Low-density lipoprotein receptor (LDLR) is an independent adverse prognostic factor in acute myeloid leukaemia

Stearoyl-CoA desaturase inhibition is toxic to acute myeloid leukemia displaying high levels of the de novo fatty acid biosynthesis and desaturation

Identification of specific and therapeutically actionable vulnerabilities, ideally present across multiple mutational backgrounds, is needed to improve acute myeloid leukemia (AML) patients' outcomes. We identify stearoyl-CoA desaturase (SCD), the key enzyme in fatty acid (FA) desaturation, as prognostic of patients' outcomes and, using the clinical-grade inhibitor SSI-4, show that SCD inhibition (SCDi) is a therapeutic vulnerability across multiple AML models in vitro and in vivo. Multiomic analysis demonstrates that SCDi causes lipotoxicity, which induces AML cell death via pleiotropic effects. Sensitivity to SCDi correlates with AML dependency on FA desaturation regardless of mutational profile and is modulated by FA biosynthesis activity. Finally, we show that lipotoxicity increases chemotherapy-induced DNA damage and standard chemotherapy further sensitizes AML cells to SCDi. Our work supports developing FA desaturase inhibitors in AML while stressing the importance of identifying predictive biomarkers of response and biologically validated combination therapies to realize their full therapeutic potential.

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

METHOD

This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods.

RESULTS

Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques.

CONCLUSIONS

This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Novel Vpx virus-like particles to improve cytarabine treatment response against acute myeloid leukemia

Knowledge of the molecular pathogenesis of acute myeloid leukemia has advanced in recent years. Despite novel treatment options, acute myeloid leukemia remains a survival challenge for elderly patients. We have recently shown that the triphosphohydrolase SAMHD1 is one of the factors determining resistance to Ara-C treatment. Here, we designed and tested novel and simpler virus-like particles incorporating the lentiviral protein Vpx to efficiently and transiently degrade SAMHD1 and increase the efficacy of Ara-C treatment. The addition of minute amounts of lentiviral Rev protein during production enhanced the generation of virus-like particles. In addition, we found that our 2nd generation of virus-like particles efficiently targeted and degraded SAMHD1 in AML cell lines with high levels of SAMHD1, thereby increasing Ara-CTP levels and response to Ara-C treatment. Primary AML blasts were generally less responsive to VLP treatment. In summary, we have been able to generate novel and simpler virus-like particles that can efficiently deliver Vpx to target cells.

NET-related gene signature for predicting AML prognosis

Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan-Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs-CFTR, ENO1, PARVB, DDIT4, MPO, LDLR-were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.

Orchestral role of lipid metabolic reprogramming in T-cell malignancy

The immune function of normal T cells partially depends on the maneuvering of lipid metabolism through various stages and subsets. Interestingly, T-cell malignancies also reprogram their lipid metabolism to fulfill bioenergetic demand for rapid division. The rewiring of lipid metabolism in T-cell malignancies not only provides survival benefits but also contributes to their stemness, invasion, metastasis, and angiogenesis. Owing to distinctive lipid metabolic programming in T-cell cancer, quantitative, qualitative, and spatial enrichment of specific lipid molecules occur. The formation of lipid rafts rich in cholesterol confers physical strength and sustains survival signals. The accumulation of lipids through de novo synthesis and uptake of free lipids contribute to the bioenergetic reserve required for robust demand during migration and metastasis. Lipid storage in cells leads to the formation of specialized structures known as lipid droplets. The inimitable changes in fatty acid synthesis (FAS) and fatty acid oxidation (FAO) are in dynamic balance in T-cell malignancies. FAO fuels the molecular pumps causing chemoresistance, while FAS offers structural and signaling lipids for rapid division. Lipid metabolism in T-cell cancer provides molecules having immunosuppressive abilities. Moreover, the distinctive composition of membrane lipids has implications for immune evasion by malignant cells of T-cell origin. Lipid droplets and lipid rafts are contributors to maintaining hallmarks of cancer in malignancies of T cells. In preclinical settings, molecular targeting of lipid metabolism in T-cell cancer potentiates the antitumor immunity and chemotherapeutic response. Thus, the direct and adjunct benefit of lipid metabolic targeting is expected to improve the clinical management of T-cell malignancies.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

Biochemical Basis of Selective Accumulation and Targeted Delivery of Photosensitizers to Tumor Tissues

