Microarray Gene Expression Data Research Articles

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Analyzing Pooled Microarray Gene Expression Data to Uncover Common Pathways in Periodontitis and Oral Squamous Cell Carcinoma from the Gene Expression Omnibus

ABSTRACT Periodontitis and oral squamous cell carcinoma (OSCC) are prevalent oral diseases with distinct etiologies, yet they share certain molecular and biological characteristics. Gene expression datasets from the gene expression omnibus (GEO) repository (GSE30784 for OSCC and GSE10334 for periodontitis) were analyzed. Data preprocessing and differential gene expression analysis using GEO2R identified common differentially expressed genes (DEGs), and FunRich software facilitated the construction of a protein-protein interaction (PPI) network on the STRING database. Cytoscape, coupled with the CytoHubba plugin, identified Cluster of Differentiation 19 (CD19) and Von Willebrand Factor (VWF) as the top hub genes, with Complement C3 (C3) also highly ranked. Functional enrichment analysis highlighted pathways such as the B-cell receptor signaling pathway, complement and coagulation cascades, and hematopoietic cell lineage. Additionally, miRNet analysis identified key miRNAs, including hsa-mir-26a-5p, hsa-mir-129-2-3p, and hsa-mir-27a-3p, associated with these pathways. These findings suggested an association of molecular mechanisms between periodontitis and OSCC, with identified hub genes and miRNAs potentially serving as therapeutic targets.

LXRα Promotes Abdominal Aortic Aneurysm Formation Through UHRF1 Epigenetic Modification of miR-26b-3p.

Abdominal aortic aneurysm (AAA) is a severe aortic disease without effective pharmacological approaches. The nuclear hormone receptor LXRα (liver X receptor α), encoded by the NR1H3 gene, serves as a critical transcriptional mediator linked to several vascular pathologies, but its role in AAA remains elusive. Through integrated analyses of human and murine AAA gene expression microarray data sets, we identified NR1H3 as a candidate gene regulating AAA formation. To investigate the role of LXRα in AAA formation, we used global Nr1h3-knockout and vascular smooth muscle cell-specific Nr1h3-knockout mice in 2 AAA mouse models induced with angiotensin II (1000 ng·kg·min; 28 days) or calcium chloride (CaCl2; 0.5 mol/L; 42 days). Upregulated LXRα was observed in the aortas of patients with AAA and in angiotensin II- or CaCl2-treated mice. Global or vascular smooth muscle cell-specific Nr1h3 knockout inhibited AAA formation in 2 mouse models. Loss of LXRα function prevented extracellular matrix degeneration, inflammation, and vascular smooth muscle cell phenotypic switching. Uhrf1, an epigenetic master regulator, was identified as a direct target gene of LXRα by integrated analysis of transcriptome sequencing and chromatin immunoprecipitation sequencing. Susceptibility to AAA development was consistently enhanced by UHRF1 (ubiquitin-like containing PHD and RING finger domains 1) in both angiotensin II- and CaCl2-induced mouse models. We then determined the CpG methylation status and promoter accessibility of UHRF1-mediated genes using CUT&Tag (cleavage under targets and tagmentation), RRBS (reduced representation bisulfite sequencing), and ATAC-seq (assay for transposase-accessible chromatin with sequencing) in vascular smooth muscle cells, which revealed that the recruitment of UHRF1 to the promoter of miR-26b led to DNA hypermethylation accompanied by relatively closed chromatin states, and caused downregulation of miR-26b expression in AAA. Regarding clinical significance, we found that underexpression of miR-26b-3p correlated with high risk in patients with AAA. Maintaining miR-26b-3p expression prevented AAA progression and alleviated the overall pathological process. Our study reveals a pivotal role of the LXRα/UHRF1/miR-26b-3p axis in AAA and provides potential biomarkers and therapeutic targets for AAA.

