Microarray Experiments Research Articles

Identification and Validation of the Hsa_circ_0001726/miR-140-3p/KRAS Axis in Hepatocellular Carcinoma Based on Microarray Analyses and Experiments.

Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Epigenetic mechanisms have revealed that noncoding RNAs, such as microRNAs (miRNAs) and circular RNAs (circRNAs), are involved in HCC progression. This study aimed to construct a circRNA-miRNA-mRNA network in HCC and validate one axis within the network. HCC-related transcriptome data were obtained from the Gene Expression Omnibus, and HCC-related genes were sourced from GeneCards to identify differentially expressed circRNAs and miRNAs. The targeting relationships between circRNA-miRNA and miRNA-mRNA interactions were predicted. The involvement of the hsa_circ_0001726/miR-140-3p/KRAS axis in HCC was evaluated through cellular experiments and survival analyses. We identified six differentially expressed circRNAs in HCC, which were linked to 13 miRNAs and 88 mRNAs. A network containing 34 circRNA-miRNA pairs and 194 miRNA-mRNA pairs was constructed. Cell proliferation and migration assays confirmed the role of hsa_circ_0001726 in promoting HCC progression, possibly through the miR-140-3p/KRAS axis. Survival analysis verified that hsa_circ_0001726 was a prognostic factor for overall survival in patients with HCC. The hsa_circ_0001726/miR-140-3p/KRAS axis also mediates lenvatinib resistance in HCC cells. The HCC circRNA/miRNA/mRNA network provides new insights into the post-transcriptional regulatory mechanism of HCC. The hsa_circ_0001726/miR-140-3p/KRAS axis is involved in HCC progression and lenvatinib resistance.

Influence of budesonide and fluticasone propionate in the anti-osteoporotic potential in human bone marrow-derived mesenchymal stem cells via stimulation of osteogenic differentiation

Osteoporosis is a prevalent bone condition with adverse effects observed in patients undergoing long-term glucocorticoid therapy, resulting in bone demineralization and tissue loss. There has been limited studies on the global response to dexamethasone in terms of comparing its expression profile to other common glucocorticoids during osteogenic differentiation. This study focused on the downregulated gene expression profile of glucocorticoid compounds; dexamethasone, budesonide, and fluticasone propionate, during osteogenic differentiation to elucidate the related target genes and pathways associated with the anti-osteoporotic potential of telomerase-immortalized human bone marrow-derived mesenchymal stem cells using a bioinformatics approach. Based on gene expression microarrays experiments and bioinformatics analysis, several key genes involved in the regulation of osteogenic differentiation and osteoporosis development in mesenchymal stem cells that were targeted by these specific glucocorticoids were determined. Network analysis using GeneCards, OMIM, and CTD databases were performed and osteoporosis-related genes were identified. LIMMA and moderated Welch test R packages were performed to determine significant downregulated differentially expressed genes for each glucocorticoid treatment. A total of 479 (dexamethasone), 84 (budesonide), and 889 (fluticasone propionate) differentially expressed genes were identified for each glucocorticoid, of which 35 common genes overlapped. Enrichment pathway analysis was conducted using Metascape, and protein-protein interaction networks were constructed using the STRING database and Cytoscape software to determine potential target genes involved with osteoporosis. Enrichment pathway analysis revealed genes involved in 3 Reactome pathways namely cytokine signaling in immune system, immune system and the interferon alpha/beta signaling pathways and identified 10 hub genes based on the PPI network to determine potential target pathways associated with osteoporosis. These findings provide preliminary insights into the relationship between the key target genes of dexamethasone, budesonide, and fluticasone propionate, and the pathways associated with regulated osteoporosis metabolism during osteogenic differentiation.

Application of Coincidence Index in the Discovery of Co-Expressed Metabolic Pathways.

Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeon Halobacterium salinarum and the bacterium Escherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

Terpene extract from the stem of Celastrus orbiculatus inhibits actin cytoskeleton remodelling in gastric cancer cells by regulating the protein interaction between PTBP1 and ACTN4

Adjuvant chemoradiotherapy, molecular targeted therapy, and immunotherapy are frequently employed to extend the survival of patients with advanced gastric cancer (GC). However, most of these treatments have toxic side effects, drug resistance, and limited improvements in survival and quality of life. Therefore, it is crucial to discover and develop new medications targeting GC that are highly effective and have minimal toxicity. In previous studies, the total terpene extract from the stem of Celastrus orbiculatus demonstrated anti-GC activity; however, the specific mechanism was unclear. Our research utilising co-immunoprecipitation-mass spectrometry (Co-IP-MS), polypyrimidine tract binding protein 1 (ptbp1) clustered regularly interspaced short palindromic repeat-associated protein 9 (Cas9)-knockout (KO) mouse model, tissue microarray, and functional experiments suggests that alpha actinin-4 (ACTN4) could be a significant biomarker of GC. PTBP1 influences actin cytoskeleton restructuring in GC cells by interacting with ACTN4. Celastrus orbiculatus stem extract (COE) may directly target ACTN4 and affect the interaction between PTBP1 and ACTN4, thereby exerting anti-GC effects.

Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies.

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

METTL3-mediated pre-miR-665/DLX3 m6A methylation facilitates the committed differentiation of stem cells from apical papilla

Methyltransferase-like 3 (METTL3) is a crucial element of N6-methyladenosine (m6A) modifications and has been extensively studied for its involvement in diverse biological and pathological processes. In this study, we explored how METTL3 affects the differentiation of stem cells from the apical papilla (SCAPs) into odonto/osteoblastic lineages through gain- and loss-of-function experiments. The m6A modification levels were assessed using m6A dot blot and activity quantification experiments. In addition, we employed Me-RIP microarray experiments to identify specific targets modified by METTL3. Furthermore, we elucidated the molecular mechanism underlying METTL3 function through dual-luciferase reporter gene experiments and rescue experiments. Our findings indicated that METTL3+/− mice exhibited significant root dysplasia and increased bone loss. The m6A level and odonto/osteoblastic differentiation capacity were affected by the overexpression or inhibition of METTL3. This effect was attributed to the acceleration of pre-miR-665 degradation by METTL3-mediated m6A methylation in cooperation with the “reader” protein YTHDF2. Additionally, the targeting of distal-less homeobox 3 (DLX3) by miR-665 and the potential direct regulation of DLX3 expression by METTL3, mediated by the “reader” protein YTHDF1, were demonstrated. Overall, the METTL3/pre-miR-665/DLX3 pathway might provide a new target for SCAP-based tooth root/maxillofacial bone tissue regeneration.

Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis.

Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as "quasi-atoms." Predictive Quantitative Structure-Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81-0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.

DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments.

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI's GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance.

Efficient Block Designs for Mixed Level Factorial Microarray Experiments based on Baseline Parameterization

A procedure of obtaining efficient block designs in block size 2 for n-factor mixed level factorial microarray experiments based on baseline parameterization has been given. A software module has been developed using C# programming language with ASP.NET platform for generation of efficient block designs in block size 2 for s1 × s2 ×× sn factorial experiments in v −1 arrays, where j s denotes the number of levels of jth factor and n denotes the number of factors and 1 n j j v s = = Π , the total number of treatment combinations. For n = 2, the software developed can also generate efficient block designs for 1 2 v −1≤ b ≤ ( v −1) + ( s −1)( s −1) , where b is the number of arrays.