The method of photodynamic therapy for treatment of malignant neoplasms is based on the selective of accumulation of photosensitizers in the tumor tissue. Insufficient selectivity of photosensitizers in relation to pathologically altered tissues and generalized distribution throughout the body leads to the development of severe toxic effects, including skin phototoxicity. The mechanisms underlying selectivity of photosensitizers for tumor tissue include selective binding to blood proteins and lipoproteins (considering that the number of receptors for those is increased on tumor cell membranes), uptake by macrophages, better solubility at low pH (acidic pH is characteristic of tumor cells), and other mechanisms. At present, increase in the efficiency of photodynamic therapy is largely associated with the additional targeting of photosensitizers to tumor tissues. Targeted delivery strategies are based on the differences in metabolism and gene expression profiles between the tumor and healthy cells. There are differences in expression of receptors, proteases, or transmembrane transporters in these cells. In particular, accelerated metabolism in many types of tumors leads to overexpression of receptors for epidermal growth factor, folic acid, transferrin, and a number of other compounds. This review considers biochemical basis for the selective accumulation of various classes of photosensitizers in tumors (chlorins, phthalocyanines, 5-aminolevulinic acid derivatives, etc.) and discusses various strategies of targeted delivery with emphasis on conjugation of photosensitizers with the receptor ligands overexpressed in tumor cells.

Mining of transcriptome identifies CD109 and LRP12 as possible biomarkers and deregulation mechanism of T cell receptor pathway in Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) is a heterogeneous disease with highest mortality compared to other types of leukemia. There is a need to find the gene abnormalities and mechanisms behind them due to their heterogenic nature. The present study is aimed to understand genes, pathways and biomarker proteins influenced by transcriptomic deregulation due to AML. Differentially expressed gene (DEG), protein-protein interaction network, gene ontology, KEGG pathway, variant analysis and secretome analyses were performed using different AML RNAseq datasets. A total of 655 DEGs including 291 up-regulated and 364 down-regulated genes, which were satisfied with a fold change of 1.5 were identified. Top hub genes for AML were identified as TP53, PTPRC and AKT1. This integrative bioinformatics approach revealed the deregulation of T Cell Receptor (TCR) pathway and altered immune response related genes. The survival analysis revealed the associated deregulation of multiple TCR pathway related genes. Variant analysis identified the benign and likely benign nature of many important target genes and markers screened, which were found to have an important role in the progression of AML. DEGs and secretome analysis found out a set of seven molecules represents potential biomarkers for AML. In vitro analytical validation showed overexpression pattern of CD109 and LRP12 in AML cell line and HL-60 cells than the normal human bone marrow-derived stromal cell line HS-5. Here we report first time for CD109 and LRP12 as a possible biomarkers for the diagnostic significance. Amino acid substitutions detected by variant analysis and deregulation of immune checkpoint molecules revealed their role in reducing immune response and inability to fight cancer cells. In conclusion, this study highlights the possibility of new biomarkers for AML and the mechanism of decrease in immune response due to the downregulation of co-stimulatory immune molecules, which needs further clinical validation investigations.

•

Using RNA-seq data of AML patients, two biomarkers including CD109 and LRP12 for the diagnostic significance were identified based on DEGs, GO/KEGG, and PPI network analysis.

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The transcriptome mining unmasked the complexity of gene alterations in AML by identifying immune response related genes deregulation and significance of TCR signalling.

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Several genes were identified as AML hub genes by network analysis, variant analysis identified non-synonymous variants in co-stimulatory checkpoint targets and the co-inhibitory targets.

Cholesterol homeostasis and cancer: a new perspective on the low-density lipoprotein receptor

BACKGROUND

Disturbance of cholesterol homeostasis is considered as one of the manifestations of cancer. Cholesterol plays an essential role in the pleiotropic functions of cancer cells, including mediating membrane trafficking, intracellular signal transduction, and production of hormones and steroids. As a single transmembrane receptor, the low-density lipoprotein receptor (LDLR) can participate in intracellular cholesterol uptake and regulate cholesterol homeostasis. It has recently been found that LDLR is aberrantly expressed in a broad range of cancers, including colon cancer, prostate cancer, lung cancer, breast cancer and liver cancer. LDLR has also been found to be involved in various signaling pathways, such as the MAPK, NF-κB and PI3K/Akt signaling pathways, which affect cancer cells and their surrounding microenvironment. Moreover, LDLR may serve as an independent prognostic factor for lung cancer, breast cancer and pancreatic cancer, and is closely related to the survival of cancer patients. However, the role of LDLR in some cancers, such as prostate cancer, remains controversial. This may be due to the lack of normal feedback regulation of LDLR expression in cancer cells and the severe imbalance between LDLR-mediated cholesterol uptake and de novo biosynthesis of cholesterol.