HIHISIV: a database of gene expression in HIV and SIV host immune response

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at https://hihisiv.github.io, provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

CHRNA6 RNA In Situ Hybridization Is a Useful Tool for the Diagnosis of Extraskeletal Myxoid Chondrosarcoma

Extraskeletal myxoid chondrosarcoma (EMC) is an uncommon mesenchymal neoplasm characteristically composed of uniform-appearing round to spindle-shaped cells with eosinophilic cytoplasm and abundant myxoid extracellular matrix. Although the majority of cases harbor a pathognomonic t(9;22) translocation that fuses EWSR1 with the orphan nuclear receptor NR4A3, there are less common variants that partner NR4A3 with TAF15, TCF12, or TFG. By immunohistochemistry, EMC has features of both cartilaginous and neuroendocrine differentiation, as evidenced by inconsistent expression of S100 protein and synaptophysin or INSM1, respectively, in a subset of cases. Given the limitations of available immunohistochemical stains for the diagnosis of EMC, we analyzed genome-wide gene expression microarray data to identify candidate biomarkers based on differential expression in EMC in comparison with other mesenchymal neoplasms. This analysis pointed to CHRNA6 as the gene with the highest relative expression in EMC (96-fold; P = 8.2 × 10−26) and the only gene with >50-fold increased expression in EMC compared with other tumors. Using RNA chromogenic in situ hybridization, we observed strong and diffuse expression of CHRNA6 in 25 cases of EMC, including both EWSR1-rearranged and TAF15-rearranged variants. All examined cases of histologic mimics were negative for CHRNA6 overexpression; however, limited CHRNA6 expression, not reaching a threshold of >5 puncta or 1 aggregate of chromogen in >25% of cells, was observed in 69 of 685 mimics (10.1%), spanning an array of mesenchymal tumors. Taken together, these findings suggest that, with careful interpretation and the use of appropriate thresholds, CHRNA6 RNA chromogenic in situ hybridization is a potentially useful ancillary histologic tool for the diagnosis of EMC.

Abstract 1694 Combined analysis of gene expression microarray data from Polyphenon E-treated prostate cancer cells identifies significant gene expression changes

Abstract 1694 Combined analysis of gene expression microarray data from Polyphenon E-treated prostate cancer cells identifies significant gene expression changes

Harmonizing multisite data with the ComBat method for enhanced Parkinson's disease diagnosis via DAT-SPECT.

Dopamine transporter single-photon emission computed tomography (DAT-SPECT) is a crucial tool for evaluating patients with Parkinson's disease (PD). However, its implication is limited by inter-site variability in large multisite clinical trials. To overcome the limitation, a conventional prospective correction method employs linear regression with phantom scanning, which is effective yet available only in a prospective manner. An alternative, although relatively underexplored, involves retrospective modeling using a statistical method known as "combatting batch effects when combining batches of gene expression microarray data" (ComBat). We analyzed DAT-SPECT-specific binding ratios (SBRs) derived from 72 healthy older adults and 81 patients with PD registered in four clinical sites. We applied both the prospective correction and the retrospective ComBat correction to the original SBRs. Next, we compared the performance of the original and two corrected SBRs to differentiate the PD patients from the healthy controls. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The original SBRs were 6.13 ± 1.54 (mean ± standard deviation) and 2.03 ± 1.41 in the control and PD groups, respectively. After the prospective correction, the mean SBRs were 6.52 ± 1.06 and 2.40 ± 0.99 in the control and PD groups, respectively. After the retrospective ComBat correction, the SBRs were 5.25 ± 0.89 and 2.01 ± 0.73 in the control and PD groups, respectively, resulting in substantial changes in mean values with fewer variances. The original SBRs demonstrated fair performance in differentiating PD from controls (Hedges's g = 2.76; AUC-ROC = 0.936). Both correction methods improved discrimination performance. The ComBat-corrected SBR demonstrated comparable performance (g = 3.99 and AUC-ROC = 0.987) to the prospectively corrected SBR (g = 4.32 and AUC-ROC = 0.992) for discrimination. Although we confirmed that SBRs fairly discriminated PD from healthy older adults without any correction, the correction methods improved their discrimination performance in a multisite setting. Our results support the utility of harmonization methods with ComBat for consolidating SBR-based diagnosis or stratification of PD in multisite studies. Nonetheless, given the substantial changes in the mean values of ComBat-corrected SBRs, caution is advised when interpreting them.

