cells within a tumor-permissive bone marrow microenvironment. Metabolic dysfunctions are emerging as key determinants in the pathobiology of MM. In this review, we highlight the metabolic features of MM, showing how alterations in various lipid pathways, mainly involving fatty acids, cholesterol and sphingolipids, affect the growth, survival and drug responsiveness of MM cells, as well as their cross-talk with other cellular components of the tumor microenvironment. These findings will provide a new path to understanding the mechanisms underlying how lipid vulnerabilities may arise and affect the phenotype of malignant plasma cells, highlighting novel druggable pathways with a significant impact on the management of MM.

Revolutionizing anti-cancer drug discovery against breast cancer and lung cancer by modification of natural genistein: an advanced computational and drug design approach

Breast and lung cancer are two of the most lethal forms of cancer, responsible for a disproportionately high number of deaths worldwide. Both doctors and cancer patients express alarm about the rising incidence of the disease globally. Although targeted treatment has achieved enormous advancements, it is not without its drawbacks. Numerous medicines and chemotherapeutic drugs have been authorized by the FDA; nevertheless, they can be quite costly and often fall short of completely curing the condition. Therefore, this investigation has been conducted to identify a potential medication against breast and lung cancer through structural modification of genistein. Genistein is the active compound in Glycyrrhiza glabra (licorice), and it exhibits solid anticancer efficiency against various cancers, including breast cancer, lung cancer, and brain cancer. Hence, the design of its analogs with the interchange of five functional groups-COOH, NH2 and OCH3, Benzene, and NH-CH2-CH2-OH-have been employed to enhance affinities compared to primary genistein. Additionally, advanced computational studies such as PASS prediction, molecular docking, ADMET, and molecular dynamics simulation were conducted. Firstly, the PASS prediction spectrum was analyzed, revealing that the designed genistein analogs exhibit improved antineoplastic activity. In the prediction data, breast and lung cancer were selected as primary targets. Subsequently, other computational investigations were gradually conducted. The mentioned compounds have shown acceptable results for in silico ADME, AMES toxicity, and hepatotoxicity estimations, which are fundamental for their oral medication. It is noteworthy that the initial binding affinity was only -8.7 kcal/mol against the breast cancer targeted protein (PDB ID: 3HB5). However, after the modification of the functional group, when calculating the binding affinities, it becomes apparent that the binding affinities increase gradually, reaching a maximum of -11.0 and -10.0 kcal/mol. Similarly, the initial binding affinity was only -8.0 kcal/mol against lung cancer (PDB ID: 2P85), but after the addition of binding affinity, it reached -9.5 kcal/mol. Finally, a molecular dynamics simulation was conducted to study the molecular models over 100 ns and examine the stability of the docked complexes. The results indicate that the selected complexes remain highly stable throughout the 100-ns molecular dynamics simulation runs, displaying strong correlations with the binding of targeted ligands within the active site of the selected protein. It is important to further investigate and proceed to clinical or wet lab experiments to determine the practical value of the proposed compounds.

Lipid Metabolism Pathway Genes and Lung Cancer: ACADSB rs12220683G&gt;C Is Associated with Better Survival Outcome in Patients with Non-Small Cell Lung Cancer

INTRODUCTION

Altered lipid metabolism has been reported to be associated with prognosis in multiple cancers. This study aimed to investigate the association of polymorphisms in lipid metabolism pathway genes with survival outcomes in patients with surgically resected non-small cell lung cancer (NSCLC).

METHODS

In total, 744 patients with surgically resected NSCLC (380 in the discovery cohort and 364 in the validation cohort) were included in this study. The association between 176 polymorphisms of lipid metabolism pathway genes and the clinical outcomes of NSCLC patients was analyzed.

