Microarray Gene Expression Profiling Research Articles

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

METTL14 promotes lipid metabolism reprogramming and sustains nasopharyngeal carcinoma progression via enhancing m6A modification of ANKRD22 mRNA.

N6-methyladenosine (m6A) modification is essential for modulating RNA processing as well as expression, particularly in the context of malignant tumour progression. However, the exploration of m6A modification in nasopharyngeal carcinoma (NPC) remains very limited. RNA m6A levels were analysed in NPC using m6A dot blot assay. The expression level of methyltransferase-like 14 (METTL14) within NPC tissues was analysed from public databases as well as RT-qPCR and immunohistochemistry. The influences on METTL14 expression on NPC proliferation and metastasis were explored via in vitro as well as in vivo functional assays. Targeted genes of METTL14 were screened using the m6A and gene expression profiling microarray data. Actinomycin D treatment and polysome analysis were used to detect the half-life and translational efficiency of ANKRD22. Flow cytometry, immunofluorescence and immunoprecipitation were used to validate the role of ANKRD22 on lipid metabolism in NPC cells. ChIP-qPCR analysis of H3K27AC signalling near the promoters of METTL14, GINS3, POLE2, PLEK2 and FERMT1 genes. We revealed METTL14, in NPC, correlating with poor patient prognosis. In vitro and in vivo assays indicated METTL14 actively promoted NPC cells proliferation and metastasis. METTL14 catalysed m6A modification on ANKRD22 messenger ribonucleic acid (mRNA), recognized by the reader IGF2BP2, leading to increased mRNA stability and higher translational efficiency. Moreover, ANKRD22, a metabolism-related protein on mitochondria, interacted with SLC25A1 to enhance citrate transport, elevating intracellular acetyl-CoA content. This dual impact of ANKRD22 promoted lipid metabolism reprogramming and cellular lipid synthesis while upregulating the expression of genes associated with the cell cycle (GINS3 and POLE2) and the cytoskeleton (PLEK2 and FERMT1) through heightened epigenetic histone acetylation levels in the nucleus. Intriguingly, our findings highlighted elevated ANKRD22-mediated histone H3 lysine 27 acetylation (H3K27AC) signals near the METTL14 promoter, which contributes to a positive feedback loop perpetuating malignant progression in NPC. The identified METTL14-ANKRD22-SLC25A1 axis emerges as a promising therapeutic target for NPC, and also these molecules may serve as novel diagnostic biomarkers.

MCM8 promotes gastric cancer progression through RPS15A and predicts poor prognosis.

Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. Minichromsome maintenance proteins family member 8 (MCM8) assists DNA repair and DNA replication. MCM8 exerts tumor promotor function in multiple digestive system tumors. MCM8 is also considered as a potential cancer therapeutic target. Bioinformatics methods were used to analyze MCM8 expression and clinicopathological significance. MCM8 expression was detected by immunohistochemistry (IHC) staining and qRT-PCR. MCM8 functions in GC cell were explored by Celigo cell counting, colony formation, wound-healing, transwell, and annexin V-APC staining assays. The target of MCM8 was determined by human gene expression profile microarray. Human phospho-kinase array kit evaluated changes in key proteins after ribosomal protein S15A (RPS15A) knockdown. MCM8 functions were reassessed in xenograft mouse model. IHC detected related proteins expression in mouse tumor sections. MCM8 was significantly upregulated and predicted poor prognosis in GC. High expression of MCM8 was positively correlated with lymph node positive (p < 0.001), grade (p < 0.05), AJCC Stage (p < 0.001), pathologic T (p < 0.01), and pathologic N (p < 0.001). MCM8 knockdown inhibited proliferation, migration, and invasion while promoting apoptosis. RPS15A expression decreased significantly after MCM8 knockdown. It was also the only candidate target, which ranked among the top 10 downregulated differentially expressed genes (DEGs) in sh-MCM8 group. RPS15A was identified as the target of MCM8 in GC. MCM8/RPS15A promoted phosphorylation of P38α, LYN, and p70S6K. Moreover, MCM8 knockdown inhibited tumor growth, RPS15A expression, and phosphorylation of P38α, LYN, and p70S6K invivo. MCM8 is an oncogene and predicts poor prognosis in GC. MCM8/RPS15A facilitates GC progression.

