DPEP1 expression promotes proliferation and survival of leukaemia cells and correlates with relapse in adults with common B cell acute lymphoblastic leukaemia

ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies

Gene selection is crucial for cancer classification using microarray data. In the interests of improving cancer classification accuracy, in this paper, we developed a new wrapper method called ieGENES for gene selection. First we proposed a parsimonious kernel machine regularization (PKMR) model by using ridge regularization in kernel machine driven classification to tackle multi-collinearity for the sake of stable estimates in high-dimensional settings. Then the ieGENES algorithm was developed to optimally identify relevant genes while iteratively eliminating redundant ones based on leave-one-out cross-validation accuracy. In particular, we developed a new methodology to optimally update model parameters upon gene removal. The ieGENES algorithm was evaluated on six cancer microarray datasets and compared to existing methods. Classification accuracy and number of differentially expressed genes (DEGs) identified were assessed. In terms of gene selection accuracy, the ieGENESoutperformed multiple wrapper methods on 5 out of 6 datasets (Colon, Leukemia, Hepato, Glioma, and Breast Cancers), with statistically significant improvements (p<0.001). For the Colon dataset, ieGENES achieved 96.21% accuracy with 167 DEGs. The proposed ieGENES technique demonstrated superior performance in identifying DEGs for cancer diagnosis comparing with existing techniques. It offers a promising tool for identifying biologically relevant genes in microarray data analysis and biomarker discovery for cancer research.

Lipid metabolism-related gene expression in the immune microenvironment predicts prognostic outcomes in renal cell carcinoma

Background

Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases.

Methods

The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays.

Results

Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration.

Conclusion

In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.

Characterization of mesenchymal stem cells in pre-B acute lymphoblastic leukemia

Components of the bone marrow microenvironment (BMM) have been shown to mediate the way in which leukemia develops, progresses and responds to treatment. Increasing evidence shows that leukemic cells hijack the BMM, altering its functioning and establishing leukemia-supportive interactions with stromal and immune cells. While previous work has highlighted functional defects in the mesenchymal stem cell (MSC) population from the BMM of acute leukemias, thorough characterization and molecular profiling of MSCs in pre-B cell acute lymphoblastic leukemia (B-ALL), the most common cancer in children, has not been conducted. Here, we investigated the cellular and transcriptome profiles of MSCs isolated from the BMM of an immunocompetent BCR-ABL1⁺ model of B-ALL. Leukemia-associated MSCs exhibited reduced self-renewal capacity in vitro and significant changes in numerous molecular signatures, including upregulation of inflammatory signaling pathways. Additionally, we found downregulation of genes involved in extracellular matrix organization and osteoblastogenesis in leukemia-associated MSCs. This study provides cellular and molecular insights into the role of MSCs during B-ALL progression.

DPEP1 promotes drug resistance in colon cancer cells by forming a positive feedback loop with ASCL2

BACKGROUND

Drug resistance is an important factor affecting the efficacy of chemotherapy in patients with colon cancer. However, clinical markers for diagnosing drug resistance of tumor cells are not only a few in number, but also low in specificity, and the mechanism of action of tumor cell drug resistance remains unclear.

METHODS

Dipeptidase 1 (DPEP1) expression was analyzed using the cancer genome atlas (TCGA) and genotype-Tissue Expression pan-cancer data. Survival analysis was performed using the survival package in R software to assess the prognostic value of DPEP1 expression in colon cancer. Correlation and Venn analyses were adopted to identify key genes. Immunohistochemistry, western blot, qRT-PCR, Co-immunoprecipitation, and dual-luciferase reporter experiments were carried out to explore the underlying associations between DPEP1 and Achaete scute-like 2 (ASCL2). MTT assays were used to evaluate the role of DPEP1 and ASCL2 in colon cancer drug resistance.

RESULTS

DPEP1 was highly expressed in colon cancer tissues. DPEP1 expression correlated negatively with disease-specific survival but not with overall survival. Bioinformatics analysis and experiments showed that the expressions of DPEP1 and ASCL2 in colon cancer tissues were markedly positively correlated. Mechanistic research indicated that DPEP1 enhanced the stability of protein ASCL2 by inhibiting its ubiquitination-mediated degradation. In turn, ASCL2 functioned as a transcription factor to activate the transcriptional activity of the DPEP1 gene and boost its expression. Furthermore, DPEP1 also could enhance the expression of colon cancer stem cell markers (LGR5, CD133, and CD44), which strengthened the tolerance of colon cancer cells to chemotherapy drugs.

CONCLUSIONS

Our findings reveal that the DPEP1 enhances the stemness of tumor cells by forming a positive feedback loop with ASCL2 to improve resistance to chemotherapy drugs.

