Prognosis and regulation of an adenylyl cyclase network in acute myeloid leukemia

Resveratrol suppresses liver cancer progression by downregulating AKR1C3: targeting HCC with HSA nanomaterial as a carrier to enhance therapeutic efficacy

The enzyme AKR1C3 plays a crucial role in hormone and drug metabolism and is associated with abnormal expression in liver cancer, leading to tumor progression and poor prognosis. Nanoparticles modified with HSA can modulate the tumor microenvironment by enhancing photodynamic therapy to induce apoptosis in tumor cells and alleviate hypoxia. Therefore, exploring the potential regulatory mechanisms of resveratrol on AKR1C3 through the construction of HSA-RSV NPs carriers holds significant theoretical and clinical implications for the treatment of liver cancer. The aim of this study is to investigate the targeted regulation of AKR1C3 expression through the loading of resveratrol (RSV) on nanomaterials HSA-RSV NPs (Nanoparticles) in order to alleviate tumor hypoxia and inhibit the progression of hepatocellular carcinoma (HCC), and to explore its molecular mechanism. PubChem database and PharmMapper server were used to screen the target genes of RSV. HCC-related differentially expressed genes (DEGs) were analyzed through the GEO dataset, and relevant genes were retrieved from the GeneCards database, resulting in the intersection of the three to obtain candidate DEGs. GO and KEGG enrichment analyses were performed on the candidate DEGs to analyze the potential cellular functions and molecular signaling pathways affected by the main target genes. The cytohubba plugin was used to screen the top 10 target genes ranked by Degree and further intersected the results of LASSO and Random Forest (RF) to obtain hub genes. The expression analysis of hub genes and the prediction of malignant tumor prognosis were conducted. Furthermore, a pharmacophore model was constructed using PharmMapper. Molecular docking simulations were performed using AutoDockTools 1.5.6 software, and ROC curve analysis was performed to determine the core target. In vitro cell experiments were carried out by selecting appropriate HCC cell lines, treating HCC cells with different concentrations of RSV, or silencing or overexpressing AKR1C3 using lentivirus. CCK-8, clone formation, flow cytometry, scratch experiment, and Transwell were used to measure cancer cell viability, proliferation, migration, invasion, and apoptosis, respectively. Cellular oxygen consumption rate was analyzed using the Seahorse XF24 analyzer. HSA-RSV NPs were prepared, and their characterization and cytotoxicity were evaluated. The biological functional changes of HCC cells after treatment were detected. An HCC subcutaneous xenograft model was established in mice using HepG2 cell lines. HSA-RSV NPs were injected via the tail vein, with a control group set, to observe changes in tumor growth, tumor targeting of NPs, and biological safety. TUNEL, Ki67, and APC-hypoxia probe staining were performed on excised tumor tissue to detect tumor cell proliferation, apoptosis, and hypoxia. Lentivirus was used to silence or overexpress AKR1C3 simultaneously with the injection of HSA-RSV NPs via the tail vein to assess the impact of AKR1C3 on the regulation of HSA-RSV NPs in HCC progression. Bioinformatics analysis revealed that AKR1C3 is an important target gene involved in the regulation of HCC by RSV, which is associated with the prognosis of HCC patients and upregulated in expression. In vitro cell experiments showed that RSV significantly inhibits the respiratory metabolism of HCC cells, suppressing their proliferation, migration, and invasion and promoting apoptosis. Silencing AKR1C3 further enhances the toxicity of RSV towards HCC cells. The characterization and cytotoxicity experiments of nanomaterials demonstrated the successful construction of HSA-RSV NPs, which exhibited stronger inhibitory effects on HCC cells. In vivo, animal experiments further confirmed that targeted downregulation of AKR1C3 by HSA-RSV NPs suppresses the progression of HCC and tumor hypoxia while exhibiting tumor targeting and biological safety. Targeted downregulation of AKR1C3 by HSA-RSV NPs can alleviate HCC tumor hypoxia and inhibit the progression of HCC.