Transcription factor ATMIN facilitates chemoresistance in nasopharyngeal carcinoma

Despite that the docectaxel-cisplatin-5-fluorouracil (TPF) induction chemotherapy has greatly improved patients’ survival and became the first-line treatment for advanced nasopharyngeal carcinoma (NPC), not all patients could benefit from this therapy. The mechanism underlying the TPF chemoresistance remains unclear. Here, by analyzing gene-expression microarray data and survival of patients who received TPF chemotherapy, we identify transcription factor ATMIN as a chemoresistance gene in response to TPF chemotherapy in NPC. Mass spectrometry and Co-IP assays reveal that USP10 deubiquitinates and stabilizes ATMIN protein, resulting the high-ATMIN expression in NPC. Knockdown of ATMIN suppresses the cell proliferation and facilitates the docetaxel-sensitivity of NPC cells both in vitro and in vivo, while overexpression of ATMIN exerts the opposite effect. Mechanistically, ChIP-seq combined with RNA-seq analysis suggests that ATMIN is associated with the cell death signaling and identifies ten candidate target genes of ATMIN. We further confirm that ATMIN transcriptionally activates the downstream target gene LCK and stabilizes it to facilitate cell proliferation and docetaxel resistance. Taken together, our findings broaden the insight into the molecular mechanism of chemoresistance in NPC, and the USP10-ATMIN-LCK axis provides potential therapeutic targets for the management of NPC.

Comparative analysis of ARID1A mutations with mRNA levels and protein expression in gastric carcinoma

The ARID1A gene is pivotal in chromatin remodeling and genomic integrity and is frequently mutated in various cancer types. ARID1A mutation is the second most frequently mutated tumor suppressor gene and has been suggested as a predictor of immunotherapeutic responsiveness in gastric carcinoma (GC). Despite its significance, the relationship among ARID1A somatic mutations, RNA expression levels, and protein expression remains unclear, particularly in GC. For this purpose, we performed comparative study in two cohorts. Cohort 1 used next-generation sequencing (NGS) to identify 112 GC cases with ARID1A mutations. These cases were compared with ARID1A immunohistochemistry (IHC) results. Cohort 2 employed microarray gene expression data to assess ARID1A RNA levels and compare them with ARID1A IHC results. In Cohort 1, 38.4% of ARID1A-mutated GC exhibited a complete loss of ARID1A protein when assessed by IHC, whereas the remaining 61.6% displayed intact ARID1A. Discordance between NGS and IHC results was not associated with specific mutation sites, variant classifications, or variant allele frequencies. In Cohort 2, 24.1% of the patients demonstrated a loss of ARID1A protein, and there was no significant difference in mRNA levels between the ARID1A protein-intact and -loss groups. Our study revealed a substantial discrepancy between ARID1A mutations detected using NGS and protein expression assessed using IHC in GC. Moreover, ARID1A mRNA expression levels did not correlate well with protein expression. These findings highlighted the complexity of ARID1A expression in GC.

Comparative analyses reveal a phenylalanine ammonia lyase dependent and salicylic acid mediated host resistance in Zingiber zerumbet against the necrotrophic soft rot pathogen Pythium myriotylum

Little is known about the molecular basis of host defense in resistant wild species Zingiber zerumbet (L.) Smith against the soil-borne, necrotrophic oomycete pathogen Pythium myriotylum Drechsler, which causes the devastating soft rot disease in the spice crop ginger (Zingiber officinale Roscoe). We investigated the pattern of host defense between Z. zerumbet and ginger in response to P. myriotylum inoculation. Analysis of gene expression microarray data revealed enrichment of phenylpropanoid biosynthetic genes, particularly lignin biosynthesis genes, in pathogen-inoculated Z. zerumbet compared to ginger. RT-qPCR analysis showed the robust activation of phenylpropanoid biosynthesis genes in Z. zerumbet, including the core genes PAL, C4H, 4CL, and the monolignol biosynthesis and polymerization genes such as CCR, CAD, C3H, CCoAOMT, F5H, COMT, and LAC. Additionally, Z. zerumbet exhibited the accumulation of the phenolic acids including p-coumaric acid, sinapic acid, and ferulic acid that are characteristic of the cell walls of commelinoid monocots like Zingiberaceae and are involved in cell wall strengthening by cross linking with lignin. Z. zerumbet also had higher total lignin and total phenolics content compared to pathogen-inoculated ginger. Phloroglucinol staining revealed the enhanced fortification of cell walls in Z. zerumbet, specifically in xylem vessels and surrounding cells. The trypan blue staining indicated inhibition of pathogen growth in Z. zerumbet at the first leaf whorl, while ginger showed complete colonization of the pith within 36 h post inoculation (hpi). Accumulation of salicylic acid (SA) and induction of SA regulator NPR1 and the signaling marker PR1 were observed in Z. zerumbet. Silencing of PAL in Z. zerumbet through VIGS suppressed downstream genes, leading to reduced phenylpropanoid accumulation and SA level, resulting in the susceptibility of plants to P. myriotylum. These findings highlight the essential role of PAL-dependent mechanisms in resistance against P. myriotylum in Z. zerumbet. Moreover, our results suggest an unconventional role for SA in mediating host resistance against a necrotroph. Targeting the phenylpropanoid pathway could be a promising strategy for the effective management of P. myriotylum in ginger.