Deliberate inclusion of multiple co-correlated gene target sets during discovery for improved biomarker translation to a clinical assay in early-stage breast cancer.

e12560 Background: Co-correlation between gene targets provides a more robust, and often nonlinear, understanding of human biology, resulting in more accurate and reliable diagnostics. When translating from discovery to a clinical assay, detectability of promising targets often hinders diagnostic development. Translating a single set of co-correlated targets becomes more challenging, as the success of the program is dependent on the interrelationship between all targets and the ability of the entire set to translate clinically. Given target translation uncertainty, we hypothesized that the deliberate inclusion of multiple independent sets of co-correlated targets during discovery could bridge the gap between promising biology and a clinically viable assay. We set out to discover and clinically validate multiple sets of co-correlated targets required for noninvasive breast cancer detection. Methods: Nonlinear statistical methods were repeatedly applied to microarray gene expression profiles from peripheral blood (PBMCs) obtained from 140 patients with and without breast cancer. Four independent cohorts were used for subsequent studies to assess efficacy and reproducibility of discovered targets. In total, five independent sets of highly efficacious co-correlated targets, totaling 26 unique genes, were discovered and analytically validated across the five patient cohorts (n=357). A sixth independent cohort of 197 plasma samples (breast cancer n=116, controls n=81) was obtained and used to clinically translate and validate the 26 targets using real-time qPCR (qPCR). Results: qPCR revealed strong and reproducible clinical signal from 8/26 previously discovered targets (Table). To evaluate the efficacy of the targets, an 8-gene signature was trained on the PBMC discovery cohort, achieving &gt;99% sensitivity, 83% specificity, indicating a promising classifier could be trained from the 8 qPCR targets. During CLIA validation of the assay, in normalized qPCR data from 87 patients, the 8-gene assay achieved a statistically significant &gt;99% sensitivity, 89% specificity in stage I breast cancer. Conclusions: Given the performance observed from the 8-gene qPCR assay, we conclude that the deliberate inclusion of multiple sets of co-correlated targets, including those considered mathematically redundant during discovery, contributed to the success of a clinically viable assay for the detection of early-stage breast cancer in plasma. [Table: see text]

Neuregulin 2 Is a Candidate Gene for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with heterogeneous and complex genetic underpinnings. Our previous microarray gene expression profiling identified significantly different neuregulin-2 gene (NRG2) expression between ASD patients and controls. Thus, we aimed to clarify whether NRG2 is a candidate gene associated with ASD. The study consisted of two stages. First, we used real-time quantitative PCR in 20 ASDs and 20 controls to confirm the microarray gene expression profiling results. The average NRG2 gene expression level in patients with ASD (3.23 ± 2.80) was significantly lower than that in the controls (9.27 ± 4.78, p &lt; 0.001). Next, we conducted resequencing of all the exons of NRG2 in a sample of 349 individuals with ASD, aiming to identify variants of the NRG2 associated with ASD. We identified three variants, including two single nucleotide variants (SNVs), IVS3 + 13A &gt; G (rs889022) and IVS10 + 32T &gt; A (rs182642591), and one small deletion at exon 11 of NRG2 (delGCCCGG, rs933769137). Using data from the Taiwan Biobank as the controls, we found no significant differences in allele frequencies of rs889022 and rs182642591 between two groups. However, there is a significant difference in the genotype and allele frequency distribution of rs933769137 between ASDs and controls (p &lt; 0.0001). The small deletion is located in the EGF-like domain at the C-terminal of the NRG2 precursor protein. Our findings suggest that NRG2 might be a susceptibility gene for ASD.