CONCLUSIONS

The imbalance of cholesterol homeostasis caused by abnormal LDLR expression provides new therapeutic opportunities for cancer. LDLR interferes with the occurrence and development of cancer by modulating cholesterol homeostasis and may become a novel target for the development of anti-cancer drugs. Herein, we systematically review the contribution of LDLR to cancer progression, especially its dysregulation and underlying mechanism in various malignancies. Besides, potential targeting and immunotherapeutic options are proposed.

Linking Late Endosomal Cholesterol with Cancer Progression and Anticancer Drug Resistance

Cancer cells undergo drastic metabolic adaptions to cover increased bioenergetic needs, contributing to resistance to therapies. This includes a higher demand for cholesterol, which often coincides with elevated cholesterol uptake from low-density lipoproteins (LDL) and overexpression of the LDL receptor in many cancers. This implies the need for cancer cells to accommodate an increased delivery of LDL along the endocytic pathway to late endosomes/lysosomes (LE/Lys), providing a rapid and effective distribution of LDL-derived cholesterol from LE/Lys to other organelles for cholesterol to foster cancer growth and spread. LDL-cholesterol exported from LE/Lys is facilitated by Niemann–Pick Type C1/2 (NPC1/2) proteins, members of the steroidogenic acute regulatory-related lipid transfer domain (StARD) and oxysterol-binding protein (OSBP) families. In addition, lysosomal membrane proteins, small Rab GTPases as well as scaffolding proteins, including annexin A6 (AnxA6), contribute to regulating cholesterol egress from LE/Lys. Here, we summarize current knowledge that links upregulated activity and expression of cholesterol transporters and related proteins in LE/Lys with cancer growth, progression and treatment outcomes. Several mechanisms on how cellular distribution of LDL-derived cholesterol from LE/Lys influences cancer cell behavior are reviewed, some of those providing opportunities for treatment strategies to reduce cancer progression and anticancer drug resistance.

New dawn for cancer cell death: Emerging role of lipid metabolism

BACKGROUND

Resistance to cell death, a protective mechanism for removing damaged cells, is a "Hallmark of Cancer" that is essential for cancer progression. Increasing attention to cancer lipid metabolism has revealed a number of pathways that induce cancer cell death.

SCOPE OF REVIEW

We summarize emerging concepts regarding lipid metabolic reprogramming in cancer that is mainly involved in lipid uptake and trafficking, de novo synthesis and esterification, fatty acid synthesis and oxidation, lipogenesis, and lipolysis. During carcinogenesis and progression, continuous metabolic adaptations are co-opted by cancer cells, to maximize their fitness to the ever-changing environmental. Lipid metabolism and the epigenetic modifying enzymes interact in a bidirectional manner which involves regulating cancer cell death. Moreover, lipids in the tumor microenvironment play unique roles beyond metabolic requirements that promote cancer progression. Finally, we posit potential therapeutic strategies targeting lipid metabolism to improve treatment efficacy and survival of cancer patient.

MAJOR CONCLUSIONS

The profound comprehension of past findings, current trends, and future research directions on resistance to cancer cell death will facilitate the development of novel therapeutic strategies targeting the lipid metabolism.

Investigation of the Effect of Curcumin on Protein Targets in NAFLD Using Bioinformatic Analysis

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder. Defects in function/expression of genes/proteins are critical in initiation/progression of NAFLD. Natural products may modulate these genes/proteins. Curcumin improves steatosis, inflammation, and fibrosis progression. Here, bioinformatic tools, gene−drug and gene-disease databases were utilized to explore targets, interactions, and pathways through which curcumin could impact NAFLD. METHODS: Significant curcumin−protein interaction was identified (high-confidence:0.7) in the STITCH database. Identified proteins were investigated to determine association with NAFLD. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for significantly involved targets (p < 0.01). Specificity of obtained targets with NAFLD was estimated and investigated in Tissue/Cells−gene associations (PanglaoDB Augmented 2021, Mouse Gene Atlas) and Disease−gene association-based EnrichR algorithms (Jensen DISEASES, DisGeNET). RESULTS: Two collections were constructed: 227 protein−curcumin interactions and 95 NAFLD-associated genes. By Venn diagram, 14 significant targets were identified, and their biological pathways evaluated. Based on gene ontology, most targets involved stress and lipid metabolism. KEGG revealed chemical carcinogenesis, the AGE-RAGE signaling pathway in diabetic complications and NAFLD as the most common significant pathways. Specificity to diseases database (EnrichR algorithm) revealed specificity for steatosis/steatohepatitis. CONCLUSION: Curcumin may improve, or inhibit, progression of NAFLD through activation/inhibition of NAFLD-related genes.