CircLIFR-007 reduces liver metastasis via promoting hnRNPA1 nuclear export and YAP phosphorylation in breast cancer

Cancer metastasis is the major cause of death in patients with breast cancer (BC). The liver is a common site of breast cancer metastasis, and the 5-year survival rate of patients with breast cancer liver metastases (BCLMs) is only about 8.5 %. CircRNAs are involved in a variety of cancer-related pathological behaviors, and their unique structure and resistance to RNA degradation enable them to serve as ideal diagnostic biomarkers and therapeutic targets. Therefore, it is important to investigate the role and molecular mechanism of circRNAs in cancer metastasis. CircLIFR-007 was identified as a critical circular RNA in BC metastasis by circRNAs microarray and qRT-PCR experiment. Cell function assays were performed to explore the effect of circLIFR-007 in breast cancer cells. Experiments in vivo validated the function of circLIFR-007. Several molecular assays were performed to investigate the underlying mechanisms. We found that circLIFR-007 acted as a negative controller in breast cancer liver metastasis. CircLIFR-007 upregulates the phosphorylation level of YAP by exporting hnRNPA1 to promote the combination between hnRNPA1 and YAP in the cytoplasm. Overexpression of circLIFR-007 suppressed the expression of liver metastasis-related proteins, SREBF1 and SNAI1, which were regulated by transcription factor YAP. Functionally, circLIFR-007 inhibits the proliferation and metastasis of breast cancer cells both in vivo and in vitro.

Comparative Screening of the Liver Gene Expression Profiles from Type 1 and Type 2 Diabetes Rat Models.

Experimental animal models of diabetes can be useful for identifying novel targets related to disease, for understanding its physiopathology, and for evaluating emerging antidiabetic treatments. This study aimed to characterize two rat diabetes models: HFD + STZ, a high-fat diet (60% fat) combined with streptozotocin administration (STZ, 35 mg/kg BW), and a model with a single STZ dose (65 mg/kg BW) in comparison with healthy rats. HFD + STZ- induced animals demonstrated a stable hyperglycemia range (350-450 mg/dL), whereas in the STZ-induced rats, we found glucose concentration values with a greater dispersion, ranging from 270 to 510 mg/dL. Moreover, in the HFD + STZ group, the AUC value of the insulin tolerance test (ITT) was found to be remarkably augmented by 6.2-fold higher than in healthy animals (33,687.0 ± 1705.7 mg/dL/min vs. 5469.0 ± 267.6, respectively), indicating insulin resistance (IR). In contrast, a more moderate AUC value was observed in the STZ group (19,059.0 ± 3037.4 mg/dL/min) resulting in a value 2.5-fold higher than the average exhibited by the control group. After microarray experiments on liver tissue from all animals, we analyzed genes exhibiting a fold change value in gene expression <-2 or >2 (p-value <0.05). We found 27,686 differentially expressed genes (DEG), identified the top 10 DEGs and detected 849 coding genes that exhibited opposite expression patterns between both diabetes models (491 upregulated genes in the STZ model and 358 upregulated genes in HFD + STZ animals). Finally, we performed an enrichment analysis of the 849 selected genes. Whereas in the STZ model we found cellular pathways related to lipid biosynthesis and metabolism, in the HFD + STZ model we identified pathways related to immunometabolism. Some phenotypic differences observed in the models could be explained by transcriptomic results; however, further studies are needed to corroborate these findings. Our data confirm that the STZ and the HFD + STZ models are reliable experimental models for human T1D and T2D, respectively. These results also provide insight into alterations in the expression of specific liver genes and could be utilized in future studies focusing on diabetes complications associated with impaired liver function.

Unveiling the Molecular Mechanism of Trastuzumab Resistance in SKBR3 and BT474 Cell Lines for HER2 Positive Breast Cancer.