RESULTS

Among the polymorphisms investigated, ACADSB rs10902859G>A was associated with significantly better overall survival (OS) in the discovery, validation, and combined cohorts. ACADSB rs10902859G>A was located in the repressed region and had strong linkage disequilibrium (D' = 1.00 and r2 = 0.94), with rs12220683G>C located in the H3K4me3 peak region, which indicates the presence of active promoters. ACADSB rs12220683G>C was also associated with better OS in the discovery, validation, and combined cohorts (in a dominant model; adjusted hazard ratio [aHR] = 0.53, 95% confidence interval [CI] = 0.30-0.94, p = 0.03; aHR = 0.37, 95% CI = 0.15-0.89, p = 0.03; and aHR = 0.47, 95% CI = 0.29-0.75, p = 0.002, respectively). In vitro luciferase assay demonstrated that the promoter activity of ACADSB was significantly increased in the rs12220683 variant C allele compared with that in the wild G allele (p = 3 × 10-5).

CONCLUSION

These results suggest that ACADSB rs12220683G>C increases promoter activity and that increased ACADSB expression may result in better OS in patients with surgically resected NSCLC.

Integrative bioinformatics analysis of ACS enzymes as candidate prognostic and diagnostic biomarkers in colon adenocarcinoma

Background and purpose

Acyl-CoA synthetase (ACS) enzymes play an important role in the activation of fatty acids. While many studies have found correlations between the expression levels of ACS enzymes with the progression, growth, and survival of cancer cells, their role and expression patterns in colon adenocarcinoma are still greatly unknown and demand further investigation.

Experimental approach

The expression data of colon adenocarcinoma samples were downloaded from the Cancer Genome Atlas (TCGA) database. Normalization and differential expression analysis were performed to identify differentially expressed genes (DEGs). Gene set enrichment analysis was applied to identify top enriched genes from ACS enzymes in cancer samples. Gene ontology and protein-protein interaction analyses were performed for the prediction of molecular functions and interactions. Survival analysis and receiver operating characteristic test (ROC) were performed to find potential prognostic and diagnostic biomarkers.

Findings/Results

ACSL6 and ACSM5 genes demonstrated more significant differential expression and LogFC value compared to other ACS enzymes and also achieved the highest enrichment scores. Gene ontology analysis predicted the involvement of top DEGs in fatty acids metabolism, while protein-protein interaction network analysis presented strong interactions between ACSLs, ACSSs, ACSMs, and ACSBG enzymes with each other. Survival analysis suggested ACSM3 and ACSM5 as potential prognostic biomarkers, while the ROC test predicted stronger diagnostic potential for ACSM5, ACSS2, and ACSF2 genes.

Conclusion and implications

Our findings revealed the expression patterns, prognostic, and diagnostic biomarker potential of ACS enzymes in colon adenocarcinoma. ACSM3, ACSM5, ACSS2, and ACSF2 genes are suggested as possible prognostic and diagnostic biomarkers.

Identification of ferroptosis and drug resistance related hub genes to predict the prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Currently, overcoming the drug resistance in HCC is a critical challenge and ferroptosis has emerged as a promising therapeutic option for cancer. We aim to construct a new gene signature related to ferroptosis and drug resistance to predict the prognosis in HCC. The RNA-seq data of HCC patients was obtained from the Cancer Genome Atlas database. Using least absolute shrinkage and selection operator cox regression, Kaplan-Meier analysis, and differential analysis, we constructed a prognostic model consisting of six hub genes (TOP2A, BIRC5, VEGFA, HIF1A, FTH1, ACSL3) related to ferroptosis and drug resistance in HCC. Functional enrichment, pathway enrichment and GSEA analysis were performed to investigate the potential molecular mechanism, and construction of PPI, mRNA-miRNA, mRNA-RBP, mRNA-TF and mRNA-drugs interaction networks to predict its interaction with different molecules. Clinical prognostic characteristics were revealed by univariate, multivariate cox regression analysis and nomogram. We also analyzed the relationship between the signature, immune checkpoints, and drug sensitivity. The expression of the gene signature was detected in HCC cell lines and HPA database. Our prognostic model classified patients into high and low-risk groups based on the risk scores and found the expression level of the genes was higher in the high-risk group than the low-risk group, demonstrating that high expression of the hub genes was associated with poor prognosis in HCC. ROC analysis revealed its high diagnostic efficacy in both HCC and normal tissues. The proportional hazards model and calibration analysis confirmed that the model's prediction was most accurate for 1- and 3-years survival. QRT-PCR showed the high expression level of the gene signature in HCC. Our study built a novel gene signature with good potential to predict the prognosis of HCC, which may provide new therapeutic targets and molecular mechanism for HCC diagnosis and treatment.

