Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, ...might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood.
The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples.
A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples.
Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.
In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the ...mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network.
Abstract
The mechanism that creates vitreous endosperm in the mature maize kernel is poorly understood. We identified
Vitreous endosperm 1
(
Ven1
) as a major QTL influencing this process.
Ven1
...encodes β-carotene hydroxylase 3, an enzyme that modulates carotenoid composition in the amyloplast envelope. The A619 inbred contains a nonfunctional
Ven1
allele, leading to a decrease in polar and an increase in non-polar carotenoids in the amyloplast. Coincidently, the stability of amyloplast membranes is increased during kernel desiccation. The lipid composition in endosperm cells in A619 is altered, giving rise to a persistent amyloplast envelope. These changes impede the gathering of protein bodies and prevent them from interacting with starch grains, creating air spaces that cause an opaque kernel phenotype. Genetic modifiers were identified that alter the effect of
Ven1
A619
, while maintaining a high β-carotene level. These studies provide insight for breeding vitreous kernel varieties and high vitamin A content in maize.
Lysosomes are essential organelles of eukaryotic cells and are responsible for various cellular functions, including endocytic degradation, extracellular secretion, and signal transduction. There are ...dozens of proteins localized to the lysosomal membrane that control the transport of ions and substances across the membrane and are integral to lysosomal function. Mutations or aberrant expression of these proteins trigger a variety of disorders, making them attractive targets for drug development for lysosomal disorder-related diseases. However, breakthroughs in R&D still await a deeper understanding of the underlying mechanisms and processes of how abnormalities in these membrane proteins induce related diseases. In this article, we summarize the current progress, challenges, and prospects for developing therapeutics targeting lysosomal membrane proteins for the treatment of lysosomal-associated diseases.
Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death and has a poor prognosis worldwide, thus, more effective drugs are urgently needed. In this article, a small ...molecule drug library composed of 1,056 approved medicines from the FDA was used to screen for anticancer drugs. The tetracyclic compound maprotiline, a highly selective noradrenergic reuptake blocker, has strong antidepressant efficacy. However, the anticancer effect of maprotiline remains unclear. Here, we investigated the anticancer potential of maprotiline in the HCC cell lines Huh7 and HepG2. We found that maprotiline not only significantly restrained cell proliferation, colony formation and metastasis
in vitro
but also exerted antitumor effects
in vivo
. In addition to the antitumor effect alone, maprotiline could also enhance the sensitivity of HCC cells to sorafenib. The depth studies revealed that maprotiline substantially decreased the phosphorylation of sterol regulatory element-binding protein 2 (SREBP2) through the ERK signaling pathway, which resulted in decreased cholesterol biosynthesis and eventually impeded HCC cell growth. Furthermore, we identified cellular retinoic acid binding protein 1 (CRABP1) as a direct target of maprotiline. In conclusion, our study provided the first evidence showing that maprotiline could attenuate cholesterol biosynthesis to inhibit the proliferation and metastasis of HCC cells through the ERK-SREBP2 signaling pathway by directly binding to CRABP1, which supports the strategy of repurposing maprotiline in the treatment of HCC.
Background
Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by morphological abnormalities and peripheral blood cytopenias, carrying a risk of progression to acute ...myeloid leukemia. Although ferroptosis is a promising target for MDS treatment, the specific roles of ferroptosis‐related genes (FRGs) in MDS diagnosis have not been elucidated.
Methods
MDS‐related microarray data were obtained from the Gene Expression Omnibus database. A comprehensive analysis of FRG expression levels in patients with MDS and controls was conducted, followed by the use of multiple machine learning methods to establish prediction models. The predictive ability of the optimal model was evaluated using nomogram analysis and an external data set. Functional analysis was applied to explore the underlying mechanisms. The mRNA levels of the model genes were verified in MDS clinical samples by quantitative real‐time polymerase chain reaction (qRT‐PCR).
Results
The extreme gradient boosting model demonstrated the best performance, leading to the identification of a panel of six signature genes: SREBF1, PTPN6, PARP9, MAP3K11, MDM4, and EZH2. Receiver operating characteristic curves indicated that the model exhibited high accuracy in predicting MDS diagnosis, with area under the curve values of 0.989 and 0.962 for the training and validation cohorts, respectively. Functional analysis revealed significant associations between these genes and the infiltrating immune cells. The expression levels of these genes were successfully verified in MDS clinical samples.
Conclusion
Our study is the first to identify a novel model using FRGs to predict the risk of developing MDS. FRGs may be implicated in MDS pathogenesis through immune‐related pathways. These findings highlight the intricate correlation between ferroptosis and MDS, offering insights that may aid in identifying potential therapeutic targets for this debilitating disorder.