Detection of Prostate Cancer using Ensemble based Bi-directional Long Short Term Memory Network

Aim and Background: In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer. Methodology: The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression. Results: The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects. Conclusion: The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.

Angiopoietin-Like Protein 2 Serves as an Independent Prognostic Biomarker and Correlates with Immune Infiltration in Osteosarcoma

Osteosarcoma mostly occurs in adolescents, with high disability and mortality rates and poor prognosis. Angiopoietin-like protein 2 (ANGPTL2) is a secreted glycoprotein protein, which plays important roles in cellular processes and various diseases. Microarray gene expression data GSE16088 and GSE36001 were downloaded from Gene Expression Omnibus (GEO, <ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov/geo">http://www.ncbi.nlm.nih.gov/geo</ext-link>). The Complex Heatmap package was used to construct heatmaps, the gglot2 R language package to plot volcanoes and differential expression data, and the “VennDiagram” R language package to plot Wayne plots. Western booting and qRT-PCR was used to detect ANGPTL2 expression in osteosarcoma tissues. proteins. In addition, the GeneMANIA database (<ext-link ext-link-type="uri" xlink:href="http://www.genemania.org">http://www.genemania.org</ext-link>) was cited to predict the ANGPTL2 interaction network. We used the single-sample gene-set enrichment analysis (ss GSEA) algorithm to quantify the immune infiltration of osteosarcoma. In the GSE16088 database there were 5005 that met the selected threshold of |log2(FC)| > 1 and P <0.05, of which 2719 were highly expressed and 2286 were lowly expressed. WB and qRT-PCR results showed that ANGPTL2 was significantly up-regulated in osteosarcoma tissues. ssGSEA showed that high ANGPTL2 expression in osteosarcoma patients had a worse prognosis. ssGSEA showed that high ANGPTL2 expression was correlated with Mast cells and Th1 cells. Collectively, ANGPTL2 can be an independent prognostic biomarker and correlated with immune infiltration in osteosarcoma.

Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.

Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.

Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness

The lethality of epithelial ovarian cancer (OC) is largely due to a high rate of recurrence and development of chemoresistance, which requires synergy between cancer cells and the tumor microenvironment (TME) and is thought to involve cancer stem cells. Our analysis of gene expression microarray data from paired primary and recurrent OC tissues revealed significantly elevated expression of the gene encoding periostin (POSTN) in recurrent OC compared to matched primary tumors (p = 0.015). Secreted POSTN plays a role in the extracellular matrix, facilitating epithelial cell migration and tissue regeneration. We therefore examined how elevated extracellular POSTN, as we found is present in recurrent OC, impacts OC cell functions and phenotypes, including stemness. OC cells cultured with conditioned media with high levels of periostin (CMPOSTNhigh) exhibited faster migration (p = 0.0044), enhanced invasiveness (p = 0.006), increased chemoresistance (p < 0.05), and decreased apoptosis as compared to the same cells cultured with control medium (CMCTL). Further, CMPOSTNhigh-cultured OC cells exhibited an elevated stem cell side population (p = 0.027) along with increased expression of cancer stem cell marker CD133 relative to CMCTL-cultured cells. POSTN-transfected 3T3-L1 cells that were used to generate CMPOSTNhigh had visibly enhanced intracellular and extracellular lipids, which was also linked to increased OC cell expression of fatty acid synthetase (FASN) that functions as a central regulator of lipid metabolism and plays a critical role in the growth and survival of tumors. Additionally, POSTN functions in the TME were linked to AKT pathway activities. The mean tumor volume in mice injected with CMPOSTNhigh-cultured OC cells was larger than that in mice injected with CMCTL-cultured OC cells (p = 0.0023). Taken together, these results show that elevated POSTN in the extracellular environment leads to more aggressive OC cell behavior and an increase in cancer stemness, suggesting that increased levels of stromal POSTN during OC recurrence contribute to more rapid disease progression and may be a novel therapeutic target. Furthermore, they also demonstrate the utility of having matched primary-recurrent OC tissues for analysis and support the need for better understanding of the molecular changes that occur with OC recurrence to develop ways to undermine those processes.