Identification of a m6A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma

BACKGROUND

Both N6-methyladenosine (m⁶A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m⁶A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m⁶A-related ferroptosis genes in lung adenocarcinoma.

METHODS

Lung adenocarcinoma sample data were downloaded from the University of California Santa Cruz Xena and Gene Expression Omnibus databases. Spearman's correlation analysis was used to screen for m⁶A-related ferroptosis genes. Univariate Cox regression, Kaplan-Meier, and Lasso analyses were conducted to identify prognostic m⁶A-related ferroptosis genes, and stepwise regression was used to construct a prognostic gene signature. The predictive value of the gene signature was assessed using a multivariate Cox analysis. In the validation cohort, survival analysis was performed to verify gene signature stability. The training cohort was divided into high- and low-risk groups according to the median risk score to assess differences between the two groups in terms of gene set variation analysis, somatic mutations, and tumor immune infiltration cells.

RESULTS

Six m⁶A-related ferroptosis genes were used to construct a gene signature in the training cohort and a multivariate Cox analysis was conducted to determine the independent prognostic value of these genes in lung adenocarcinoma. In the validation cohort, Kaplan-Meier and receiver operating characteristic analyses confirmed the strong predictive power of this signature for the prognosis of lung adenocarcinoma. Gene set variation analysis showed that the low-risk group was mainly related to immunity, and the high-risk group was mainly related to DNA replication. Somatic mutation analysis revealed that the TP53 gene had the highest mutation rate in the high-risk group. Tumor immune infiltration cell analysis showed that the low-risk group had higher levels of resting CD4 memory T cells and lower levels of M0 macrophages.

CONCLUSION

Our study identified a novel m⁶A-related ferroptosis-associated six-gene signature (comprising SLC2A1, HERPUD1, EIF2S1, ACSL3, NCOA4, and CISD1) for predicting lung adenocarcinoma prognosis, yielding a useful prognostic biomarker and potential therapeutic target.

Clinical features and lipid metabolism genes as potential biomarkers in advanced lung cancer

BACKGROUND

Lung cancer is one of the most lethal tumors with a poor survival rate even in those patients receiving new therapies. Metabolism is considered one of the hallmarks in carcinogenesis and lipid metabolism is emerging as a significant contributor to tumor metabolic reprogramming. We previously described a profile of some lipid metabolism related genes with potential prognostic value in advanced lung cancer.

AIM

To analyze clinical and pathological characteristics related to a specific metabolic lipid genomic signature from patients with advanced lung cancer and to define differential outcome.

METHODS

Ninety samples from NSCLC (non-small cell lung cancer) and 61 from SCLC (small cell lung cancer) patients were obtained. We performed a survival analysis based on lipid metabolic genes expression and clinical characteristics. The primary end point of the study was the correlation between gene expression, clinical characteristics and survival.

RESULTS

Clinical variables associated with overall survival (OS) in NSCLC patients were clinical stage, adenocarcinoma histology, Eastern Cooperative Oncology Group (ECOG), number and site of metastasis, plasma albumin levels and first-line treatment with platinum. As for SCLC patients, clinical variables that impacted OS were ECOG, number of metastasis locations, second-line treatment administration and Diabetes Mellitus (DM). None of them was associated with gene expression, indicating that alterations in lipid metabolism are independent molecular variables providing complementary information of lung cancer patient outcome.

CONCLUSIONS

Specific clinical features as well as the expression of lipid metabolism-related genes might be potential biomarkers with differential outcomes.

Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

ACSL3 and ACSL4, Distinct Roles in Ferroptosis and Cancers

The long-chain fatty acyl CoA synthetase (ACSLs) family of enzymes contributes significantly to lipid metabolism and produces acyl-coenzyme A by catalyzing fatty acid oxidation. The dysregulation of ACSL3 and ACSL4, which belong to the five isoforms of ACSLs, plays a key role in cancer initiation, development, metastasis, and tumor immunity and may provide several possible therapeutic strategies. Moreover, ACSL3 and ACSL4 are crucial for ferroptosis, a non-apoptotic cell death triggered by the accumulation of membrane lipid peroxides due to iron overload. Here, we present a summary of the current knowledge on ACSL3 and ACSL4 and their functions in various cancers. Research on the molecular mechanisms involved in the regulation of ferroptosis is critical to developing targeted therapies for cancer.

Phenolic diterpenes from Rosemary supercritical extract inhibit non-small cell lung cancer lipid metabolism and synergise with therapeutic drugs in the clinic

Lung cancer is one of the most deadly and common cancers in the world. The molecular features of patient's tumours dictate the different therapeutic decisions, which combines targeted therapy, chemotherapy, and immunotherapy. Altered cellular metabolism is one of the hallmarks of cancer. Tumour cells reprogram their metabolism to adapt to their novel requirements of growth, proliferation, and survival. Together with the Warburg effect, the role of lipid metabolism alterations in cancer development and prognosis has been highlighted. Several lipid related genes have been shown to promote transformation and progression of cancer cells and have been proposed as biomarkers for prognosis. Nevertheless, the exact mechanisms of the regulation of lipid metabolism and the biological consequences in non-small cell lung cancer (NSCLC) have not been elucidated yet. There is an urgent necessity to develop multidisciplinary and complementary strategies to improve NSCLC patients´ well-being and treatment response. Nutrients can directly affect fundamental cellular processes and some diet-derived ingredients, bioactive natural compounds and natural extracts have been shown to inhibit the tumour growth in preclinical and clinical trials. Previously, we described a supercritical extract of rosemary (SFRE) (12 - 16% composition of phenolic diterpenes carnosic acid and carnosol) as a potential antitumoral agent in colon and breast cancer due to its effects on the inhibition of lipid metabolism and DNA synthesis, and in the reduction of resistance to 5-FluoroUracil (5-FU). Herein, we demonstrate SFRE inhibits NSCLC cell bioenergetics identifying several lipid metabolism implicated targets. Moreover, SFRE synergises with standard therapeutic drugs used in the clinic, such as cisplatin, pemetrexed and pembrolizumab to inhibit of cell viability of NSCLC cells. Importantly, the clinical relevance of SFRE as a complement in the treatment of NSCLC patients is suggested based on the results of a pilot clinical trial where SFRE formulated with bioactive lipids (PCT/ES2017/070263) diminishes metabolic and inflammatory targets in peripheral-blood mononuclear cells (PBMC), such as MAPK (p=0.04), NLRP3 (p=0.044), and SREBF1 (p=0.047), which may augment the immune antitumour function. Based on these results, SFRE merits further investigation as a co-adjuvant in the treatment of NSCLC.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT05080920.

Label-Free Quantitative Proteomics Analysis of Adriamycin Selected Multidrug Resistant Human Lung Cancer Cells

The development of drug resistance in lung cancer is a major clinical challenge, leading to a 5-year survival rate of only 18%. Therefore, unravelling the mechanisms of drug resistance and developing novel therapeutic strategies is of crucial importance. This study systematically explores the novel biomarkers of drug resistance using a lung cancer model (DLKP) with a series of drug-resistant variants. In-depth label-free quantitative mass spectrometry-based proteomics and gene ontology analysis shows that parental DLKP cells significantly differ from drug-resistant variants, and the cellular proteome changes even among the drug-resistant subpopulations. Overall, ABC transporter proteins and lipid metabolism were determined to play a significant role in the formation of drug resistance in DKLP cells. A series of membrane-related proteins such as HMOX1, TMB1, EPHX2 and NEU1 were identified to be correlated with levels of drug resistance in the DLKP subpopulations. The study also showed enrichment in biological processes and molecular functions such as drug metabolism, cellular response to the drug and drug binding. In gene ontology analysis, 18 proteins were determined to be positively or negatively correlated with resistance levels. Overall, 34 proteins which potentially have a therapeutic and diagnostic value were identified.