HER2-positive breast cancer is one of the most prevalent forms of cancer among women worldwide. Generally, the molecular characteristics of this breast cancer include activation of human epidermal growth factor receptor-2 (HER2) and hormone receptor activation. HER2-positive is associated with a higher death rate, which led to the development of a monoclonal antibody called trastuzumab, specifically targeting HER2. The success rate of HER2-positive breast cancer treatment has been increased; however, drug resistance remains a challenge. This fact motivated us to explore the underlying molecular mechanisms of trastuzumab resistance. For this purpose, a two-fold approach was taken by considering well-known breast cancer cell lines SKBR3 and BT474. In the first fold, trastuzumab treatment doses were optimized separately for both cell lines. This was done based on the proliferation rate of cells in response to a wide variety of medication dosages. Thereafter, each cell line was cultivated with a steady dosage of herceptin for several months. During this period, six time points were selected for further in vitro analysis, ranging from the untreated cell line at the beginning to a fully resistant cell line at the end of the experiment. In the second fold, nucleic acids were extracted for further high throughput-based microarray experiments of gene and microRNA expression. Such expression data were further analyzed in order to infer the molecular mechanisms involved in the underlying development of trastuzumab resistance. In the list of differentially expressed genes and miRNAs, multiple genes (e.g., BIRC5, E2F1, TFRC, and USP1) and miRNAs (e.g., hsa miR 574 3p, hsa miR 4530, and hsa miR 197 3p) responsible for trastuzumab resistance were found. Downstream analysis showed that TFRC, E2F1, and USP1 were also targeted by hsa-miR-8485. Moreover, it indicated that miR-4701-5p was highly expressed as compared to TFRC in the SKBR3 cell line. These results unveil key genes and miRNAs as molecular regulators for trastuzumab resistance.

Differential Expression of Noncoding RNAs Revealed Enhancer RNA AC016735.2 as a Potential Pathogenic Marker of Congenital Microtia Patients.

Congenital microtia is a complex maxillofacial malformation with various risk factors. This study aimed to find potential pathogenic noncoding RNAs for congenital microtia patients. We collected 3 pairs of residual ear cartilage samples and corresponding normal ear cartilage samples from nonsyndromic congenital microtia patients for microarray experiments. The differentially expressed RNAs were screened, and enrichment analysis and correlation expression analysis were performed to elucidate the function of the differentially expressed genes (DEGs). We further investigated the most significantly differentially expressed long noncoding RNA (lncRNA), AC016735.2, through follow-up analyses including RT-qPCR and Western blotting, to validate its differential expression in residual ear cartilage compared with normal ear cartilage. SiRNA was designed to study the regulatory role of AC016735.2, and cell proliferation experiments were conducted to explore its impact on residual ear chondrocytes. Analysis of the microarray data revealed a total of 1079 differentially expressed RNAs, including 305 mRNAs and x lncRNAs, using a threshold of FC>1.5 and P<0.05 for mRNA, and FC>1.0 and P<0.05 for lncRNA. Enrichment analysis indicated that these genes are mainly involved in extracellular matrix regulation and embryonic development. AC016735.2 showed the highest differential expression among the eRNAs, being upregulated in residual ear cartilage. It acts in cis to regulate the nearby coding gene ZFP36L2, indirectly affecting downstream genes such as BMP4, TWSG1, COL2A1, and COL9A2. Significant differences were observed in the expression of lncRNAs and mRNAs between residual ear cartilage and normal auricular cartilage tissues in the same genetic background of congenital microtia. These differentially expressed lncRNAs and mRNAs may play crucial roles in the occurrence and development of microtia through pathways associated with extracellular matrix regulation and gastrulation. Particularly, AC016735.2, an eRNA acting in cis, could serve as a potential pathogenic noncoding gene.

