Analysis of Differentially Expressed Genes in Endothelial Cells Following Tumor Cell Adhesion, and the Role of PRKAA2 and miR-124-3p

Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins

Extracellular vesicles (EVs) are crucial mediators of cell-to-cell communication in physiological and pathological conditions. Specifically, EVs released from the vasculature into blood were found to be quantitatively and qualitatively different in diseases compared to healthy states. However, our understanding of EVs derived from the lymphatic system is still scarce. In this study, we compared the mRNA and microRNA (miRNA) expression in blood vascular (BEC) and lymphatic (LEC) endothelial cells. After characterization of the EVs by fluorescence-triggered flow cytometry, nanoparticle tracking analysis and cryo-transmission electron microscopy (cryo-TEM) we utilized small RNA-sequencing to characterize miRNA signatures in the EVs and identify cell-type specific miRNAs in BEC and LEC. We found miRNAs specifically enriched in BEC and LEC on the cellular as well as the extracellular vesicle level. Our data provide a solid basis for further functional in vitro and in vivo studies addressing the role of EVs in the blood and lymphatic vasculature.

Fatty Acid Metabolism Signature Contributes to the Molecular Diagnosis of a Malignant Gastric Cancer Subtype with Poor Prognosis and Lower Mutation Burden

BACKGROUND

Gastric cancer (GC) is a common gastrointestinal tumor with high morbidity and mortality. Fatty acid metabolism (FAM) contributes to GC development. Patents have been issued for the use of compositions comprising fatty acid analogues for the treatment of many clinical conditions. However, its clinical significance and its relationship with tumor-related mutations have not been thoroughly discovered. This study was conducted to analyze and explore FAM-related genes' molecular characteristics, prognostic significance, and association with tumor- related mutations.

METHODS

The gastric adenocarcinoma's transcriptome, clinical data, and tumor mutation load (TMB) data were downloaded from TCGA and GEO databases. The differentially expressed FAM genes (FAM DEGs) between cancer and control samples were screened, and their correlation with TMB and survival was analyzed. A PPI network of FAM DEGs was constructed, and a downscaling clustering analysis was performed based on the expression of the FAM DEGs. Further immuno- infiltration and GO/KEGG enrichment analyses of the identified FAM clusters were performed to explore their heterogeneity in biological functions. The effects of FAM score and gastric cancer (STAD) on TMB, MSI, survival prognosis, and drug sensitivity were jointly analyzed, and finally, a single-gene analysis of the obtained core targets was performed.

RESULTS

Through differential analysis, 68 FAM DEGs were obtained, and they were highly associated with STAD tumor mutation load. In addition, a high FAM DEGs CNV rate was observed. The PPI network showed a complex mutual correlation between the FAM DEGs. Consensus clustering classified the patients into three clusters based on the FAM DEGs, and the clusters presented different survival rates. The GSVA and immune infiltration analysis revealed that metabolism, apoptosis, and immune infiltration-related pathways were variated. In addition, FAM genes, STAD prognostic risk genes, and PCA scores were closely associated with the survival status of STAD patients. FAM score was closely correlated with STAD TMB, MSI, and immunotherapy, and the TMB values in the low FAM score group were significantly higher than those in the high FAM score group. Finally, combining the above results, it was found that the core gene PTGS1 performed best in predicting STAD survival prognosis and TMB/MSI/immunotherapy.

CONCLUSION

Fatty acid metabolism genes affect the development of gastric adenocarcinoma and can predict the survival prognosis, tumor mutational load characteristics, and drug therapy sensitivity of STAD patients, which can help explore more effective immunotherapy targets for GC.

Integration of Chemoinformatics and Multi-Omics Analysis Defines ECT2 as a Potential Target for Cancer Drug Therapy