Effects of Cigarette Smoke Exposure on the Gut Microbiota and Liver Transcriptome in Mice Reveal Gut–Liver Interactions

Cigarette smoke exposure has a harmful impact on health and increases the risk of disease. However, studies on cigarette-smoke-induced adverse effects from the perspective of the gut–liver axis are lacking. In this study, we evaluated the adverse effects of cigarette smoke exposure on mice through physiological, biochemical, and histopathological analyses and explored cigarette-smoke-induced gut microbiota imbalance and changes in liver gene expression through a multiomics analysis. We demonstrated that cigarette smoke exposure caused abnormal physiological indices (including reduced body weight, blood lipids, and food intake) in mice, which also triggered liver injury and induced disorders of the gut microbiota and liver transcriptome (especially lipid metabolism). A significant correlation between intestinal bacterial abundance and the expression of lipid-metabolism-related genes was detected, suggesting the coordinated regulation of lipid metabolism by gut microbiota and liver metabolism. Specifically, Salmonella (harmful bacterium) was negatively and positively correlated with up- (such as Acsl3 and Me1) and downregulated genes (such as Angptl4, Cyp4a12a, and Plin5) involved in lipid metabolism, while Ligilactobacillus (beneficial bacterium) showed opposite trends with these genes. Our results clarified the key role of gut microbiota in liver damage and metabolism and improved the understanding of gut–liver interactions caused by cigarette smoke exposure.

Screening for Potential Therapeutic Agents for Non-Small Cell Lung Cancer by Targeting Ferroptosis

Ferroptosis is a form of non-apoptotic and iron-dependent cell death originally identified in cancer cells. Recently, emerging evidence showed that ferroptosis-targeting therapy could be a novel promising anti-tumour treatment. However, systematic analyses of ferroptosis-related genes for the prognosis of non-small cell lung cancer (NSCLC) and the development of antitumor drugs exploiting the ferroptosis process remain rare. This study aimed to identify genes related to ferroptosis and NSCLC and to initially screen lead compounds that induce ferroptosis in tumor cells. We downloaded mRNA expression profiles and NSCLC clinical data from The Cancer Genome Atlas database to explore the prognostic role of ferroptosis-related genes. Four prognosis-associated ferroptosis-related genes were screened using univariate Cox regression analysis and the lasso Cox regression analysis, which could divide patients with NSCLC into high- and low-risk groups. Then, based on differentially expressed risk- and ferroptosis-related genes, the negatively correlated lead compound flufenamic acid (FFA) was screened through the Connective Map database. This project confirmed that FFA induced ferroptosis in A549 cells and inhibited growth and migration in a dose-dependent manner through CCK-8, scratch, and immunofluorescence assays. In conclusion, targeting ferroptosis might be a therapeutic alternative for NSCLC.

Effect of Statins on Lung Cancer Molecular Pathways: A Possible Therapeutic Role

Lung cancer is a common neoplasm, usually treated through chemotherapy, radiotherapy and/or surgery. Both clinical and experimental studies on cancer cells suggest that some drugs (e.g., statins) have the potential to improve the prognosis of cancer. In fact, statins blocking the enzyme "hydroxy-3-methylglutaryl-coenzyme A reductase" exert pleiotropic effects on different genes involved in the pathogenesis of lung cancer. In this narrative review, we presented the experimental and clinical studies that evaluated the effects of statins on lung cancer and described data on the effectiveness and safety of these compounds. We also evaluated gender differences in the treatment of lung cancer to understand the possibility of personalized therapy based on the modulation of the mevalonate pathway. In conclusion, according to the literature data, statins exert multiple effects on lung cancer cells, even if the evidence for their use in clinical practice is lacking.