Abstract PO1-28-06: Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer

Abstract Background Over 12 million US women undergo follow-up breast imaging and exams annually due to breast cancer suspicions, incurring an $8 billion total cost, of which $2.18 billion is attributed to false-positive breast biopsies. Recent updates published to the Mammography Quality Standards Act (MQSA) now recommend supplemental imaging for women with heterogeneously dense and extremely dense breasts, regardless of suspicious findings. Given sensitivity limitations associated with mammography in dense-breasted women, as low as 30-47%, and non-specificity associated with ultrasound and MRI, these recent recommendations are likely to further strain the healthcare system and place substantial burden on patients and providers. With an estimated 25 million dense-breasted women who regularly screen for breast cancer now considered at-risk, there is a critical need for affordable and highly accurate testing, offering better specificity without sacrificing sensitivity. To address this gap, we developed an affordable and accurate plasma-based gene expression assay using real-time qPCR for targeted screening and diagnosis of early-stage breast cancer. Methods In previous studies, 26 cross-correlated mRNA gene targets were discovered and independently validated for non-invasive breast cancer detection across five independent patient cohorts using microarray gene expression profiling, comprised of peripheral blood mononuclear cells (n=337) and saliva (n=20), demonstrating strong efficacy, reproducibility, and concordance across bio-fluids and assay platforms in all studies. A sixth independent validation cohort comprised of 203 plasma samples was subsequently obtained for real-time qPCR clinical assay development and validation in our CLIA laboratory. The CLIA validation cohort was designed to be representative of different diagnosis status, stage, and ethnicity, and included Caucasian, Black/African American, Hispanic, and Asian women. After normalizing mRNA expression and clustering analysis, the signature was refined to 8 target genes and was assessed in a cohort of 87 plasma samples [Table 1] to verify the assay’s diagnostic performance for stage I breast cancer detection, under locked laboratory protocols. We leveraged machine learning methods derived from XGBoost classification, a supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. Results The XGBoost model, selecting from the 8 mRNA gene targets and patient age, achieved &amp;gt;99% sensitivity, 89% specificity for stage I breast cancer detection in the held-out test set, with an overall diagnostic accuracy of 94.5%. Normalized data showed significant differences in gene expression between healthy controls and stage I patients, and distinct clustering was observed for the 8 mRNA gene signature, including patient age. Conclusions Our plasma-based real-time qPCR gene expression clinical assay, with machine learning, is positioned as a highly accurate non-invasive tool for targeted screening and diagnosis of early-stage breast cancer. Furthermore, a clinically validated assay in this space addresses the need for more targeted diagnostics, while serving as a promising tool for clinicians as they make critical care decisions across the breast health continuum, accelerating time to earlier and more precise intervention and treatment, mitigating unnecessary healthcare expenditures, and reducing the mental, emotional, and financial burden on patients and their families. Citation Format: Martin Keiser, Elizabeth Cormier-May, Matthew Alderdice, Joy Kavanagh, William Guesdon, Heather Healy, Nathalie Jean-Charles, Jay Harness. Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-28-06.

The Microglial Transcriptome of Age-Associated Deep Subcortical White Matter Lesions Suggests a Neuroprotective Response to Blood-Brain Barrier Dysfunction.

Age-associated deep-subcortical white matter lesions (DSCLs) are an independent risk factor for dementia, displaying high levels of CD68+ microglia. This study aimed to characterize the transcriptomic profile of microglia in DSCLs and surrounding radiologically normal-appearing white matter (NAWM) compared to non-lesional control white matter. CD68+ microglia were isolated from white matter groups (n = 4 cases per group) from the Cognitive Function and Ageing Study neuropathology cohort using immuno-laser capture microdissection. Microarray gene expression profiling, but not RNA-sequencing, was found to be compatible with immuno-LCM-ed post-mortem material in the CFAS cohort and identified significantly differentially expressed genes (DEGs). Functional grouping and pathway analysis were assessed using the Database for Annotation Visualization and Integrated Discovery (DAVID) software, and immunohistochemistry was performed to validate gene expression changes at the protein level. Transcriptomic profiling of microglia in DSCLs compared to non-lesional control white matter identified 181 significant DEGs (93 upregulated and 88 downregulated). Functional clustering analysis in DAVID revealed dysregulation of haptoglobin-haemoglobin binding (Enrichment score 2.5, p = 0.017), confirmed using CD163 immunostaining, suggesting a neuroprotective microglial response to blood-brain barrier dysfunction in DSCLs. In NAWM versus control white matter, microglia exhibited 347 DEGs (209 upregulated, 138 downregulated), with significant dysregulation of protein de-ubiquitination (Enrichment score 5.14, p < 0.001), implying an inability to maintain protein homeostasis in NAWM that may contribute to lesion spread. These findings enhance understanding of microglial transcriptomic changes in ageing white matter pathology, highlighting a neuroprotective adaptation in DSCLs microglia and a potentially lesion-promoting phenotype in NAWM microglia.

Targeting the Cdc2-like kinase 2 for overcoming platinum resistance in ovarian cancer.