Epithelial cell transforming 2 (ECT2) is a potential oncogene and a number of recent studies have correlated it with the progression of several human cancers. Despite this elevated attention for ECT2 in oncology-related reports, there is no collective study to combine and integrate the expression and oncogenic behavior of ECT2 in a panel of human cancers. The current study started with a differential expression analysis of ECT2 in cancerous versus normal tissue. Following that, the study asked for the correlation between ECT2 upregulation and tumor stage, grade, and metastasis, along with its effect on patient survival. Moreover, the methylation and phosphorylation status of ECT2 in tumor versus normal tissue was assessed, in addition to the investigation of the ECT2 effect on the immune cell infiltration in the tumor microenvironment. The current study revealed that ECT2 was upregulated as mRNA and protein levels in a list of human tumors, a feature that allowed for the increased filtration of myeloid-derived suppressor cells (MDSC) and decreased the level of natural killer T (NKT) cells, which ultimately led to a poor prognosis survival. Lastly, we screened for several drugs that could inhibit ECT2 and act as antitumor agents. Collectively, this study nominated ECT2 as a prognostic and immunological biomarker, with reported inhibitors that represent potential antitumor drugs.

Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery

BACKGROUND

Postoperative progression and chemotherapy resistance is the major cause of treatment failure in patients with triple-negative breast cancer (TNBC). Currently, there is a lack of an ideal predictive model for the progression and drug sensitivity of postoperative TNBC patients. Diverse programmed cell death (PCD) patterns play an important role in tumor progression, which has the potential to be a prognostic and drug sensitivity indicator for TNBC after surgery.

MATERIALS AND METHODS

Twelve PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis) were analyzed for model construction. Bulk transcriptome, single-cell transcriptome, genomics, and clinical information were collected from TCGA-BRCA, METABRIC, GSE58812, GSE21653, GSE176078, GSE75688, and KM-plotter cohorts to validate the model.

RESULTS

The machine learning algorithm established a cell death index (CDI) with a 12-gene signature. Validated in five independent datasets, TNBC patients with high CDI had a worse prognosis after surgery. Two molecular subtypes of TNBC with distinct vital biological processes were identified by an unsupervised clustering model. A nomogram with high predictive performance was constructed by incorporating CDI with clinical features. Furthermore, CDI was associated with immune checkpoint genes and key tumor microenvironment components by integrated analysis of bulk and single-cell transcriptome. TNBC patients with high CDI are resistant to standard adjuvant chemotherapy regimens (docetaxel, oxaliplatin, etc.); however, they might be sensitive to palbociclib (an FDA-approved drug for luminal breast cancer).

CONCLUSION

Generally, we established a novel CDI model by comprehensively analyzing diverse cell death patterns, which can accurately predict clinical prognosis and drug sensitivity of TNBC after surgery. A user-friendly website was created to facilitate the application of this prediction model (https://tnbc.shinyapps.io/CDI_Model/).

Circular CPM promotes chemoresistance of gastric cancer via activating PRKAA2-mediated autophagy

BACKGROUND

Chemotherapy can significantly improve the disease-free survival and overall survival of patients with advanced gastric cancer (GC). 5-fluorouracil (5-FU) is frequently applied in the clinic, acting as a first-line chemotherapy drug of advanced GC, which could be used alone or combining platinum drugs. However, its efficacy is significantly attenuated by chemoresistance, which is associated with patients' poor survival. Recently, there is evidence suggesting that dysregulation of autophagy may contribute to drug resistance in cancer, and circular RNAs (circRNAs) also take part in chemoresistance. However, whether circRNAs participate in 5-FU chemoresistance through autophagy remains largely unknown.

METHODS

RNA sequencing technologies and bioinformatics analysis were performed in GC. Sanger sequencing, Actinomycin D assay and RNase R assay confirmed the circular structure of circular CPM (circCPM). Various cell line models and animal models were used to explore related functions in vitro and in vivo. Quantitative Real-time PCR (qRT-PCR), fluorescence in situ hybridization, ribonucleic acid; (RNA) pulldown assays, RNA binding protein immunoprecipitation assays and Luciferase reporter assays were applied to explore involved pathways.

RESULTS

circCPM was up-regulated in 5-FU resistant GC cell lines and tissue. Moreover, high circCPM expression is positively associated with poor survival. Silencing circCPM greatly improved chemosensitivity in vitro and in vivo. Mechanistically, it directly binds to miR-21-3p in the cytoplasm and therefore increases the expression of PRKAA2, contributing to the activation of autophagy and chemoresistance.

CONCLUSION

Our results reveal that circCPM has a crucial role in regulating GC autophagy and 5-FU resistance by targeting PRKAA2. It may function as a new theory basis for assessing the curative effect of GC and reversing 5-FU chemoresistance.