Development and Validation of a Novel Metabolic Signature-Based Prognostic Model for Uveal Melanoma

Purpose

Uveal melanoma (UM) is the most common primary malignant tumor with poor prognosis. The role of metabolism-related genes in the prognosis of UM remains unrevealed. This study aimed to establish and validate a prognostic prediction model for UM based on metabolism-related genes.

Methods

Gene expression profiles and clinicopathological information were downloaded from The Cancer Genome Atlas, and the Gene Expression Omnibus database. Univariable Cox regression, least absolute shrinkage and selection operator Cox regression, and stepwise regression were performed to establish the model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and calibration and discrimination analyses were used to evaluate the prognostic model.

Results

Three metabolism-related genes, carbonic anhydrase 12, acyl-CoA synthetase long-chain family member 3, and synaptojanin 2, and three clinicopathological parameters (i.e., age, gender, and metastasis staging) were identified to establish the model. The risk score was found to be an independent prognostic factor for UM survival. High-risk patients demonstrated significantly poorer prognosis than low-risk patients. ROC analysis suggested the promising prognostic efficiency of the model. The calibration curve manifested satisfactory agreement between the predicted and observed risk. A nomogram and online survival calculator were developed to predict the survival probability.

Conclusions

The novel metabolism-based prognostic model could accurately predict the prognosis of UM patients, which facilitates the prediction of the survival probability by both ophthalmologists and patients with the online dynamic nomogram.

Translational Relevance

The dynamic nomogram links gene expression profiles to clinical prognosis of UM and is useful to evaluate the survival probability.

A Novel Predictive Model Incorporating Ferroptosis-Related Gene Signatures for Overall Survival in Patients with Lung Adenocarcinoma

BACKGROUND Lung adenocarcinoma (LUAD) is the predominant histological type of lung cancer with high morbidity and mortality. Ferroptosis is regarded as a new pattern of programmed cell death concerned with the progression of lung cancer characterized by lipid peroxidation. Nevertheless, the prognostic role of ferroptosis-related genes for LUAD warrant to be explored. MATERIAL AND METHODS RNA sequencing and relevant clinical patient data were obtained from public-access databanks. A prognostic model was constructed through the LASSO Cox regression in the cancer genome atlas cohort. The diagnostic value of the prognostic model was further evaluated in the gene expression omnibus cohort. RESULTS Most of the ferroptosis-related genes (69.9%) were differentially expressed between tumor and adjacent non-cancerous tissues. 43 differentially expressed genes showed a close association with the prognosis of LUAD patients (adjusted p-value <0.05). An 18-gene signature was built and applied to assign patients into high vs low-risk groups. Compared with the high-risk group, patients defined as the low-risk group suffered significantly prolonged OS. Both uni- and multivariate analyses demonstrated that the signature-based score served as a crucial role in influencing the OS of LUAD patients (hazard ratio >1, p<0.001). The immunity-related signaling pathway was enriched in the functional analysis and the infiltration of the immune cells showed a great difference between groups. CONCLUSIONS The predictive model could be applied for prognostic prediction for LUAD. Targeting ferroptosis could be a possible curative strategy against LUAD, and immunomodulation may be one of the potential mechanisms.

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.