Target Deconvolution of Fenofibrate in Nonalcoholic Fatty Liver Disease Using Bioinformatics Analysis

Background

Nonalcoholic fatty liver disease (NAFLD) is a prevalent form of liver damage, affecting ~25% of the global population. NAFLD comprises a spectrum of liver pathologies, from hepatic steatosis to nonalcoholic steatohepatitis (NASH), and may progress to liver fibrosis and cirrhosis. The presence of NAFLD correlates with metabolic disorders such as hyperlipidemia, obesity, blood hypertension, cardiovascular, and insulin resistance. Fenofibrate is an agonist drug for peroxisome proliferator-activated receptor alpha (PPARα), used principally for treatment of hyperlipidemia. However, fenofibrate has recently been investigated in clinical trials for treatment of other metabolic disorders such as diabetes, cardiovascular disease, and NAFLD. The evidence to date indicates that fenofibrate could improve NAFLD. While PPARα is considered to be the main target of fenofibrate, fenofibrate may exert its effect through impact on other genes and pathways thereby alleviating, and possibly reversing, NAFLD. In this study, using bioinformatics tools and gene-drug, gene-diseases databases, we sought to explore possible targets, interactions, and pathways involved in fenofibrate and NAFLD.

Methods

We first determined significant protein interactions with fenofibrate in the STITCH database with high confidence (0.7). Next, we investigated the identified proteins on curated targets in two databases, including the DisGeNET and DISEASES databases, to determine their association with NAFLD. We finally constructed a Venn diagram for these two collections (curated genes-NAFLD and fenofibrate-STITCH) to uncover possible primary targets of fenofibrate. Then, Gene Ontology (GO) and KEGG were analyzed to detect the significantly involved targets in molecular function, biological process, cellular component, and biological pathways. A P value < 0.01 was considered the cut-off criterion. We also estimated the specificity of targets with NAFLD by investigating them in disease-gene associations (STRING) and EnrichR (DisGeNET). Finally, we verified our findings in the scientific literature.

Results

We constructed two collections, one with 80 protein-drug interactions and the other with 95 genes associated with NAFLD. Using the Venn diagram, we identified 11 significant targets including LEP, SIRT1, ADIPOQ, PPARA, SREBF1, LDLR, GSTP1, VLDLR, SCARB1, MMP1, and APOC3 and then evaluated their biological pathways. Based on Gene Ontology, most of the targets are involved in lipid metabolism, and KEGG enrichment pathways showed the PPAR signaling pathway, AMPK signaling pathway, and NAFLD as the most significant pathways. The interrogation of those targets on authentic disease databases showed they were more specific to both steatosis and steatohepatitis liver injury than to any other diseases in these databases. Finally, we identified three significant genes, APOC3, PPARA, and SREBF1, that showed robust drug interaction with fenofibrate.

Conclusion

Fenofibrate may exert its effect directly or indirectly, via modulation of several key targets and pathways, in the treatment of NAFLD.

The characteristics of circRNA as competing endogenous RNA in pathogenesis of acute myeloid leukemia

Background

As one of the novel molecules, circRNA has been identified closely involved in the pathogenesis of many diseases. However, the function of circRNA in acute myeloid leukemia (AML) still remains unknown.

Methods

In the current study, the RNA expression profiles were obtained from Gene Expression Omnibus (GEO) datasets. The differentially expressed RNAs were identified using R software and the competing endogenous RNA (ceRNA) network was constructed using Cytoscape. Functional and pathway enrichment analyses were performed to identify the candidate circRNA-mediated aberrant signaling pathways. The hub genes were identified by MCODE and CytoHubba plugins of Cytoscape, and then a subnetwork regulatory module was established.

Results

A total of 27 circRNA-miRNA pairs and 208 miRNA-mRNA pairs, including 12 circRNAs, 24 miRNAs and 112 mRNAs were included in the ceRNA network. Subsequently, a subnetwork, including 4 circRNAs, 5 miRNAs and 6 mRNAs, was established based on related circRNA-miRNA-mRNA regulatory modules.

Conclusions

In summary, this work analyzes the characteristics of circRNA as competing endogenous RNA in AML pathogenesis, which would provide hints for developing novel prognostic, diagnostic and therapeutic strategy for AML.