Single-Cell Analysis Uncovers Osteoblast Factor Growth Differentiation Factor 10 as Mediator of Vascular Smooth Muscle Cell Phenotypic Modulation Associated with Plaque Rupture in Human Carotid Artery Disease

(1) Background: Vascular smooth muscle cells (VSMCs) undergo a complex phenotypic switch in response to atherosclerosis environmental triggers, contributing to atherosclerosis disease progression. However, the complex heterogeneity of VSMCs and how VSMC dedifferentiation affects human carotid artery disease (CAD) risk has not been clearly established. (2) Method: A single-cell RNA sequencing analysis of CD45- cells derived from the atherosclerotic aorta of Apolipoprotein E-deficient (Apoe-/-) mice on a normal cholesterol diet (NCD) or a high cholesterol diet (HCD), respecting the site-specific predisposition to atherosclerosis was performed. Growth Differentiation Factor 10 (GDF10) role in VSMCs phenotypic switch was investigated via flow cytometry, immunofluorescence in human atherosclerotic plaques. (3) Results: scRNAseq analysis revealed the transcriptomic profile of seven clusters, five of which showed disease-relevant gene signature of VSMC macrophagic calcific phenotype, VSMC mesenchymal chondrogenic phenotype, VSMC inflammatory and fibro-phenotype and VSMC inflammatory phenotype. Osteoblast factor GDF10 involved in ossification and osteoblast differentiation emerged as a hallmark of VSMCs undergoing phenotypic switch. Under hypercholesteremia, GDF10 triggered VSMC osteogenic switch in vitro. The abundance of GDF10 expressing osteogenic-like VSMCs cells was linked to the occurrence of carotid artery disease (CAD) events. (4) Conclusions: Taken together, these results provide evidence about GDF10-mediated VSMC osteogenic switch, with a likely detrimental role in atherosclerotic plaque stability.

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Numerous studies have been performed over the last decade to exploit the complexity of genomic and transcriptomic lesions driving the initiation of acute myeloid leukemia (AML). These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples is sparse, however; meanwhile, relapse and therapy resistance represent the main challenges in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse, and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1 downregulation and DPEP1 upregulation were associated with AML relapse both in adults and children. Finally, machine learning-based and network-based analysis identified overexpressed CD6 and downregulated INSR as highly copredictive genes depicting important relapse-associated characteristics among adult patients with AML. Our findings highlight the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments to improve cure rates in AML.

Identifying the Predictive Role of Oxidative Stress Genes in the Prognosis of Glioma Patients

Background

Gliomas are primary aggressive brain tumors with poor prognoses. Oxidative stress plays a crucial role in the tumorigenesis and drug resistance of gliomas. The aim of the present study was to use integrated bioinformatics analyses to evaluate the prognostic value of oxidative stress-related genes (OSRGs) in glioma.

Material/Methods

Disease- and prognosis-associated OSRGs were identified using microarray and clinical data from the Chinese Glioma Genome Atlas database. Functional enrichment, gene-gene interaction, protein-protein interaction, and survival analyses were performed in screened OSRGs. The protein expression was validated by the Human Protein Atlas database. A risk score model was constructed and verified through Cox regression, receiver operating characteristic curve, principal component, and stratified analyses. The Cancer Genome Atlas (TCGA) database was used for external validation. A nomogram was constructed to facilitate the clinical application.

Results

Twenty-one disease-associated and 14 prognosis-associated OSRGs were identified. Enrichment analyses indicated that these signature OSRGs were involved in tumorigenesis and drug resistance of glioma. The risk score model demonstrated a significant difference in overall survival between the high- and low-risk groups. The area under the curve and hazard ratio (1.296) revealed the independent prognostic value of the model. The model exhibited good predictive efficacy in the TCGA cohort. A clinical nomogram was constructed to calculate survival rates in glioma patients at 1, 3, and 5 years.

Conclusions

Our comprehensive study indicated that OSRGs were valuable for prognosis prediction in glioma, which provides a novel insight into the relationship between oxidative stress and glioma and a potential therapeutic strategy for glioma patients.

DPEP1 promotes the proliferation of colon cancer cells via the DPEP1/MYC feedback loop regulation

DPEP1 is highly expressed in the colorectal carcinoma tissues and colon cancer cells. However, the function and underlying mechanism of DPEP1 in the colon cancer cells are still poorly understood. Here, we found that transcription factor MYC could occupy on the DPEP1 promoter and activate its activities, and DPEP1 was up-regulated by MYC proteins in mRNA and protein levels in a dose-dependent manner in colon cancer cells. The expression levels of DPEP1 were positively correlated with that of MYC in colorectal tumor tissues. Moreover, Laser confocal images and Co-immunoprecipitation (Co-IP) revealed that DPEP1 and MYC proteins could bind to each other in the colon cancer cells. In turn, DPEP1 could enhance the stability of MYC proteins by extending the half-life of MYC proteins in colon cancer cells. Thus, DPEP1 and MYC proteins might form a positive feedback loop to maintain their high expression levels in colon cancer cells. In function, the MTT, EdU, Clone Formation assays and xenograft tumors assays demonstrated that DPEP1 could boost the proliferation of colon cancer cells through the DPEP1/MYC positive feedback loop in vitro and in vivo. Theoretically, DPEP1 may serve as a colon cancer biomarker and a novel target of colorectal carcinogenesis therapy.