Loss of SMARCA4 Leads to an Impaired Hematopoiesis in Mice

SMARCA4 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a) is the central ATPase containing enzyme in the SWI/SNF- (switch/sucrose non-fermentable) complex, which regulates chromatin accessibility and thereby influences gene transcription. By acting as a transcriptional activator or repressor in different cell types, SMARCA4 and the SWI/SNF complex have various functions. Our aim was to understand the function of SMARCA4 in healthy hematopoiesis in order to then apply our findings to different hematologic diseases in a second step. To investigate the role of SMARCA4 in haematopoiesis and hematopoietic stem cells, we created a transgenic Smarca4 knockout mouse model. An inducible knockout system was chosen since early embryonic knockout of SMARCA4, which is crucially involved in embryonic development leads to a lethal phenotype. The Cre/loxP system was used to induce homozygous Smarca4 knockout under the control of the hematopoiesis-specific promotor Mx-1. We then performed blood counts, bone marrow smears and RNA sequencing, including analysis of RNA signature of the different cell types in the bone marrow to decipher the composition of the bone marrow of Smarca 4 deficient mice. Blood count analysis showed that Smarca4 knockout mice have significantly lower leukocyte, reticulocyte, and thrombocyte counts. In addition, bone marrow showed lower colony forming capacity in colony formation assays compared to healthy cre-negative control mice. Bone marrow smears displayed aplasia of all cell lines in Smarca4 knockout mice with additional signs of dysplasia as hypolobulatedmegakaryocytes or nuclear blasts. RNA-sequencing revealed 749 upregulated and 520 downregulated genes with a log fold change ≥ +/- 1 in the bone marrow of Smarca4 knockout mice compared to cre-negative control mice. Relevant over represented pathways according to REACTOME data base included genes of generation of second messenger molecules, cell surface interactions at the vascular wall, and immunoregulatory interactions between lymphoid and non-lymphoid cells. Through cell type deconvolution (CIBERSORTx) of the bulk microarray gene expression data we estimated abundances for different cell types. Results revealed a decrease in granulocyte and macrophage progenitors (statistically significant) as well as in eosinophil progenitor cells, and in erythroid pre-colony forming units. Interestingly, both common myeloid and common lymphoid progenitors were increased. It seems as if the progenitor cells accumulate and take a different path when Smarca4 is knocked out in these cells (figure 2). We conclude that SMARCA4 is crucial for cell growth and differentiation of progenitor cells in the bone marrow during hematopoiesis since a Smarca4 knockout leads to differentiation arrest and accumulation of distinct progenitor cells and pancytopenia in peripheral blood. Figure 1: Abundances for 23 cell populations in Smarca4 knockout (KO) and wildtype (WT) samples were estimated using CIBERSORTx. Bubble plot showing Log2 fold changes of average abundances in KO vs WT with accumulation of distinct progenitor cells in the bone marrow of Smarca4 deficient mice. As underlying signature-matrix the haemopedia gene set was used. Bubbles with black borders indicate significance of difference between samples.

Gene selection and tumor identification based on a hybrid of the multi-filter embedded recursive mountain gazelle algorithm

Microarray gene expression data are useful for identifying gene expression patterns associated with cancer outcomes; however, their high dimensionality make it difficult to extract meaningful information and accurately classify tumors. Hence, developing effective methods for reducing dimensionality while preserving relevant information is a crucial task. Hybrid-based gene selection methods are widely proposed in the gene expression analysis domain and can still be enhanced in terms of efficiency and reliability. This study proposes a new hybrid-based gene selection method, called multi-filter embedded mountain gazelle optimizer (MUL-MGO), which utilizes two filters and an embedded method to remove irrelevant genes, followed by selecting the most relevant genes using recently developed MGO algorithm. To the best of our knowledge, this is the first work to exploit MGO as a gene or feature selection method. A new version of MGO, called recursive mountain gazelle optimizer (RMGO), which implements MGO algorithm recursively to avoid local optima, minimize search space, and obtain minimum gene count without decreasing the classifier’s performance, is developed. The proposed RMGO is used to develop a new hybrid gene selection method employing similar filters and embedded methods as MUL-MGO, but with a recursive MGO algorithm version. The resulting method is called multi-filter embedded recursive mountain gazelle optimizer (MUL-RMGO). Several classifiers are used for cancer classification. Accordingly, several experimental studies are performed on eight microarray gene expression datasets to demonstrate the proficiencies of MUL-MGO and MUL-RMGO methods. The experimental findings indicate the efficiency and productivity of the suggested MUL-MGO and MUL-RMGO methods for gene selection. The methods outperform cutting-edge methods in the literature, with MUL-RMGO exceeding MUL-MGO in terms of accuracy and selected gene count.