Nutraceuticals and Cancer: Potential for Natural Polyphenols

Cancer is one of the leading causes of death globally, associated with multifactorial pathophysiological components. In particular, genetic mutations, infection or inflammation, unhealthy eating habits, exposition to radiation, work stress, and/or intake of toxins have been found to contribute to the development and progression of cancer disease states. Early detection of cancer and proper treatment have been found to enhance the chances of survival and healing, but the side effects of anticancer drugs still produce detrimental responses that counteract the benefits of treatment in terms of hospitalization and survival. Recently, several natural bioactive compounds were found to possess anticancer properties, capable of killing transformed or cancerous cells without being toxic to their normal counterparts. This effect occurs when natural products are associated with conventional treatments, thereby suggesting that nutraceutical supplementation may contribute to successful anticancer therapy. This review aims to discuss the current literature on four natural bioactive extracts mostly characterized by a specific polyphenolic profile. In particular, several activities have been reported to contribute to nutraceutical support in anticancer treatment: (1) inhibition of cell proliferation, (2) antioxidant activity, and (3) anti-inflammatory activity. On the other hand, owing to their attenuation of the toxic effect of current anticancer therapies, natural antioxidants may contribute to improving the compliance of patients undergoing anticancer treatment. Thus, nutraceutical supplementation, along with current anticancer drug treatment, may be considered for better responses and compliance in patients with cancer. It should be noted, however, that when data from studies with bioactive plant preparations are discussed, it is appropriate to ensure that experiments have been conducted in accordance with accepted pharmacological research practices so as not to disclose information that is only partially correct.

Cyy260, a novel small molecule inhibitor, suppresses non-small cell lung cancer cell growth via JAK2/STAT3 pathway

Non-small cell lung cancer (NSCLC) is a malignant tumor that accounts for the most new cancer cases and cancer-related deaths worldwide, and the proliferation and metastasis of NSCLC are the main reasons for treatment failure and patient death. Traditional chemotherapeutic drugs have low selectivity, which can kill cancer cells and cause damage to normal cells at the same time. Therefore, it is particularly important to study therapies that target cancer cells and to find low-toxicity, high-efficiency anticancer drugs. Cyy260 is a novel small molecule inhibitor that we synthesized for the first time. Here, we investigated the in vitro and in vivo antitumor activities of Cyy260 and explored the underlying mechanisms in NSCLC. Cyy260 had a concentration- and time-dependent inhibitory effect on NSCLC cells, but it was less toxic to normal cells. Cyy260 regulated apoptosis through intracellular and extracellular apoptotic pathways. In addition, Cyy260 could also induce cell cycle arrest, thereby inhibiting cell proliferation. Further analysis of molecular mechanisms showed that the JAK2/STAT3 signaling pathway was involved in the antitumor effect mediated by Cyy260. Analysis of subcutaneously transplanted tumors in mice showed that Cyy260 suppressed tumor growth in vivo. Our results proved that Cyy260 is a novel inhibitor of the JAK2/STAT3 pathway thus may have potential in therapy of NSCLC and other cancers.

ACSL family: The regulatory mechanisms and therapeutic implications in cancer

Accumulating evidence shows that deregulation of fatty acid (FA) metabolism is associated with the development of cancer. Long-chain acyl-coenzyme A synthases (ACSLs) are responsible for activating long-chain FAs and are frequently deregulated in cancers. Among the five mammalian ACSL family members, ACSL1 is involved in the TNFα-mediated pro-inflammatory phenotype and mainly facilitates cancer progression. ACSL3 is an androgen-responsive gene. High ACSL3 expression has been detected in a variety of cancers, including melanoma, triple-negative breast cancer (TNBC) and high-grade non-small cell lung carcinoma (NSCLC), and correlates with worse prognosis of patients with these diseases. ACSL4 can exert opposing roles acting as a tumor suppressor or as an oncogene depending on the specific cancer type and tissue environment. Moreover, ACSL4 behaves as a crucial regulator in ferroptosis that is defined as a cell death process caused by iron-dependent peroxidation of lipids. ACSL5 is nuclear-coded and expressed in the mitochondria and physiologically participates in the pro-apoptotic sensing of cells. ACSL5 mainly acts as a tumor suppressor in cancers. ACSL6 downregulation has been observed in many forms of cancers, except in colorectal cancer (CRC). Here, we address the differential regulatory mechanisms of the ACSL family members as well as their functions in carcinogenesis. Moreover, we enumerate the clinical therapeutic implications of ACSLs, which might serve as valuable biomarkers and therapeutic targets for precision cancer treatment.