Potential drug-drug interactions with mitogen-activated protein kinase (MEK) inhibitors used to treat melanoma

INTRODUCTION

The management of patients with BRAF-mutated advanced melanoma who are undergoing targeted therapy with MEK inhibitors can be complicated by the co-administration of multiple medications, which can give rise to drug-drug interactions of clinical significance.

COVERED AREAS

Our review presents a comprehensive analysis of the pharmacokinetic and pharmacodynamic interactions of the three approved for advanced melanoma MEK inhibitor drugs - binimetinib, cobimetinib, and trametinib. MEDLINE (PubMed) was utilized for the literature search, comprising clinical studies, observational studies, and preclinical research. The review discusses the impact of these interactions on efficacy and safety of the treatments and differentiates between interactions supported by pharmacokinetic or pharmacodynamic mechanisms, those encountered in clinical practice, and those observed in preclinical studies.

EXPERT OPINION

Physicians should be aware about potential benefits, but also increased toxicity caused by drug interactions between MEK inhibitors and other drugs in the management of patients with metastatic melanoma.

A Pan-Cancer Analysis of the Prognostic Value and Expression of Adenylate Cyclase 7 (ADCY7) in Human Tumors

BACKGROUND

The role of adenylate cyclase 7 (ADCY7) in cancer is still unclear. This study analyzed the interrelationship between the expression and immune function of ADCY7.

METHODS

ADCY7 expression in multiple human cancers was analyzed using the databases of Genotype-Tissue Expression Project (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA). Correlations among ADCY7 gene expression, mismatch repair (MMR) gene expression, and DNA methyltransferase (DNMT) expression were assessed using Spearman correlation analysis. Univariate survival analysis and Kaplan-Meier (KM) curve were used to examine the effect of ADCY7 expression on prognosis. The Tumor Immune Estimation Resource (TIMER) database was used to evaluate the relationship between ADCY7 gene expression and tumor immune invasion or immune checkpoint gene (ICG) expression.

RESULTS

ADCY7 was abnormally expressed in multiple human cancers and was correlated with MMR genes and DNMT expression. Univariate survival analysis and KM curve showed that ADCY7 expression influences the overall survival (OS) of six types of cancer, disease-specific survival (DSS) of eight, and progression-free interval (PFI) of three. The high expression of ADCY7 in OS, DSS, and PFI was strongly associated with poor outcomes in patients with breast cancer and lung squamous cell carcinoma. ADCY7 expression was strongly associated with immune cell infiltration and ICG expression.

CONCLUSION

The results of this study indicated that ADCY7 may be a prognostic biomarker of tumorigenesis. The study may also provide a new perspective on the role of ADCY7 in human cancers.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach

Simple Summary

We applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells.

Abstract

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.

An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma

In this study, we performed bioinformatics and statistical analyses to investigate the prognostic significance of metabolic genes in clear cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissues from TCGA database. We identified 79 upregulated and 45 downregulated (n=124) metabolic genes in ccRCC tissues. Eleven prognostic metabolic genes (NOS1, ALAD, ALDH3B2, ACADM, ITPKA, IMPDH1, SCD5, FADS2, ACHE, CA4, and HK3) were identified by further analysis. We then constructed an 11-metabolic gene signature-based prognostic risk score model and classified ccRCC patients into high- and low-risk groups. Overall survival (OS) among the high-risk ccRCC patients was significantly shorter than among the low-risk ccRCC patients. Receiver operating characteristic (ROC) curve analysis of the prognostic risk score model showed that the areas under the ROC curve for the 1-, 3-, and 5-year OS were 0.810, 0.738, and 0.771, respectively. Thus, our prognostic model showed favorable predictive power in the TCGA and E-MTAB-1980 ccRCC patient cohorts. We also established a nomogram based on these eleven metabolic genes and validated internally in the TCGA cohort, showing an accurate prediction for prognosis in ccRCC.