Platinum resistance represents a major barrier to the survival of patients with ovarian cancer (OC). Cdc2-like kinase 2 (CLK2) is a major protein kinase associated with oncogenic phenotype and development in some solid tumors. However, the exact role and underlying mechanism of CLK2 in the progression of OC is currently unknown. Using microarray gene expression profiling and immunostaining on OC tissues, we found that CLK2 was upregulated in OC tissues and was associated with a short platinum-free interval in patients. Functional assays showed that CLK2 protected OC cells from platinum-induced apoptosis and allowed tumor xenografts to be more resistant to platinum. Mechanistically, CLK2 phosphorylated breast cancer gene 1 (BRCA1) at serine 1423 (Ser1423) to enhance DNA damage repair, resulting in platinum resistance in OC cells. Meanwhile, in OC cells treated with platinum, p38 stabilized CLK2 protein through phosphorylating at threonine 343 of CLK2. Consequently, the combination of CLK2 and poly ADP-ribose polymerase inhibitors achieved synergistic lethal effect to overcome platinum resistance in patient-derived xenografts, especially those with wild-type BRCA1. These findings provide evidence for a potential strategy to overcome platinum resistance in OC patients by targeting CLK2.

Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach.

Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein-protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. The t-test was used through R programming to generate the P values for a test of the significance of expressed genes. A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.

SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach

ObjectiveUsing bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD). MethodsWe obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments. ResultsA total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited. ConclusionSH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.

DNA methylation profiling identifies epigenetic signatures of early gastric cancer.

Research on the DNA methylation status of gastric cancer (GC) has primarily focused on identifying invasive GC to develop biomarkers for diagnostic. However, DNA methylation in noninvasive GC remains unclear. We conducted a comprehensive DNA methylation profiling study of differentiated-type intramucosal GCs (IMCs). Illumina 850K microarrays were utilized to assess the DNA methylation profiles of formalin-fixed paraffin-embedded tissues from eight patients who were Epstein-Barr virus-negative and DNA mismatch repair proficient, including IMCs and paired adjacent nontumor mucosa. Gene expression profiling microarray data from the GEO database were analyzed via bioinformatics to identify candidate methylation genes. The final validation was conducted using quantitative real-time PCR, the TCGA methylation database, and single-sample gene set enrichment analysis (GSEA). Genome-wide DNA methylation profiling revealed a global decrease in methylation in IMCs compared with nontumor tissues. Differential methylation analysis between IMCs and nontumor tissues identified 449 differentially methylated probes, with a majority of sites showing hypomethylation in IMCs compared with nontumor tissues (66.1% vs 33.9%). Integrating two RNA-seq microarray datasets, we found one hypomethylation-upregulated gene: eEF1A2, overlapped with our DNA methylation data. The mRNA expression of eEF1A2 was higher in twenty-four IMC tissues than in their paired adjacent nontumor tissues. GSEA indicated that the functions of eEF1A2 were associated with the development of IMCs. Furthermore, TCGA data indicated that eEF1A2 is hypomethylated in advanced GC. Our study illustrates the implications of DNA methylation alterations in IMCs and suggests that aberrant hypomethylation and high mRNA expression of eEF1A2 might play a role in IMCs development.

Molecular mechanism of a novel root-end filling material containing zirconium oxide on the osteogenic/odontogenic differentiation of human osteosarcoma MG-63 cells.

Although the novel root-end filling material containing zirconium oxide (NRFM-Zr) which is hydroxyapatite-based may promote osteoblast differentiation, the molecular mechanism remains unclear. The aim of this study is to investigate it underlying the osteogenic/odontogenic differentiation of human osteosarcoma MG-63 cells induced by NRFM-Zr, compared with calcium silicate-based mineral trioxide aggregate (MTA), and glass ionomer cement (GIC). Firstly, three different types of root filling materials were co-cultured with MG-63 cells, and their cell toxicity, alkaline phosphatase (ALP) activity, and calcium ion concentration were evaluated. Next, gene expression profiling microarray was employed to analyze the impact of the materials on the gene expression profile of MG-63 cells. The results of cell viability revealed that NRFM-Zr group had no significant difference compared to the negative control group. After 5 and 7days of cultivation, both the NRFM-Zr and MTA groups exhibited significantly higher ALP activity compared to the negative control (p < 0.05). Moreover, the NRFM-Zr group had the highest calcium ion concentration, while the GIC group was the lowest (p < 0.05). Gene expression profiling microarray analysis identified 2915 (NRFM-Zr), 2254 (MTA) and 392 (GIC) differentially expressed genes, respectively. GO functional and KEGG pathway analysis revealed that differentially expressed genes of NRFM-Zr, MTA and GIC participated in 8, 6 and 0 differentiation-related pathways, respectively. Comparing the molecular mechanisms of osteogenic/odontogenic differentiation induced by hydroxyapatite-based NRFM-Zr and calcium silicate-based MTA, it was found that they shared similarities in their molecular mechanisms of promoting osteogenic differentiation. NRFM-Zr primarily promotes differentiation and inhibits cell apoptosis, thereby enhancing osteogenic/odontogenic differentiation of MG-63 cells. Furthermore, the inducing efficacy of NRFM-Zr was found to be superior to MTA.