High throughput biological sequence analysis using machine learning-based integrative pipeline for extracting functional annotation and visualization

The Differential Gene Expression (DGE) approach to find out the expressed genes relies on measures such as log-fold change and adjusted p-values. Although fold change is commonly employed in gene expression studies, especially in microarray and RNA sequencing experiments to quantify alterations in a gene’s expression level, a limitation and potential hazard of relying on fold change in this context is its inherent bias. As a consequence, it might incorrectly categorize genes that have significant differences but minor ratios, resulting in poor detection of mutations in genes with high expression levels. In contrast, machine learning offers a more comprehensive view, adept at capturing the non-linear complexities of gene expression data and providing robustness against noise that inspired us to utilize machine learning models to explore differential gene expression based on feature importance in Type 2 Diabetes (T2D), a significant global health concern, in this study. Moreover, we validated biomarkers based on our findings expressed genes with previous studies to ensure the effectiveness of our ML models in this work which led us to go through to analysis pathways, gene ontologies, protein-protein interactions, transcription factors, miRNAs, and drug predictions to deal with T2D. This study aims to consider the machine learning technique as a good way to know about expressed genes profoundly not relying on the DGE approach, and to control or reduce the risk of T2D patients by helping drug developer researchers.

Benchmarking enrichment analysis methods with the disease pathway network.

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Hydroxysafflor yellow A induced ferroptosis of Osteosarcoma cancer cells by HIF-1α/HK2 and SLC7A11 pathway.

Osteosarcoma is a very serious primary bone cancer with a high death rate and a dismal prognosis. Since there is no permanent therapy for this condition, it is necessary to develop a cure. Therefore, this investigation was carried out to assess the impacts and biological functions of hydroxysafflor yellow A (HYSA) in osteosarcoma cell lines (MG63). In this investigational study, MG63 cells were utilized. Microarray experiments, quantitative polymerase chain reaction (qPCR), immunofluorescent staining, extracellular acidification rate (ECAR), oxygen consumption rate (OCR), glucose consumption, lactate production, and ATP levels, proliferation assay, 5-Ethynyl-2'-deoxyuridine (EDU) staining, and Western blot were performed. In MG63 cells, HYSA lowered cell proliferation and metastasis rates, suppressed EDU cell number, and enhanced caspase-3/9 activity levels. HYSA reduced the Warburg effect and induced ferroptosis (FPT) in MG63 cells. Inhibiting ferroptosis diminished HYSA's anti-cancer activities in MG63 cells. The stimulation of the HIF-1α/SLC7A11 pathway decreased HYSA's anti-cancer activities in MG63 cells. HIF-1α is one target spot for HYSA in a model of osteosarcoma cancer (OC). HYSA altered HIF-1α's thermophoretic activity; following binding with HYSA, HIF-1α's melting point increased from ~55°C to ~60°C. HYSA significantly enhanced the thermal stability of exogenous WT HIF-1α while not affecting Mut HIF-1α, suggesting that ARG-311, GLY-312, GLN-347, and GLN-387 may be involved in the interaction between HIF-1α and HYSA. Conclusively, our study revealed that HYSA induced FPT and reduced the Warburg effect of OC through mitochondrial damage by HIF-1α/HK2/SLC7A11 pathway. HYSA is a possible therapeutic option for OC or other cancers.

Anti-Proliferative and Apoptotic Activities of Rumex crispus.

Colorectal cancer is the fourth leading cause of cancer death and the third most common cancer diagnosed in the United States. Several anticancer compounds from natural products have been of great interest in cancer chemotherapy and are currently in clinical trials. Natural products that present the targeted killing of cancerous cell and are soluble in water with minimal side effects are ideal candidates. In this study, water-soluble compounds from Rumex crispus plants were screened for anti-proliferative and apoptotic activity against human colorectal adenocarcinoma (DLD-1) cells. The most potent fraction with the highest cell killing and caspase fold change rates was selected for further experiments. The observed changes were further validated by measuring the caspase fold change using RT-qPCR. Furthermore, gene transcript levels were evaluated using an RT2 Profiler assay and a microarray experiment. Our results showed that the most potent L19 fraction exhibits anti-proliferative activity in a dose-dependent manner. The L19 fraction was found to induce apoptotic pathways by triggering different caspases and inflammatory pathways through the activation of non-apoptotic genes. Our study identified and validated the anticancer property of the L19 fraction, which can serve as a strong lead compound for the synthesis of other novel potent analogues.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