Our study represents the first identification of the involvement of ferroptosis‐related genes (FRGs) in myelodysplastic syndromes (MDS) and the establishment of a novel risk model based on six FRGs for assessing MDS development. These genes potentially contribute to MDS pathogenesis and progression through immune‐related pathways. The findings underscore the intricate correlation between ferroptosis and MDS, providing insights that could facilitate the identification of therapeutic targets for this disorder.
With the process of poverty eradication and economic growth, hydropower development becomes increasingly important because of its huge potential advantages in the Lancang-Mekong River Basin. However, ...the complex topography and rich resource endowments in the Lancang-Mekong River Basin bring a variety of potential risks and uncertainties in hydropower development, which has an important impact on the sustainable livelihood of farmers. There is an urgent need for countries in the Lancang-Mekong River Basin to systematically assess hydropower projects, especially their impact on the sustainable livelihoods of farmers. Based on the systematic analysis of relevant literature, this study established a collaborative framework of hydropower development and farmers’ sustainable livelihood, including theoretical framework, indicator system and model structure. The purpose is to explore the interaction mechanism of energy and water resources utilization, food security and sustainable livelihood of farmers in hydropower development. The findings can provide scientific and technological support for the Belt and Road Initiative, poverty reduction and sustainable development in the river basin.
Background
Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for ...cancer. However, no research has unveiled the particular roles of cuproptosis‐related genes (CRGs) in the prediction of MM diagnosis.
Methods
Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real‐time polymerase chain reaction (qRT‐PCR) in MM cell samples.
Results
The optimal gene signature was identified using LASSO and SVM‐RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature‐based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune‐related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings.
Conclusion
We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune‐related pathways.
We identified a novel signature associated with cuproptosis‐related genes, which exhibited a strong diagnostic value for multiple myeloma (MM). Our study highlights the involvement of cuproptosis‐related genes in the diagnosis of MM and investigates the underlying molecular mechanisms, offering novel insights into the pathogenesis and progression of MM.
Dent and flint kernel architectures are important characteristics that affect the physical properties of maize kernels and their grain end uses. The genes controlling these traits are unknown, so it ...is difficult to combine the advantageous kernel traits of both. We found mutation of ARFTF17 in a dent genetic background reduces IAA content in the seed pericarp, creating a flint-like kernel phenotype. ARFTF17 is highly expressed in the pericarp and encodes a protein that interacts with and inhibits MYB40, a transcription factor with the dual functions of repressing PIN1 expression and transactivating genes for flavonoid biosynthesis. Enhanced flavonoid biosynthesis could reduce the metabolic flux responsible for auxin biosynthesis. The decreased IAA content of the dent pericarp appears to reduce cell division and expansion, creating a shorter, denser kernel. Introgression of the ARFTF17 mutation into dent inbreds and hybrids improved their kernel texture, integrity, and desiccation, without affecting yield.
As one of the hallmarks of cancer, metabolic reprogramming leads to cancer progression, and targeting glycolytic enzymes could be useful strategies for cancer therapy. By screening a small molecule ...library consisting of 1320 FDA-approved drugs, we found that penfluridol, an antipsychotic drug used to treat schizophrenia, could inhibit glycolysis and induce apoptosis in esophageal squamous cell carcinoma (ESCC). Gene profiling and Ingenuity Pathway Analysis suggested the important role of AMPK in action mechanism of penfluridol. By using drug affinity responsive target stability (DARTS) technology and proteomics, we identified phosphofructokinase, liver type (PFKL), a key enzyme in glycolysis, as a direct target of penfluridol. Penfluridol could not exhibit its anticancer property in PFKL-deficient cancer cells, illustrating that PFKL is essential for the bioactivity of penfluridol. High PFKL expression is correlated with advanced stages and poor survival of ESCC patients, and silencing of PFKL significantly suppressed tumor growth. Mechanistically, direct binding of penfluridol and PFKL inhibits glucose consumption, lactate and ATP production, leads to nuclear translocation of FOXO3a and subsequent transcriptional activation of BIM in an AMPK-dependent manner. Taken together, PFKL is a potential prognostic biomarker and therapeutic target in ESCC, and penfluridol may be a new therapeutic option for management of this lethal disease.
PFKL may be a promising prognostic biomarker and therapeutic target in esophageal cancer, and our study provides a preclinical rationale for antipsychotic drug penfluridol as strategy for cancer treatment. Display omitted
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