Baicalin suppresses colorectal cancer cell proliferation, potentially via ARRDC4: Bioinformatics and experimental analysis

Colorectal cancer (CRC) is a malignant digestive tumor mostly prevalent among adolescents and has great proliferation potential. Baicalin is a flavonoid derived from the dried root of Scutellaria baicalensis, it has tumor-suppressing effects on various kinds of tumors. Although baicalin has been shown to regulate genes associated with CRC, its role in cancer proliferation remains poorly illustrated. In this work, we evaluated the effect of baicalin on the proliferative ability of three CRC cell lines using a cell counting kit-8 assay. Furthermore, we screened for differentially expressed genes in HCT-116 cells using baicalin-treated samples and control samples on a gene expression profile microarray. We identified 19 genes were identified significantly downregulated by baicalin, which may potentially play an indispensable role in the proliferation of HCT-116 cells. High-content screening evaluated whether silencing the 19 genes affected HCT-116 cell growth. The results showed that knocking down ARRDC4 exhibited the most potent inhibition of cell proliferation. ARRDC4 expression was knocked down by transfecting CRC cells with lentiviral shRNA. MTT assay, Caspase-Glo 3/7 assay and immunohistochemistry analysis were performed to explore the effects of baicalin on proliferation and apoptosis of CRC cells. Besides, RT-PCR showed that the expression levels of ARRDC4 mRNA decreased significantly in HCT-116 cells after baicalin treatment. In conclusion, these findings indicated that baicalin may attenuated the expression of ARRDC4 via regulating the levels of LCN2, Claudin-2 and GSTA4 to inhibit HCT-116 cell proliferation. The theoretical underpinnings for the investigation of the pharmacological effects of baicalin in the treatment of human CRC were also established in this study.

Overlapping group screening for binary cancer classification with TCGA high-dimensional genomic data.

Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients' survival. Since disease developments involve complex interplay among multiple factors such as gene-gene interactions, cancer classifications based on microarray gene expression profiling data are expected to be effective, and hence, have attracted extensive attention in computational biology and medicine. However, when using genomic data to build a diagnostic model, there exist several problems to be overcome, including the high-dimensional feature space and feature contamination. In this paper, we propose using the overlapping group screening (OGS) approach to build an accurate cancer diagnosis model and predict the probability of a patient falling into some disease classification category in the logistic regression framework. This new proposal integrates gene pathway information into the procedure for identifying genes and gene-gene interactions associated with the classification of cancer outcome groups. We conduct a series of simulation studies to compare the predictive accuracy of our proposed method for cancer diagnosis with some existing machine learning methods, and find the better performances of the former method. We apply the proposed method to the genomic data of The Cancer Genome Atlas related to lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LHC), and thyroid carcinoma (THCA), to establish accurate cancer diagnosis models.

Identification of Key Genes and Regulatory Pathways in Multiple Sclerosis Brain Samples: A Meta-Analysis of Micro-Array Datasets.

Multiple sclerosis (MS) is an autoimmune disorder of the central nervous system (CNS) whose aetiology is only partly understood. Investigating the intricate transcriptional changes occurring in MS brains is critical to unravel novel pathogenic mechanisms and therapeutic targets. Unfortunately, this process is often hindered by the difficulty in retrieving an adequate number of samples. However, by merging data from publicly available datasets, it is possible to identify alterations in gene expression profiles and regulatory pathways that were previously overlooked. Here, we merged microarray gene expression profiles obtained from CNS white matter samples taken from MS donors to identify novel differentially expressed genes (DEGs) linked with MS. Data from three independent datasets (GSE38010, GSE32915, and GSE108000) were combined and used to detect novel DEGs using the Stouffer's Z-score method. Corresponding regulatory pathways were analysed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. Finally, top up- and down-regulated transcripts were validated by real-time quantitative PCR (qPCR) using an independent set of white matter tissue samples obtained from MS donors with different disease subtypes. There were a total of 1446 DEGs, of which 742 were up-regulated and 704 genes were down-regulated. DEGs were associated with several myelin-related pathways and protein metabolism pathways. Validation studies of selected top up- or down-regulated genes highlighted MS subtype-specific differences in the expression of some of the identified genes, underlining a more complex scenario of white matter pathology amongst people afflicted by this devastating disease.