An embedded feature selection method based on generalized classifier neural network for cancer classification

The selection of relevant genes plays a vital role in classifying high-dimensional microarray gene expression data. Sparse group Lasso and its variants have been employed for gene selection to capture the interactions of genes within a group. Most of the embedded methods are linear sparse learning models that fail to capture the non-linear interactions. Additionally, very less attention is given to solving multi-class problems. The existing methods create overlapping groups, which further increases dimensionality. The paper proposes a neural network-based embedded feature selection method that can represent the non-linear relationship. In an effort toward an explainable model, a generalized classifier neural network (GCNN) is adopted as the model for the proposed embedded feature selection. GCNN has well-defined architecture in terms of the number of layers and neurons within each layer. Each layer has a distinct functionality, eliminating the obscure nature of most neural networks. The paper proposes a feature selection approach called Weighted GCNN (WGCNN) that embeds feature weighting as a part of training the neural network. Since the gene expression data comprises a large number of features, to avoid overfitting of the model a statistical guided dropout is implemented at the input layer. The proposed method works for binary as well as multi-class classification problems likewise. Experimental validation is carried out on seven microarray datasets on three learning models and compared with six state-of-art methods that are popularly employed for feature selection. The WGCNN performs well in terms of the F1 score and the number of features selected.

A Comprehensive Survey on Different Machine Learning Approaches for Breast Cancer Prediction based on Medical Imaging Modalities and Microarray Gene Expression.

Cancer is a complex global health problem that causes a high death rate. Breast cancer (BC) is the second most common death-causing disease in women worldwide. BC develops in the cells of the ducts or lobules of the glandular tissue when breast cells become uncontrollably proliferative. It can be controlled if diagnosed early enough. There are many techniques used to diagnose or classify BC. Machine learning (ML) has a significant effect on BC classification. This article provides a comparative study of different ML approaches for BC prediction based on medical imaging and microarray gene expression (MGE) data. DT, KNN, RF, SVM, Naïve Bayes, ANN, etc. perform much better in their respective fields. Another method named ensemble, incorporates more than one single classifier to solve the same problem. The study shows how ML with supervised, unsupervised, and ensemble learning might help with BC prognosis. This paper observes ensemble methods provide better performance than a single classifier. Finally, a comprehensive review of various imaging modalities and microarray gene expression, different datasets, performance metrics and outcomes, challenges, and prospective research directions are provided for the new researchers in this fast-growing field.

Weighted Gene Co-expression Network Analysis of Glioblastoma Gene Expression Microarray Data

Glioblastoma is a highly aggressive and lethal form of brain cancer characterized by its complex molecular landscape. Understanding the underlying gene expression patterns and their relationships is essential for unraveling the mechanisms driving this disease. In this study, we conducted a Weighted Gene Co-expression Network Analysis (WGCNA) on Glioblastoma gene expression microarray data to identify co-expressed gene modules and potential key regulatory genes associated with the disease. Utilizing a comprehensive dataset of Glioblastoma samples, we performed quality control and preprocessing to ensure the reliability of the data. WGCNA was employed to construct a weighted gene co-expression network, enabling the identification of modules of co-expressed genes. The correlation between these modules and clinical characteristics such as patient survival, tumor grade, and other relevant factors was assessed. Additionally, we conducted functional enrichment analysis to gain insights into the biological processes and pathways associated with the identified gene modules. Our findings revealed distinct gene modules associated with Glioblastoma progression and patient outcomes. Notably, we identified key hub genes within these modules, which may serve as potential biomarkers or therapeutic targets. Furthermore, functional enrichment analysis provided a comprehensive understanding of the biological processes and pathways influenced by these co-expressed gene modules. In conclusion, our Weighted Gene Co-expression Network analysis of Glioblastoma gene expression microarray data has shed light on the complex gene interactions and regulatory networks underlying this aggressive brain cancer. This knowledge may ultimately contribute to the development of novel diagnostic and therapeutic strategies, improving the prognosis for Glioblastoma patients.