Transcriptomic Signature of a Patient with Chronic Lymphocytic Leukemia Exhibiting Profound Fluctuations in the Lymphocytosis during Nutrition and Prolonged Fasting Periods

Introduction This study aimed to investigate the underlying reasons behind sustained significant continuous deep fluctuations in the absolute lymphocyte count (ALC) in an untreated patient with Chronic Lymphocytic Leukemia (CLL), who has maintained a favorable prognosis since the time of diagnosis in 2016. The patient has voluntarily chosen to adopt a mainly vegetarian and fruitarian diet, including annual periods of prolonged total fasting (lasting from 4 to 39 days). Methods For this purpose, we decided to conduct a comprehensive analysis of the whole transcriptome profiling of peripheral blood (PB) CD19+ cells from patient (#1) at different time-points, in comparison to the corresponding cells from 5 other untreated CLL patients who followed a varied diet. As a result, the CLL patients were classified as follows: the 1st group consisted of patient #1 at 20 different time-points (16 time-points during nutrition and 4 time-points during fasting), whereas the 2nd group included only one time point for each of the patients (#2, #3, #4, #5, and #6) due to their varied dietary practices. We have performed microarray experiments on the Peripheral Bone Marrow Cells (PBMCs) CD19+ cells utilizing the Affymetrix Human Clariom™ D Pico Assay, a powerful tool for generating high-fidelity biomarker signatures. The data was preprocessed and normalized using the Robust Multiarray Average (RMA) method. Significance Analysis of Microarrays (SAM version 3.0) was utilized to assess significant differentially expressed genes between the 1st group and 2nd group of CLL patients. The Storey and Tibshirani procedure was applied to control the False Discovery Rate (FDR). Results The lymphocytosis trend of patient #1 displayed recurring significant deep fluctuations since the time of diagnosis. Interestingly, we observed that approximately 4-6 weeks after the conclusion of fasting periods, the absolute lymphocyte count was reduced by about half (Figure 1A). On the other hand, in the 2nd group, all patients did not show fluctuations in the absolute lymphocyte count, but instead demonstrated a progressive increase in lymphocytes over time. Differential expression analysis, after correction for multiple testing, identified 18 differentially expressed genes (DEGs) in the comparison between CD19+ cells of patient #1 and patients #2, #3, #4, #5, and #6. Among them, our focus was on 9 genes with functional annotation: 4 genes were downregulated ( IGLC3, RPS26, CHPT1, and PCDH9), and 5 genes were upregulated ( IGHV3-43, IGKV3D-20, PLEKHA1, CYBB, and GABRB2) in the 1st group (patient #1) vs. the 2nd group of patients (#2, #3, #4, #5, and #6), respectively. In particular, IGLC3, RPS26, PLEKHA1, CYBB, and GABRB2 showed high Fold Change (FC) &amp;gt; 3. Additionally, we generated a heat map on log2 expression of the 9 differentially expressed genes across different patients/conditions (Figure 1B). Furthermore, hierarchical clustering analysis revealed that patient #1 during nutrition and fasting (1st group) clustered distinctly from all CLL patients (2nd group), indicating a gene expression signature that differentiated patient #1 from patient #2, #3, #4, #5, and #6. Conclusions In conclusion, our transcriptomic study revealed a small set of 9 genes that distinguished an untreated CLL patient who underwent a prolonged periods of total fasting and maintained a slow-growing tendency of lymphocytosis, in comparison to 5 untreated CLL patients with a varied diet. Future investigations of patient #1 could demonstrate the potential role of prolonged periodic fasting and the involvement of the 9 genes in sustaining the trend of lymphocytosis and the benign course of the disease.

A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG

BackgroundHeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model.MethodsTranscriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry.ResultsThe three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG.ConclusionsWe proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.