RECENT DEVELOPMENTS IN SOFT COMPUTING BASED TECHNIQUES FOR FEATURE SELECTION AND DISEASE CLASSIFICATION

Computational prediction of diseases is vital in medical research that contributes to computer-aided diagnostics and helps doctors and medical practitioners in critical decision-making for various diseases such as bacterial and viral kinds of disease, including COVID-19 of the current pandemic situation. Feature selection techniques function as a preprocessing phase for classification and prediction algorithms. For disease prediction, these features may be the patient’s clinical profiles or genomic features such as gene expression profiles from microarray and read counts from RNA-Seq. The performance of a classifier depends primarily on the selected features. In addition, genomic features are too large in numbers, resulting in the curse of dimensionality problem. In the last few years, several feature selection algorithms have been developed to overcome the existing problems to get rid of eliminating chronic diseases, such as various cancers, Zika virus, Ebola virus, and the COVID-19 pandemic. In this review article, we systematically associate soft computing-based approaches for feature selection and disease prediction by applying three data types: patients’ clinical profiles, microarray gene expression profiles, and RNA-Seq sample profiles. According to related work, when the discussion took place, the percentage of medical data types highlighted through pictorial representation and the respective ratio of percentages mentioned were 52%, 27%, 9% and 12% for clinical symptoms, gene expression, MRI-Image and other data types such as signal or text-based utilized, respectively. We also highlight the significant challenges and future directions in this research domain.&#x0D;

ITGB1BP1, a Novel Transcriptional Target of CD44-Downstream Signaling Promoting Cancer Cell Invasion.

Breast cancer (BC) is the most common malignancy worldwide and has a poor prognosis, because it begins in the breast and disseminates to lymph nodes and distant organs. While invading, BC cells acquire aggressive characteristics from the tumor microenvironment through several mechanisms. Thus, understanding the mechanisms underlying the process of BC cell invasion can pave the way towards the development of targeted therapeutics focused on metastasis. We have previously reported that the activation of CD44 receptor with its major ligand hyaluronan (HA) promotes BC metastasis to the liverin vivo. Next, a gene expression profiling microarray analysis was conducted to identify and validate CD44-downstream transcriptional targets mediating its pro-metastatic function from RNA samples collected from Tet CD44-inducedversuscontrol MCF7-B5 cells. We have already validated a number of novel CD44-target genes and published their underlying signaling pathways in promoting BC cell invasion. From the same microarray analysis, Integrin subunit beta 1 binding protein 1 (ITGB1BP1) was also identified as a potential CD44-target gene that was upregulated (2-fold) upon HA activation of CD44. This report will review the lines of evidence collected from the literature to support our hypothesis, and further discuss the possible mechanisms linking HA activation of CD44 to its novel potential transcriptional target ITGB1BP1.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma

BackgroundPancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC.MethodsIn this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast–enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses.ResultsSurvival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05).ConclusionsA CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.

Multilevel Feature Selection Method for Improving Classification of Microarray Gene Expression Data

Microarray gene expression profiles provide valuable answers to a variety of problems, and contributes to advances in clinical medicine. Gene expression data typically has a high dimension and a small sample size. Gene selection from microarray gene expression data is a challenge due to high dimensionality of the data. The number of samples in the microarray dataset is much smaller compared to the number of genes as features. To extract useful gene information from cancer microarray data and reduce dimensionality, selection of significant genes is necessary. An effective method of gene feature selection helps in dimensionality reduction and improves the classification performance. Experimental results suggest that appropriate combination of filter gene selection methods is more effective than individual techniques for microarray data classification. In this paper, we propose a two-layered feature selection method. In the first layer, t-test statistical method is used to remove the features that have little correlation with the classification results. In the second layer, line segment approximation method is used to transform the feature subset into a less dimensional feature space. Four well known classifiers kNN, SVM, NBC, DT were used to verify the performance of the proposed feature selection algorithm on binary class microarray data. The experimental results show that the proposed method can effectively select relevant gene subsets, and achieves higher classification accuracy.