Development and Validation of a Robust Ferroptosis-Related Prognostic Signature in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Ferroptosis is a newly recognized process of cell death, which is different from other forms of cell death in morphology, biochemistry, and genetics, and has played a vital role in cancer biology. This study aimed to identify a ferroptosis-related gene signature associated with LUAD prognosis.

Methods

Dataset TCGA-LUAD which came from the TCGA portal was taken as the training cohort. GSE72094 and GSE68465 from the GEO database were treated as validation cohorts. Two hundred fifty-nine ferroptosis-related genes were retrieved from the FerrDb database. In the training cohort, Kaplan–Meier and univariate Cox analyses were conducted for preliminary screening of ferroptosis-related genes with potential prognostic capacity. These genes then entered into the LASSO Cox regression model, constructing a gene signature. The latter was then evaluated in the training and validation cohorts via Kaplan–Meier, Cox, and ROC analyses. In addition, the correlations between risk score and autophagy were examined by Pearson correlation coefficient. The analyses of GSEA and immune infiltrating were performed for better studying the function annotation of the gene signature and the character of each kind of immune cells played in the tumor microenvironment.

Results

A 15-gene signature was found from the training cohort and validated by Kaplan–Meier and Cox regression analyses, revealing its independent prognosis value in LUAD. Moreover, the ROC analysis was conducted, confirming a strong predictive ability that this signature owned for LUAD prognosis. One hundred fifty-one of 222 (68.01%) autophagy-related genes were discovered significantly correlated with risk scores. Analyses of GSEA and immune infiltration exhibited in detail the specific pathways that associate with the 15-gene signature and identified the crucial roles of resting mast cells and resting dendritic cells owned in the prognosis of the 15-gene signature.

Conclusion

In this present study, a novel ferroptosis-related 15-gene signature (RELA, ACSL3, YWHAE, EIF2S1, CISD1, DDIT4, RRM2, PANX1, TLR4, ARNTL, LPIN1, HERPUD1, NCOA4, PEBP1, and GLS2) was built. It could accurately predict the prognosis of LUAD and was related to resting mast cells and resting dendritic cells, which provide potential for the personalized outcome prediction and the development of new therapies in LUAD population.

Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients

Simple Summary

Despite the progress in surgery and therapies, small cell lung cancer (SCLC) is still one of the most lethal types of cancer. The disease control remains heterogeneous and consequently, the ability to predict patient survival would be of great clinical value. Here, we propose for the first time, a metabolic precision approach for SCLC patients. We found that a healthy metabolic status contributes to increasing SCLC survival. Moreover, we discovered that two lipid metabolism-related genes, racemase and perilipin 1, and a genetic risk score of both genes, predict better SCLC survival. Our results show that a metabolic scenario characterized by metabolic health, lipid gene expression and environmental factors, is crucial for increase SCLC survival.

Abstract

Small cell lung cancer (SCLC) prognosis is the poorest of all types of lung cancer. Its clinical management remains heterogeneous and therefore, the capability to predict survival would be of great clinical value. Metabolic health (MH) status and lipid metabolism are two relevant factors in cancer prevention and prognosis. Nevertheless, their contributions in SCLC outcome have not yet been analyzed. We analyzed MH status and a transcriptomic panel of lipid metabolism genes in SCLC patients, and we developed a predictive genetic risk score (GRS). MH and two lipid metabolism genes, racemase and perilipin 1, are biomarkers of SCLC survival (HR = 1.99 (CI95%: 1.11–3.61) p = 0.02, HR = 0.36 (CI95%: 0.19–0.67), p = 0.03 and HR = 0.21 (CI95%: 0.09–0.47), respectively). Importantly, a lipid GRS of these genes predict better survival (c-index = 0.691). Finally, in a Cox multivariate regression model, MH, lipid GRS and smoking history are the main predictors of SCLC survival (c-index = 0.702). Our results indicate that the control of MH, lipid gene expression and environmental factors associated with lifestyle is crucial for increased SCLC survival. Here, we propose for the first time, a metabolic precision approach for SCLC patients.